CONCEPTUAL FRAMEWORK ON ECONOMIC VULNERABILITY
Identifying vulnerability or exposure of countries, regions, communities, households and individuals to exogenous economic shocks has become an important issue lately. Working on this issue, the first most important step to take is to define vulnerability. Vulnerability is not a straightforward concept, and there is no consensus as to its precise meaning. But, generally speaking, vulnerability refers to potential for loss or damage from external/exogenous shocks. In the economic area, economic vulnerability refers to risks caused by external/exogenous shocks to system of production, distribution and consumption. In Adger, et al.(2004), the vulnerability of a system, population or individual to a threat relates to its capacity to be harmed by that threat; in Briguglio et al.'s (2008), it is ascribed to inherent conditions affecting a country's (or a region or an household) exposure to exogenous shocks.
In Guillaumon (2007), it is stated that the economic vulnerability of a country can be defined by the risk for countries to see their development hampered by exogenous shocks they face. According to him, there are two main kinds of exogenous shocks, or two main sources of vulnerability: 1) environmental or “natural” shocks, namely natural disasters, such as earthquakes or volcanic eruptions, and the more frequent climatic shocks, such as typhoons and hurricanes, droughts, floods, etc. ; 2) external (trade and exchange related) shocks, such as slumps in external demand, world commodity prices instability (and correlated instability of terms of trade), international fluctuations of interest rates, etc. ” (page 6).
Hoddinott and Quisumbing (2003) define vulnerability as the likelohood that at a given time in the future, an individual will have a level of welfare below some norm or benchmark. More concretely, for instance, the likelihood of being poor next year, in ten years time, being poor in old age. From the economic point of view, the welfare is generally expressed in terms of level of consumption or income, and the norm or benchmark as the poverty line. Now, the problem of this definition is to determine norm or benchmark which may vary by different individuals, households, communities or regions.
The concept of vulnerability is often used as a synonym for poverty. But it is not the same as the concept of poverty. As in their own words, Hoddinott and Quisumbing (2003) say that concepts of vulnerability and poverty are linked but are not identical. Poverty measures are generally fixed in time, thus poverty is essentially a static concept. For instance, based on BPS data, the poverty rate in Indonesia in 2008 was about 15.42% (March), while in 2007 it was 16.58%. By contrast, as explained in Lipton and Maxwell (1992), vulnerability is a dynamic process: it captures changes processes of people/individuals move in and out of poverty. For instance, the financial consition of an household has been deteriorated in the past few years. If this financial condition is considered as an indicator for identifying vulnerability, then it can be said that the level of vulnerability of the household has been increased in the past 5 years, ceteris paribus, other factors which can improve the financial condition do not change. Therefore, in assessing vulnerability, time-series data are required in order to capture long-term process of changes of indicators (which will be discussed in next chapter) of vulnerability.
In Moser (1998) it is stated that, although poor people are usually among the most vulnerable, not all vulnerable people are poor, a distinction which facilitates differentiation among low-income populations (page 3). In Chaudhuri et al. (2002) it is writen: vulnerability is an ex ante (forward-looking) rather than an ex post concept. Poverty status can be observed at a specific time period, given the welfare measure and the poverty threshold. By contrast, household vulnerability is not directly observed, rather it can only by predicted...(page 12). Back to the above given example given. Wit the fact that its financial condition has been deteriorated in the past few years, it can only be predicted that the financial consition of the household will further deteriorate and so the household will become more vulnerable to shocks only with the assumption that no changes from other factors will take place that will make otherwise.
Given the definition and the link between vulnerability and poverty as discussed above, vulnerability can be assessed through different approaches. Hoddinott and Quisumbing (2003) have mentioned three principal approaches: (1) vulnerability as expected poverty, (2) vulnerability as low expected utility, and (3) vulnerability as uninsured exposure to risk. All share a common characteristic, namely they predict a measure of welfare, which can be consumption, or in other alternative welfare indicators could also be used instead. Among those three approaches, the first one seems to be easier to be used since it is not only relatively straight forward to calculate, but it also can be estimated with a single cross-section using BPS data.If the welfare is defined in terms of consumption (using SUSENAS data), so in a simple way it can be said that vulnerability of an household in, say, 2010, is the probability that the household's level of consumption at 2011 will be below the consumption poverty line.
Further, vulnerability has several dimensions which should be taken into account in the process of identification of determinants (or indicators) of vulnerability. According to many, such as Blaike and Brookfield (1987), Bayliss-Smith (1991), and Moser (1998), there are two dimensions of vulnerability: (1) its sensitivity, i.e. the magnitude of individuals', households', and communities' response to a shock, and (2) its resilience, i.e. the ease and rapidity of individuals', households', and communities' recovery from a shock. As stated in Moser (1998), analyzing vulnerability involves identifying not only the threat but also the “resilience”, or responsiveness in exploiting opportunities, and in resisting or recovering from negative effects of a changing environment (page 3). He see vulnerability as insecurity and sensitivity in the well-being or welfare (which is generally measured by total income or value of total assets owned) of individuals, households and communities in the face of a negative changing environment (which can be ecological, economic,social and political), and implicit in this, their responsiveness and resilience to risks that they face during such a negative change. Whereas, for Briguglio et al. (2008) economic resilience is associated with actions undertaken by policymakers and private economic agents which enable a country to withstand or recover from negative impacts (e.g. production dclines, poverty increases) of shocks (page 2). In Allen (2003), vulnerability is viewed as representing the set of socio-economic factors that determine people's ability to cope with stress or change.
Guillaumon (2007) also agreed with those two dimensions, but he added another dimension, namely the nature of the shocks. Precisely, he argues that vulnerability can be seen as the result of three components: (a) the size and frequency of the exogenous shocks, either observed (ex post vulnerability) or anticipated (ex ante vulnerability); (b) the exposure to the shocks ; (c) the capacity to react to the shocks, or “resilience” (pages 6&7). While, in Hoddinott and Quisumbing (2003), vulnerability is stated to be dependent on 4 main factors: (1) the nature of the shock (e.g large-scale disasters such as droughts, earthquakes, floods or landslides; world market instability, political instability), (2) the availability of additional sources of income, (3) the functioning of labor, credit and insurance markets, and (4) the extent of public assistance. The second, third and fourth factors are related to incomes or money available and thus they are related to resilience.
Thus in a simple way, it can be concluded that the level of vulnerability of , say an household or a region depends on or a function of three factors (Figure 1): the degree of sensitivity, the degree of resilience of the household/region and the nature of the shocks. At least theoretically, it can be hypothesized that the function has a positive relationship with the first factor (i.e. more sensitive, more vulnerable), a negative relationship with the second factor (more resilience or more capacity/capability to recover, less vulnerable), and a positive relationship with the third factor (larger or more serious shocks, more vulnerable. Each of the three factors has a number of indicators.
Vulnerability indicators are the most common methodology in vulnerability assessment. The standard practice is to compile a list of indicators using criteria such as: suitability, following a conceptual framework or definitions; availability of data; and sensitivity to shocks. Based on the above discussion, this section of the paper proposed a list of most important indicators that can be used to assess the economic vulnerability.
As discussed earlier, the level of vulnerability depends on three main factors, i.e. degree of sensitivity, degree of resilience (capacity/capability to recover), and the nature of the shocks. In the literature on vulnerability, within those three factors, the attention has been given more on the capabilities of individuals or households to recover from a shock/crisis. The main reason is that sensitivity is to a certain extent is a natural given. For instance, Singapore is a very vulnerable to any global economic crisis, because it is very small country which makes it fully dependent on international economy (trade, investment, finance, energy). Whereas, resilience is about required actions that people have capacity to take in coping with a crisis/shock. Thus, it has to do with what he/she or a community can do to escape/recover as soon as possible from negative effects of an exogenous shocks. For policy makers on poverty alleviation policies, knowing individuals'/households' recovery capability is very important since it determines the right choice of forms/types of interventions required to help effectively and efficiently the poor in the case of an economic crisis.
Now the question is, what are the suitable indicators to reflect the crisis-coping capability of regions or individuals or households? According to many, among others, such as Streeten et al. (1981), the capabilities of individuals or households to recover from a shock are deeply influenced by factors ranging from prospects of earning a living, to the social and psychological effects of deprivation and exclusion. These include individuals' basic needs, employment at reasonable wages and health and education facilities. Swift (1989) analyzes vulnerability and security as a function of assets. In his model, he classifies assets into three categories: (1) investments (i.e. human investments in education and health, and physical investments in housing,equipment and land); (2) stores (e.g. food, money or valuables such as jewelry); and (3) claims on others for assistance (e.g. frienship, kinship, networks and patrons in the community,government and international community). Whereas, in Moser (1998), it is stated that the capability of an individual to recover from the negative effects of, say, an economic shock strongly depends on the means owned by the individual, which are the assets and entitlements (e.g. labor, land) that he/she can mobilize and manage in the face of hardship caused by the shock. Vulnerability is therefore, according to Moser (1998), closely linked to asset ownership: the more assets a person has, the less vulnerable he/she is, and the greater the erosion/reductionof his/her assets, the greater his/her insecurity.
If economic vulnerability is defined, in a general sense, as the welfare loss associated with poverty or with shock, then, based on the above discussion, in identifying vulnerability indicators, determinants (directly and indirectly) of welfare or income should be identified first. For example, if employment is considered as an important source/determinant of welfare, then employment should be also considered as a vulnerability indicator.
By level of aggregation, economic vulnerability can be assessed at macro-level: country, region or community level, and micro-level: an individual or an household level. At the macro-level, to assess the economic vulnerability of region, the most used indicators are given below.Further, some of the macro-level indicators are considered as sensitivity indicators and others as resilience indicators, or both. Sensitivity indicators refer to inherent and permanent characteristics (which may not subject to policy or governance) which render regions/countries prone to exogenous shocks. Whereas, resilience indicators refer to a crisis coping capability.
1) Size (a resilience/sensitivity indicator).
2) Population density and structure (a sensitivity/resilience indicator)
3) Geographical location (a sensitivity indicator)
4) Economic openness (a sensitivity indicator).
5) Export dependency and concentration (a sensitivity indicator)
6) Import dependency and concentration (a sensitivity indicator)
7) The share of manufacturing industry/agricuulture in GDP (a sensitivity indicator)
8) The share of total employment by sector (sensitivity indicator).
9) Real income per capita and income distribution (a resilience indicator).
10) Percentage of population living under the poverty (a resilience indicator)
11) Adult literacy rate and school enrollment ratios (a resilience indicator)
12) Health condition (a resilience indicator)
13) Technology capability (a resilience indicator)
14) Social and economic infrastructure (a resilience indicator)
15) Social capital (a resilience indicator)
16) Women participation in employment/economic activities (a resilience indicator)
17) Macroeconomic stability (a resilience indicator)
18) Microeconomic market efficiency (a resilience indicator)
Ad.1) Economic Size
Small size of a region limits the ability to reap the economies of scale benefits and constrains production possibilities. Therefore, the size should be considered as resilience as well as a sensitivity or exposure indicator to exogenous shocks. There is no generally agreed definition as to which variable should be used to measure the size of regions or countries and as to what should be the cut-off point between a small region and a large region.
Population often used as an indicator of size of region (province and district) or country. Guillaumont (2007) also states that, among several ways by which the size of a region can be measured, the most meaningful is the number of its inhabitants. Especially to assess the main economic consequences of the size of a region, independently from its income per capita, the most usual measure is the number of its population. He argues that when investigating the channels by which size matters for economic development, links with economic vulnerability appear very clearly. There are at least three main channels (or intermediate variables) through which small size influences the exposure components of economic vulnerability: trade (export and import) intensity, government size and social cohesion. For instance, as one hypothesis, the smaller the (population) size, the higher (ceteris paribus) the trade to GDP ratio is (and the more “dependent” the economy), ceteris paribus.
Economic size also described with output capacity. Region with large economic size have capability to produce more output than region with small economic size. Output has direct linkage with population to describe economies of scale. Population as market of output if we views from demand side, and population as production factor (employment) if we views from supply side of output. The large population enable to produce more output than small population, ceteris paribus.
With these reasons above, the indicator to measured of economic size using the basis of output (GDP or GRDP) and population. In Indonesia, the data of output in sub-national level only available annually data, whereas population data only available 5 yearly from population census an inter-census survey of population. However, BPS periodically (annually) providing of population data with projection data. All of the data above provide by BPS.
Ad.2) Demographic structure
As explained above, total population is positive for the economy regarding economies of scale and production possibilities. More population has more manpower for production activities. However, there is a limit for the positive side of large population. Beyond that economic efficient limit (which is difficult to determine), it is then regarded as an over-population which have negative effects on production and hence welfare. The related hypothesis is that, beyond that limit, population density and production capability or income per capita tend to be negatively related: too much population for a given land, less space for production, ceteris paribus (thus, beyond that limit, population density and vulnerability tend to be positively related). Moreover, according to such as Cova and Church (1997), Mitchell (1999), and Cutter, et al. (2000), high-density areas complicate evacuation out of harm's way; although this is more relevant for natural disasters.
Population structure by gender and age is also important in determining the vulnerability of a region. Regions or countries where marginalization of women is a serious problem is more vulnerable to shocks than those where there are no gender discriminations (see further point ad.16). Also, regions where fraction of unproductive population is high are more vulnerable to shocks than those where the category of productive age as a percentage of total population is high. In many studies, it is shown that extremes of the age spectrum affect the movement out of harm's way. For instance, parents lose time and money caring for children when daycare facilities are affected, and elderly may have mobility constraints or mobility concerns increasing the burden of care and lack of resilience. 
Demographic structure identification using population density, productive aged proportion and gender proportion. According to explanation above, proportion of productive aged to total population less vulnerable than low proportion of productive aged. Large proportion of productive aged to total population has more manpower to contribute an output, vice versa. Large proportion of woman to total population more vulnerable than small proportions, ceteris paribus.
Ad.3) Geographical location
Location isolation, i.e. insularity, peripherality and remoteness leads to high transport costs and marginalization. Degree of economic openness of a region is also affected by, among other factors, the geographical location of the region. According to many studies, remoteness from world markets (for output as well as inputs) is a structural handicap not only because it is a factor of vulnerability: even if transport costs have decreased, distance remains an important obstacle to trade.Thus, as a hypothesis, the more remote a region the higher is its sensitivity to exogenous shocks, ceteris paribus.
Ad.4) Economic Openness
A high degree of economic openness of a region means that the region does intensively export and import (which can be measured as a ratio of international trade (export plus import) on GDP). According to Briguglio et al. (2008), economic openness is to a signifcant extent an inherent feature of an economy, conditioned mainly by two factors: (1) the size of the country's domestic market affecting the exports-to-GDP ratio (i.e. smaller domestic market leads to more export, ceteris paribus), and (2) the country's availability of resources and its ability to efficiently produce the range of goods and services to meet domestic market demand, affecting the imports-to-GDP ratio (i.e. poorer in resources and less capacity to produce efficiently lead to more import, ceteris paribus). Besides trade, a high degree of economic openess of a region can also be reflected by its high ratio of foreign investment (capital inflow plus capital outflow) to GDP. No doubt that trade (especially export) and investment have a positive effect on economic growth. But, a region with high degree of economic openess is particularly susceptible to outside economic conditions. As stated in Briguglio et al. (2008), economic vulnerability is defined as the exposure of an economy to exogenous shocks, arising out of economic openness.....(page i).  (see further discussion in ad.4) and ad.5). Thus, the hypothesis related to this indicator is that, regions with open economy face more vulnerable to exogenous shocks than those with protected economy, ceteris paribus.
Ad.5) Export Dependency and Concentration
Regions with a strong export dependency has a greater exposure to exogenous shocks compared to regions with less exports. The higher (ceteris paribus) the degree of export dependency, the more ‘dependent' the economy, the greater the exposure to exogenous shocks (e.g. world market instability). The risks of being negatively affected by export instability is exacerbated when high export dependency is only on a narrow range of exports (for instance, Indonesia on exports of oil during the Soeharto era). Or, in Briguglio et al.'s (2008) words, dependence on a narrow range of exports gives rise to risks associated with lack of diversification, and therefore exacerbates vulnerability associated with economic openess (page 5). In other words, within the economically opened countries, those with low export market diversification (higher export concentration) are more vulnerable than those with the reverse situation to external shocks.Thus, it can be hypothesized that, given the exports-to-GDP ratio, there is a positive relationship between the level of export concentration and the level of vulnerability, ceteris paribus.
As a theoretical illustration, a shock happens can be caused by significant declines in world prices or drops in world demand for key commodities. Suppose that the world prices or demand for Indonesian key exports of agricultural commodities decline, then farm/agricultural incomes in Indonesia will decrease (Figure 2). This process however will not stop here. There is a multiplier effect: declined farm incomes will reduce consumption as well as intermediate demands within the rural areas, and as a result, rural poverty will increase. In this case, the trasmitted indicators are export revenues, consumption and intermediate demands, and employment. The most vulnerable groups are farmers and exporting companies, including their employees. According a report from the World Bank and the ASEAN Secretariat in 2009, in Indonesia, the 2008/09 crisis is most likely to be felt among a few regions that produce commodities such as estate crops, for which world prices have dropped significantly.Regions where export‐oriented factories, such as garment factories, are located (e.g. Bandung/West Java, and Greater Jakarta) are also more likely to feel the impacts (World Bank and ASEAN, 2009).
In export of services, it is a crisis for exporting countries when for instance the number of foreign tourists declines sharply or a significant drop in remittance receipts from overseas migrants. According to a report from the World Bank and the ASEAN Secretariat (World Bank and ASEAN, 2009), the 2008/09 crisis has been transmitted to ASEAN economies through several transmission channels, and among the most important are fewer tourist arrivalsand reduced remittance receipts from oversea s migrants.
Ad.6) Import dependency and Concentration
Regions with high degree of import dependency, especially strategic imports such as energy (i.e. fuel),other crucial natural resources, and industrial supplies, exacerbated by limited import substitution possibilities are very sensitive to instability in world supplies (availability) or world prices (costs of import) on that particular items. Thus, on one hand, as one hypothesis, the ratio of imports to GDP and the level of sensitivity to external shocks are positively related, ceteris paribus. On the other hand, as another one hypothesis, given the ratio, the lower is import market diversification (higher import concentration), the higher is the sensitivity to external shocks, ceteris paribus.
As a theoretical illustration, a significant increase in world price or a sudden and huge drop in world stock for a worldwide tradable commodity can become a crisis for importing countries if it is a crucial commodity for the countries, for instance rice or oil. For instance, in 1974, the sudden decision made by the organisation of petroleum exporting countries (OPEC) to increase the price of their produced oil as a response to the Arab-Israel conflict was a big crisis for oil importing countries. With no change in total oil import, this ‘first oil crisis' had led energy cost and hence domestic production costs in the oil importing countries to increase and resulted in hyper inflation in the world (Figure 3). At least, theoretically, in such a crisis, the impact can be relatively moderate if the oil importing countries make an adjustment by subsidizing imported oil with alternative energies or by improving efficiency in the use of oil as energy for their domestic production, as happened in the second ‘oil crisis' in end of 1970s/early 1980s. The worldwide impact of this second oil crisis was much smaller than that of the first one. In this case, the trasmitted indicators are cost of imported oil, inflation and employment. The most vulnerable group are, first, the importing companies and their employees, and, second, through domestic production linkages, other related companies or sectors, including their employees.
Ad.7) The share of manufacturing industry/agricuulture in GDP
This share is a good indicator of economic diversification. The higher is the percentage share of manufacturing industry or agriculture in GDP, the higher is the economic concentration or the lower is the economic diversification. Furthermore, given the level of domestic market demand (which determined by population size, among other determinants), high level of economic concentration also means high import dependency (for other sectors which have small GDP contributions). Thus, the related hypothesis is that the higher is the economic concentration in a region, the more vulnerable is the region to external shocks, ceteris paribus. But, of course, it depends on which sectors the shocks hit most.
Ad.8) The share of total employment by sector
Basically, this ratio tells the same story as the ration in point ad.7. A singular reliance on one economic sector
for income generation creates a form of economic vulnerability for counties/regions. As explained in Cutter, et al. (2003), the boom and bust economies of oil development, fishing, or tourism-based coastal areas are good examples—in the heyday of prosperity, income levels are high, but when the industry sees hard times or is affected by a natural hazard, the recovery may take longer. The agricultural sector is no exception and is, perhaps, even more vulnerable given its dependence on climate. Any change in weather conditions or increases in hydrometeorological hazards, such as flooding, drought, or hail, can affect annual and decadal incomes and the sustainability of the resource base (page 253). Thus, as an hypothesis, it can be expected that regions where the majority of workforce work in one sector are less resilience to shocks than those with relatively equal distribution of employment by sector, ceteris paribus (as in the case of point ad.7, it depends on which sectors the shocks hit most).
Ad.9) Real income per capita and income distribution
Real income per capita is often used as a welfare indicator, which indicate the purchasing power of an economy. Theoretically, total income of a family is the sum of income earned from employment and income actually earned or potentially can be earned from her tangible assets (excluding human capital). This total income is often mentioned as total welfare. Thus, ideally, total welfare (in real value) per capita instead of real income (from employment) per capita should be measured. This total welfare-to-total population ratio is more appropriate to indicate the ability to absorb losses and enhance resilience to hazard impacts of shocks. It can be that someone has no income because he/she has no work, but he/she is potentialy rich because he/she owns many lands and has many houses that he/she (can) rent/sell, or many livestocks that he/she can sell. Thus, the related hypothesis is that communities in the wealthy regions are able to absorb and recover from losses more quickly than those in poor regionsHowever, high real income or wealth per capita will meaningless when the achieved total income/wealth is not equally distributed among the population. In other words, even when real income per capita is high, poverty rate can also high if income disparity is high. The income inequality is often measured by a gini coefficient. Thus, the related hypothesis is that, given the level of real income per capita, the higher the gini coefficient is (approaching one), the higher is the level of vulnerability, ceteris paribus.
Ad.10) Percentage of population living under the poverty line
Proportion of population in a region or community living under current poverty line (the poverty rate) indicates the level of sensitivity as well as the level of resilience of the region/community to external/exogenous shocks, as it is generally believed that only individuals or households who are not poor (i.e. who have money or assets) are more able to face a crisis than the poor. Since the poverty rate and the rate of employment (unemployment/underemployment) are negatively (positively) related, unemployment/underemployment (or employment) rate can be used as an alternative indicator of poverty, and hence of vulnerability. Moreover, according to such as Mileti (1999) and Cutter et al.(2003), the potential loss of employment following a shock exacerbates the number of unemployed workers in a community or region, contributing to a slower recovery from the shock. Thus, one hypothesis is that poor regions are more vulnerable and face more difficulties to cope with crises than wealthy regions, ceteris paribus.
Ad.11) Adult literacy rate
Educational advancement, measured by two human capital indicators, i.e. adult literacy rate and school enrollment ratios, is generally considered as an important determinant of regions/communities' crisis coping capability. Briguglio, et al. (2008) argue that social develpment is another essential component of economic resilience, and they consider educational advancement is a good indicator of social development. It is also appropriate to make a distiction between male and female literacy rate, or female literacy as a ratio to total population who can read and write. As one hypothesis from this theoretical thought is that regions with high educated population are less vulnerable to shocks than those where the majority of the population have only primary school, ceteris paribus.
Ad.12) Health condition
As in the case of educational advancement, health condition is also another crucial human capital indicator, since high educational advancement can never be achived in a unhealthy society. In other words, education and health go together, or they are complementer to each other. Briguglio, et al. (2008) also consider advancement in health standard to be conducive to economic resilience. The related hypothesis is that healthy communities are more able to face a crisis with minimum damage/loss compared to unhealthy communities, ceteris paribus.
Ad.13) Technology capability
It is generally recognized that technology is the most important determinant, besides human capital, of economic development or economic well being. Technology capability of a region is determined by many factors, including people access to advanced technologies, eithert through education, vocational training, workshops, or self-learning with full access to information (e.g. internet, newspapers, television, etc.). Thus, the related hypothesis is that regions with higher technology capability are more resilience than those with low technology capability to exogenous shocks, ceteris paribus.
At the national/country level, the most used indicators are R&D investment/expenditure as a percentage of GDP, number of scientists and engineers in R&D per million population, and tertiary enrolment. At the regional/provincial level, besides tertiary enrolment, number of R&D institutes (litbang), politeknik school, number of scientists and engineers as a percentage of total population, and number of people with technical universities certificates as a percentage of total population can be used as alternative indicators.
Ad.14) Social and economic infrastructure
Social and economic infrastructure, e.g. school, hospital, public utilities, roads, bridges, harbors, telecommunication facilities, transportation facilities, sanitation, clean water supply, industrial estates, electricity, irrigated areas (for agricultural-based regions), etc. are very important determinants of vulnerability or resilience of a region. The related hypothesis is that regions with well-developed social and economic infrastructures face lower vulnerability or have higher coping capability to shocks compared to regios with underdeveloped infrastructures.
Ad.15) Social capital
It is generally acknowledged the importance of social capital as critical in building and maintaining the trust necessary for social cohesion and change. In the economic area, social capital is important as a determinant of the feasibility and productivity of economic activities. In general, social capital can be defined as reciprocity within communities and between households based on trust deriving from social ties. Putnam (1993), for instance, defines the “stocks” of social capital as the informal (unorganized) and formal (organized) reciprocal networks of trust and norms embedded in the social organization of communities, with social institutions both hierarchical and horizontal in structure. In Adger, et al. (2004), social capital is seen as the ability to act colectively. According to Hoddinott and Quisumbing (2003), social capital includes networks, norms and social trust that facilitates cordination and cooperation. In relation to the issue of vulnerability, Adger, et al. (2004) emphasize that the way in which society at large acts collectively to confront hazards and reduce risk is a complex, yet extremely important, factor in determining vulnerability. Thus, the related hypothesis is that a community with well-developed social capital (reflected in strong community level trust and collaboration), faces low vulnerability (or high resilience) to a shock, ceteris paribus.
At the micro-or household-level, social capital or social cohesion is embedded in household and intra-household level relationships. According to Moser (1998), the capacity of a region or community to respond to a shock depends not only on community level trust and collaboration, but also on households' social cohesion. Thus, social capital (at the macro-/community-level) and social cohesion (at the micro-/household-level) are two important invisible intangible assets which determine the response capacity to a crisis. Household relations are expecially important in an economic crisis because they are a mechanism for pooling income and consumption sharing. For instance, in response to negative changes in economic circumstances, household strategies may include increase reliance on extended family support networks or increase labour migratio and remittances. While, the importance of social capital at the time of an economic crisis, may in the forms of increase reliance on informal credit arrangement (e.g. through rural cooperatives instead of banks) or increase informal support netwworks among households or farmers through farmers association, or increase community-level activity (Morse, 1998).
Ad.16) Women participation in employment/economic activities
Probably gender issue is more relevant in Indonesia than in more developed countries or countries with higher empowered female or women emancipation rate. Due to many constraints facing women in Indonesia (e.g. culture, religious, norm, customs, male-biased practices), the level of marginalisation of women in Indonesia (as in many other less developed countries) is generally believed to be higher than in the developed world.While, on the other hand, women empowerment or more opportunities for women to get good education and employment/economic activities will reduce poverty. At the time of crises, as argued in the literature, women can have a more difficult time during recovery than men, often due to sector-specific employment, lower wages, and family care responsibilities.Thus, the related hypothesis is that, regions with low marginalized women are less vulnerable to external shocks than those with low level of women emancipation, ceteris paribus.
Ad.17) Macroeconomic stability
Following the work of Briguglio et al. (2008) in the construction of a resilience index, macroeconomic stability is considered as an important variable that captures the effect of shock absorption or shock counteraction policies. Macroeconomic stability relates to an internal economic equilibrium (i.e. aggregate demand equals aggregate supply), which is manifested in a sustainable fiscal or government budget position (expenditures relative to tax and other incomes), low inflation rate, and an unemployment rate close to the natural rate, as well as by external balance. The latter is reflected in the balance of payment (international current account plus capital balance), or trade balance (export minus import in goods and services) or international current account position (i.e. trade balance plus balance in international payments not included in the capital balance), or by the level of external debt (e.g. external debt-to-GDP ratio).
With respect to fiscal position, the related hypothesis is that, higher fiscal deficit (more expenditure than incomes), lower sustainability of government budget, less resilience, and thus, higher vulnerability, ceteris paribus. At the regional level in Indonesia, APBD-to-PDRB ratio or government expenditure as a ratio to government income can be used as indicators of regional fiscal sustainability.
With regard to inflation and unemployment, the related hypothesis is that, higher inflation and unemployment rates (higher government expenditures of fiscal deficit), higher welfare costs caused by a shock, lower resilience, ceteris paribus. Therefore, unemployment and inflation are often associated with resilience of a shock-absorbing nature (Briguglio et al., 2008).
With respect to external debt, following Adger, et al. (2004), a country's ability to pay for emergency planning or to finance recovery programs such as social safety nets during a crisis will be affected by its indebtedness, i.e. the extent to which the national wealth is diverted into servicing loans. Moreover, economic policy in highly indebted countries is very often determined by the international financial institutions, which impose structural readjustment and trade liberalisation programmes (as in the case of Indonesia during the 1997/98 Asian financial crisis with the International Monetary Fund/IMF) that reduce the capacity of governments in those countries to pursue policies that reduce vulnerability associated with poverty. Ndikumana and Boyce (2003) also find that debt can encourage capital flight, further exacerbating national economic well-being.
Thus, the related hypothesis is that, higher external debt (measured by e.g. debt as a ratio of GDP), more difficult to mobilize resources in order to offset the negative effects of external shocks, and thus less resilience. For the application of this aspect at the regional/provincial level in Indonesia, loans or fund from central government (DAU) as a percentage of PDRB or as a ratio to PAD can be used as an alternative indicator.
Ad.18) Microeconomic market efficiency
Microeconomic market efficiency is also considered as an important component of the resilience index proposed by Briguglio et al. (2008). The theoretical justification of using this component is the following: an economy will gain more benefits of all existing resources are efficiently allocated through the undistorted price mechanism. Following an external shock, the more efficient an economy is, the more rapidly the market adjustment process to achieve equilibrium, the less are the costs of recovery. To apply this component at the regional level in Indonesia, since not many indicators of market efficiency are available, such as the percentage of private companies (or regional state-owned companies/BUMD) in total enterprises; the percentage of small and medium enterprises (SMEs) as a percentage of total enterprises; total private employment as a percentage of total employment; percentage of credits from private banks (or from state-owned banks) in total credits; and total non-tax retributions (or as a percentage of total tax incomes), can be used as alternative indicators. The basic idea here is that bureaucratic control of business activities (reflected by e.g. high percentage of BUMN in total enterprises) is also thought to inhibit market efficiency. Even, it can be hypothesized that in regions where government interventions/controls in daily social life, private initiatives and own capability to cope with an economic crisis are much weakers than in regions with the reverse situation,ceteris paribus.
At the micro-level, the most used indicators on vulnerability of an household are the followings (household characteristics:
1) Employment condition and status of the head of the family
It is generally expected that families with unemployed heads, ceteris paribus, are more vulnerable than those with heads having permanent jobs. Further, there is a positive correlation between the status of employment and the level of wage/income, ceteris paribus. In this respect, one hypothesis is that, the better the employment status of the head, ceteris paribus, the higher the degree of resilience, the lower the household's vulnerability
2) Education of the head of family
Theoretically, level of formal education is positively correlated with employment condition and status or with wage/income (as level of education is positively correlated with productivity, ceteris paribus). From their study on vulnerability in Bulgaria using a panel dataset 1994, Ligon and Schechter (2003) found that households with employed, educated heads are less vulnerable to shocks than are other households. An hypothesis derived from this theoretical thought is that the higher the level of formal education of the family head, the higher the degree of resilience and the lower the vulnerability of the family, ceteris paribus.
3) Gender and age status of the head of family
As already explained in discussing vulnerability indicators at the macro-level, gender issue of the head of family is more relevant in Indonesia than in more developed countries or countries with higher empowered female or women emancipation rate. Given many constraints facing women in Indonesia (e.g. culture, religious, norm, male-biased practices), it is generally expected that families with female head, ceteris paribus, are more vulnerable to or have more difficulties to cope with external shocks compared to those with male head (see also discussion in ad.16 with respect to macro-level indicators). With respect to age, as age is negatively related with productivity, beyond a certain age which is considered as the optimal productive age, it can be hypothesized that there is a positive link between the age of the household head and the level of household's vulnerability; with the assumption, other factors keep constant.
3) Household size and structure by employment and education
A large household size has more economies of scale than a smaller household size. But a large household size is more vulnerable to a crisis than a smaller household size, when the large sized household consists of a large number of dependent members (higher dependency ratio), or unproductive/unemployment members (lower productive age proportion), or low educated members. In Cutter,et al. (2003), it is explained that families with large numbers of dependents (or single-parent households) often have limited finances to outsource care for dependents, and thus must juggle work responsibilities and care for family members. All affect the resilience to and recovery from shocks. In other words, diverse livelihood activities by a range of household members is a good hedge against the failure of one or another of these income streams due to shocks. Also, during the recovery period, even if the pre-shocks employment or livelihood pursuit has been taken up again, the income or revenue (in the case that the househods have own businesses) may be lower due to missing equipment, use of damaged equipment, and marketing problems.
Thus, family structure by productive/working members or dependent/unemployed members (or educated member as an alternative indicator) also plays an important role in determinng the level of resilience/vulnerability of household. As empirical evidence, from their study in southern China, Chaudhuri and Christiaensen (2002) compared a number of characteristics of the poorest 26% (say group A) and the most vulnerable 26% (group B) of survey households, they find that average family size as well as fraction with high dependency ratio in group A are higher than in group B. The hypothesis related to this issue is that the smaller the size, given the family structure, or, the better the structure (low dependency ratio; low illiteracy rate), given the size, the higher the level of resilience and the lower the vulnerability, ceteris paribus.
4) Health conditions
Human capital consists of education/skill and health. As education, health condition of family members is also a very important determinant of the family's capability to response to a cri sis. The related hypothesis is that, the better the health condition of a family, ceteris paribus, the higher and the lower, respectively, the family's resilience and vulnerability.
5) Assets ownership
The capability of a family to response to an economic crisis is not only determined by income but by its total welfare, which is employment income plus income which can be generated from its total assets, e.g. natural capital (land and livestock), physical capital (house, transportation means, agricultural tools); financial capital (e.g. bank account/saving, net loans outstanding), and other non-labor assets/human capital. According to Hoddinott and Quisumbing (2003), all those assets (including social capital) through their allocation across a number of activities, e.g. food productionm cash crop production, and other income-generating determine the capability of households to response to shocks. Cutter, et al. (2003) constructed an index of social vulnerability to environmental hazards (called the Social Vulnerability Index) for the United Stated based on 1990 county-level socioeconomic and demographic data. In their model, personal wealth is measured besides by employment income also by median house values and median rents. By using a factor analytic approach, their analysis show that the wealth factor explains 12.4 percent of the variance. Based on this finding, they conclude that wealth enables households to quickly absorb and recover from losses. On the other hand, lack of wealth is a primary contributor to social vulnerability as fewer individual and community resources for recovery are available, thereby making the community less resilient to the hazard impacts. Thus, the related hypothesis is that the more assets owned by a family, the higher the family's resilience, and the lower its vulnerability, ceteris paribus.
According to such as Cova and Church (1997), Mitchell (1999), and Cutter, et al. (2000), rural residents may be more vulnerable than urban residents to shocks due to lower incomes and more dependent on locally based resource extraction economies (e.g., farming, fishing). The related hypothesis is that, given the above household characteristics, households located in remoted areas (e.g. mountain, hill, rural) are more vulnerable, as they are less resilience (they face many contraints to recover) , than households in open/fully accessible locations/urban, to a shock, ceteris paribus.
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Other advantages of the first approach: (1) it produces a “headline” vulnerability figure, (2) it can identify vulnerable but not poor households. See further Hoddinott and Quisumbing (2003) for further discussion on models regarding their proposed approaches.
 Swift (1989) argues that in the case of need (e.g. during a changing economic environment), assets can be transformed into production inputs or directly into consumption. Reducing assets increases vulnerability although this may not be visible.
 See many studies discussing economic vulnerability indicators, which include, among others, Briguglio (1992, 1995), Briguglio et al. (2008), Pantin (1997), UNCTAD (1997), Crowards and Coulter (1998), and Atkins et al. (1998).
However, many small countries manage to generate a relatively high GDP per capita or economic stability in comparison with other larger countries in spite of their high exposure to external/exogenous economic shocks. One good example is Singapore. Although this country is highly exposed to exogenous shocks as the country is fully linked to world economy through trade and investment, the country has managed to generate high rates of economic growth and macroeconomic stability. Briguglio (2003) terms this phenomenon as the “Singapore paradox”. See also such as Briguglio (1995) and most recent works of e.g. Alesina and Spolaore (2004) and Winters and Martins (2004) on the links between the vulnerability and the consequences of the size of countries.
 See, for instance, Hewitt (1997), Ngo (2001), and Cutter, et al. (2000).
See e.g. Brun et al.(2005), Carrère and Schiff (2005), and Guillaumont (2007),
Empirical work on the construction of an economic vulnerability index is often based on the premise that a country's vulnerability to exogenous shocks stems from a number of inherent economic features, including high degree of economic openness, export concentration and dependency on strategic imports. See e.g. Briguglio (1995), Briguglio and Galea (2003), Farrugia (2004), and Briguglio et al. (2008).
 The effects of export declines or export instability, a main source of economic vulnerability in especially primary commodities export dependending countries, have been discussed for many years in the literature using economic growth regressions. From those discussions/studies, there is a general consensus to conclude that export instability (or in some studies terms of trade instability) has a negative effect on economic growth (see, e.g. Bleany and Greenaway, 2001; Fosu, 1992, 2002; Guillaumont et al. 1999, Combes and Guillaumont, 2002, and Mendoza 2000). More significant effects are found when the studies test simultaneously the positive effects of export growth and the negative effects of export declines and when the export instability (size of the shocks) is either weighted by the average export to GDP ratio during the studied period, as found in, for instance, Combes and Guillaumont (2002).
According to the same report, the average prices of crude petroleum, copper, palm oil, coffee, and rice have fallen by 25‐50 percent since the start of the crisis and through the first quarter of 2009, although prices of major commodities have started to rebound since then. ASEAN countries exporting primary commodities have experienced delines in their export values for such commodities. For example, Myanmar has experienced a decline in the value of exports of natural gas due to decreasing global demand and falling commodity prices. The value of natural gas exports to Thailand, its biggest trade partner, was expected to drop by 50 percent year‐on‐year (World Bank and ASEAN, 2009).
Official data from the ASEAN Secretariat show that during the first seven months of 2009, foreign tourist arrivals fell by 14 percent in Indonesia and 20 percent in Vietnam, compared with the same period in 2008. In Cambodia, the number of tourists declined by 2.2 percent in the first two months of 2009 compared to a year
Earlier (World Bank and ASEAN, 2009).
According to a World Bank estimation, during the crisis period, international remittances have fallen by 7‐10 percent (World Bank, 2009). In Indonesia, Koser (2009) estimates that remittances have declined to US$3 billion in 2009, from US$6 billion in 2007. A qualitative assessment of migrant workers from the Philippines from the Philippines Central Bank also suggests that the frequency and size of remittances sent home were declining in recent months, though recent estimates indicate that overall remittance growth in the Philippines remained low but positive in the first half of 2009 (BSP, 2009).
 Import dependency on energy can also be used as an indicator (e.g. imported energy as a ratio of total energy consumed showing the degree of energy dependency.
In Downing et al. (2001), GDP per capita and Gini ratio are used as indicators of economic coping capacity (resilience). See also, e.g. Puente (1999), Platt (1999), Cutter, et al. (2000), and Peacock, et al. (2000).
 An alternative indicator for ad.11 and ad.12, is the Human Development Index (HDI), which is probably the most well-known national-level aggregate index relating to human welfare developed by UNDP. The HDI is based on the earlier Physical Quality of Life Index and related to the Human Poverty Index, and is an aggregate measure of well-being based on education and health status, as well as income and inequality. See e.g. Downing et al (2001), and Adger, et al. (2004) for further discussion on HDI. Downing et al (2001) propose the HDI as a reasonable measure of “present criticality”, which is equivalent to vulnerability.
For more discussion on the importance of social capital, see, among others, Lochner, et al (1999), Paldam and Svendsen (2000),and Adger, et al (2003.
See e.g. Tambunan (2009) for his research on women entrepreneurship in Asian developing countries and his review on literature on the issue.
See, among others, Enarson and Scanlon (1999), Morrow and Phillips (1999), Peacock, et al. (2000), and Cutter, et al. (2003).
In their proposed resilience index, Briguglio et al. (2008) included the sum of these two variables, also known as the economic discomfort index (or often said as economic misery index).
In Downing et al. (2001), dependency ratio, together with completed fertility, literacy, and lifeexpectation are used as main indicators of human resources coping capacity. See also, e.g. Morrow (1999), Puente (1999), and Heinz Center for Science, Economics, and the Environment (2000).
If there are data on employment income and data on total value (or total expected incomes) of all assets (not inlcuding human capital/labor), both data should be used as total family incomes.