rafmacalaba/data-use-mentions-extended huggingface dataset:
including for the effects of JOHAR irrigation on Area under Irrigation, High-Value Crop Cultivation, and Gross Value of Output in Rabi 2021 (Tables A.8 through A.10 in the appendix). This enables us to conclude with some confidence that (i) beneficiary farmers were not better off before the installation of the JCLIS system, and that (ii) their increase in GVO and area under high value crop cultivation can be attributed to the increase in irrigation delivered by the JOHAR systems.

JCLIS operators and managers do not keep written records of system use. Therefore, our assessment of system performance is also based on self-reported recall data from these respondents which may contain recall errors or systematic biases, including the possibility of deliberate misreporting. Data on system performance collected from beneficiary farmers were also used to validate operators’ responses. However, data on important aspects like the total operating hours of the system and the total command area served in each season could only be collected from the operators.

Disruptions from the COVID-19 pandemic may have led to lower price realizations for high-value crops grown by farmers and may even have affected their crop choices. It is therefore possible that the impacts of JCLIS may become measurably greater once the pandemic subsides. These limitations of the study should be kept in mind when interpreting the results of this analysis.

# **4. Data**

# _4.1. System characteristics_

50 system operators were interviewed. (Table A.1 in the Appendix reports the summary statistics for systems and their operators.) 48 of them were men. 36 of the 50 operators had received training from the JSLPS team. Most of them had been trained in the operation and management of the irrigation system including basic troubleshooting and care. Only a few of the operators had any training in record keeping or financial management, and none of them maintained written records of hours of irrigation services provided to farmers, fees collected, or area irrigated. The rest of the discussion in this section, therefore, relies on the
(2) legal structure and security of property rights; (3) access to sound money; (4) freedom to trade internationally; and (5) regulation of credit, labor and business. This index is also normalized between 0-1.

# Migration

For emigration data, we use the estimates provided in Defoort (2008). Focusing on the six major destination countries (USA, Canada, Australia, Germany, UK and France), she computed skilled emigration stocks and rates by educational attainment from 1975 to 2000 (one observation every 5 years). On the whole, the six destination countries represent about 75 percent of the OECD total immigration stock.<sup>17</sup>

> 17However, for some sending countries, the coverage by the Defoort dataset may be quite low. For

11
18 indicators of child mortality are more pronounced in large cities than in towns and medium sized cities. This study finds that while infant mortality amongst the wealthiest classes in large cities are particularly low, infant mortality rates amongst the poorest classes are quite pronounced – and indeed are higher than amongst the poorer segments in small and medium sized towns. These are suggestive findings and may be related to the particularly unhealthy living conditions in over-crowded slum areas of large cities. However, evidence on health outcomes across city sizes categories remains scarce and there does not appear to be a broad consensus in the literature on the relatively higher health risks in large cities. For example, Kapadia-Kundu and Kanitkar (2002) argue, also with reference to India, that urban public health services generally place greater emphasis on mega-cities and metro-centers, to the relative neglect of smaller cities and towns.

We probe this concern by examining the gradient across city size categories in Mexico of child anthropometric outcomes. We draw on a small area estimation effort undertaken by Rascón (2010) that parallels the work reported in preceding sections, but focuses on anthropometric outcomes rather than income poverty. Rascón combines the Mexican National Survey of Health and Nutrition 2006 with the Second Count of the Population and Dwellings 2005 in order to apply a variant of the Elbers et al. (2003) small area estimation procedure to the incidence of stunting and underweight amongst children aged 5 and below in Mexico.<sup>20</sup> Lanjouw and Rascón (2010) examine the correlation of child health outcomes in urban areas with city size. Table 8 summarizes their results and documents that the incidence of low height for age (stunting) and low weight for age (underweight) among children displays a similar gradient across city sizes as we have seen for income poverty. In Mexican cities that are larger than 500,000 inhabitants, the incidence of stunting and of underweight among children is 9%. In the case of stunting the incidence rises monotonically as cities decline in size. Amongst the smallest
Figure 16 displays the Gini coefficients from the various checks for 2010 and 2016. These results indicate no significant differences across the scenarios, reinforcing one of the analysis's central messages that inequality has remained relatively stable over the past 12 years.

Figure 16. Gini coefficients in 2010 and 2016 under different scenarios <!-- Start of picture text --> 36<br>35<br>34<br>33.49 33.47<br>33.08<br>33 33.02<br>32.96 33.04 32.67<br>32.67<br>32<br>31<br>30<br>Current approach Sc.2: Remove decile Sc.3: Use only 2022 in Sc. 2 + Sc. 3<br>constraint training stage<br>2010 2016<br><!-- End of picture text --> Source: Own estimations based on 2010, 2016, and 2022 HIES.

These additional scenarios and their outcomes enhances the robustness of the analysis. None of the alternative scenarios significantly change the overall trends or conclusions drawn from the study of poverty and inequality evolution in Bangladesh over the past 12 years.

# b. The SWIFT method

A second round of robustness checks was conducted using the SWIFT methodology (Yoshida et al., 2022) as an alternative method. SWIFT typically is implemented for estimating total household consumption. However, it was adapted to predict only the additional consumption from previous rounds. This adjustment allowed for a comparison of the SWIFT results with those obtained through the methodology outlined.

The application of the SWIFT method necessitated a cross-validation process before prediction, resulting in a p-value of 0.04. This value indicated the most suitable fit for minimizing the mean square error and the mean absolute difference in poverty rate estimations within the test sample. Multiple imputations were conducted, each with 20 repetitions. Findings showed that the estimated poverty rates for both 2016 and 2010 were consistent
**Figure 3:** **_Adequate Housing Index_ and Slum Population**

<!-- Start of picture text --> CRIEGY TUNTURMAR ROU ARGDOMMEXBRA UKR BLR UZB<br>VNM COL PER<br>ZAF IDN IRQ<br>GAB<br>GHA IND PAKNICPHL<br>LAO GMBSEN CMR BGDAGONPL CIV<br>RWA KENCOG NGAMMR DJI SDN<br>TZA BDIUGA TGOZMB BEN MWIHTI<br>MLI GIN BFA<br>SLE ETH MOZ<br>GNB COD<br>MDG<br>TCD<br>NER<br>0 20 40 60 80<br>Slum population in % ( UN - Habitat 2018 )<br>Source: UN Habitat<br>1<br>.8<br>.6<br>Adequate Housing Index .4<br>.2<br><!-- End of picture text -->

**Note** :  Relationship between the _Adequate Housing Index_ and urban slum population. The correlation coefficient is - .83. (Source: Authors’ calculation based on countries’ latest available household consumption and expenditure survey and UN-Habitat 2018).

# **_4.2 AHI – Within-Country Estimates_**

As the _AHI_ is micro-founded it can be disaggregated along segments of the population within countries. In this section, we present disaggregated estimates of the _AHI_ focused along several key dimensions.

## 4.2.1 AHI – Disaggregated by Household Income

In all 64 economies, adequate housing is often not delivered to the lower end of the income spectrum. With growing population and urbanization rates in the next decade, this issue will become even more acute. To reflect within-country differences in housing adequacy, we calculate the Gini coefficient of adequate housing per country, which reflects inequality in access to adequate housing (Table 2). The Gini coefficient is frequently applied in economic literature to assess the state of income inequality. Over time, the methodology for estimating the Gini coefficient has
survey data which include detailed information on households’ sanitation infrastructure and behaviour as well as information on borrowing, (iii) administrative MFI data on sanitation loan uptake from the micro-finance institution providing the newly introduced sanitation loan product as well as official administrative credit bureau data on the amount and date of all loans disbursed by all MFIs active in the study area.

The first key result of the paper is that the sanitation micro-loan is availed of by both subsidy eligible and ineligible households and the percentage uptake within these two groups is statistically equivalent at on average 19 percentage points. We however also observe that while the loan to new toilet conversion rate is about 86% for subsidy ineligible households it is, on average, only about half of this for those eligible for the government subsidy.

Turning to mechanisms, we combine predictions from a theoretical model outlined in ACGMS 2019 and the evidence available on changes in borrowing and investment behaviour, to conclude that sanitation loan uptake and investment of subsidy ineligible households is driven by the new loan product alleviating credit constraints for sanitation. We can conclude that one channel of micro-credit supporting the subsidy scheme is through encompassing _de facto_ vulnerable households not included in the government scheme. However, our results also raise caution that the sanitation loans only partially lift financial constraints these households seem to be facing. We observe that the uptake of sanitation loans is accompanied by a decrease in uptake of education loans. Whether this substitution also leads to a reduction in education investments remains an important question for further research.

For subsidy eligible households, we are not able to identify the exact mechanisms through which the introduction of the sanitation loan induced a significant increase in sanitation loan uptake. However, we provide evidence consistent with micro-credit helping overcome features of the subsidy design which impose possible constraints to its effectiveness. In particular, to minimise misuse of funds, the scheme 3
Breiman, L., 2001. Random Forests. Machine Learning 45, 5–32. https://doi.org/10.1023/A:1010933404324

- Chisty, K.U., 2014. Landslide in Chittagong City: A perspective on hill cutting. Journal of Bangladesh Institute of Planners 7, 1–17.

- Comprehensive Disaster Management Programme II, 2012. Landslide Inventory & Land-use Mapping, DEM Preparation, Precipitation Threshold Value & Establishment of Early Warning Devices.

- Crozier, M.J., 2010. Deciphering the effect of climate change on landslide activity: A review. Geomorphology 124, 260–267. https://doi.org/10.1016/j.geomorph.2010.04.009

- Feizizadeh, B., Shadman Roodposhti, M., Jankowski, P., Blaschke, T., 2014. A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping. Computers and Geosciences 73, 208–221. https://doi.org/10.1016/j.cageo.2014.08.001

- Fell, R., Corominas, J., Bonnard, C., Cascini, L., Leroi, E., Savage, W.Z., 2008. Guidelines for

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_Table 4. Longitudinal Relationships between Number of Papers Using Data and Statistical Performance_

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Log GDP per capita|0.58**|||0.51|0.58**|0.62**|
||(0.20)|||(0.33)|(0.22)|(0.22)|
|Log Population||0.18||1.09|0.40|0.38|
|||(0.35)||(0.69)|(0.37)|(0.37)|
|SPI Overall Score|||0.00|0.00|||
||||(0.00)|(0.00)|||
|SPI Overall Score<br>(Extended Series)|||||0.01+<br>(0.00)||
|SPI Data Sources Score<br>(Extended Series)||||||0.00+|

4 The SCI does not contain as many indicators as the SPI (25 indicators versus 51 in the SPI), and not all the indicators are similar. Eleven indicators in the SCI overlap with the SPI: national accounts base year, consumer price index, balance of payments manual in use, government finance accounting, population census, agriculture census, health surveys, household consumption/income survey, CRVS availability, and the IMF SDDS status of the country. Using these indicators, the extended SPI series contains 35 indicators with data back to 2004. These include 5 indicators covering data use, 1 covering data services, 16 covering
# **1 Introduction**

Self-employment and entrepreneurial activities create opportunities for better jobs and higher incomes, leading to overall growth in the wider economy. In the case of low-income countries, they are also among the main paths out of poverty for a large share of the population. Yet, many institutional voids in financial markets, such as weak legal regimes and regulations, absence of credit information systems, or costly insolvency proceedings, lead creditors to limit loans and charge prohibitively high interest rates. As a result, many potential entrepreneurs cannot obtain the needed capital to pursue profitable investment opportunities, resulting in constrained labor market outcomes for the individuals and lower levels of economic development for the country.

One commonly observed strategy to circumvent such constraints is temporary migration to higher income countries where workers accumulate savings at a faster rate and use them when they return home for self-employment and entrepreneurial activities. This paper provides empirical evidence on how such temporary migration episodes are used as common _intermediate_ steps to accumulate the required capital and overcome the institutional voids prevalent on the financial markets for entrepreneurial loans, a relation visualized in Figure 1. Our analysis utilizes a newly collected dataset with detailed information on the economic decisions and activities of 5,000 temporary migrants who returned to Bangladesh. The data are unique in providing the complete work trajectories of the respondents, including their economic activities and outcomes before migration, their expectations prior to departure about earning and saving prospects abroad, migration expenditures, sources of income and savings, and employment histories at the foreign destination. Most importantly, we link these trajectories with their economic activities and earnings after their return, showing how the savings from temporary migration finance self-employment and entrepreneurial investments.

International migration itself shares many common features with classical entrepreneurial investments. Temporary migration typically requires considerable upfront investment to cover the expenses and fees, with the promise of high returns afterwards. At the same time, migration involves risks. Earnings potential abroad may be misjudged or the migrant may be forced to return home prematurely, before a savings target is
relative to the baseline. This implies that imports are essentially orthogonal to log DVA, given the controls. This is sensible, since we have already removed a substantial component of import variation by interacting import decile indicators with the importer-industry-year fixed effects.

The second set of controls are measures of bilateral characteristics (often used as proxies for trade costs in the gravity literature), including distance, colonial linkages, common language, and contiguity (common border).<sup>72</sup> We do this to rule out that omitted variables (either the proxies themselves, or variables that are correlated with the proxies) spuriously drive our results.

In Table C2, columns (3)-(6) report OLS estimates and columns (7)-(10) report IV estimates with DVA-in-Services as the instrument. As is evident, the coefficient on log DVA remains negative and significant after adding these controls, while the proxy variables themselves are almost never significant.

> 72We obtain these variables from the CEPII GeoDistance Database: `http://www.cepii.org/CEPII/fr/ bdd_modele/presentation.asp?id=6` . One complication is that these characteristics pertain to individual bilateral country pairs, but we have a composite non-country entity (the EU) in our data. We therefore define bilateral characteristics vis-a-vis the EU by taking GDP-weighted averages of bilateral characteristics defined for each individual EU country. This implies that colonial linkages, common language, and contiguity are not strict indicator variables, as their weighted averages can lie between zero and one when the EU is a trading partner.

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# **5 Conclusion**

Deciding to adhere to their tax obligations is a complex decision for taxpayers, and several factors motivate it. A large body of the literature has sought to understand the determinants of tax morale with limited evidence on the role of trust.

As a novelty, this paper considers simultaneously the role of vertical and horizontal relationships, captured by trust in public institutions and the neighborhood, on attitude towards taxes. First, the paper proposes a theoretical framework, where both trust in public institutions and the neighborhood are included in the standard model of tax evasion. Further exploiting the outcomes of the model, the paper examines the effects of both types of trust on tax morale in Algeria, Ghana, Morocco, and Nigeria using the WVS. As the relationship between trust and tax morale is potentially endogenous, the paper devotes efforts to address such issues employing the instrumental variable method. The identification strategy takes advantage of historical data on slave shipments by ethnic group and community-level social norms about trust in public institutions and in the neighborhood. The underlying hypothesis of using historical data on slavery is that the slave trade caused a culture of mistrust in Africa, which may persist to this day and alter taxpayers trust in public institutions and the neighborhood. It is well documented that the slave trade generated a general environment of insecurity as slaves were captured through state-organized raids and warfare, and caused individuals to turn against each other. So, I rely on the intensity of the slave trade measured as the ratio the number of slaves exported and the initial land area of the ethnic group. I use two additional instruments, which are the average trust in public institutions and the neighborhood at the community level. Both instruments rely on the literature on the cultural heritage of trust according to which cultural heritage about trust is a critical component of individual-level trust.

The estimation results indicate that both trust in public institutions and the neighborhood are significant determinants of individual tax morale in Algeria, Ghana, Morocco, and Nigeria. The magnitude of the effects
the caloric intake distribution experience the largest percentage declines in caloric intake for a given increase in wheat flour prices. Households at the bottom of the caloric intake distribution make very small reductions in caloric intake due to the price increases. Such households live near subsistence levels and are forced to make adjustments to the quality of their diets in order to maintain energy levels; households at the 25<sup>th</sup> quantile of the dietary diversity distribution make the largest reduction in dietary quality – about one and a half times larger than that of households at the 75<sup>th</sup> quantile.

The findings have several implications. First, if policymakers focus exclusively on changes in caloric intake that result from price shocks, they may miss an important component of the big picture. While poorer households do not cut back on calories very much, it is likely that they reduce dietary quality. Such findings underscore the importance 21
correlation which could be interpreted as evidence that better social safety nets reduce (precautionary) savings. Thus, we conclude that our indicator of savings, while partially based on respondents’ perceptions, is a meaningful measure.

# **3.4. Informal and bank savings are the predominant saving instruments**

Theoretical models of household investment behavior predict that all households should participate in all financial markets and thus hold a diversified portfolio which includes risky financial assets (Panizza, 2015). Empirical evidence on actual investment behavior including investment in risky assets is scarce and mainly limited to high income countries. Guiso and Sodini (2013) gather evidence on direct and indirect stock holding for 12 countries; more recently the Household Finance and Consumption Survey in the euro area collected new data on the saving behavior of households in 15 euro area countries. On average 33% of households in the euro area have any contractual savings and 20.2 % of households across the euro area hold capital market savings (Arrondel et al, 2014).

In CEEU and WB, among those individuals who save (40% on average, see Figure 1), an equal percentage of households have informal savings (74.6%) and bank savings (74.1%). The very high percentage of informal savings is dominated by savings in cash, a feature of households’ saving behavior in transition economies which has been documented and analyzed in depth by previous research (Stix, 2013). With three out of four savers holding bank savings, current accounts and/or saving deposits constitute the predominant form of formal savings. Contractual savings are held by 23% of savers or 9% of households, thus well below the euro area average. In line with the wellknown “stock-market participation puzzle”, participation in capital market savings is very low at less than 3 % of households or 7% of savers.

Heterogeneities across countries in the use of different saving instruments are substantial. Bank savings are held by only 50% of savers
, those differences are narrowing. Finally, foreign investors may be more sensitive than local investors to “ _push factors_ ” of capital flows into the domestic economy. For instance, global investors are more prone to invest their capital in the domestic economy if the returns in their economies (say, the interest rate in the foreign country) are lower. If local investors may still invest in the foreign country despite the lower world interest rate, considerations on foreign assets as safe assets (say, US T-bills) may prevail. This paper also presents some empirical evidence on the different time-bunching of sudden stops by source (Figure 4) and the differences in their macroeconomic consequences (Figure 5 and Table 6). These findings may indicate differences in the determinants of sudden stops driven by foreign investors ( _inflow-driven_ ) vis-à-vis those driven by decisions of local investors ( _outflow-driven_ ). If that is the case, preventive policies implemented by governments will also differ.

In order to analyze the questions in this paper we gather information on gross capital flows for 185 countries from 1970 to 2007. Then our _effective sample_ is reduced to 82 countries while eliminating small countries ( _i.e._ with population less than 1 million in 2007 and gross national income per capita lower than US$ 2000) and countries with few observations ( _i.e._ less than 9 consecutive annual observations). We follow the procedure outlined in Guidotti, Sturzenegger and Villar (2004) to define a sudden stop as the reduction in the financial account that is at least one standard deviation below the mean and exceed 5 percent of GDP. To ascertain whether the

> 4 Kose, Elekdag and Cardarelli (2009) have recently documented the policy responses to surges in private capital inflows across a wide array of developing and industrial countries.

4