rafmacalaba/data-use-mentions-extended huggingface dataset:
labor force in informal activity. The developing country context also requires that we capture informal sector housing – slums – which we do with the distinction between modern and traditional sectors. They use different building technologies, one expensive and the other cheaper and delivering lower amenity value per unit land area.

The literature on quantitative spatial models has grown substantially in recent years, as summarized in the Redding and Rossi-Hansberg 2017. Most of this work has focused on developed countries, including in Berlin where Ahlfeldt et al. 2015 use the shock of the Berlin wall to examine the impacts of connectivity within a city on urban structure and production. Monte et al. 2015 use a related approach to quantify the benefits from a reduction in commuting costs across cities in the United States. Papers focused on developing countries are more limited however, mainly as a result of data limitations. The model in this paper develops a model set out in Bird and Venables (2019) on Kampala, Uganda, which uses a CGE approach to analyzing the impact of land tenure on the city, and considering the potential welfare impacts of land reform.

Our core results take a benchmark data set of Dhaka in 2035. This data set is based on detailed spatial data for the period 2010-2011 (Li et al. 2016), combined with projections (supplied by the World Bank) of aggregate variables to 2035, knowledge of planned transport improvements, and supplemented by using the model to fill in projections of disaggregate detail. Some economic activity currently exists in East Dhaka, and this is projected to increase over time due to private infilling of land to create low-quality but developable areas. We refer to this as scenario A, or Business as Usual. Given this benchmark we then simulate the effect of increasing the quantity and quality of available land in East Dhaka following the construction of an embankment and related flood protection measures (Bird et al. 2018). The assumed changes are: Scenario B: Reduction in the share of land that floods according to estimates from the Institute
in 1986. Section 3 is a literature review of demand analyses concerning Vietnam, with a focus on approaches to deal with quality substitution and endogeneity of unit values. Sections 4 and 5 explain the data and method applied in this paper, respectively, and are followed by results in section 6. Finally, section 7 concludes with some policy discussion.

# **2. Economic, social development and policies in Vietnman**

In 1986 the Đổi Mới reforms produced a decisive change in the economic and social policies in Vietnam. The country shifted from a centrally-planned, highly subsidiezed Soviet-based economic system to a multi-sector, market-oriented and globally integrated economy (Hien, 2019; Khoa, 2021). Policy reforms and favourable global trends allowed progress and economic growth, so that in 2010 Vietnam transitioned from being one of the world's poorest countries to becoming a lower middleincome economy (World Bank, 2020).

Several factors helped the transition. A young population expanded the labor force while agricultural productivity increased thanks to new technologies, resulting in 30% of the workforce moving from the rural sector to the manufacturing and industrial sectors (World Bank, 2020). Domestic migration between regions is still significant. In 2018, the provinces of Sóc Trăng and Trà Vinh had a net migration of -14.5% and -11.2% respectively, while Ho Chi Minh City and the surrounding province of Bình Dương of 6.1% and 47.9% respectively (GSO data). The expansion of global trade particularly benefited Vietnam’s exports, initially exporting important commodities such as rice thanks to a relative abundance of fertile land and water, and in a second step exporting labor-intensive products such as textiles and electronics (World Bank, 2020). The development of manufacturing industries was further improved by inflows of foreign direct investments (FDI) (Hien, 2019).

Since 2010 the yearly average GDP growth rate has been 6.3% (GSO data). In 2018, the main employing sectors were
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Panel C: Birth district of mothers with at least one born in 1966-1970 (comparison cohort) and at least one born in 1971-1975 (treatment cohort)
average of the EPU index from November of the previous year through October of the current year).

Economic policy uncertainty may influence trade by affecting GDP. But policy uncertainty can also have a direct impact on trade. This is the case, for example, when policy uncertainty affects the behavior of foreign firms, leading them to reduce the investment in serving the domestic market. Insofar as the specification above controls for the impact on trade of GDP changes, the coefficient of the policy uncertainty variable can be seen as reflecting the direct effect on trade.

The regression results indicate a statistically significant relationship between economic policy uncertainty and trade growth: a 1 percent increase in uncertainty is associated with a 0.02percentage point reduction in import volume growth. The relationship with goods alone is as strong as for goods and services together, even though border policies affecting goods trade (e.g. tariffs) are easier for governments to change and therefore should be more sensitive to uncertainty than behind-the-border policies affecting services trade (e.g. domestic regulations).<sup>4</sup> Growth in Real GDP and REER have the expected signs and are significant. The results are robust to specifications that allow dynamic effects in both the dependent variable and covariates such as growth in real GDP and growth in REER (table A1).

**Table 2.** **_Association between import volume growth and economic policy uncertainty: estimations using yearly data_**

|Dependent variables:|Growth in goo<br>import|ds and services<br>volume|Growth in g<br>volu|oods import<br>me|
|---|---|---|---|---|
||(1a)|(1b)|(2a)|(2b)|
|Log of Economic Policy Uncertainty (EPU) index|‐0.0223**<br>(0.00800)|‐0.0242**
and supervision survey) to reflect that central bankers are more likely to choose bank regulation that promotes systemic stability (see Goodhart, 2002). A final instrument is the five-year moving average of the Gini index measuring income inequality (from the WDI), as regulation is in part shaped by its distributional consequences (see Beck, Levine, and Levkov, 2010).<sup>21</sup> The resulting IV regressions are reported in columns 7-9 of Table 4. In the foreign subsidiary share and third country subsidiary share regressions 7 and 9, the instrumented capital regulation variable is estimated with negative significant coefficients, suggesting less foreign subsidiary usage in case of more stringent borrower-country capital regulation. In the IV regression 7, foreign subsidiary usage is positively related to Creditor rights (borrower) and negatively to Time to enforce contracts (borrower). Thus, in the IV regressions the picture remains one of subsidiary usage being negatively related to borrower country regulatory stringency, but positively to borrower-country institutional quality.

As a specification test, for regressions 7-9 we conducted an overidentification test based on Hansen’s J statistic with as the null hypothesis that the instruments are valid, i.e. uncorrelated with the error term and correctly excluded from the estimating equation. As indicated in the table, this null hypothesis is not rejected for the three regressions. In addition, we conducted an under-identification test based on the Kleibergen-Paap rk Wald statistic with as the null hypothesis that the model is not identified, as the excluded instruments are not sufficiently correlated with the endogenous regressors. As seen in the table, in this instance the null hypothesis is rejected for the three regressions. Thus, the IV regressions 7-9 appear to be correctly specified.

# **3.2 Bilateral loan flows between borrower and lender countries**

> 21 <mark>Other instrumental variables used by Houston et al. (2012) are subsumed by included fixed effects in our setting.</mark>

19
Impact of Disaster Funds on Economic Reconstruction in Mexico.”

- Elbers, C., Gunning, J.W., Kinsey, B., 2007. Growth and Risk: Methodology and Micro Evidence. World Bank Econ. Rev. 21, 1–20.

- Engle, N.L., Bremond, A. de, Malone, E.L., Moss, R.H., 2013. Towards a resilience indicator framework for making climate‐change adaptation decisions. Mitig. Adapt. Strateg. Glob. Change 19, 1295–1312.

- Fleurbaey, Marc, and Peter J. Hammond. 2004. “Interpersonally Comparable Utility.” In _Handbook of Utility Theory_ , edited by Salvador Barberà, Peter J. Hammond, and Christian Seidl, 1179–1285. Springer US. http://link.springer.com.gate3.inist.fr/chapter/10.1007/978‐1‐4020‐7964‐1_8.

- Foa, Roberto. 2013. “Household Risk Preparation Indices—Construction and Diagnostics.”

- G7, 2015. Leadersʼ Declara�on G7 Summit Germany [WWW Document]. URL http://www.international.gc.ca/g8/g7_germany_declaration‐

- g7_allemagne_declaration.aspx?lang=eng (accessed 6.14.15).

- Geographic Information Science and Technology, 2015. Landscan Population Data. Oak Ridge National Laboratory.

- GIZ. 2014. “The Vulnerability Sourcebook: Concept and Guidelines for Standardised Vulnerability Assessments.”

- https://gc21.giz.de/ibt/var/app/wp342deP/1443/index.php/knowledge/vulnerability‐ assessment/vulnerability‐sourcebook/.

41
through latent agro-climatic factors that could be captured by district-level fixed effects. They found significant productivity gains from rural infrastructure.

In another example, Duflo (2001) estimated the impact on schooling and earnings in Indonesia of building schools. A feature of the assignment mechanism was known, namely that more schools were built in locations with low enrolment rates. Also, the age cohorts that participated in the program could be easily identified. The fact that the gains in schooling attainments of the first cohorts exposed to the program were greater in areas that received more schools was taken to indicate that building schools promoted better education. Frankenberg et al. (2005) use a similar method to assess the impacts of providing basic health care services through midwives on children’s nutritional status (height-for-age), also in Indonesia.

Galiani et al. (2005) used a _DD_ design to study the impact of privatizing water services on child mortality in Argentina, exploiting the joint geographic (across municipalities) and intertemporal variation in both child mortality and ownership of water services. Their results suggest that privatization of water services reduced child mortality.

A DD design can also be used to address possible biases in a social experiment, whereby there is some form of selective compliance or other distortion to the randomized assignment (as discussed in section 4). An example can be found in Thomas et al. (2003) who randomized assignment of iron-supplementation pills in Indonesia, with a randomized-out group receiving a placebo. By also collecting pre-intervention baseline data on both groups, the authors were able to address concerns about compliance bias.

While the classic design for a DD estimator tracks the differences over time between participants and non-participants, that is not the only possibility. Jacoby (2002) used a DD design to test whether intra-household resource allocation shifted in response to a school-feeding program, to neutralize the latter’s effect on child nutrition. Some schools had the feeding program and some did not, and some children attended school and some did not. The author’s DD
a credit at an agricultural input supplier for the financed production inputs. For further details on CP, MRFC, and the loan particulars, please see Online Appendix A.<sup>5</sup> At the time of the study, the vast majority of farmers in the sample had no access to formal-sector credit. In our baseline survey, only 6.7% of farmers had any formal loans in the previous year. Among these few farmers with formal-sector credit, MRFC was the largest single lender, providing 34% of loans (more than twice the share of the next largest lender).<sup>6</sup> Farmers therefore had a strong interest in maintaining good credit history with MRFC so as to maintain access to what would likely be their primary source of formal credit going forward.

In the absence of fingerprinting, farmer identification relies on the personal knowledge of loan officers (who may also rely on local informants such as village and locality leaders). While loan officers could build up reliable knowledge of borrowers over time, this identification “technology” is imperfect. Loan officers are sometimes promoted and routinely rotated to other localities. Among the 11 loan officers who handle our study areas, the median number of years at the branch is only two, while the median number of years working for the lender is 13.<sup>7</sup> In the absence of an independent mechanism for identifying borrowers, the institutional memory is lost when the loan officer is transferred to another location. Even when loan officers remain in a given location over time, the large number of borrowers can lead them to make mistakes in identification. In this project, loan officers issued an average of 104 loans, and also handled other loan customers not associated with the project.

The timeline of the experiment is presented in Appendix Figure 1. Our study sample consists of 249 clubs with approximately 3,500 farmers in Dedza, Mchinji, Dowa and Kasungu districts. Farmer clubs in the study were randomly
.<sup>24</sup> Global settlement footprint data sets with high spatiotemporal resolution have become available recently, but have thus far not been used in global flood hazard assessments<sup>25,26</sup> .

By using global historical inventories of recorded flood events, such as EM-DAT, studies have estimated exposure indicators at the country level<sup>27</sup> . Yet, the lack of data on the spatial distribution and coincidence of flood hazards and populations means that continuously evolving urban shapes cannot be taken into 4
|Month FE|Yes|Yes|Yes|Yes|
|Individual FE|No|No|Yes|Yes|

Standard errors in parentheses, *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Note: this table shows the impact of Formosa on fishery income (monthly;’000 VND; log-transformed) in 2016. “Treat” indicates saltwater fishers living in Ha Tinh, Quang Binh, Quang Tri, and Hue. “Post” indicates May-2016 or after. Geographic control group (Panel A) consists of all saltwater fishers living in provinces south of Phu Yen (i.e. distant from Formosa region). Industry control group (Panel B) consists of workers in unaffected industries (i.e. manufacturing, construction, and retail) living in the four Formosa-affected provinces. Columns 1-2 report results using a sample consisting of all individuals identified as saltwater fishery workers (for Panel A) and workers in the control industries (for Panel B) before May-2016. Columns 3-4 report results using a sub-sample restricting to only individuals who were surveyed twice in the Labor Force Survey 2016 – before and after May-2016. Standard errors are clustered at the district level.

32
. Following consultation with transport specialists at the World Bank and corroboration by specialists from the DRVN and the Transport Development and Strategy Institute (TDSI) in Vietnam, we arrived at the following as an average estimate of the life span of each type of RTP3 activity:

- **Routine maintenance** leads to an improvement in road quality that lasts a maximum of one year since these activities typically prevent road quality from deteriorating during the rainy season. We use one year as the life span of a road improved with routine maintenance.

-

- **Periodic maintenance** leads to improvements that last longer, typically around two years.<sup>13</sup>

- **Rehabilitation** improves road quality<sup>14</sup> for longer than five years, the maximum length of the postintervention period in our study, so we treated it as a permanent improvement _within our study period_ (i.e., a road rehabilitated in 2009 remains “improved” until 2014). This is consistent with

> 11 Since we had household-level fixed effects and the households in our sample did not move in the duration of the study, these absorbed a potential commune-level fixed effect.

> 12 We controlled for number of dependents in the household rather than household size. The number of dependents likely correlated with the household size, but did not open up a channel for omitted variable bias arising from the fact that household size is affected by migration caused by road improvements.

> 13 The administrative data do not distinguish between periodic maintenance on unsealed and sealed roads. The lifespan of this intervention on sealed roads is usually longer, up to six to eight years. Therefore, the average lifespan proposed by transport specialists at the World Bank, DRVN, and TDSI is a conservative estimate of the duration of improvement.

> 14 As noted in Section 2, activities classified in the administrative data as “road rehabilitation” include rehabilitation and upgrading. The life span for upgrading is typically much longer than six years
decisionmaking – a study by Corsetti et. al. (2011) using data from the U.S. detects significant spending reversals (falling below trend levels) that follow periods of stimulus in the country‘s economic history. These spending reversals, likely motivated by an effort to stabilize the country‘s debt, also affect public expectations profoundly through subsequent cycles. Understanding how exactly government stimulus plays out into different economic sectors, however, hinges on reliable estimates of the multiplier effects of government spending, which can vary considerably across models (Auerbach et. al. 2010).

There have been few studies that examine the effects of the 2008 crisis on households in low- and middle-income countries.<sup>1</sup> In the case of East Asia, the most-recent relevant studies have typically looked at the 1997 financial crisis in which the external environment was much more favorable and it was possible to stimulate the crisis-hit economies through sharp increases in net exports. The distributional impacts of that crisis are particularly interesting. Bresciani et al. (2002), for example, studied the impact of the Asian financial crisis of 1997-98 on farm households in Thailand and Indonesia; they found that cross-country effects on similar household demographic groups can vary considerably - poor farmers were hard hit in Thailand, but not in Indonesia, and that in both countries farmers specializing in export crops benefited from the currency devaluation associated with that crisis. Friedman and Levinsohn (2002) also find, as in the case of Thailand, that following the crisis in Indonesia, the urban poor were hit the hardest compared to the rural poor, who being able to produce food were able to absorb some of the effects of rapid inflation.

Although the broad issue we are addressing – household responses to shocks and the role that policy might play in softening the effects of these shocks – is of universal importance, our study focuses on just one country, Thailand. This choice is driven not only by its exposure to the 2008 crisis as a heavily export-oriented
inputs by up to 21%. This effect remains unchanged when I condition on household characteristics proxying for overall household wealth such as education, household size and total land owned. The estimate for households below the median is indistinguishable from zero throughout.

What alternative channels might explain the relationship between debt relief and the observed decline in household investment and productivity? Duflo, Kremer and Robinson (2011) study the investment decision of farmers in Kenya and show that time-inconsistent preferences limit profitable investments. In principle, it is possible that the same mechanism is operational in the sample of farmers studied here. However, in order to explain the results reported in the preceding sections, debt relief would need to induce differences in the time-preferences between treatment and control households. In non-reported robustness checks I test the impact of debt relief on measures of time preferences and find no effect. Hence time-inconsistent preferences may constrain the investment decisions of households in the sample overall, but not differentially so among the treatment and control group. Another potential mechanism that might induce a reduction in investment expenditure after debt relief is the possibility that households perceive debt relief as a windfall and shift their expenditures away from investment and towards consumption (see e.g. a discussion in de Mel, McKenzie and Woodruff 2008).<sup>23</sup> Two observations make this mechanism unlikely to operate in the setting studied here. First, note that the program eliminated debt on the books, but did not involve cash payments. Additionally few households used debt relief to obtain a new loan so that the effect on household liquidity and the potential to increase consumption was limited. Second, when I examine the consumption expenditures of households after the program I find no difference between households in the treatment group. I also find no evidence of an impact of debt relief on savings, which suggests that, households lack sufficient liquidity to finance investments from precautionary savings prior to the program, and that debt relief does not lead households to increase savings ex-post.

> 23The authors find that
predictors, for example the distance to urban centers and the distance to the electricity grid. Such variables are informative especially with respect to their impact on poverty and can be better predictors than binary variables indicating access based on a cutoff might be. However, they also can have difficulty predicting “pockets of poverty” sitting in otherwise wealthier areas, for example slums in urban areas, or the converse. This could exacerbate the spatially smooth predictions already introduced by the assumption of constant coefficients from the linear regression. Unfortunately, the impact this may have had on the estimation is difficult to test using cross‐validation with survey data that was designed to be widely distributed geographically. Therefore, it is impossible to test what the share of variation in welfare across EAs is dampened by the use of these spatially smooth predictors. This is an area which warrants future research given the predictive power of such variables and could be better tested using data collected more finely over large areas such as a Census.

Secondly, there is a very poor understanding of the population distribution in South Sudan and no reliable sampling frame against which to extrapolate our predictions. The implications of this are that while the model can predict into geographic pixels based on the existing data, it is difficult to aggregate by county without knowing how to weight each pixel according to the population present within it. Thus, poverty maps aggregated by area are likely to over‐ estimate poverty rates as most areas within each county are likely to have lower population density and high poverty. The solution to this problem is to define a new sampling frame for the country, then re‐calculate county‐ level predictions based on this sampling frame. This was attempted in this study by estimating an urban gradient based on multiple data sources and their relationship with urbanicity. However, some of these data are likely to be out of date for many of the same reasons that a traditional Census exercise is complicated. The rapid and enormous movement of people caused by the conflict is likely to have compounded this