The vulnerability and resilience of households in Vanuatu and Solomon Islands to global macroeconomic shocks (PhD Thesis) (original) (raw)

Abstract

The Small Island Developing States of Vanuatu and Solomon Islands are renowned for their exposure to exogenous shocks. However, the vulnerability and resilience of households in these countries to experiencing poverty as a result of economic shocks is less understood. Recent global macroeconomic shocks, including the spike in international food and fuel prices in 2007 and 2008 and the subsequent Global Economic Crisis (GEC), pushed many households in developing countries into poverty. However, a lack of household-level quantitative data means that there is little information on how these shocks affected households in either Vanuatu or Solomon Islands. This research uses empirical data from a cross-sectional survey of households that was specifically designed to examine the vulnerability and resilience of households to global macroeconomic shocks. In early 2011 six communities in each country were surveyed, ranging from inner-urban squatter settlements of the respective capital cities to some of the most geographically distant areas in each country. The original contribution of this thesis is that it marks the first time that the micro effects of macroeconomic events are studied in such a broad range of communities in either country. Household poverty is identified using the nascent, though well-known, Multidimensional Poverty Index (MPI). A new Melanesian MPI (MMPI) is also created, which accounts for distinctly local aspects of well-being, such as food gardens and social support systems. Both the MPI and MMPI indicate that poverty is highest in both urban squatter settlements where land is limited and the most isolated communities where infrastructure is limited. In contrast, poverty is lowest in rural communities with good transport links to central markets. Households were almost universally exposed to price shocks, largely through their purchases of imported food and fuel, which provide a direct link to international commodity price fluctuations. In contrast, the dualistic structure of the economy tended to insulate most rural households from weakening labour demand associated with the GEC. These effects were therefore limited to urban areas and communities with direct links to agricultural exports. While households used a variety of different mechanisms to cope with the shocks, it is clear that domestic food gardens and informal systems of social support are integral to households’ ability to manage risk. Yet, it is also clear that neither provides households with full insurance against shocks. Urban squatter communities and female-headed households were among the most acutely vulnerable. Combining empirical information on poverty, exposure to past shocks and households’ revealed preference for coping with shocks, the analysis also formally estimates households’ vulnerability to future poverty. The results indicate that vulnerability is widespread, with exposure to risk being the key determinant. This is an important finding as it illustrates the nexus between the well-known vulnerabilities of each country at a national level and household-level vulnerability. In order to strengthen households’ resilience to future shocks, social protection policies must shift the burden of managing risk away from individual households. Importantly, these policies must also build upon, rather than weaken, the unique assets of the local context.

Figures (91)

prominent recipients of ODA from Australia — the regional and economic power.® dual economy, combining large rural populations involved in subsistence agriculture and

he fastest growing economies in the region, largely driven by continued strength in tourism

Figure 1.2: GNI per capita Vanuatu and Solomon Islands 1.3.1. The unique vulnerabilities of Vanuatu and Solomon Islands

is Arithmetic average between the FAO and World Bank monthly food price indices. ** Crude oil, average spot price of Brent, Dubai and West Texas Intermediate monthly prices, equally weighed. Sources: FAO; World Bank; WTO. ee eta a ie, < ans win Price data index: (2004=100); GDP data annual percentage change, nominal Figure 1.3: International food and fuel prices and global output

Sources: Reserve Bank of Vanuatu; Solomon Islands National Statistics Office. Quarterly data; Year-ended percentage change; bars indicate contribution to growth Figure 1.4: Inflation in Vanuatu and Solomon Islands

Figure 1.5: GDP per capita in Vanuatu and Solomon Islands The vulnerability and resilience of households in Vanuatu and Solomon Islands The combination of the acute vulnerabilities of Vanuatu and Solomon Islands to economic

See Appendix A for a detailed list of the villages surveyed in each community. Urban and rural distinctions mirror, to the extent possible, the regional distinctions outlined in the respective national Censes. They are also a convenient approximation of the respective features of the dualistic economy structure in each country, described in Section 1.3 above. Islands, both in terms of the travel costs and the time costs of researchers, the research budget

As part of the broader mixed-methods research being undertaken under the same research grant provides a breakdown of the number of surveys in each community.

Table 3.1: Multidimensional Poverty Index (MPI)

Table 3.2: Melanesian Multidimensional Poverty Index (MMPI)

Notes: Sample size N=955. Source: Author. Table 3.3: Multidimensional Poverty Indices

Based on the geographical characterisation of basic needs poverty used by the Government of Vanuatu and the Government of Solomon Islands in Solomon Islands) according to the basic needs approach. Looking at the poverty profile at the individual community level, some further interesting children in rural households experienced three times the rate of severe deprivations than urban

Headcount multiplied by average intensity; by location and country* *Communities have been assembled, broadly, in terms of their increasing remoteness from central markets (as per Figure 3.1). Source: Author.

*Communities have been assembled, broadly, in terms of their increasing remoteness from central markets Source: Author. Percentage of households in multidimensional poverty by location and country*

* Communities have been assembled, broadly, in terms of their increasing remoteness from central markets (as per Figure 3.1). Source: Author. Percentage contributions of each dimension of well-being to total poverty; by community* Figure 3.3: Dimensions of multidimensional poverty

Table 3.5: MMPI poverty vs. MPI poverty that is MMPI poor will not be classified as MPI poor.

Percentage contributions of each dimension of well-being to total poverty; by country ‘igure 3.4: Dimensions of multidimensional poverty out in terms of their lack of access to gardens. 28 per cent of households, on average, in the 29 per cent average contribution of the remaining communities (Appendix D)

Percentage of total sample that is poor, vulnerable and non-poor; by country

provides a useful taxonomy of various shocks that can affect households (Table 4.1). such as a spike in inflation, an unexpected illness or even a military coup. The World Ban hat the focus should be on the effects of negative shocks since a household’s vulnerability to

asked whether they had experienced an event that had “made life hard” from a list, or hac In addition to price shocks, households were also asked to nominate whether they had

Table 4.3: Primary shock experiences of households in Vanuatu and Solomon Islands Percentace of camnle in narentheces

*Communities have been assembled, broadly, in terms of their increasing remoteness from central markets (as per Figure 3.1). Source: Author.

Percentage of sample; rural and urban communities* Figure 4.2: Reliance on environmental and market-based systems for food

*Formal employment includes private employee, self-employed, waged, or employer; informal employment includes voluntary work, unpaid family work, producing goods for sale, producing goods for own consumption and household duties. Sources: Vanuatu Census 2009, Table 7.8; Solomon Islands Census 2009 Table 1. Census data; total individuals employed as a percentage of workforce in each region* Figure 4.3: Employment types by location in Vanuatu and Solomon Islands

Percentage of households in the individual settlements of each urban community Figure 4.4: Gardens and crop failure shocks in urban communities )f the remaining shocks, urban households were statistically more likely than rural areas to

Percentage of households, by community* *Communities have been assembled, broadly, in terms of their increasing remoteness from central markets (as per Figure 3.1). Source: Author.

Source: Author. Figure 4.6: Number of adverse shocks experienced by households Per cent of total sample 4.4.3. Shock experience and well-being: dealing with endogeneity

Table 4.4: Shock experience and well-being A model of household shock experience

Table 4.5: Details of variables used in the model of households’ shock experience

Table 4.6: Probit estimations — selected shocks; total sample; marginal effects *** n<0.01, ** p<0.05, * p<0.10. Dependent variable takes the value one if a household reported experiencing a given shock, and zero otherwise; standard errors are robust, and Model | clusters standard errors on the basis of individual community. Luganville dropped from the sample. Hosmer-Lemeshow statistic is a lack-of-fit test: significant p values indicate a rejection of a goodness-of-fit test. Weather Coast had zero households experiencing a “Move in” shock. For a definition of urban and rural refer to Table 2.1 in Chapter 2.

Table 4.6 (continued): Probit estimations — selected shocks; total sample; marginal effects #*** N<0.01, ** p<0.05, * p<0.10. Dependent variable takes the value one if a household reported experiencing a given shock, and zero otherwise; standard errors are robust, and Model 1| clusters standard errors on the basis of individual community. Luganville dropped from the sample. Hosmer-Lemeshow statistic is a lack-of-fit test: significant p values indicate a rejection of a goodness-of-fit test. Weather Coast had zero households experiencing a “Move in” shock. For a definition of urban and rural refer to Table 2.1 in Chapter 2.

Example references in parentheses

Percentage of households that experienced recent global macroeconomic shocks* : * Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Coping responses are not mutually exclusive. *, **, *** indicate the results of t-tests of significance between rural and urban households at the 10, 5 and | per cent levels of significance, respectively. t For a definition of urban and rural refer to Table 2.1 in Chapter 2. Source: Author. Table 5.2: Dominant household coping responses to recent global macroeconomic shock in Vanuatu and Solomon Islands

* Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Source: Author. Figure 5.1: Gardens in urban communities and coping with shocks

* Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Communities have been assembled, broadly, in terms of their increasing remoteness from central markets (as per Figure 3.1 in Chapter 3). Source: Author. Percentage of households that experienced recent global macroeconomic shocks* Figure 5.2: Bank account ownership and coping with shocks The vulnerability and resilience of households to the effects of recent global macroeconomic shocks

Percentage of households that experienced recent global macroeconomic shocks* * Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Eight households were dropped from the sample because of no response to the question. For a definition of urban and rural refer to Table 2.1 in Chapter 2. Source: Author. he experience of food security tended to be more uniformly distributed along conventio ographic lines (Figure 5.5). Urban squatter communities had much higher rates of fc security (24 per cent) than rural areas (11 per cent). The worst rates of food insecurity w oserved in the urban communities of Auki, Port Vila and Honiara. In contrast, the w mnected rural areas of GPPOL and Hog Harbour and the geographically isolated Weat oast are noticeable for their very low level of food insecurity. This may be a capturing b e effects of the Melanesian “sweet spot” identified in Chapter 3 as well as the near s liance of the isolated communities when it comes to food.!*4

* Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Communities have been assembled, broadly, in terms of their increasing remoteness from central markets (as per Figure 3.1 in Chapter 3). Eight households were dropped from the sample because of no response to the question. Source: Author.

* Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Indicator of fooc insecurity is whether “an adult had not eaten for one day during the past 12 months”. Communities have been assembled, broadly, in terms o: their increasing remoteness from central markets (as per Figure 3.1 in Chapter 3). Source: Author. Percentage of households that experienced recent global macroeconomic shocks; by tenant tt

. ** = p<0.01, ** = p<0.05, * = p<0.10; Standard errors are robust and Model A clusters standard errors on the basis of individual community. Models 1A and 1B dependent variable derived from a five-point Lickert scale, where 3 = no change. Models 2A and 2B dependent variable takes the value one if a household reported experiencing food insecurity and zero otherwise; Luganville dropped from the sample. Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Hosmer-Lemeshow statistic is a lack-of-fit test: significant p values indicate a rejection of a goodness-of-fit test. Square brackets show the maximum and minimum values for each coefficient from the separate models of well-being using a dummy variable for each dominant coping response. For a definition of urban and rural refer to Table 2.1 in Chapter 2. Qonree: Anthar

* Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). *** p<0.01, ** p<0.05, * p<0.10; standard errors (not shown) are robust can clustered on the basis of individual community (i.e. Model A). Results presented are the coefficients on the dummy variables of each coping variable in a separately specified model of household well-being during the recent global macroeconomic shocks. Other explanatory variables (not shown) include two separate indices of household wealth, household size, dependency ratio, adult education, and dummy variables for the gender of the household head, access to various livelihood sources, and the dominant source of food. Separate dummy variables were also included for households’ experiences of shocks as well as dummy variables for urban households and Vanuatu households. Full regression output of these 15 separate models available upon request. Source: Author. Coefficients on coping variables from Equation 5.1* rable 5.4: Effectiveness of households’ dominant coping mechanisms in providing resilience from recent global macroeconomic shocks shocks and a recent idiosyncratic shock, respectively.

Table 6.2: Model of the estimation of vulnerability to MPI poverty

Z-statistics in parentheses; *** p<0.01, ** p<0.05, * p<0.10. Standard errors are robust and clustered errors on the basis of individual community. Non-capital city includes all communities except Port Vila and Honiara. Luganville dropped from the sample. For models of MPI score a negative coefficient implies a reduction in expected weighted MPI deprivations and thus an improvement in well-being. For a definition of urban and rural refer to Table 2.1 in Table 6.2 (cont.): Model of the estimation of vulnerability to MPI povert:

*** Statistically significant difference between the predicted and actual at the 1 per cent level according to a t-test. Table 6.3: Goodness of fit: predicted versus actual MPI deprivations \ number of tests were also run to check the specification of the model. Importantly, there was consistent with other FGLS models of household well-being (see Del Ninnio et al. 2006, p10).

Table 6.4: Vulnerability and poverty groupings Unless otherwise specified, relative vulnerability and high vulnerability are conflated as “vulnerability”. Source: Author, based on and Chaudhuri et al. (2002). the two are not exact substitutes.!7!

Figure 6.1: Vulnerability and expected weighted sum of deprivations in Vanuatu anc Solomon Islands NB: a higher deprivation score represents lower household well-being. The poverty threshold (line GHIJ) is set at one third. Source: Author.

Table 6.5: Vulnerability to MPI poverty and observed headcount MPI poverty

Source: Author. Figure 6.2: Estimated incidence of vulnerability at various vulnerability thresholds 6.4.4. Vulnerability and MPI-poverty by individual communities

Communities have been assembled, broadly, in terms of their increasing remoteness from central markets (as per Figure 3.1 in Chapter 3) Source: Author. Figure 6.3: Vulnerability to MPI poverty and MPI poverty headcounts is, the mean probability of experiencing poverty in each community. For instance, the mear

By community and country *The proportion of poor households is the headcount poverty rate, while the fraction vulnerable is the headcount vulnerability rate. Poverty and vulnerability are not mutually exclusive (see Table 6.5). Mean vulnerability is the mean probability of experiencing poverty in the future in the cohort. For a definition of urban and rural refer to Table 2.1 in Chapter 2.

*Expected incidence of poverty is slightly different to the probability of experiencing poverty in that the former only includes the expected mean, not the expected variance, of well-being. For a definition of urban and rural refer to Table 2.1 in Chapter 2. Source: Author. Weighted MPI deprivations greater than poverty line; by community and country Table 6.7: Depth of expected MPI poverty

6.4 and Appendix Z). In almost all communities there is statistically no significant difference with the MPI (36.4 per cent) though the difference is not statistically significant. Also similat

Communities have been assembled, broadly, in terms of their increasing remoteness from central markets (as per Figure 3.1 in Chapter 3) Source: Author. This chapter estimates the vulnerability of households in Vanuatu and Solomon Islands in

Appendix A: Detailed list of villages surveyed:

a — Proportion of households with access to improved water; improved sanitation; electricity; secondary school / primary school / medical centre / bank within halt an hour s travel (source: household survey). b — Households were asked to rank their most important food source (source: household survey). c — Transport infrastructure and primary income-generating activity (unless otherwise states) from author’s observation. d-— Population estimates obtained from key informant interviews with community leaders. e — Euclidean (straight-line) distances between location and the respective capital city; calculated using Google maps. f — Cultural heterogeneity measured as the proportion of people in each community surveyed who originate from the “incumbent island” compared with those who originate from another location (source: household Source: Author (unless otherwise specified).

For a definition of urban and rural refer to Table 2.1 in Chapter 2. Source: Author. Percentage of households in each community that are deprived in the relevant indicator

For a definition of urban and rural refer to Table 2.1 in Chapter 2. Numbers may not sum due to rounding. Source: Author. Percentage contribution each dimension makes to overall poverty in each community

Share of households in each level of poverty intensity, by community Total non-poor includes both non-poor, non-vulnerable households and vulnerable (near poor) households. Total poor includes less-severe poor households and severe poor households. Total non-poor and total poor sum tc Non-poor, non-vulnerable households have a weighted average deprivation score of less than 0.20. Vulnerable (near-poor) households have a weighted average deprivation score greater than or equal to 0.20 and less than 0 Less-severe poor households have a weighted average deprivation score greater than or equal to 0.33 and less than 0.50. Severe poor households have weighted average deprivation score greater than or equal to 0.50. Numt not sum due to rounding. Source: Author.

Appendix F: Types of shocks experienced by households Percentage of sample For a definition of urban and rural refer to Table 2.1 in Chapter 2. *, **, *** indicate the results of t-tests of significance between rural and urban households at the 10, 5 and 1 per cent levels of significance, respectively. Some shocks identified in Table 4.2 (including default on a loan, divorce/ separation) and are not specified because of a lack of observations. Source: Author. Appendix F: Types of shocks experienced by households Percentage of sample

Source: Author. Appendix G: Tetrachoric correlation matrix of households’ shock experience

a? Re a Reported statistic: factor loadings (pattern matrix) and unique variances *Calculated using tetrachoric correlation matrix. Several shock types were dropped from the factor analysis owing to the lack of a positive semi- definite tetrachoric correlation matrix. Other shocks were dropped on account of being wholly-contained subsets of the relevant inflation shock, including: severe food inflation; severe fuel inflation; severe real food inflation and severe real fuel inflation. Shocks relating to fall in demand fall in supply and fall in remittances and ‘other’ were excluded owing to a lack of data. Source: Author.

Percentage of households that experienced each shock; by community; standard errors in parentheses Source: Author.

* Labour market events include a new job or an increase in wages. Commodity income increases include commodity price increases or more crops growing; one off transactions include setting up a stall at Independence Da celebration in Vanuatu, receipt of a bride price, arrangement of a rental lease, and a bumper catch of fish. Other includes receipt of a loan, an inheritance, and an unexpected increase in remittances Source: Author. Percentage of households that experienced each shock; by community; standard errors in parentheses

* According to WHO/UNICEF (2013). ** High quality building materials include concrete wood or tin, while high quality roofing materials include tin or tiles. The first Eigenvalue is 5.49 and the second is 1.59 Source: Author. in the index and the polychoric PCA coefficients.

Table 2: Traditional wealth index traditional wealth index and the polychoric PCA coefficients.

*Conventional wealth is an index of long-term socioeconomic status of the household based on household durable assets; Traditional wealth is an index of long-term socioeconomic status based on traditional assets. Higher values of wealth indices represent better-off households. Ten households were dropped from the sample to construct an index of traditional wealth, owing to missing data on decision making within the household. For a definition of urban and rural refer to Table 2.1 in Chapter 2. Table 3: Conventional and traditional wealth scores by location type* reverse is the case for the traditional wealth index. average score than rural households according to the conventional wealth index, while average score than Solomon Islands in both indices (Table 3). Urban households have a higher

Selected shocks; coefficients and standard errors

Selected shocks; coefficients and standard errors

Selected shocks; coefficients and standard errors

Selected shocks; marginal effects mes p<0.01, ** p<0.05, * p<0.10; Dependent variable takes the value one if a household reported experiencing a certain shock, and zero otherwise; standard errors are robust, and clustered on the basis of individual community. osmer Lemeshow statistic is a lack-of-fit test: significant p values indicate a rejection of a goodness-of-fit test; Luganville dropped from the sample, Weather Coast had zero households exper Community effects dropped because of a lack of data in a number of communities when the sample was split between poor and non-poor. Food peddler dropped from real inflation shock model 1 definition of urban and rural refer to Table 2.1 in Chapter 2. Source: Author.

ome ps¥.U1, ** p<¥.U5, * p<u. 13 Dependent variable takes the value one if a household reported experiencing a certain shock, and zero otherwise; standard errors are robust, and clustered on the basis of individual community. Hosmer Lemeshow (H/L) statistic is a lack-of-fit test: significant p values indicate a rejection of a goodness-of-fit test; Luganville dropped from the sample, Weather Coast had zero households experiencing a “ Community effects dropped because of a lack of data in a number of communities when the sample was split between poor and non-poor. v Source: Author.

Total sample, poor households and non-poor households; marginal effects

Source: Author based on Heitzmann et al. (2002); Holzmann and Jgrgensen (2000); Moser (1998); Siegel and Alwang (1999).

! ! * Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Includes only the responses for which data were available. A number of responses are also not shown, including those with less than a 5 per cent response rate and those with responses similar to those already included in the Table. For a definition of urban and rural refer to Table 2.1 in Chapter 2. Coping responses are not mutually exclusive.*, **, *** indicate the results of t-tests of significance between rural and urban households at the 10, 5 and | per cent levels of significance, respectively. Onenee Auth ae Percentage of households that experienced recent global macroeconomic shocks

*Idiosyncratic shock is a death / illness shock (see Chapter 4 for details). For a definition of urban and rural refer to Table 2.1 in Chapter 2. Coping responses are not mutually exclusive. *, **, *** indicate the results of t-tests of significance between rural and urban households at the 10, 5 and | per cent levels of significance, respectively. Source: Author. Percentage of households that experienced the shock Appendix S: Dominant household responses to an idiosyncratic shock in Vanuatu and Solomon Islands *

Percentage of households that experienced the shock Appendix T: Households’ responses to an idiosyncratic shock in Vanuatu and Solomon Islands *

nple limited to the 816 households that experienced recent global macroeconomic shocks*

* Global macroeconomic shocks include a real inflation shock and a labour market shock (see Chapter 4 for details). Standard errors (not shown) are robust; *** p<0.01, ** p<0.05, * p<0.10. Coefficients on the dummy variables of each coping variable in a separately specified model of household well- being during the recent global macroeconomic shocks. Other explanatory variables (not shown) include two separate indices of household wealth, household size, dependency ratio, adult education, and dummy variables for the gender of the household head, access to various livelihood sources, and the dominant source of food. Separate dummy variables were also included for households’ experiences of shocks as well as dummy variables for urban households and Vanuatu households (i.e. Model A). Moved in with family/wantok coping mechanisms both dropped from Appendix R because of insufficient data. Full regression output of 41 separate models available upon request. Source: Author. Coefficients on coping variables from Equation 5.1 Appendix V: Effectiveness of households’ coping mechanisms in providing resilience from recent global macroeconomic shocks*

Appendix W: Effectiveness of households’ coping mechanisms in providing resilience from an idiosyncratic shock *

A ppendix Y (cont.): Model of the estimation of vulnerability to MMPI poverty

‘The proportion of poor households is the headcount poverty rate, while the fraction vulnerable is the headcount vulnerability rate. Poverty and vulnerability are not mutually exclusive (see Table 6.5). Mean vulnerability is nean probability of experiencing poverty in the future in the cohort. For a definition of urban and rural refer to Table 2.1 in Chapter 2. Source: Author.

Source: Author.

Weighted MMPI deprivations greater than poverty line; by country and community *Expected incidence of poverty is slightly different to the probability of experiencing poverty in that the former only includes the expected mean, not the expected variance, of well-being. For a definition of urban and rural refer to Table 2.1 in Chapter 2. Source: Author.

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