metadata: authors: 0: author: Nose,Manabu
count: Indonesia
docna: 0: docna: Enduring impacts of aid quality on job
            choices : the case of the 2004 tsunami in Aceh
docty: Policy Research Working Paper
subtopic: Urban Housing and Land Settlements,Labor Markets,Urban Governance and Management,Urban Housing,Municipal Management and Reform,Natural Disasters
teratopic: Environment,Agriculture,Social Protections and Labor,Water Resources
abstracts: cdata!: After the tsunami in Aceh, Indonesia,
            the recovery of fishing was limited while non-fishing
            sectors temporarily expanded. This paper shows that
            fishermen's ex-post labor supply responses continued to
            be constrained by the provision of low quality production
            assets. The average fishing productivity also declined for
            the negative selection in response to the aggregate shock.
            In a natural experiment set-up, it shows widening income
            inequality after the tsunami for income losses to the
            recipients of poor quality aid. It suggests the importance
            of quality monitoring and private market access to
            sustainably promote rural development after the tsunami.
geo_reg_key: 517198,517191
repnme: repnme: Enduring impacts of aid quality on job
            choices : the case of the 2004 tsunami in Aceh
display_title: Enduring impacts of aid quality on
            job choices : the case of the 2004 tsunami in Aceh
listing_relative_url: /research/2013/06/17934839/enduring-impacts-aid-quality-job-choices-case-2004-tsunami-aceh
topicv3: Land and Housing m1327648 817,Tsunami m1503625 3160,Jobs m1503931 2670,Labor Supply m1504055 2751,Labor Demand m1583073 4422
publishtoextweb_dt: 2013-06-01T00:00:00Z
historic_topic: Environment,Agriculture,Social Protections and Labor,Water Resources
no_of_pages: 51
author_keys: 0: author_key: 1172766
model_extractions: WPS6514 Policy Research Working Paper 6514 Enduring Impacts of Aid Quality on Job Choices The Case of the 2004 Tsunami in Aceh _Manabu Nose_ The World Bank Multilateral Investment Guarantee Agency Economics and Sustainability Group June 2013
Policy Research Working Paper 6514

# **Abstract**

After the tsunami in Aceh, Indonesia, the recovery of fishing was limited while non-fishing sectors temporarily expanded. This paper shows that fishermen’s ex-post labor supply responses continued to be constrained by the provision of low quality production assets. The average fishing productivity also declined for the negative selection in response to the aggregate shock. In a natural experiment set-up, it shows widening income inequality after the tsunami for income losses to the recipients of poor quality aid. It suggests the importance of quality monitoring and private market access to sustainably promote rural development after the tsunami.

This paper is a product of the Economics and Sustainability Group, Multilateral Investment Guarantee Agency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mnose@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Enduring Impacts of Aid Quality on Job Choices: The Case of the 2004 Tsunami in Aceh<sup>1</sup> Manabu Nose<sup>2</sup>

**<mark>JEL classification:</mark>** Q54, H84, J43, O12

**Keywords:** Rural development, Natural disaster, Aid effectiveness, Coping strategy, Inequality

**Sector Boards** :  POV, SDN, PSD

> 1 I am grateful to Vernon Henderson, Andrew Foster, and Sriniketh Nagavarapu for their guidance and supports on this paper. I thank Ari Kunkoro, Zakir Machmud, Ifa Isfandiarni, and Hamdan Bintra of the University of Indonesia for the supervision of the Aceh tsunami survey. Useful insights from Glenn Loury, Blaise Melly, Pedro Dal Bo, Oded Galor, Nathaniel Baum-Snow, Mark Pitt, and Adam Storeygard, and Takashi Kurosaki are greatly acknowledged. I also acknowledge the support from World Bank Jakarta office for the access to Indonesian household surveys. The findings, interpretations and conclusions expressed in this paper are entirely those of the author, and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent

> 2 The World Bank, 1818 H Street, N.W., Washington, DC, 20433 USA; Email: <u>mnose@worldbank.org; Tel:</u> +1-(202)294-5370
# **1.       Introduction**

After the complete destruction of the agricultural labor market by disasters, rural households suddenly lose jobs and need to consider how they reallocate their labor between farm and off-farm employment. In such situation, it is essential to restore the agricultural sector to move underemployed households back to work, but investments in the non-agricultural sector may also help reduce rural poverty (Lanjouw and Lanjouw, 2001; Foster and Rosenzweig, 2004). Whether the economy reverts back to the pre-disaster situation or is transformed to a new structure could be driven by the relative aid effectiveness on farm and non-farm growth after the shock.<sup>i</sup> This paper considers the recovery process of the fishing villages in Aceh, Indonesia after the 2004 Indian Ocean tsunami. Following the almost complete destruction of production assets in Aceh, there was a huge influx of humanitarian relief (such as housing and fishing boats) and a reconstruction boom to restore destroyed public infrastructure, funded by the Indonesian government and international agencies. During this recovery process, the share of fishing sector fell, accompanied by a temporary expansion of non-fishing sector as shown in Figure 1.

My main theme is how aid _quality_ constrains the well-being of households after natural disasters. A few recent papers have examined the effectiveness of in-kind aid transfers on jobs, but the results are rather mixed.<sup>ii</sup> In Aceh, the in-kind transfer programs experienced a major challenge in trade-offs between aid quality, aid quantity, and the speed of delivery (Henderson and Lee, 2012; Nose, 2013). In particular, fishing boats from aid agencies were made of low quality materials or were improperly constructed due to a lack of quality monitoring by NGOs, shortage of qualified boat builders, a rush of boat orders with unrealistic delivery times, and limited amounts of resources (FAO, 2005).<sup>iii</sup> Due to the imperfect credit market, such quality shocks created a sizable income loss for those who were heavily affected by the tsunami.

Using household-level panel data, this paper provides rigorous evaluations of the short-term and long-term impacts of in-kind aid quality on fishermen’s job recovery and income growth after the tsunami. It further considers whether an alternative form of disaster-relief (i.e., the development 2
of private market) could better help fishermen to plan their career and cope with aid quality problem.

Empirical section considers the potential beneficiary selection bias from the non-random quality assignment between treated (the recipients of bad quality aid) and untreated (good aid recipients) groups similar to Chen, Mu, and Ravallion (2009). I offer a balancing test in the later section which supports the exogeneity of the aid quality variation conditional on observable characteristics. Using the survey data on the quantity and quality of housing and fishing reliefs, it also deals with the confounding effect from the multiple treatment problem.

This paper found that the aggregate quality shock (adverse labor demand shock common within a village borne by a supply of faulty aid boats) created substantial income losses, driving many fishermen to choose non-fishing jobs. Moreover, the aggregate shock created a _negative selection effect_ , leading to a small-scale and less productive fishing sector. A subset of aid recipients further faced the idiosyncratic shock (loss of production capital due to the imperfect durability) in later years. Households needed to take costly coping actions if they experienced the shocks, creating a new form of income inequality. As private market gradually developed over time, better access to private market helped fishermen to undo the idiosyncratic shock. <mark>These results imply that the quality monitoring of in-kind aid and the soft aid for developing private market would be key policy agenda for donors in the post-disaster context.</mark> The paper is structured as follows. Section 2 explains the context and survey. Section 3 provides theoretical predictions. Section 4 and 5 carry out empirical analyses on job choices, while Section 6 studies the impact of aid quality on inequality. Section 7 concludes.

# **2.     Background and survey data**

# _2.1   Labor market developments after the tsunami_

Creating permanent jobs is an important challenge in the aftermath of the natural disaster. Figure 1 shows the change in the fraction of workers in each of the four sectors and the underemployment rate.

3
Each bar is the mean value of employment shares from the overlap sample of 80 villages which were surveyed in all three survey rounds.

In the pre-tsunami period, fishing was the dominant industry in the coastal villages in Aceh.<sup>iv</sup> The impact of the tsunami on fishing community was substantial: the underemployment rate spiked up to 46% from the pre-tsunami level of 12%, and the fishing reefs and mangroves were devastated. During the first six months after the tsunami, the majority of households worked for cash-for-work programs or made a life by receiving living cost allowances from local governments, which explains the largest share of workers in the “others” job category in 2005 (29%) in Figure 1. Fishing sector temporarily recovered in 2007, but by 2009 the share of fishing industry had declined again.

On the other hand, the non-fishing industry continued to develop due to an increased labor demand in the construction sector (called “spending effect” by Corden (1984)) for rebuilding private houses and public buildings (e.g. hospitals, schools, and mosques). While aid activities were heavily clustered in the north-west part of mainland Aceh, there were only a few agencies which provided assistance in remote areas (further down the south part in Aceh Jaya) and in the isolated island in Pulo Aceh. Except for construction business, n <mark>on-fishing sector in Aceh was mainly trading (e.g., fish-related retail business and transportation) which relies on fishing activity growth. This is household production on a very small scale, producing simple goods, unlike small modern factories which produce exportable goods in India (Foster and Rosenzweig, 2004). As most aid efforts came close to the end in 2009, public investments wind down, increasing underemployment rate.</mark> Table 2 comes from the household survey and shows a big shift of fishing status from being a captain to crew from 2007 and 2009. This shift indicates limited fishing opportunities for crew in 2007. The fall in the number of boat owners and captains in 2009 also implies that some household-specific shocks occurred in later years. The distribution of workers also became somewhat diversified: many former fishermen, especially those with higher education and with trauma (psychological and physical reactions) from the tsunami, started working in non-fishing jobs.

4
# [insert Table 2]

Figure 2 shows the lasting negative effects of the low aid quality on the post-tsunami labor market developments. It divides villages into two groups: 49 villages with a minor quality problem (i.e., less than 50% of aid boats were in bad quality; left panel) and 31 villages with a major quality problem (right panel). In the former type of villages, the fishing sector recovered to above the pre-tsunami level in 2007, but declined in 2009. On the other hand, in the latter type of villages, fishing did not recover to the pre-tsunami level in 2007 and continued to decline in 2009.

# [insert Figure 2]

_2.2 Survey_ The data are drawn from a large field survey on fishermen and coastal villages in Aceh, Indonesia (“Population and Economic Recovery in Coastal Aceh: Aid and Village Institutions” principally investigated by J. Vernon Henderson (Brown University/NBER) which has been financially supported by the National Science Foundation (SES 0416840) and the National Institutes of Health (R01 HD057188)). The data were gathered at three levels (household, village head, and local heads of fishermen’s association ( _Panglima Laot_ )) in 2005, 2007, and 2009. In 2005, information on pre-tsunami household characteristics was also retrospectively collected.

The initial survey was conducted in early 2005 and covered 544 pre-tsunami fishermen in 111 fishing villages. The survey covers the universe of fishing villages in the tsunami affected kabupatens (Banda Aceh and Aceh Besar).<sup>v</sup> In the fishermen’s survey, the targeted population is fishing households living in these coastal villages which were most heavily damaged by the tsunami.<sup>vi</sup> The survey on fishing households represents 25% of pre-tsunami boat owners and 45% of surviving boat owners in these villages. The sampling frame was drawn from the list of former fishing boat owners assembled by _Panglima Laot_ , and the fishing households were proportionally sampled from the list in each village.

Among 544 households surveyed in 2005, I use 348 pre-tsunami boat owners or captains 5
who were re-surveyed in late 2007 as a baseline sample ( **_original sample_** ). This sample is used to analyze factors affecting initial job decisions in 2007.

In 2007, the survey added 151 new fishing families to our original sample; these families had never owned a boat before the tsunami but donors gave them a chance to be the first time owners. Besides, 288 previous owners were additionally added from Aceh Jaya (kabupaten which is heavy devoted to fishing) which is located in the south of Aceh and was not originally covered in 2005. Of the 439 households, only 244 households could be re-surveyed in 2009.<sup>vii</sup> I added these to 313 households (which were surveyed in all three rounds among 348 original sample), and formed 557 families ( **_supplementary sample_** ). This is the two-year panel between 2007 and 2009 to examine the job switching behaviors (see Figure 3).

Summary statistics for the original sample and the supplementary sample are reported in Table 1. In supplementary sample, new boat owners were oversampled in Aceh Jaya. This sampling heterogeneity needs to be controlled in the later sections.

# **3.      Theoretical predictions**

The model is built in two stages where fishermen make career decisions in face of aggregate and idiosyncratic quality shocks (see Appendix 1 for details).

First, donors provide production assets (i.e., fishing boats) with heterogeneous quality across villages. In the first stage, some villages encounter serious quality problem. Households in such communities face a decrease in labor demand (aggregate shock; _AGGSHOCK_ ) as it reduces the number of boat owners in the community, suppressing labor demand for crew as well. Besides, it could also have a price effect by reducing compensations for crew. In the first stage, actual boat quality is imperfectly observed by individual fishermen.<sup>viii</sup> After they use the fishing boats, they may discover that their boat is not durable (idiosyncratic shock; _IDIOSHOCK_ ). If the aggregate quality shock continues to constrain job opportunities, those who initially resumed fishing may have common motives to switch jobs in the second stage.

6
Under the Roy model, fishermen compare the fishing income and the reservation wage for non-fishing activities in the first stage. Duration of initial job depends on people’s earnings in the first job as well as the idiosyncratic shock happened afterwards.

With heterogeneity in two skill factors (fishing and market skills), the optimal career decision depends on the comparative advantage in each sector as well as the type of capitals given by aid agencies. With the capital-skill complementarity, negative selection in fishing skill would be expected if a household is likely to receive a faulty boat.

# **4.   Empirical framework**

The empirical analysis exploits quality variations of fishing boats at village and household levels to estimate their short-term and long-term effects on the labor supply decision. The analysis starts from probit regressions, defined in Eq. (1) and (2) below, to understand job choices in 2007 and 2009. In 2007, the empirical model is only concerned about the aggregate quality shock _B_ , as almost nobody (only 0.6%) in my sample experienced the idiosyncratic shock (see Table 1).

where F07 equals one if household _i_ in village _v_ in kabupaten (or kechamatan) _k_ chose to fish in 2007, and zero otherwise. _X_ is a vector of households’ pre-tsunami attributes such as their boat size (capturing fishing productivity), fishing status as a boat owner and captain, relationship with middlemen through credit repayment obligation, a higher education dummy, and the number of surviving captains after the tsunami (capturing the aggregate labor demand for crew). An outside option variable ( _w_ ) controls for the confounding effect due to the _quantity_ of other aid programs. With greater number of non-fishing aid implemented in the community, I expect higher elasticity of labor supply to the aggregate quality shock as labor demand in the non-fishing sector increases, providing more attractive market job opportunities. The benchmark regression uses the log of 7
number of NGOs as a proxy for outside options, and regressions in Appendix 2 use alternative outside option measures to check the empirical robustness. Fixed effect _μ_ purges region specific unobserved characteristics. To adjust seasonal working patterns (e.g., fishing in the peak season and farming in the slack season), it also controls for the months when interview took place.

Eq. (2) estimates career decision to continue fishing in 2009 conditional on those who were fishing in 2007 (see Table 3 for the career transition matrix).

where _q_ is the actual quality of aid boats; it equals one if the boat became unusable in 2009. Even in 2009, there would be a lack of job opportunities to become crew in villages with many bad boats, which might prevent households from remaining in fishing sector. If the aggregate shock posed a long-term constraint for fishermen’s job choices, β2 will be negatively signed. _X_ includes similar household characteristics as well as the fraction of fishermen in each village, a proxy for the size of fishing sector, capturing an agglomeration effect. l�sorig = 1� controls for sampling heterogeneity for the supplementary sample. _μ_ controls for the regional differences in the market environment and other unobservable factors.

Households' unobservable characteristics, such as mental conditions or risk preferences, might have driven job choices. To confirm the empirical robustness, endogenous switching regression is employed to account for the self-selectivity between two sectors. (see Appendix 3)

# _4.1  Endogenous self-financing_

Besides a risk-mitigation through labor supply responses, self-financing may be another optimal choice for agents to deal with the quality shocks depending on the level of wealth and/or an access to a private market. With this option, agents compare the relative payoffs of three options: fishing as crew (V1), fishing as an owner by buying a boat (V2), and working for non-fishing jobs (V3).

8
Assuming the logit error structure, the probability for households _i_ to take each option can be written as follows and the parameters can be estimated using the multinomial logit model.

# _4.2 Identification issues_

_A. Exogeneity of quality shock variables_ The empirical identifications in Eq. (1)-(3) rely on the exogeneity of _B_ and _q._ Table 4 provides the balancing test of two quality shock variables.

The aggregate quality shock _B_ can be systematically different by the type of villages. For example, the aid quality could be worse in remote rural areas with poor access to road or in heavily devastated places which were too dangerous for aid agencies to operate. Moreover, as shown by Henderson and Lee (2011), the aid quality is also highly correlated with the type of aid agencies (donor) and village governments (recipient). The upper panel of Table 4 splits villages with major quality issue ( _Bad50=1_ ) vs. not ( _Bad50=0_ ), and shows no significant differences with regard to most village attributes (i.e., the survival rate of population, the supply constraint of aid relative to demand from surviving captains, distance from Banda Aceh, the involvement of donors with inadequate quality monitoring,<sup>ix</sup> and the local governance structure). Only exception is that the boat quality was worse in places where local government/BRR decided an aid delivery. I include kecamatan fixed effects to balance village characteristics between two groups.

To identify the idiosyncratic effect of _q_ , the characteristics of fishermen who experienced the shocks vs. not needs to be balanced. The bottom panel of Table 4 shows higher likelihood of the idiosyncratic quality failure to smaller boat owners pre-tsunami, especially around a remote region (i.e., Aceh Jaya). Thus, regressions need to control for kecamatan fixed effects and pre-tsunami boat size for _q_ to be plausibly exogenous. Intuitively, non-randomness of _q_ is less of a concern for two reasons. From supply side, although aid boats tended to be targeted to people with stronger social 9
connection (Nose, 2013), it is unlikely that different quality of boats were selectively assigned to a particular type of fishermen. From the demand side, when fishermen lobbied with village leaders to ask for aid, they were concerned about receiving any boats rather than the actual aid quality given the limited supply of aid. Controlling for pre-tsunami social status (as a boat owner and captain) will minimize the non-randomness associated with the endogenous placement of aid within each village.

# _B. Confounding effect from the quality of housing aid_

Another major form of hard aid was house constructions which also had serious quality problem (Henderson and Lee, 2011). Almost all households in my sample received houses from donors by 2009, but they were often constructed with leaky roofs, cracked walls and poor foundations (see Table 1). By 2007, 41% of house recipients reported at least one form of the quality deficiencies, which rose to 51% by 2009. The faulty houses were more commonly observed for the poor and were clustered in Banda Aceh. Living conditions, such as ownership of houses and land tenure, is found to affect labor supply decisions in developing countries (Field, 2007). As almost all houses were destroyed by the tsunami (survival rate was only 9%), a failure to secure a good quality houses could pose a major constraint for the job and income recoveries. The qualities of boats and houses were not significantly correlated, so I separately control for the quality of housing aid to remove the potential bias.

# _C. Attrition bias_

Figure 3 shows the selection of my sample over time. Internal validity of the estimates faces a potential threat of attrition bias if those dropped out of the study (“droppers”) are systematically different from those who remain in the study (“stayers”). A simple t-test reveals that droppers were more likely to be those who did not own a boat in 2007 and they tended to live in Aceh Besar or Banda Aceh. Intuitively, as the idiosyncratic shock is more serious for those living in Aceh Jaya, which is more likely to remain in my sample, _IDIOSHOCK_ would be overestimated.

10
I employ inverse probability weighted (IPW) estimation which can extend to a general non-linear model and does not require an exclusion restriction (Wooldridge, 2002). The attrition probability is predicted in the first stage based on a regression of the attrition dummy on socio-economic covariates and regional fixed effects. Then, I non-parametrically weight each observation using the inverse probability of non-attrition to obtain consistent and efficient estimates which account for attrition bias.

# **5.  Empirical results**

_5.1 Assessing the impact of quality shocks_

# _A. Initial career decision_

Eq. (1) is separately estimated for boat owners and non-owners in 2007 as the aggregate shock should matter only for the latter group.

Column (1) shows estimates for 154 owners, among which 74% chose fishing. Their decision was not significantly affected by the aggregate shock as they already had a fishing boat and did not anticipate the possibility that their boats could be unusable in a few years.

The rest of columns are for non-owners. In column (2) and (3), a unit increase in the supply of bad aid boats limits opportunities to be crew and reduces the probability of fishing by 50.4 percentage point with kabupaten fixed effects, which rises to 68.7 percentage point with kecamatan fixed effects. Column (4) controls for the confounding effect of housing aid quality. A receipt of a faulty house significantly reduces fishing probability. Omitting the housing quality creates downward bias to this aggregate shock variable: the magnitude of _B_ rises to 79.6 percentage point after removing the confounding effect. Column (5) includes two dummy variables (which indicates no aggregate shock ( _Bad0=1_ ) and more than half aid boats in poor quality ( _Bad50=1_ )) to examine the non-linear effect. The marginal effect of the second dummy is -47.9 percentage point (significant at 99%), showing that the aggregate quality shock was critical in the villages with _B_ greater than 0.5.

11
As a robustness check, column (6) drops unemployed people from the sample and column (7) uses alternative definition of fishing (households are now defined as “fishing families” if any family member reported fishing outputs regardless whether it is their primary or secondary occupation). As the villages with a major quality issue seemed to be more constrained by demand (relative to the supply of _good_ aid boats; see upper panel of Table 4), column (8) and (9) separately estimate the number of good and bad aid boats which explicitly accounts for this demand constraint. Even in these specifications, the negative effect of the aggregate shock remains to be robust.

# _B. Job switching decision_

Table 6 shows the regression coefficients of Eq. (2). In column (1) with kabupaten fixed effects, receiving a low quality boat reduced the probability of fishing by -11.4% (significant at 95%) while _AGGSHOCK_ is insignificant. This implies that people changed jobs at the extensive margin in response to the idiosyncratic shock, but the aggregate shock did not pose a long-term constraint for job seeking behaviors. In column (2), the interaction term between _IDIOSHOCK_ and _AGGSHOCK_ is included to examine whether fishermen could better cope with the idiosyncratic shock or not when their villages faced the aggregate quality shock, but there is no interaction effect. In column (3), the sample is restricted to the aid recipients in 2007. As expected, the negative effect of _IDIOSHOCK_ increased in magnitude to -15.8% (significant at 95%) as it is specific to aid recipients. In column (4) and (5), kecamatan fixed effects are included instead. Although the magnitude of _q_ decreases (as _q_ has smaller variations within kecamatans), it is negative and significant.

Those who received bad quality houses tended to maintain fishing jobs suggesting that poor housing conditions constrained their job mobility into the market sector. The positive coefficient on fraction of fishing population in 2007 captures the positive externality as interactions in a fishing team would increase if fishing market was larger.

12
# _C. Selection effect_

In Eq. (1), regressions in Table 7 add the interaction term of the aggregate quality shock with productivity variables (controlling kecamatan fixed effects) to examine whether a particular type of fishermen was disproportionally affected. With negative selection into fishing, the interaction term should be negatively signed.

I use the type of technology adopted pre-tsunami (i.e., the log of boat length and engine capacity, outliers above 95 percentile are trimmed out), fishing labor productivity (total fishing outputs per boat length), and the location of fishing activities pre-tsunami (near coast within lagoon which does not require a skill to fish in the deep sea) to examine whether skill compositions between two sectors changed due to the aggregate shock. The new fishing boats provided by aid agencies were smaller and homogeneous compared to the pre-tsunami old boats, creating skill mismatch (Nose, 2013) which was likely to affect the incentive on occupational choices.

In column (1), the negative marginal effect of the aggregate shock increases for households with bigger pre-tsunami boat. For those with average sized boats (the average of the log of boat length is 2.151), the marginal effect is equal to -0.859 (=2.066-1.360×2.151). Column (2) and (4) show similar findings when I use pre-tsunami engine capacity and the location of fishing activity as proxies of fishing productivity. The empirical results remain robust when alternative outside option measures are used (results available upon requests). This suggests that technologically advanced fisherman or those who had engaged in in-shore fishing pre-tsunami were differentially more affected by quality deficiencies of boats, confirming capital-skill complementarity. Figure 4 describes faster decline of fishing probability for non-owners with more advanced technological fishing skill than for less productive group as the provision of low quality boats increased.<sup>x</sup> Another possible interpretation is that the cost of job mobility is lower for the productive group simply because they had higher education, and thus had better outside options in their village. However, market and fishing skills (i.e., ability to use better technology boats) are not positively correlated, eliminating this story of positive selection based on market skill. It is thus more 13
plausible in our context that productive fishermen simply did not prefer to fish as crew since the new boat type did not fit the fishing technology which they used to work pre-tsunami.

As a result of this negative selection effect, the average fish productivity (defined by total fish catch in 2007) is significantly lower in the villages with major quality problem compared to those with minor problem (significant at 99%). This drove the former villages into a small-scale fishing with lower compensations for crew, widening income gap between fishing and non-fishing jobs in such communities.

[insert Figure 4]

# _5.2        Private market transaction_

Table 8 examines another coping strategy to deal with the quality shocks by purchasing a private boat in the local boat market. Column (1) and (2) present the probit estimation with different levels of fixed effects, showing that the self-financing decision was not significantly affected by _AGGSHOCK_ . Those receiving a faulty house were less likely to buy a boat, and people had less incentive to buy a boat if more market opportunities were available in the village. The multinomial logit estimation in column (3) also finds that _AGGSHOCK_ did not trigger self-financing strategy in the initial period of 2007 as private markets were less developed right after the tsunami.

On the other hand, self-financing became a feasible coping strategy in the later period of 2009 after local markets developed. In column (4), _IDIOSHOCK_ increases the probability of self-financing by 17.8% (significant at 95%). The result also shows that wealthier households were more likely to buy one to undo the idiosyncratic shock. Those living further away from Banda Aceh were less able to take this option, implying that the transaction cost was higher in the remote area.

Column (5) examines the hypothesis that self-financing is easier if households have access to credit unions as it reduces transaction costs. The sample is divided based on the access to credit unions in 2007, and the estimates show that those living in villages with the credit access were more likely to self-finance a boat in response to both aggregate and idiosyncratic shocks. This suggests 14
that access to credit remained to be the key constraints for adopting self-financing strategy. Column (6) employs the multinomial logit regression which confirms the same narrative that private resources available in the local economy helped recipients of poor aid boats undo it in the later stage.

# **6.      Post-tsunami evolution of income inequality**

Finally, I examine how the two types of quality shocks affected income distribution over time. In the rural economy without formal social insurance system, the loss of jobs or production capacity could bring fishermen into poverty. Therefore, if they could not cope with the quality shock in each stage through two measures (labor adjustment and self-financing), I would expect to see a significant income disparity between the treated group (those affected by the quality shock) and the control group (those not affected).

Figure 5 shows the change in per capital income distribution (in real term) from pre-tsunami to 2009. The income inequality was determined by the initial wealth endowments before the tsunami, but was equalized as almost all financial and physical assets were wiped out by the tsunami ( _leveling effect_ ). However, income inequality has diverged over time, especially with increasing number of poor households at the lower tail of income distribution in 2009. This suggests that both aggregate and idiosyncratic quality shocks created a _new form of inequality_ depending on the type of aid and jobs household obtained after the tsunami. The following first-differenced (FD) model is used to estimate the effect of the quality shocks on income growth.

where Δln(yivk) is real income growth for household _i_ , in village _v_ and kecamatan _k_ . It is the log difference of real per capita income: ∆ln(yivk) = ln ~~�~~ nI0909,ivk,ivk � −ln �nI0707,ivk,ivk ~~�~~ where It is weekly earning (in real term, deflated by the consumer price index in Aceh province from BPS ( _Badan Pusat Statistik_ ) website) and nt is household size in year _t_ . Right hand side includes a dummy for fishing 15
decision in 2007, two quality dummies, other controls, and kecamatan fixed effects.

This regression controls for the pre-tsunami boat size (as it is correlated with _q_ ; see Table 4).

FD model removes time invariant unobservables, such as households’ taste for fishing and inherent wealthiness, which might be correlated with their income growth. I control for kecamatan fixed effects to remove a concern that local governments provided other welfare programs during the same time period. The fixed effect also controls for the change in job opportunities and production activities at each region which may affect income growth. There is a potential concern of “spill-over” effect of the quality shock happened in a community to neighboring places which could bias our estimates (Chen, Mu, and Ravallion, 2009). However, bias from spill-over effects of the quality shock is less of a concern in my case as people rarely move across communities after the tsunami and the inter-regional trade was also not common in 2007.

To better identify the effect of idiosyncratic quality shock on income growth, the sample is restricted to those who initially chose to fish in 2007.

The idiosyncratic shock is expected to dampen income growth, which could be mitigated by switching into a non-fishing job or self-financing a new boat. The relative magnitude of two types of quality shocks can be examined by comparing β1 and β2. β2 captures the lasting effect of initial inter-village quality differences which could constrain income growth.

The top panel of Figure 6 compares income distribution between two groups divided by the aggregate shock, while the lower panel compares two groups by the idiosyncratic shock. The figure confirms that those who experienced each shock experienced slower income growth.

In Table 9, column (1) shows estimates of Eq. (4) for the original sample and column (2)-(8) for the supplementary sample. If households were in the villages with a major quality problem, their fishing income would be lower due to lower equilibrium wage for the increase in _B_ and for the decrease in average fishing productivity.<sup>xi</sup> 16
In column (2)-(8), the negative effect of idiosyncratic shock on real income growth was marginal when holding other covariates constant, suggesting that self-insurance actions helped to mitigate negative income shocks. In column (3), I added the interaction term of two shocks, but found no interactive effect. Column (4) estimates Eq. (5) for those who were fishing in 2007, and column (6) further restricts the sample to those who received aid boats by 2007. Even in these specifications, the negative effect of the idiosyncratic shock remains to be insignificant while the aggregate shock had significant negative impacts on income growth. Column (6)-(8) use alternative outside option measures which are explained in Appendix 2. In any specifications, the negative effect of _AGGSHOCK_ on the real income growth is significantly large, implying that the initial covariate demand shock was difficult for fishermen to cope with and it persistently had negative impact on their income recovery. All outside option measures except the predicted reservation wage are positively signed, showing faster income growth when more aid related business were available.

# **7.    Conclusion**

During the recovery process after the tsunami in Aceh, disaster aid programs offered various job opportunities. However, whether the aid helped the sustainable recovery of the rural economy remains an open question. My finding shows that the deficiency of in-kind aid quality persistently constrained fishermen’s job choices and income growth. Driven by a large labor demand in construction, non-fishing jobs provide alternative income sources, but the expansion of the non-fishing sector was a temporary event. I also found that the aggregate shock created the negative selection effect, driving down average fishing productivity, and it created a new form of income inequality in the post-tsunami economy.

<mark>As there is a positive linkage between fishing and non-fishing activity growth, it is essential for aid agencies to improve the quality monitoring in order to effectively recover the fishing sector as a basis for the sustainable rural development after the tsunami.</mark> On the other hand, I also found 17
that <mark>soft aid in the private sector could be an alternative to complement the q</mark> uality deficiencies associated with public transfer programs to improve the aid efficacy.

For my next research, it is very important to investigate whether the new form of income inequality between those affected and unaffected by the quality shocks could persist or not. Especially, in the face of capital market imperfection, inequality can persist with the differences in post-tsunami wealth given by aid agencies (Galor and Zeira, 1993; Banerjee and Newman, 1993; Mookherjee and Ray, 2002). The next round of survey in 2011 will tell us how households’ job and income have evolved after the termination of international aid efforts.

18
# **Note**

i There are number of papers which look at the short-term and long-term effects of natural disasters or civil conflicts on asset distribution (Carter et al, 2007), capital-labor ratio (Hutchinson and Margo, 2006), wage responses (Mueller and Quisumbing, 2011), and demographic mobility (Hornbeck, 2012) after the shocks. However, there appears to be less focus on the long-term economic impact of a disaster aid on households’ welfare.

ii In the post-disaster context, de Mel, McKenzie, and Woodruff (2008) found no statistical difference for the effect of cash and in-kind aid on micro-enterprise’s production decisions. However, in Fafchamps, McKenzie, Quinn, and Woodruff (2011), in-kind transfer turns out to be more effective than cash transfer as it overcomes self-control problem on producer’s decision making.

iii FAO reports that it would take a boat builder 12 days to make a boat before the tsunami; the surge in demand for boats after the tsunami required that a boat be completed in one day. This evidence is also confirmed by our field survey; 24.7% of aid boats were not operational immediately after they were delivered in 2007. In addition, 19% of fishermen who started fishing with a new boat from NGOs found they were not durable in two years and needed to re-engage in a different job.

ivBased on pre-tsunami SUSENAS survey, the major industry in other parts of Aceh (both inland and coastal) was agriculture. Compared to the inland area, the coastal communities in Aceh, which is the focus of this survey, exhibit higher share of fishing industry (over 30%) before the tsunami as shown in Figure 1.

> v A district ( _kabupaten_ ) is a political subdivision of a province in Indonesia.  Districts are divided into sub-districts ( _kecamatan_ ).

vi For this reason, our sample is not representative of the general population of Aceh, but it rather represents the average fishermen in the province and complements the SUSENAS sample (see Nose (2013) for detailed explanations on the sample representativeness).

vii Among the additional 439 households, 50 observations are removed as they did not report any fish outputs. viii The aggregate shock is the labor demand shock due to the provision of faulty boats only for a subset of fishermen (i.e., those who could not receive boat from donors). The nature of the shock is not same as a regular weather shock which creates harvest failures and direct production risks for all farmers. However, this is the common labor demand shock for non-owners in the same community which affects labor conditions and recovery speed of rural cities in the aftermath of the tsunami. In the empirical section, the share of faulty boats in the village is used to capture the degree of such community-wide shocks on the rural labor market.

ix Several NGOs (e.g. Oxfam, Trianangle Generation Humanitaire), local governments and the BRR, and bilateral donors (e.g. Kuwait government) are reported to have serious quality deficiencies of boat aid.

x Figure 4 depicts the result using the length of boats as a fishing productivity measure, but I found a similar correlation when the engine capacity is used instead.

19
> xi As shown in section 5, fishermen’s job decision was self-selective.  I also match their characteristics by occupation and run a regressions where fishing sample is weighted by 1/p(X) and non-fishing sample by 1/(1 −p(X)).   The propensity score p(X) of being the 2007 fishermen is predicted based on covariates included in Eq. (1) with those outside (0.15, 0.85) are trimmed out.  The results in Table 9 are robust even when the regression is weighted by this propensity score.

20
# **References**

Banerjee, A. and Newman, A. (1993) Occupational choice and the process of development. _Journal of Political Economy_ , 101(2) pp. 274-298 Carter, M., Little, P., Mogues, T., and Negatu, W. (2007) Poverty trap and natural disasters in Ethiopia and Honduras. _World Development_ , 35(5), pp. 835-856 Chen, S., Mu, R. and Ravallion, M. (2009) Are there lasting impacts of aid to poor areas?. _Journal of Public Economics_ , 93(3-4), pp. 512–528.

Corden, W. (1984) Booming sector and Dutch disease economics: survey and consolidation _. Oxford Economic Papers_ , 36(3), pp. 359-380 de Mel, S., McKenzie, D. and Woodruff, C. (2008) Returns to capital in microenterprises: Evidence from a field experiment. _Quarterly Journal of Economics_ , 123(4), pp. 1329–1372.

Dercon, S. (2002) Income risk, coping strategies, and safety nets. _World Bank Research Observer_ , 17(2), pp. 141–166.

Fafchamps, M., McKenzie, D., Quinn, S., and Woodruff, C (2011) When is capital enough to get female microenterprises growing?: evidence from a randomized experiment in Ghana. _NBER Working Paper #17207_

> FAO (2005) Fishing vessel quality issues [Report]. Retrieved from <u>ftp://ftp.fao.org/ Fi/DOCUMENT/ tsunamis_05/indonesia/livelihoods/BoatbuildingBookEnglish.pdf</u> Field, E. (2007) Entitled to work: urban property rights and the labor supply in Peru. _Quarterly Journal of Economics_ , vol. 122, no. 4, pp. 1561-1602

Foster, A. and Rosenzweig, M (2004) Agricultural productivity growth, rural economic diversity, and economic reforms: India, 1970-2000. _Economic Development and Cultural Change_ , 52(3), pp. 509-542 Frankenberg, E., Gillespie, T., Preston, S., Sikoki, B., and Thomas, D. (2011) Mortality, the family and the Indian Ocean tsunami. _Economic Journal_ , 121(554), pp. 162-182 Galor, O. and Zeira, J. (1993) Income distribution and macroeconomics. _Review of Economic_
_Studies_ . 60(1), pp. 35-52 Glaeser, E. and Gyourko, J. (2005) Urban decline and durable housing. _Journal of Political Economy_ , 113(2), pp. 345-375 Henderson, V. and Lee, Y. (2011) Organization of disaster aid delivery: spending your donations. _NBER Working Paper 17707_

> Hornbeck, R. (2012) The enduring impact of the American Dust Bowl: Short and Long-run adjustments to environmental catastrophe. _American Economic Review_ ,102(4),pp. 1477-1507 Hutchinson, W. and Margo, R. (2006) The impact of the civil war on capital intensity and labor productivity in southern manufacturing. _Explorations in Economic History_ , 43(4), pp. 689-704

Jovanovic, B. and Moffitt, R. (1990) An estimate of a sectoral model of labor mobility. _Journal of Political Economy_ , vol. 98, no. 4, pp. 827-852 Lanjouw, J. and Lanjouw, P. (2001) The rural non-farm sector: issues and evidence from developing countries. _Agricultural Economics_ , 26(1), pp. 1-23 Mookherjee, D. and Ray, D (2002) Is equality stable?. _American Economic Review_ , 92(2), pp. 253-259 Mueller, V. and Quisumbing, A. (2011) How resilient are labor markets to natural disasters? The case of the 1998 Bangladesh Flood. _Journal of Development Studies_ , 47(12), pp. 1954-1971 Nose, M. (2013) Micro responses to disaster-relief aid: design problems for aid efficacy. Mimeo. Washington , DC: World Bank.

Wooldridge, J. (2002) Inverse probability weighted M-estimators for sample selection, attrition, and stratification. _Portuguese Economic Journal_ , 1, pp. 117-139 22
# **Table 1:  Summary statistics**

|**Variables**|**N**|**Mean**|**S.D.**|**N**|**Mean**|**S.D.**|
|---|---|---|---|---|---|---|
|||**Original sa**|**mple**|**Supp**|**lementary **|**sample**|
|Fish in 07|348|0.583|0.494|557|0.734|0.442|
|Fish in 09|348|0.580|0.494|557|0.661|0.474|
|Report catch in 07|348|0.644|0.480|557|0.806|0.396|
|Boat aid in 07|348|0.371|0.484|557|0.587|0.493|
|Boat aid in 09||||557|0.724|0.448|
|Buy in 07|348|0.144|0.351|557|0.120|0.326|
|Buy in 09||||557|0.298|0.458|
|Boat owner in 07|348|0.511|0.501|557|0.716|0.451|
|Boat owner in 09||||557|0.488|0.500|
|**AGGSHOCK**|||||||
|Fraction of bad aid boats in 07|313|0.247|0.384||||
|Bad0 1/|313|0.617|0.487||||
|Bad50 1/|313|0.227|0.419||||
|**IDIOSHOCK**|||||||
|D_have a bad aid boat in 07|348|0.006|0.076|557|0.097|0.296|
|D_have a bad aid boat in 09||||557|0.190|0.393|
|D_bad house in 07|348|0.382|0.486|557|0.409|0.492|
|D_bad house in 09||||557|0.510|0.500|
|D_have leaky roof in 09||||557|0.255|0.436|
|D_have cracked wall in 09||||557|0.329|0.470|
|D_have poor foundation in 09||||557|0.221|0.415|
|<br>D_have faulty plumbing in 09||||557|0.246|0.431|
|**Outside option**|||||||
|Number of NGOs in 07|329|15.833|7.267||||
|Number of aid projects in 07|329|37.246|18.280||||
|Total housing aid in 07 2/|331|252.752|271.663||||
|**Fishing productivity**|||||||
|Ln (boat length pre-tsunami) 3/|320|2.159|0.226||||
|Ln (engine capacity pre-tsunami) 3/|326|3.061|0.479||||
|Total fish catch/length pre-tsunami 3/|326|26.319|38.056||||
|In-shore fishing skill|348|0.348|0.476||||
|**Socio-economic characteristics**|||||||
|Boat owner and captain pre-tsunami|348|0.736|0.442|557|0.610|0.488|
|D_large boat pre-tsunami|348|0.330|0.471|557|0.305|0.461|
|High education pre-tsunami|341|0.141|0.348||||
|D_debt to Toke Bangku|348|0.201|0.401||||
|High income in 07||||556|0.252|0.434|
|**Community characteristics**|||||||
|Number of boat aid in 07|313|18.217|20.132||||
|Number of surviving captains in 05|309|23.379|19.561||||
|D_Banda Aceh|348|0.175|0.381|557|0.158|0.365|
|D_Aceh Jaya||||557|0.140|0.347|
|Fraction of fishing population in 07||||491|0.271|0.270|
|Credit union in village in 07||||522|0.318|0.466|

1/ Bad0=1[Fraction of bad aid boats=0], Bad50=1[Fraction of bad aid boats>0.5] 2/ Both temporary and permanent houses.

3/ Observations above the top 5 percentile are trimmed out.
Table 2: Breakdown of primary job of household heads

||2007|2009|
|---|---|---|
|Fish (Captain)|**374**|**293**|
|Fish (Crew)|**35**|**71**|
|Other fishing|3|9|
|Agriculture|20|43|
|Construction|23|19|
|Trade|52|73|
|Service sector|24|22|
|Unemployed|23|20|
||554|554|

(Note) Other fishing includes aquaculture and fish processing; trade includes fish related trader, coffee shop, retail shop, restaurant, transportation, and technician (boat builder, mechanics, plumber, electrician, carpenter); service sector includes private service (banker, administrative office), public service (government official, teacher, doctor, police), and manufacturing.

Table 3: Transition matrix of occupations

||Fish in 09|Not fish in09||
|---|---|---|---|
|Fish in07|310|99|409|
|Notfish in07|58|90|148|
||368|189|557|
Table 4: Balancing test of quality shocks

# **In 2007 (for original sample)**

||**Bad50=1**<br>**(N=14)**|**Bad50=0**<br>**(N=39)**|**Difference**|
|---|---|---|---|
|Survival rate of village population in 2005|0.54|0.62|-0.08|
|Good aid boat/Survivingcaptains in 05 1/|0.38|1.76|**-1.38***|
|(ExcludingLhok Nga and Pulo Aceh)|0.38|0.66|-0.28|
|Ln(distance to Banda Aceh)|2.82|2.64|0.18|
|**Agencies’ involvement**2/||||
|Oxfam|0.07|0.06|-0.01|
|Kuwaitgovernment|0.14|0.06|0.08|
|Localgovernment/BRR|0.71|0.30|**0.41*****|
|TriangleGeneration Humanitaire|0.21|0.28|-0.07|
|**Village governance**2/||||
|Old village head survived|0.36|0.38|-0.02|
|Old PL head still in the office|0.79|0.69|0.10|

*** p<0.01, ** p<0.05, * p<0.1

1/ Total number of good boats provided by donors by 2007 divided by the number of survived captains in 2005. T-test which excludes two outliers (11 villages in Lhok Nga and Pulo Aceh where aid boats were overfunded) is also reported.

2/ Dummy variables.

# **In 2009 (for supplementary sample)**

||**_q=1_**<br>**(N=93)**|**_q=0_**<br>**(N=322)**|**Difference**|
|---|---|---|---|
|Boat ownerpre-tsunami|0.59|0.61|-0.02|
|Captainpre-tsunami|0.89|0.85|0.04|
|D_large boatpre-tsunami|0.18|0.32|**-0.14****|
|High education in2007|0.01|0.01|0|
|Good aid boat/Survivingcaptains in 05|1.68|1.42|0.26|
|(ExcludingLhok Nga and Pulo Aceh)|0.95|0.71|0.24|
|D_BandaAceh|0.06|0.17|**-0.11*****|
|D_AcehJaya|0.39|0.11|**0.28*****|

*** p<0.01, ** p<0.05, * p<0.1
Table 5: Fishing decision in 2007

||Owners||||Non|-owners||||
|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Fish in 07|||Fish in 07||||Report catch in|07|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|**AGGSHOCK**||||||||||
|Fraction of bad aid boats|-0.156|-0.504***|-0.687***|-0.796***||-1.071***|-0.830***|||
||(0.144)|(0.157)|(0.179)|(0.182)||(0.270)|(0.180)|||
|Bad0|||||0.151|||||
||||||(0.120)|||||
|Bad50|||||-0.479***|||||
||||||(0.0959)|||||
|Ln(number of good aid boats by 07)||||||||0.0631**|0.0419|
|||||||||(0.0321)|(0.0296)|
|Ln(number of bad aid boats by 07)||||||||-0.288***|-0.263***|
|||||||||(0.0641)|(0.0618)|
|D_bad house in 07||||-0.230**|-0.224**|-0.231**|-0.227**|-0.176*|-0.219**|
|||||(0.0900)|(0.0902)|(0.109)|(0.0945)|(0.0937)|(0.0992)|
|Boat owner and captain pre-tsunami|0.0413|-0.0601|0.00713|0.00233|0.00593|0.0437|-0.0191|-0.0485|0.00405|
||(0.0903)|(0.0965)|(0.105)|(0.111)|(0.111)|(0.126)|(0.115)|(0.114)|(0.122)|
|D_large boat pre-tsunami|-0.0349|0.0610|0.0366|0.0372|0.0291|-0.0241|0.0289|0.0240|-0.00797|
||(0.0763)|(0.0814)|(0.101)|(0.110)|(0.110)|(0.140)|(0.113)|(0.111)|(0.117)|
|Ln(number of NGOs in 2007)|-0.0995|-0.324***|-0.796***|-0.936***|-0.943***|-1.571***|-0.883***|-0.700***|-0.314|
||(0.0836)|(0.109)|(0.241)|(0.255)|(0.246)|(0.330)|(0.259)|(0.262)|(0.209)|
|D_debt to Toke Bangku|0.234***|0.150|0.176|0.193|0.193|0.204|0.200|0.108|0.222|
||(0.0718)|(0.151)|(0.144)|(0.152)|(0.144)|(0.207)|(0.152)|(0.161)|(0.177)|
|High education pre-tsunami|-0.162|-0.125|-0.112|-0.117|-0.0988|-0.186|-0.133|-0.134|-0.0738|
||(0.124)|(0.100)|(0.103)|(0.0999)|(0.104)|(0.159)|(0.0984)|(0.0947)|(0.101)|
|Number of surviving captains in 05||0.00563***|0.00752**|0.00994***|0.00755*|0.0109***|0.0100***|0.0118***|0.00793***|
|||(0.00157)|(0.00325)|(0.00335)|(0.00395)|(0.00408)|(0.00350)|(0.00368)|(0.00300)|
|Observations|154|148|147|147|147|115|147|147|128|
|Sample|Original||||Or|iginal||||
|Mean of dependent variable|0.740|0.453|0.449|0.449|0.449|0.574|0.463|0.463|0.445|
|Seasonality is controlled|Y|Y|Y|Y|Y|Y|Y|Y|Y|
|Regional fixed effects|Kab|Kab|Kec|Kec|Kec|Kec|Kec|Kec|Kec|

*** p<0.01, ** p<0.05, * p<0.1; Estimates in column (1)-(9) are marginal effects of probit model.  Column (6) drops unemployed households.  Column (9) drops households living in two kecamatans (Lhok Nga and Pulo Aceh).  Robust standard errors clustered at the village level are in parentheses.
Table 6: Regression coefficients in job switching equation (for fisherman in 07)

|VARIABLES|||Fish in 09|||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|IDIOSHOCK|-0.114**|-0.138**|-0.158**|-0.0881*|-0.145**|
||(0.0540)|(0.0679)|(0.0648)|(0.0531)|(0.0651)|
|AGGSHOCK|0.00203|-0.0151|-0.0663|-0.0643|-0.112|
||(0.0597)|(0.0673)|(0.0769)|(0.0661)|(0.0978)|
|AGGSHOCK * IDIOSHOCK||0.0639||||
|||(0.0884)||||
|Boat aid in 09|0.233**|0.237**||0.225**||
||(0.103)|(0.104)||(0.107)||
|D_bad house in 09|0.0382|0.0380|0.0963*|0.0431|0.0876*|
||(0.0455)|(0.0454)|(0.0536)|(0.0462)|(0.0497)|
|D_large boat pre-tsunami|0.0444|0.0433|-0.0326|0.0272|-0.00530|
||(0.0512)|(0.0512)|(0.0628)|(0.0458)|(0.0605)|
|Boat owner and captain pre-tsunami|0.0397|0.0382|0.142**|0.0610|0.127***|
||(0.0509)|(0.0514)|(0.0570)|(0.0451)|(0.0479)|
|Fraction of fish population in 07|0.121|0.119|0.213*|0.194*|0.130|
||(0.103)|(0.103)|(0.115)|(0.117)|(0.143)|
|Observations|357|357|242|355|241|
|Sample||Su|pplementary& Fish in|07=1||
||All|All|Boat aid in 07=1|All|Boat aid in 07=1|
|Mean of dependent variable|0.762|0.762|0.747|0.763|0.746|
|Seasonality is controlled|Y|Y|Y|Y|Y|
|Sampling heterogeneity is controlled 1/|Y|Y|Y|Y|Y|
|Regional fixed effects|Kab|Kab|Kab|Kec|Kec|

*** p<0.01, ** p<0.05, * p<0.1; All estimates are marginal effects of probit model. Robust standard errors clustered at the village level are in parentheses. The inverse probability of remaining in the sample ("stayers") is used as a weight in Eq. (2) to account for attrition bias and Sampling heterogeneity is controlled by including 1[ _s_orig=1_ ].

AGGSHOCK=Fraction of bad aid boats IDIOSHOCK=D_have a bad aid boat in 09
Table 7: Heterogeneity by fishing productivity (for non-owners)

|VARIABLES||Fish|in 07||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|AGGSHOCK|2.066**|1.112|-0.626**|-0.545**|
||(0.938)|(0.843)|(0.254)|(0.213)|
|AGGSHOCK*Ln (boat length pre-tsunami)|**-1.360*****||||
||**(0.446)**||||
|Ln (boat length pre-tsunami)|0.726***||||
||(0.263)||||
|AGGSHOCK*Ln (engine capacity pre-tsunami)||**-0.668****|||
|||**(0.295)**|||
|Ln (engine capacity pre-tsunami)||0.180*|||
|||(0.107)|||
|AGGSHOCK*Total fish catch/length pre-tsunami|||**-0.00473**||
||||**(0.00503)**||
|Total fish catch/length pre-tsunami|||0.00295*<br>(0.00173)||
|AGGSHOCK*In-shore fishing skill||||**-0.725*****|
|||||**(0.254)**|
|In-shore fishing skill||||-0.00111|
|||||(0.111)|
|D_bad house in 07|-0.195**|-0.168*|-0.236**|-0.229**|
||(0.0952)|(0.0883)|(0.0989)|(0.0897)|
|Boat owner and captain pre-tsunami|0.116|0.0367|0.0947|-0.0189|
||(0.107)|(0.104)|(0.114)|(0.112)|
|Ln(number of NGOs in 2007)|-1.048***|-0.989***|-1.181***|-0.957***|
||(0.270)|(0.229)|(0.303)|(0.258)|
|D_debt to Toke Bangku|0.177|0.183|0.305**|0.170|
||(0.195)|(0.151)|(0.149)|(0.151)|
|High education pre-tsunami|-0.0511|-0.125|-0.240***|-0.103|
||(0.126)|(0.108)|(0.0789)|(0.0940)|
|Number of surviving captains in 05|0.00960***|0.0113***|0.0155***|0.0115***|
||(0.00322)|(0.00310)|(0.00435)|(0.00400)|
|Observations|127|135|135|147|
|Sample||Ori|ginal||
|Mean of dependent variable|0.441|0.437|0.444|0.449|
|Seasonality is controlled|Y|Y|Y|Y|
|Regional fixed effects|Kec|Kec|Kec|Kec|

*** p<0.01, ** p<0.05, * p<0.1; All estimates are marginal effects of probit model. Robust standard errors clustered at the village level are in parentheses.

AGGSHOCK=Fraction of bad aid boats
Table 8: Self-financing decision in 2007 and 2009

|||**In 20**|**07 **||||**In 2009 1/**|||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(|3)|(4)|(|5)||(6)|
||Pro|bit|Multino|mial logit|Probit|Pr|obit|Multin|omial logit|
||||V1|V2||||V1|V2|
|VARIABLES|Buyi|n 07|(Crew in<br>07)|(Buy in<br>07)|Buyin 09|Buy|in 09|(Crew in<br>09)|<br>(Buy in<br>09)|
|IDIOSHOCK|||||0.178**|0.301**|0.134|-0.204|0.691**|
||||||(0.0694)|(0.127)|(0.0880)|(0.349)|(0.328)|
|AGGSHOCK|-0.0133|-0.0732|-1.466**|-1.306**|-0.0150|0.336***|-0.0591|-0.0368|-0.164|
||(0.0503)|(0.0569)|(0.594)|(0.616)|(0.0622)|(0.111)|(0.0752)|(0.428)|(0.430)|
|D_bad house in 07|-0.0476|-0.0693*|-0.672***|-0.942**||||||
||(0.0354)|(0.0363)|(0.226)|(0.368)||||||
|D_bad house in 09|||||0.0576|0.0706|0.0640|-0.00851|<br>0.310|
||||||(0.0501)|(0.0704)|(0.0694)|(0.279)|(0.320)|
|Boat owner and captain pre-tsunami|0.00884|0.00151|0.452|0.258|0.0696|0.0903|0.0517|0.626**|0.826**|
||(0.0398)|(0.0441)|(0.381)|(0.464)|(0.0570)|(0.0880)|(0.0791)|(0.312)|(0.323)|
|D_large boat pre-tsunami|0.0620|0.0497|-0.303|0.196|-0.00225|0.0279|0.0294|0.0679|0.0563|
||(0.0544)|(0.0544)|(0.314)|(0.355)|(0.0712)|(0.125)|(0.0990)|(0.325)|(0.450)|
|D_high income in 07|||||0.106|0.314*|0.0202|0.801**|1.038**|
||||||(0.0706)|(0.178)|(0.0802)|(0.370)|(0.436)|
|Ln(number of NGOs in 07)|-0.0819**|-0.186***|-1.368**|-2.087***||||||
||(0.0331)|(0.0562)|(0.626)|(0.572)||||||
|Ln (distance to BA)|-0.0536|-0.0844**|-0.283|-0.827**|-0.163***|0.710***|-0.173**|-0.638**|<br>-1.381***|
||(0.0416)|(0.0351)|(0.342)|(0.405)|(0.0542)|(0.191)|(0.0689)|(0.273)|(0.367)|
|Aid boats/survived fish captains|-0.0274***|-0.00753|0.176**|0.0426||||||
||(0.00938)|(0.0120)|(0.0875)|(0.100)||||||
|Constant|||4.178*|6.865***||||2.263**|2.462***|
||||(2.333)|(2.467)||||(0.914)|(0.915)|
|Observations|311|285|3|11|376|117|236||377|
|Sample||Origi|nal||||Supplementary|||
|||||||Credit<br>Union in<br>07=1|Credit<br>Union in<br>07=0|||
|Mean of dependent variable|0.148|0.161|0.486|0.148|0.312|0.299|0.342|0.470|0.313|
|Seasonality is controlled|Y|Y||Y|Y||Y||Y|
|Sampling heterogeneity is controlled 1/|||||Y||Y||Y|
|Regional fixed effects|Kab|Kec|K|ec|Kec|K|ec||Kec|

*** p<0.01, ** p<0.05, * p<0.1; Estimates in column (1), (2), (4) and (5) are marginal effects of probit model.  Column (3) and (6) provide multinomial logit estimates.  Robust standard errors clustered at the village level are in parentheses.

1/ In column (4)-(6), the inverse probability of remaining in the sample ("stayers") is used as a weight to account for attrition bias and sampling heterogeneity is controlled by including 1[ _s_orig=1_ ]. AGGSHOCK=Fraction of bad aid boats IDIOSHOCK=D_have a bad aid boat in 09
Table 9: Effect of aid quality on income growth

|VARIABLES||||∆y|||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Fish in 07|-0.511***|-0.375***|-0.375***|||-0.367***|-0.367***|-0.419***|
||(0.139)|(0.116)|(0.116)|||(0.115)|(0.111)|(0.123)|
|IDIOSHOCK||**-0.152**|**-0.177**|**-0.137**|**-0.0818**|**-0.139**|**-0.138**|**-0.183**|
|||**(0.103)**|**(0.125)**|**(0.108)**|**(0.125)**|**(0.104)**|**(0.106)**|**(0.113)**|
|AGGSHOCK|**-0.257***|**-0.248****|**-0.264****|**-0.253***|**-0.277**|**-0.225****|**-0.192***|**-0.343*****|
||**(0.138)**|**(0.102)**|**(0.108)**|**(0.138)**|**(0.170)**|**(0.103)**|**(0.107)**|**(0.117)**|
|AGGSHOCK * IDIOSHOCK|||0.0856||||||
||||(0.194)||||||
|D_bad house in 09|0.136|0.0500|0.0495|-0.0188|0.0405|0.0514|0.0195|0.0248|
||(0.107)|(0.0766)|(0.0769)|(0.0864)|(0.0812)|(0.0765)|(0.0800)|(0.0804)|
|Ln (number of NGOs in 07)|0.159|0.351***|0.350***|0.396***|0.442***||||
||(0.160)|(0.109)|(0.109)|(0.133)|(0.141)||||
|Ln (number of aid projects in 07)||||||0.385***<br>(0.116)|||
|Ln (total housing aid in 07)|||||||0.182***||
||||||||(0.0651)||
|Imputed market shadow wage in 07||||||||-0.0420<br>(0.0849)|
|Constant|-0.767|-1.554***|-1.549***|-1.820***|-2.160***|-1.990***|-2.073***|0.0515|
||(0.552)|(0.426)|(0.427)|(0.473)|(0.485)|(0.514)|(0.657)|(1.084)|
|Observations|282|505|505|377<br>Supp & Fish|260<br> <br>Supp & Fish in<br>07=1 & Boat|505|506|467|
|Sample|Original|Supp|Supp|in 07=1|aid in 07=1|Supp|Supp|Supp|
|Covariates are included|Y|Y|Y|Y|Y|Y|Y|Y|
|Sampling heterogeneity is controlled||Y|Y|Y|Y|Y|Y|Y|
|Regional fixed effects|Kec|Kec|Kec|Kec|Kec|Kec|Kec|Kec|
|R-squared|0.182|0.223|0.223|0.247|0.285|0.228|0.230|0.206|

*** p<0.01, ** p<0.05, * p<0.1; Robust standard errors clustered at the village level are in parentheses.  Control variables include boat aid in 09, boat ower and captain pre-tsunami, D_large boat pre-tsunami, and Ln (distance to BA).  The inverse probability of remaining in the sample ("stayers") is used as a weight in Eq. (5) to account for attrition bias.  Sampling heterogeneity is controlled by including _1[s_orig=1]._

AGGSHOCK=Bad50(=1[Fraction of bad aid boats>0.5]) IDIOSHOCK=D_have a bad aid boat in 09
<!-- Start of picture text --> Pre 2005 2007 2009<br>Fish Agriculture Trade Other Underemployed<br>40<br>30<br>%<br>20<br>10<br>0<br><!-- End of picture text --> (Note) “Other” sector includes transportation, construction, public services and manufacturing industries.

Figure 1: Occupational structure from pre-tsunami to 2009 <!-- Start of picture text --> Good quality villages Bad quality villages<br>Pre 2005 2007 2009 Pre 2005 2007 2009<br>40 40<br>30 30<br>% %<br>20 20<br>10 10<br>0 0<br><!-- End of picture text --> Figure 2: Share of fishing sector: Good vs. Bad quality villages (from pre-tsunami to 2009)
<!-- Start of picture text --> Attrited in 09<br>35<br>Original sample Supplementary sample<br>(05-07 surveyed)<br>348 Re-surveyed in 09<br>313<br>Re-surveyed in 09<br>Additional households 244<br>(added in 07 survey)<br>389<br>Attrited in 09<br>145<br><!-- End of picture text -->
<!-- Start of picture text --> 0 .2 .4 .6 .8 1<br>Fraction of badboat in 2007<br>High productivtiy Low productivity<br>Owner<br>.8<br>.6<br>.4<br>Pr(Fish=1) .2<br>0<br>-.2<br><!-- End of picture text --> (Note) High productivity group uses more advanced fishing technology (whose pre-tsunami boat size was greater than 9 meters) Figure 4: Relationship between fishing probability and the aggregate quality shock <!-- Start of picture text --> ln(pc inc_pre) ln(pc inc_05) ln(pc inc_07) ln(pc inc_09)<br>14<br>12<br>10<br>8<br><!-- End of picture text --> (Note) The log of households’ weekly income (per household member, in Rupiah) is adjusted based on consumer price index in August 2005 (2005=100).

Figure 5: Widening income inequality
# <u>Aggregate quality shock</u>

<!-- Start of picture text -->
-4 -2 0 2 4<br>Bad50=1 Bad50=0<br>.5<br>.4<br>.3<br>.2<br>kdensity d_inc<br>.1<br>0<br><!-- End of picture text -->

# <u>Idiosyncratic quality shock</u>

<!-- Start of picture text -->
-4 -2 0 2 4<br>Low quality (if fish in 2007) High quality (if fish in 2007)<br>.5<br>.4<br>.3<br>.2<br>kdensity d_inc<br>.1<br>0<br><!-- End of picture text -->

(Note) _d_inc_ is the log difference of real per capita income between 2007 and 2009 (i.e., income growth).

Figure 6: Distribution of income growth
# **Appendix 1: Theoretical framework**

This note lays out the predictions about how the provision of low quality in-kind aid translates into income loss and affects labor supply decisions at extensive margin (fishing vs. non-fishing employment). This is related to the risk-coping literature on agricultural households’ labor supply decisions in response to natural disasters (Kochar (1999), Rose (2001) and Jayachandran (2006) for weather risks in India; Belasen and Polachek (2009) for hurricanes in Florida; Mueller and Quisumbing (2011) for the 1998 flood in Bangladesh).

# **1.   Timing of events**

There are two stages. In _stage 1_ , donors arrive at the community and distribute productive assets (i.e., fishing boats). In some villages, a number of low quality boats are provided, creating a labor demand shock to fishermen (aggregate quality shock). In such places, those who could not be the beneficiaries of aid boats faced lower wage of fishing as crew and needed to seek other opportunities. Aid recipients cannot fully observe the actual quality of their boats until they experience fishing activities after the tsunami. In _stage 2_ , a part of beneficiaries face idiosyncratic quality shock, i.e. they might discover that their aid boats were not durable, requiring them to stop fishing or self-finance a high-quality boat. The household makes both production and labor supply decisions in each stage.

# **2.   Stage 1:**

Assume that a total of _N_ aid boats are provided by NGOs in the village compared to a total village population of _P_ . There is limited supply of boats relative to demand ( _N<P_ ) which divided fishing families into two groups: owners and non-owners _._ When the number of unusable aid boats _B_ increases, there are only _N − B_ good aid boats available to be boat owners. A fixed number of fishermen, θ, might choose to purchase private boats and hire crew. While the owners will not be affected by _B_ , how will the non-owners decide their jobs when _B_ increases?

35
Fishing is conducted in a team which consists of a captain and crew. The captain needs a boat and hires crew: the fishing production function depends on the actual boat quality qi (which cannot be observed in stage 1), average fishing productivity of the team λ<sup>�</sup> ı, and the number of crew hi: yi : = qi f�λ<sup>�</sup> ı, hi�. Each fishing household is indexed by _i_ . Boat owners need to pay each crew member αi. Owners maximize profit, written as πi = qi f�λ<sup>�</sup> ı, hi� −αihi, by choosing the optimal number of crew h<sup>∗</sup> . Profit is maximized at the point where the marginal product of labor is equal to the compensation for crew αi members: f<sup>_′_</sup> �λ<sup>�</sup> ı, hi� = . Assuming decreasing returns to scale of f(λ<sup>�</sup> ı, hi), the labor demand curve is qi ∂h<sup>∗</sup> downward sloping: < 0. The aggregate labor demand LD, which is the total job opportunities for ∂αi non-owners, is defined as follows.

Those who cannot receive aid may find an opportunity to be crew. Under the imperfect credit market, each household will consume all his earning in the same period. When the utility comes only from consumption, the aggregate labor supply function LS depends on the compensation for crew αi, the reservation wage in outside job options _w_ , households’ observed characteristics Xi, and the number of non-owners (which is expressed as the square bracket in Eq. (A.2)) _._ I model the labor supply decision of each individual fisherman as a discrete choice between two sectors (fishing vs. non-fishing). Each fisherman chooses to fish if the utility from fishing dominates the reservation wage, i.e. l(αi, Xi, w) = 1[γ0 + γ1αi + γ2Xi + εi > w] where εi is an individual specific error term. 1[] is the indicator function.

36
∂l(αi,Xi,w) If the labor supply curve is upward sloping, > 0. Non-owners have a strong incentive ∂αi ∂l(αi,Xi,w) to opt out from fishing as _w_ rises: < 0. Fishermen have incentives to shift into the ∂w non-fishing sector until the reservation wage is equalized to the compensation wage for crew. The degree of sectoral adjustment depends on the elasticity of labor supply with respect to wage (αi)<sup>xii</sup> and the mobility cost.<sup>xiii</sup> The fishing market clears when the labor demanded by captains equals the labor supplied by crew. Assuming a closed economy, the labor market is segmented at each village. Within each village, the labor market is competitive and clears with equilibrium compensation for crew α<sup>∗</sup> when LD = LS.

When _B_ rises, the labor demand curve shifts down which reduces the wage ( _price effect_ ; Figure A.1).

On the other hand, it could also constrain the labor supply to be crew as fishing opportunities are limited when _B_ rises ( _supply effect_ ). Both effects work in the same direction and make agents more willing to exit from fishing, reducing the number of fishermen.

# _<u>Selection effect (heterogeneous labor adjustment by productivity)</u>_

Who is affected most by the quality shock? After a large aggregate shock like the tsunami, there could be a compositional change in the affected society.<sup>xiv</sup> In Eq. (A.2), the wage elasticity of labor supply might differ by the fishing productivity; 37
where<sup>∂l(αi,Xi,λi,w)</sup> = γ1 + γ3λi. If the wage elasticity is higher for productive fishermen (i.e., capital ∂αi and fishing skill are complementary), I expect γ3 > 0. In this case, the labor supply curve for productive fishermen is flatter (Figure A.2).

On the other hand, if capital and fishing skill are substitutable, I expect γ3 < 0.

# **3.  Stage 2:**

In stage 2, if boats were discovered to be non-durable, they needed to switch jobs, become crew, or self-finance private boats. In other words, the actual quality of boat _q_ is revealed to be either low or high ex-post: qi ∈{qi,L, qi,H}. As shown in Table 1, the number of fishermen whose aid boats became not durable jumped up to 19% in 2009, suggesting that actual boat quality could not be fully anticipated when boats were delivered from aid agencies in 2007.

# **4.  Endogenous self-financing**

So far, the theory assumes the exogeneity of θ. However, self-financing may be another optimal choice for agents to deal with the quality shocks depending on the level of wealth and/or an access to private market. With this option, agents compare the relative payoffs of three options: working as a crew (V1), working as an owner by buying a boat (V2), and working for non-fishing jobs (V3). Then, the selection equation can be re-written as follows.

38
where V1 = αi + ei,1, V2 = qi f�λ<sup>�</sup> ı, hi� −αihi −I+ ei,2, and V3 = w. _I_ is investment costs plus the transaction cost of the market transactions, and (ei,1, ei,2) are individual-level error terms. The transaction cost should be the highest right after the tsunami, but I expect _I_ to decrease as the economy recovers from the tsunami in later years.

The effect of the aggregate quality shock on self-financing decision depends on the wage elasticity to the shock. As ∂αi < 0, it should have a distributional impact by decreasing V1 and ∂B increasing V2. If the wage elasticity is large (i.e., the distributional effect is substantial), this might drive agents to self-select into a private transaction in stage 1. On the other hand, due to the negative selection effect discussed in Eq. (A.4), the average productivity level λ<sup>�</sup> ı will decrease as _B_ increases, making the return to non-fishing ( _w_ ) relatively more attractive than the return to fishing. Due to the two competing effects, the impact of the aggregate shock on a self-financing choice is expected to be small in stage 1, i.e.

∂θ ∂B ≈0.

In stage 2, if aid boats become non-durable, V1 = 0. As it is the idiosyncratic shock, this will not affect the average fishing productivity level. In this case, the number of people who self-financed a boat, θ, is expected to increase in stage 2 when qi = qi,L, i.e. ∂<sup>∂θ</sup> qi > 0.

39
<!-- Start of picture text --> a L,<br>Loo L<br><!-- End of picture text --> <!-- Start of picture text --> a EA)<br>Ls(Ay)<br><!-- End of picture text -->
# **References:**

Abdulai, A. and Delgado, C. (1999) Determinants of nonfarm earnings of farm based husbands and wives in northern Ghana. _American Journal of Agricultural Economics_ , 81(1) pp. 117-130 Bardhan, P. (1979). Labor supply functions in a poor agrarian economy. _American Economic Review_ , 69(1) pp. 73-83

- Belasen, A. and Polachek, S (2009) How disasters affect local labor markets: the effects of hurricanes in Florida. _Journal of Human Resources_ , 44(1), pp. 251-276

Bell, C. (1988) Credit markets and interlinked transactions. _Handbook of Development Economics_ , vol. 1, pp. 763-830 Hornbeck, R and Naidu, S (2012) When the Levee Breaks: labor mobility and economic development in the American South. _NBER Working Paper 18296_ Jayachandran, S. (2006) Selling labor low: wage responses to productivity shocks in developing countries. _Journal of Political Economy_ , 14 (3), pp. 538–575.

Kochar, A. (1999) Smoothing consumption by smoothing income: hours-of-work responses to idiosyncratic agricultural shocks in rural India. _Review of Economics and Statistics_ , 81(1), pp. 50-61 Rose, E. (2001) Ex ante and ex post labor supply response to risk in a low-income area. _Journal of Development Economics_ , 64(2), pp. 371–388.

Rosenzweig, M. (1980) Neoclassical theory and the optimizing peasant: an econometric analysis of market family labor supply in a developing country. _Quarterly Journal of Economics_ , 94(1), pp. 31-55

> xii In previous studies, labor supply elasticities in developing countries are shown to be small (Bardhan (1979) for rural households in West Bengal; Abdulai and Delgado (1999) in Ghana). Rosenzweig (1980) estimates that the long-run labor supply curve for men is even backward-bending.

xiii For example, fishermen might not be able to exit from fishing if they owed a debt obligation of replaying debt to the middlemen (called _Toke Bangku_ in Aceh). This type of interlinkage between borrower and lender through trade-credit arrangement is widespread in many agricultural economies (Bell, 1988). When borrowers default on their contractual obligation, lenders have various options (such as bonding labor and extracting resources of borrowers) until borrowers satisfy the debt obligation.

xiv Hornbeck and Naidu (2012) shows a large compositional change in the black-white ratios after the 1980s Dust Bowl in the U.S. south, and claims that the compositional shift actually promoted the mechanization of American agriculture.

41
# **<mark>Appendix 2: Measures of outside options</mark>**

<mark>Non-fishing sector has rapidly developed during the reconstruction period when donors put a large amount of money to reconstruct private houses as well as public road and buildings. The survey contains detailed information on the scale of such infrastructure rebuilding programs (such as the total number of temporary and permanent houses which were constructed by donors and local governments). I use the data to control for the confounding impacts of other aid programs on households job choices and their income growth to identify the quality impacts if production assets (i.e., fishing boats).</mark> The survey collects detailed village-level variations in housing aid program besides fishing aid. In addition, the data contains secondary information of the number of total humanitarian relief projects (including all types of aid) and NGOs at the village level from RAN (Reconstruction Aceh Nias) dataset. Aid programs provided direct labor demand for non-fishing job opportunities from donors (as construction workers, interpreters etc), and they also generated indirect service jobs demanded through reconstruction efforts (retail, food, and hotel business etc) which were all closely related to the scale of aid works.

<mark>As shown in the below correlation matrix, three measures are significantly positively correlated (at 99%). The negative correlation between the distance to Banda Aceh and three outside option measures suggest that the aid program tended to be clustered near Banda Aceh, the capital city of Aceh.</mark>

||Number of<br>NGOs in 07|Number of aid<br>projects in 07|Total housing<br>aid in 07|Ln (distance to<br>BA)|
|---|---|---|---|---|
|Number of NGOs<br>in 07|1||||
|Number of aid<br>projects in 07|0.904***<br>(0.000)|1|||
|Total housing aid<br>in 07|0.508***<br>(0.000)|0.568***<br>(0.000)|1||
|Ln (distance to|-0.377***|-0.201***|-0.423***||
|BA)|(0.000)|(0.000)|(0.000)|1|

***p<0.01, **p<0.05, *p<0.1; p-values are in the parentheses.  Supplementary sample is used (N=529).

42
<mark>As there is no reliable labor data (such as wage and labor supply) in the market jobs, I cannot directly measure the reservation wage. The majority of households which were engaged in informal jobs (in agriculture, trading, and service sector) were not wage labor. As a result, I cannot estimate the shadow wage from the production function. As an alternative, I impute the average shadow wage of non-fishing job at each village using the following step:</mark>

- (1) <mark>Regress the real per capita household income in 2007 on a dummy on fishing, village</mark>

<mark>dummies, and their interaction terms. Obtain the predicted shadow wage of non-fishing households which differs across villages.</mark>

- (2) <mark>Take the average of the predicted per capita income w�</mark> ıvk <mark>to obtain the average reservation wage at each village.</mark>

<mark>Table A.1 shows that the negative effects of</mark> _<mark>AGGSHOCK</mark>_ <mark>on fishing in 2007 (robustness check for Table 5) remain to be robust when using any proxy variables and the imputed reservation wage. People living further away from Banda Aceh tended to be engaged in fishing, and they were likely to opt out from fishing if higher reservation wage was offered in the non-fishing sector. The main results in Table 7 and Table 9 are also shown to be robust.</mark>

# **References**

Jacoby, H. (1993) Shadow wages and peasant family labour supply: an econometric application to the Peruvian Sierra. _Review of Economic Studies_ . 60(4), pp. 903-921 43
Table A.1: Robustness check using alternative outside option measures

|VARIABLES||Non-o|wners||
|---|---|---|---|---|
|||Fish<br>|in 07<br>||
||(1)|(2)|(3)|(4)|
|Fraction of bad aid boats|**-0.776*****<br>**(0.193)**|**-0.605*****<br>**(0.168)**|**-0.505*****<br>**(0.192)**|**-0.651*****<br>**(0.164)**|
|D_bad house in 07|-0.212**|-0.166*|-0.134|-0.162*|
||(0.0891)|(0.0924)|(0.0980)|(0.0967)|
|Boat owner and captain pre-tsunami|-0.0206|-0.0187|-0.0248|-0.0120|
||(0.110)|(0.111)|(0.111)|(0.108)|
|D_large boat pre-tsunami|0.0150|0.0870|0.0714|-0.0308|
||(0.111)|(0.117)|(0.109)|(0.0972)|
|Ln (number of aid projects in 07)|**-0.661*****<br>**(0.186)**||||
|Ln (total housing aid in 07)||**-0.166****<br>**(0.0657)**|||
|Ln (distance to BA)|||**0.311****<br>**(0.126)**||
|Imputed market shadow wage in 07||||**-0.158***<br>**(0.0945)**|
|D_debt to Toke Bangku|0.189|0.105|0.149|0.208|
||(0.143)|(0.175)|(0.170)|(0.155)|
|High education pre-tsunami|-0.136|-0.110|-0.125|-0.138|
||(0.0923)|(0.0945)|(0.0948)|(0.0975)|
|Number of captains in 05|0.00665**|0.00471*|0.00292|0.000176|
||(0.00270)|(0.00270)|(0.00268)|(0.00223)|
|Observations|147|149|149|149|
|Sample||Orig|inal||
|Seasonality is controlled|Y|Y|Y|Y|
|Regional fixed effects|Kec|Kec|Kec|Kec|

*** p<0.01, ** p<0.05, * p<0.1; Estimates in coumn (1)-(4) are marginal effects of probit model.  Robust standard errors clustered at the village level are in parentheses.

44
# **Appendix 3: Robustness check using endogenous switching regression**

To confirm the empirical robustness, endogenous switching regression (Maddala, 1986) defined in Eq. (A.5) below is employed to examine the initial decision in 2007, which accounts for the self-selectivity between fishing (j=F) and non-fishing (j=NF). I use pre-tsunami social capital endowments (social relationship with middlemen called _Toke Bangku_ ) and the number of surviving captains after tsunami as instruments _Z_ in the selection equation. Stronger social ties are likely to constrain the switching decision out of fishing but should be uncorrelated with the log of real per capita household income in each sector: ln�y07,j,ivk� = ln ~~�~~ nI0707,j,ivk,j,ivk �. In the main equation, _X_ includes a similar set of covariates as in Eq. (1), but the regression could only control kabupaten fixed effects for this maximum likelihood procedure to converge.

Similarly, endogenous switching model defined in Eq. (A.6) is applied to find robust estimates on the job switching and income growth between 2007 and 2009. I similarly use two pre-tsunami social capital endowments (households’ economic relationship with middlemen and social relationship with neighbors through ROSCA called _Arisan group_ ) as instruments in the selection equation, and the main equation controls for the sampling heterogeneity between original and supplementary samples as well as kabupaten fixed effects.

Δln (yj,ivk) = β0j + β1jqivk + β2jBvk + γjXivk + l�sorig = 1� + μk + ∆εj,ivk , _j=F,NF_ P(F09,ivk = 1|F07,ivk = 1) = φ{δ1[Δln (yF,ivk) −Δln(yNF,ivk)] + δ2Zivk + uivk} (A.6)
# **<u>Empirical result:</u>**

Column (1) in Table A.2 confirms relatively large labor supply elasticity to income risks caused by the aggregate shock in the selection equation of Eq. (A.5). The estimates in the main equation shows that the impact of _AGGSHOCK_ on the initial income gaps between two sectors in 2007 is statistically zero. The correlation coefficients (ρεF,u, ρεNF,u) are not statistically significant, showing that the switching regression is close to exogenous.

Column (2) shows the estimates of Eq. (A.6). In the selection equation, my instrument on the affiliation with Arisan group are statistically valid and the correlation coefficients suggest that households which chose to work in the fishing sector experienced slower income growth which is consistent with my findings in Table 9. In the main equation, only _AGGSHOCK_ significantly reduces the income growth for fishing sector, but the negative effect of _IDIOSHOCK_ is not statistically significant. After explicitly controlling for the self-selectivity bias, the negative effect of _AGGSHOCK_ is still robust and supports my OLS estimates in Table 9 that the aggregate quality shock had long-term negative impacts on income growth during the recovery stage.

# **References**

Lokshin, M. and Sajaia, Z. (2004) Maximum likelihood estimation of endogenous switching regression models. _The Stata Journal_ , 4(3), pp. 282-289 Maddala, G. S. (1986). _Limited-dependent and qualitative variable in Econometrics_ . United Kingdom: Cambridge University Press 46
Table A.2: Robustness check using endogenous switching regression

|||In 2007<br>||Fr|om 2007 to 2<br>|009|
|---|---|---|---|---|---|---|
|||(1)|||(2)||
||Mai|n eq.|Selection<br>eq.|Main|eq.|Selection<br>eq.|
|VARIABLES|y_F,07|y_NF,07|Fish in 07|∆y_F|∆y_NF|Fish in 09|
|AGGSHOCK|0.480|0.0468|-1.382***|-0.350*|-0.218|0.0704|
||(0.503)|(0.339)|(0.457)|(0.189)|(0.217)|(0.183)|
|IDIOSHOCK||||0.0319|-0.0484|-0.145|
|||||(0.173)|(0.211)|(0.196)|
|D_bad house in 07|0.0346|-0.170|-0.367||||
||(0.283)|(0.184)|(0.293)||||
|D_bad house in 09||||-0.0533|-0.113|0.250*|
|||||(0.143)|(0.249)|(0.145)|
|Boat aid in 09||||-0.397|-0.338|0.492*|
|||||(0.263)|(0.300)|(0.253)|
|Boat owner and captain pre-tsunami|0.240|-0.119|-0.113|-0.151|0.0680|0.214|
||(0.212)|(0.178)|(0.260)|(0.165)|(0.199)|(0.188)|
|D_large boat pre-tsunami|0.221|-0.293*|0.146||||
||(0.210)|(0.176)|(0.264)||||
|Ln (number of NGOs in 07)|-0.127|0.213|-0.751***||||
||(0.236)|(0.230)|(0.286)||||
|Fraction of fish population in 07||||-0.806***|-0.838**|0.151|
|||||(0.244)|(0.382)|(0.320)|
|Constant|10.66***|10.80***|1.727**|0.490|-0.552|0.666|
||(0.570)|(0.841)|(0.864)|(0.357)|(0.746)|(0.490)|
|**Instrumental variables**|||||||
|D_debt to Toke Bangku pre-tsunami|||0.564*||||
||||(0.309)||||
|Number of captains in 05|||0.0156**||||
||||(0.00721)||||
|D_arisan group in 07||||||0.493**|
|||||||(0.223)|
|D_debt to Toke Bangku in 07||||||0.544|
|||||||(0.398)|
|**Correlation coefficients:**|||||||
|ρ_F|||0.667|||-0.808***|
||||(0.749)|||(0.295)|
|ρ_NF|||0.077|||-0.052|
||||(0.443)|||(0.218)|
|Observations||148|||357||
|Sample||Original||Su|pplementary|1/|
|Regional fixed effects||Y|||Y||
|Samplingheterogeneityis controlled||Y|||Y 2/||

*** p<0.01, ** p<0.05, * p<0.1; All estimates are marginal effects of probit model.  Robust standard errors clustered at the village level are in parentheses.  The regression implements the maximum likelihood estimation using _movestay_ command developed by Lokshin and Sajaia (2004).

1/ In column (2), the inverse probability of staying in the sample ("stayers") is used as a weight to account for attrition bias and sampling heterogeneity is controlled by including _1[s_orig=1]._ AGGSHOCK=Fraction of bad aid boats IDIOSHOCK=D_have a bad aid boat in 09 47
# **Appendix 4: Variable definitions**

# **1. Quality shock variable:**

# **<u>Aggregate shock (</u>** **_<u>AGGSHOCK</u>_** **<u>)</u>**

- _Fraction of bad aid boats in 07_ :  Proportion of faulty boats from donors in the village by 2007, which is the number of faulty aid boats divided by the total number of aid boats in the village. (from the _Panglima Laot_ survey in 2007)

# **<u>Idiosyncratic shock (</u>** **_<u>IDIOSHOCK</u>_** **<u>)</u>**

- _D_have a bad aid boat in 07/09_ :  Dummy variable indicating whether a household received an aid boat which was discovered to be poor quality by 2007/09 or not. (from the household survey in 2007/2009)

- _D_bad house in 07/09_ :  Dummy variable indicating whether a household received a temporary or permanent house from donors which had at least one type of quality issues such as a leaky roof, cracked wall, poor foundation, or faulty plumbing by 2007/09 or not. The summary statistics in Table 1 also reports the fraction of households facing each type of housing quality problem for the supplementary sample. (from the household survey in 2007/2009)

# **2. Household characteristics:**

- _Fish in 07/09_ :  Dummy variable indicating fishing as a primary job in 2007/09 or not.(from the household survey in 2007/2009)

- _Report catch in 07:_ Dummy variable indicating any family members reported some amounts of fish outputs in 2007 or not. (from the household survey in 2007/2009)

- _Boat aid in 07/09:_ Dummy variable indicating the receipt of a boat from donors by 2007/09 or not. (from the household survey in 2007/2009)

- _Buy in 07/09:_ Dummy variable indicating the receipt of a boat by investing own money by 2007/09 or not. (from the household survey in 2007/2009)

- _Boat owner in 07/09:_ Dummy variable indicating households owned a boat in 2007/09 or not. (from the household survey in 2007/2009)

- _Boat owner and captain pre-tsunami:_ Dummy variable indicating pre-tsunami boat owner and captain or not.  (from retrospective data in the 2005 household survey)

- _D_large boat pre-tsunami:_ Dummy variable indicating the use of boat with above average boat length (9 meter) pre-tsunami or not.  (from retrospective data in the 2005and 2009  household surveys)

- _High education pre-tsunami:_ Dummy variable indicating household heads' education level was above senior high school pre-tsunami or not.  (from retrospective data in the 2005 household survey)

- _D_debt to Toke Bangku:_ Dummy variable indicating household owed traditional debt to middlemen ( _Toke Bangku_ ) and was still obliged to repay after the tsunami or not. (from retrospective data in the 2005 household survey)

48
- _High income in 07:_ Dummy variable indicating household's weekly earning was more than Rp 500,000 (US $50) in 2007 or not. (from the 2007 household survey)

- _Ln(boat length pre-tsunami):_ Log of the length of pre-tsunami boat (in meter, outliers above the 95 percentile are trimmed). (from retrospective data in the 2005 household survey)

- _Ln(engine capacity pre-tsunami):_ Log of the boat engine capacity pre-tsunami (in HP/PK, outliers above the 95 percentile are trimmed).  (from retrospective data in the 2005 household survey)

- _Total fish catch/length pre-tsunami:_ The total amount of fish (which is the sum of reported small fish and tuna catch excluding arthropods, in kilos, outliers above the 95 percentile are trimmed) before the tsunami divided by the length of the pre-tsunami boat (in meter). (from retrospective data in the 2005 household survey)

- _In-shore fishing skill:_ Dummy variable indicating a household which was engaged in in-shore fishing (near shore or within lagoon) before the tsunami.(from retrospective data in the 2005 household survey)

# **3. Village characteristics:**

- _Number of NGOs in 07:_ Total number of aid agencies which were operating in the village in 2007. (from RAN dataset)

- _Number of aid projects in 07:_ Total number of aid projects in the village in 2007.  (from RAN dataset)

- _Total housing aid in 07:_ Total number of temporary and permanent houses constructed by 2007. (from the 2007 village head survey)

- _Number of boat aid in 07:_ Total number of boats received from donors by 2007. (from the _Panglima Laot_ survey in 2007)

- _Number of captains in 05:_ Number of surviving fishermen working as a boat captain in the village right after the tsunami in 2005.  (from the 2005 village head survey)

- _D_Banda Aceh/D_Aceh Jaya:_ Dummy variable indicating households living in Banda Aceh/Aceh Jaya or not.

- _Fraction of fishing population in 07:_ The faction of village workers in the fishing sector in 2007. (from the 2007 village head survey)

- _Credit union in village in 07:_ Dummy variable indicating the existence of credit unions in the village in 2007 or not.  (from the 2007 village head survey)

49