rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix - Geospatial analysis of displacement in Afghanistan, a case study

_Anais Dahmani-Scuitti_<sup>_1_</sup> _, Erwin Knippenberg, Walker Kosmidou-Bradley, Johanna Lee Belanger_ _(_ Poverty & Equity Global Practice, The World Bank)

# Appendix A: variables definitions

## Definitions of IOM variables

<u>Returnees</u> are Afghan nationals who have returned to Afghanistan in the assessed location after having spent at least six months abroad. This includes both documented returnees (Afghans who were registered refugees in host countries and requested voluntary return with UNHCR and relevant national authorities) and undocumented returnees (Afghans who returned spontaneously or were deported from host countries, irrespective of whether or not they were registered refugees with UNHCR and relevant national authorities).

<u>Arrival IDPs</u> are Afghans who fled from other settlements in Afghanistan and have arrived and presently reside at the assessed location - host community, as a result of, or in order to avoid, the effects of armed conflict, generalized violence, human rights violations, protection concerns, or natural and human-made disasters.

### <u>Net Inflow</u> = _Returnees + IDPs_

<u>Returned IDPs</u> are Afghans who have returned to their home or place of origin in the assessed location or settlement from which they had fled as IDPs in the past, as a result of, or in order to avoid, the effects of armed conflict, generalized violence, human rights violations, protection concerns, or natural and humanmade disasters.

<u>Fled IDPs</u> are Afghans who have fled from an assessed location or settlement within which they previously resided and now currently reside in a different settlement in Afghanistan, as a result of, or in order to avoid, the effects of armed conflict, generalized violence, human rights violations, protection concerns, or natural and human-made disasters.

## Construction of total inflow

We construct the variable _Net Inflow_ as the cumulative net inflow (labeled _net_inflow_ ) at final date (December 2020). For each settlement _i,_ the variable is hence defined with _local net inflowi= number of returnees from abroadi + number of returning IDPsi + number of new IDPs in settlementi - (Out Migrantsi + Fled IDPsi)_

# Appendix B: Geospatial Data and Methodology

The geospatial methodology used for this analysis extracts monthly descriptive statistics of Nighttime Lights data to individual buffers around IOM settlement locations. This process is known in Geographic Information Systems (GIS) as “zonal statistics”, and is a commonly used method for collecting raster, or gridded data (e.g., NTL, precipitation) into zones or areas of interest to the user (e.g., buffers, administrative districts). This general approach was used in Google Earth Engine, a free cloud computing and data repository, which enables largescale, satellite imagery and other analyses, including the extraction of zonal statistics.<sup>48</sup>

> 1 Corresponding author, <u>adahmaniscuitti@worldbank.org</u>
|**Appendix Table A1: Attrition**|**Rates by Round**<br>All firms|Control|Cash|In‐kind|P‐value test<br>of equality|
|---|---|---|---|---|---|
|_Didn't Answer Survey_||||||
|Wave 1|0|0|0|0|1|
|Wave 2|0|0|0|0|1|
|Wave 3|0.029|0.031|0.010|0.042|0.106|
|Wave 4|0.073|0.086|0.068|0.052|0.303|
|Wave 5|0.112|0.131|0.099|0.089|0.262|
|Wave 6|0.080|0.102|0.047|0.068|0.050|
|Any Wave|0.166|0.196|0.131|0.141|0.070|
|_Missing profits data_||||||
|Wave 1|0.080|0.091|0.071|0.071|0.615|
|Wave 2|0.016|0.013|0.025|0.010|0.477|
|Wave 3|0.069|0.076|0.061|0.071|0.740|
|Wave 4|0.098|0.123|0.076|0.071|0.064|
|Wave 5|0.129|0.149|0.121|0.106|0.207|
|Wave 6|0.114|0.141|0.086|0.086|0.059|
|Any Wave|0.285|0.329|0.236|0.246|0.019|
|_Ever close business_<br>Note: Test of equality if based<br>with controls for stratification|0.064<br>on regression of a<br>groups and robust|0.073<br>ttrition on t<br>standard e|0.063<br>reatment g<br>rrors.|0.047<br>roup|0.463|

**Appendix Table A2: Robustness of Treatment Effect to Lee Bounds** Dependent Variable: Real Monthly Profits (Cedis)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|FE|OLS|OLS|FE|FE|
|Cash Treatment*Female|5.167|‐2.298|6.093|1.148|‐1.441|‐3.297|
||(8.545)|(8.768)|(8.767)|(7.106)|(8.927)|(7.226)|
|In‐kind Treatment*Female|37.65**|32.87**|40.88***|9.378|35.34***|11.06|
||(14.94)|(13.21)|(15.41)|(7.066)|(13.59)|(7.661)|
|Cash Treatment*Male|16.81|5.132|21.82|6.218|9.154|‐5.718|
||(13.25)|(16.10)|(13.28)|(11.28)|(16.02)|(13.87)|
|In‐kind Treatment*Male|35.45**|27.83|37.26***|14.71|28.11|8.421|
||(14.04)|(18.15)|(14.07)|(10.14)|(18.21)|(14.07)|
|Lee Bounding|No|No|Upper|Lower|Upper|Lower|
|Number of Observations|4203|4203|4165|4167|4165|4167|
|Number of Firms|764|764|764|764|764|764|

Notes: All estimation includes wave effects. Standard errors in parentheses, clustered at the firm level. Trimmed Sample used for all columns OLS estimation includes dummies for the matched quadruplets. *, ** and *** denote significant at the 10%, 5% and 1% levels.
Crowley (2013a) construct measures of nontariff protection from U.S. application of antidumping and safeguards over 1997-2006 in order to investigate the repeated game model of Bagwell and Staiger (1990). Evidence is consistent with the theory that countries in self-enforcing trade agreements increase their levels of import protection in response to positive trade volume shocks, with variation arising according to industry-level trade elasticities. Market power may thus still affect the _levels_ of import protection that governments impose, even when applied MFN import tariffs are constrained by trade agreement commitments, provided that measures of import protection reflect the influence of the appropriate additional nontariff instruments.

Studies on different countries and time periods examine other aspects of the relationship between tariff and nontariff protection. Limão and Tovar (2011) examine Turkey’s experience during the 1990s period in which it Turkey signed a customs union arrangement with the European Union and it legally bound some of its tariffs under the WTO. Evidence suggests that Turkey’s tariff commitments increased the likelihood and restrictiveness of its government’s subsequent use of nontariff instruments of import protection.

Another interesting case study is India, which made massive cuts to its applied MFN import tariffs in the 1990s and subsequently became the trading system’s heaviest user of the antidumping and safeguards. Bown and Tovar (2011) estimate structural determinants of the Grossman and Helpman (1994) political economy model of protection on repeated cross-sections of Indian data – before and after India’s unilateral applied MFN import tariff rate cuts associated with its 1991–1992 standby arrangement with the IMF. Changes to India's applied MFN tariffs during this period appear to have resulted in an exogenous, trade-liberalizing shock to India's trade policy. Nevertheless, by 2002, India had unwound much of the applied MFN tariff reductions of the 1990s by accumulating a substantial stock of imposed antidumping and safeguards nontariff restrictions.

Finally, a number of studies provide cross-country evidence that an important determinant of import protection through ‘new’ policies such as temporary trade barriers is the commitment that governments take on that limit their access to ‘old’ instruments of protection. For example, combined 16
of natural shocks, and historical data only offer limited guidance on the future. So, when taking long-term investment and planning decisions, selecting options that offer robust performance under a variety of scenarios is crucial (Hallegatte et al. 2019).

This study considers a 1-in-100 year return flood intensity to reflect a relatively rare and intense disaster. However, the concept of return periods is easily misunderstood and the probability underestimated. A 1- in-100 year flood has, on average, a 1% probability of occurrence in any given year. But this translates to or 10% probability in a decade, or 50% in a lifetime (68 years). These are significant probabilities that lie well within reasonable planning horizons of governments. For comparison, the Dutch flood protection system protects against events up to 1-in-10,000 years. In addition, it should be noted that these probabilities apply independently to a given river basin or microclimate. For the purpose of this study, we consider hundreds of thousands of such locations. This means that, globally, hundreds of 1-in-100 year flood events happen every year.

**Figure 1. The extent of 1-in-25 and 1-in-100-year floods in Vietnam’s North Central Coast Region**

_Source: Braese et al. 2020._

# **3.2. Population density**

This study estimates the location of people using the Global Human Settlement Layer (GHSL), produced by the EC JRC. It offers global coverage and is available for the years 1975, 1990, 2000, 2015, and 2020. While GHSL provides several layers (including those specifying built-up areas, population, and settlements), this study uses the population density map (GHSL-POP 2020). In a raster format, this data set provides the number of inhabitants per cell, with a resolution of 3 arc seconds (approximately 90 by 90 meters), thus specifying the distribution of population. This information is based on administrative or census-based population data, which is then disaggregated to grid cells based on distribution and density of built-up area, which in turn is derived from satellite imagery. For details of the methodology, see Freire et al. (2016).

6
The total impact of changes in the price of rice on poverty is obtained by taking both consumers and producers into account, and the results are given in table 6. If the price of rice is reduced by 20 percent, poverty is reduced in the population as a whole to 60.5 percent, while if the price of rice increases by 20 percent, poverty would increase to 67.7 percent. These are relatively large effects for a single commodity, and they underscore why the population’s feelings about the price of rice run high in Liberia.

# **5. Conclusion**

When assessing the potential impact of a change in the price of cereals on poverty, it is important to consider both the impact on producers (who tend to benefit from an increase in prices) and consumers (who tend to loose out when the price increases). If producers tend to be poor and if consumers live in urban areas and are better off, an increase in the price of rice, despite its impact on the cost of food, may very well be poverty reducing.

In Liberia however, the impact of a change in the price of rice is not ambiguous. A majority of the rice consumed is imported, and a majority of the rice that is locally produced is used by farmers for their auto-consumption. Therefore, any increase (decrease) in the price of rice, whether imported or locally produced, will clearly result in an increase (decrease) in poverty, and this impact is likely to be large given the important share of food consumption allocated to rice in the country. Using data from the 2007 CWIQ survey implemented by Liberia’s Institute of Statistics, we find that a change in the price of rice of 20 percent could lead to an increase or decrease of three to four percentage points in the share of the population in poverty, which is indeed very large for a single commodity.
using the mathematics component of the transformed and extended tests shown in Figure 4.1, replicating and strengthening the previous results by using test data from a larger number of countries, controlling for a larger number of potentially confounding variables and extending the time period of the analysis. Using the panel structure of their growth data, they suggest that education seems to improve income levels mainly though speeding up technological progress, rather than shifting the level of the production function or increasing the impact of an additional year of schooling.

In sum, the existing evidence suggests that the quality of education, measured by the knowledge that students gain as depicted in tests of cognitive skills, is substantially more important for economic growth than the mere quantity of education.

# **_4.2 Issues of Endogeneity_**

As noted, growth modeling is naturally subject to a common concern: Do the identified factors represent truly causal influences or mere associations that will not affect growth if altered by policy?

The concerns about causality in the relationship of cognitive skills and growth have been addressed in detail by Hanushek and Kimko (2000). They conclude that causation concerns are very different in the case of quality than quantity, being much less of an issue in interpreting the results. Because causality issues are important for assessing our results below, we describe the Hanushek and Kimko direct investigations.

One common concern in analyses such as this is that schooling might not be the actual cause of growth but, in fact, may just reflect other attributes of the economy that are beneficial to growth. For example, the East Asian countries consistently score very highly on the international tests (see Figure 4.1), and they also had extraordinarily high growth over the 1960–1990 period. It may be that other aspects of these East Asian economies have driven their growth and that the statistical analysis of labor force quality simply is picking out these countries. But in fact, even if the East Asian countries are excluded from the analysis, a strong—albeit slightly smaller—relationship is still observed with test performance. This test of sensitivity of the results seems to reflect a basic importance of school quality, a factor that contributes also to the observed growth of East Asian countries.

Another concern is that other factors that affect growth, such as efficient market organizations, are also associated with efficient and productive schools—so that, again, the test measures are really a proxy for other attributes of the country. To investigate this, Hanushek and Kimko concentrate on immigrants to the United States who received their education in their home countries. They find that immigrants who were schooled in countries that have higher scores on the international math and science examinations earn more in the United States. On the other hand immigrants receiving part or all 29
- 45 -

C8te d'Ivoire publishes its payscales on a monthly basis, and frequently the private sector uses monthly terms in wage contracts. Hence, monthly earnings constitute the relevant transaction price for wage labor in C8te d'Ivore, since labor transactions and bargaining rarely take place in smaller time units. Hence, the earnings analysis below uses the log of monthly earnings as dependent variable.

The basic estimation results of equation (3) are reported in Table 17, with ln(W1) as dependent variable. On the average, a year of schooling adds 13% to earnings, which is more than a year of work experience does (Column 1). An average worker with ten years of schooling would receive a monthly starting salary of CFA 53,370, but an unschooled worker with ten years of experience would only earn CFA 31,729, which is 40% less. A year of primary education contributes least to raising - - earnings 7.5% per year while a year of secondary education raises earnings by 17.9% (Column 2). As can be expected, the effect of experience in the previous occupation is less than that in the current one (Column 3). A year of apprenticeship contributes 4% to earnings, which is much less than formal VTE. Within the latter, there are large differences by level: a year of secondary VTE adds 8.5% to earnings, but a year of teacher training adds 18.7% (Column 4).

An important issue is the role of diplomas and certificates in hiring and remuneration decisions. The results in Column 5 suggest that primary and lower secondary diplomas do not add significantly to earnings
country policies since the 1950s, beginning of period values could also have little explanatory power. Consequently, the regressions were redone using annual data for the same dependent and independent variables. To control for unobserved countryspecific differences in growth rates, we included dummy variables for each country--the so-called fixed effects model. The results are given in Tables 6 and 7.

The results presented in Tables 6 and 7 show very different effects of the seven openness measures on growth. Four of the seven measures have a statistically significant impact at the 5 percent level of significance; all seven measures are significant--in either levels or differences--at the 10 percent level. All the measures have the expected sign--movements toward openness positively affect growth; greater distortions (as measured by the black market premium and modified Dollar (1991) measure) negatively affect growth. In addition, the F-tests reported in Tables 6 and 7 reject the null hypothesis that country effects are not important. In nearly all the specifications, the country dummies are jointly significant-suggesting the presence of country-specific differences that persist over time, even after accounting for changes in policy and increases in the labor force, education, and capital stock.

One problem with using annual data to identify the determinants of long run growth is that short term or cyclical fluctuations could be responsible for the strong relationship between policy variables and GDP growth. Quah and Rauch (1990) 16
_from Reconciled and Combined Household Survey and Tax Return Data_ . Tech. rept. 21991. National Bureau of Economic Research, Inc.

- Czajka, L ́eo. 2020 (May). _Income Inequality in Cˆote d’Ivoire: 1985-2014_ . World Inequality Lab Working Paper. HAL.

- De Rosa, Mauricio, & Vil ́a, Joan. 2023. Beyond tax-survey combination: inequality and the blurry household-firm border. _The Journal of Economic Inequality_ , Apr.

- De Rosa, Mauricio, Flores, Ignacio, & Morgan, Marc. 2022 (Aug.). _More Unequal or Not as Rich? Revisiting the Latin American Exception_ .

- Fairfield, Tasha, & Jorratt De Luis, Michel. 2016. Top Income Shares, Business Profits, and Effective Tax Rates in Contemporary Chile. _Review of Income and Wealth_ , **62** (S1), S120–S144. ~~e~~ print: https://onlinelibrary.wiley.com/doi/pdf/10.1111/roiw.12196.

- Flores, Ignacio, Sanhueza, Claudia, Atria, Jorge, & Mayer, Ricardo. 2020. Top Incomes in Chile: A Historical Perspective on Income Inequality, 1964–2017. _Review of Income and Wealth_ , **66** (4), 850–874.

- Gasparini, L., & Tornarolli, L. 2009. Labor informality in Latin America and the Caribbean: Patterns and trends from household microdata. _Revista Desar-_ survey

- _rollo y Sociedad_ , **63** .

- Gasparini, Leonardo, & Lustig, Nora. 2011. The Rise and Fall of Income Inequality in Latin America. _Page 0 of:_ Ocampo, Jos ́e Antonio, & Ros, Jaime (eds), _The Oxford_ .

- _Handbook of Latin American Economics_ Oxford University Press.

- Ham, Andr ́es. 2011. La distribuci ́on del ingreso en Honduras: Un an ́alisis de las principales tendencias desde los a ̃nos 1990. _Revista Centroamericana de Ciencias Sociales ( RCCS )_ , **8** (2), 123–165.

- Hernandez Ore, Marco Antonio, Sousa, Liliana D., & Lopez, J. Humberto. 2015. _Honduras: Unlocking Economic Potential for Greater Opportunities_ . Systematic Country Diagnostic. World Bank.

- H ́erault, Nicolas, Hyslop, Dean, Jenkins, Stephen P., & Wilkins, Roger. 2022. Rising top-income persistence in Australia: Evidence from income tax data. _Review of Income and Wealth_ , Dec., roiw.12628.

- Lustig, Nora, Lopez-Calva, Luis F., & Ortiz-Juarez, Eduardo. 2013. Deconstructing the Decline in Inequality in Latin America. July. Publisher: World Bank, Washington, D.C.

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# **I. Introduction**<sup>**1**</sup>

The decreasing relative importance of the agricultural sector is a pervasive phenomenon of economic development which often entails sizeable population movements out of rural areas. These flows, whether towards urban areas or abroad, have traditionally been seen by local governments and international donors concerned with rural development issues as either something to favor – in order to meet labor demands of an expanding industrial sector or to alleviate poverty and unemployment in depressed rural areas by means of relocation of people – or something to prevent – in order to curb the hemorrhaging of the most productive workforce from agriculture and reduce pressure on overcrowded urban areas. Only recently policy makers have started realizing the potential of migration, and the remittances it generates, to improve conditions in the communities of origin and among households and individuals left behind.

Migration may affect sending households through a number of channels. The loss of family labor due to a temporary or permanent relocation can be (partly) offset by the potential income gains deriving from migrants’ remittances. These positive income effects, which derive in part from a relaxation of credit and insurance constraints, may arise even in the absence of actual remittances, as the mere presence of a family member abroad may alter the investment and risk-taking behavior of individuals in sending households. However, members in these households may decide to reduce the amount of work effort and increase leisure time as a result of higher income from remittances. This may be particularly true for low-return and less attractive types of labor activities such as agriculture, thus raising concerns on the potentially deleterious impact of remittances on agricultural productivity and total production, both at the household and at the country level.

The basic premise to be investigated in this paper is that as a result of the changes in labor endowments and the potential income effects due to migration, household members left behind are likely to adjust their farm’s technical efficiency and allocation of

> 1 We would like to thank for their useful comments and suggestions J.E. Taylor, Thomas Hertz, Paul Winters and other participants to the workshop “Migration, Transfers, and Household Economic Decision Making Workshop” held at the Food and Agriculture Organization of the United Nations in Rome, Italy. We are also grateful to the Albanian Institute of Statistics (INSTAT) for granting access to the data. We dedicate this work to the memory of Dini Llagami of INSTAT. The usual disclaimer applies: the views expressed are the sole responsibility of the authors and not of the institutions they represent.

2