rafmacalaba/data-use-mentions-extended huggingface dataset:
variable assumed to be distributed identically and independently across individuals according to the standard normal distribution. We estimate equation (14) by ordinary least squares.

# **c. Decomposition approach**

We estimate models (13) and (14) for the first and final year, and simulate the impact of occupational changes by substituting the estimated parameters for one year with the parameters of the other year. We then use this hypothetical income to calculate a series of distributional statistics and compare them against those estimated using the actual income data.

 **C.1** Accounting for the impact of occupational changes To carry out this simulation, we assign the estimated coefficients of equation (13) in year t′ to the household survey in year t . To allow individuals to change occupations in the simulation, we need the � residual terms ε� of the multinomial logit in equation (13), which are unobserved. Following Inchauste et al. (2014) and Train and Wilson (2008), we draw the residuals from an extreme value distribution in a way that is consistent with observed choices. The simulated earnings for individual i are given by: The difference between this simulated distribution and the earnings distribution observed in year _t_ is accounted by the change in the occupational structure, which in turn is due to changes in structural parameters ( Φ ) of the occupation model. This is the main focus of the decomposition in this paper. Note that the occupation structure can also shift because the exogenous characteristics change. However, while the impact of shifting characteristics can be used to decompose the distribution of occupations, one cannot separate the _direct_ impact of shifting characteristics on earnings from the _indirect_ impact of shifting characteristics on occupations and from these on earnings. This simultaneous direct and indirect impacts of shifting characteristics are thus jointly accounted in step C.2 below as follows.

 **C.2** Accounting for the impact of changes in relevant exogenous characteristics In order to study the role played by changes in
of men able to travel alone outside of<br>community in the previous year<br>•Percentage of females accessing formal institutions<br>in the previous year<br>•Percentage of males accessing formal institutions in<br>the previous year<br>•Number of community advocacy and awareness<br>campaigns against domestic violence and sexual<br>abuse in the previous year|(1) Office of the UN High<br>Commissioner for Human<br>Rights<br>(2) World Bank Country Policy<br>and Institutional<br>Assessment<br>(3) US State Department –<br>Country Reports on Human<br>Rights Practices<br>Where no data source is<br>identified, data must be<br>collected using the national<br>survey or in-depth research<br>techniques.|
||**Sub-domain: Community**||
|**Presence of rules (institutions)**|**Enactment of rules (institutions)**|**Existing data**<br>**sources/indices**|

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often than firms elsewhere. Competition from goods smugglers is also perceived as a major competitive threat in Azerbaijan and Georgia, much more than in any other countries, confirming that informal and illegal activities in these countries are a major phenomenon.

_Predictability of changes in policies and legislation_ . Perceived predictability of the business environment determined by changes in economic and financial policies or changes in laws and regulations is an important aspect that affects business decisions, particularly investments and other long-term decisions. According to enterprises and in relation to predictability of economic and financial policies, the CIS-7 rank well across transitional economies with an average predictability higher than in both other CIS countries and non CIS countries. Within the CIS-7, there are however great differences with Azerbaijan and Uzbekistan being the most predictable environments and with Kyrgyzstan and Moldova being the least predictable. The situation is very much the same relatively to predictability of laws and regulations affecting businesses with Azerbaijan showing best predictability and Kyrgyzstan the lowest.

_Infrastructures_ . Some of the essential services for the functioning of firms are ranked very poorly in the CIS-7. On average, postal services are ranked as the worst service in all three groups of countries considered. Telephones and electricity also rank for all groups within the four worst services in a list of 14 services. As already remarked in previous sections, telephones density is extremely low in the CIS-7 (Pomfret 2003) and the cost of telephones calls per minute is higher than anywhere else (WBI<sup>15</sup> ). Electricity consumption per capita in the CIS-7 is only about twothirds than elsewhere in transition and increased only marginally between 1998 and 2000 while commercial energy use was only about one-third during the same period (WBI), both measures reflecting relatively lower income and output. Generally, much better ranked are roads and public works. WBI data show that the percentage of paved roads in the CIS-7 is rather high as compared to the average in other transitional economies with over 92% of roads paved although we do not have statistics on the general state and maintenance
winners had migrated internationally, compared to 19 percent of the lottery losers who form our control group. We find that low-skilled international migration, instrumented by the outcome of a randomized lottery, increases migrant income by over 200 percent and doubles their household income, mostly through increases in remittances sent from abroad. This further leads to improvement in welfare of the household along several measures of consumption, expenditures, and household debt position. Male migration drastically increases female involvement in key household decisions by over 0.5 standard-deviation, although this effect is likely to be, at least for now, driven mechanically by the absence of a key male member. We do not observe any differences in labor supply of other household members or changes in educational attainment of children (except an increase in educational expenditures). Household involvement in entrepreneurial activities falls, potentially due to the absence of the most entrepreneurial member of the household.

Our estimates provide a well-identified, microdata-based foundation to the results found from analysis of macroeconomic data (e.g. Clemens 2011, Clemens et al. 2018, Benhabib and Jovanovic 2012). Like the experimental estimates we report here, the macro literature reports income gains that are multiples of incomes at home, even for internal migration from rural to urban areas (Gollin et al. 2014).

We present evidence on the returns to migration in an especially consequential context. International migration is a very popular livelihood strategy in South Asia, and 10 percent of the Bangladeshi male labor force works abroad. South Asians moving temporarily to richer nations in Asia is one of the most popular global migration corridors. Each year, 2.2 million workers from four South Asian countries – Bangladesh, India, Nepal, and Pakistan – seek work-permit clearances to work abroad.<sup>1</sup> Of the 270 million migrants in the world, over 31 million are South Asians living in Asia outside their country of birth (UNDESA 2019). Bangladesh alone supplies almost 7 million migrants to other Asian countries. Malaysia now
Policy Research Working Paper 10616

# **Abstract**

Close to half of the population of Afghanistan was living below the national poverty line prior to the regime change in August 2021, with no additional information on poverty collected in the country since the last official household survey in 2019/20. This paper fills this knowledge gap through survey-to-survey imputation using a SWIFT-plus methodology. The analysis trains a predictive model on data from the 2019/20 Expenditure and Labor Force survey and imputes poverty in the latest Afghanistan Welfare Monitoring Survey. The analysis accounts for seasonality in welfare patterns and implements several tests to assess the model’s predictive capacity. The results show that 48.3 percent of the Afghan population was poor as of April–June 2023, a relative decline of 4 percentage points compared to poverty levels observed over the same months in 2020. The reduction in poverty was concentrated among rural households, with a decline from 51 to 44 percent, while it stagnated in urban areas at around 58 percent. Although no poverty data exists since 2020, the evolution of self-reported welfare and food security makes it reasonable to conclude that poverty first increased during the immediate economic contraction following the regime change and has progressively declined since then.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at obarriga@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
Figure 5. Box plot showing debt to GSDP ratio and interest burden ratio <!-- Start of picture text --> Mizoram<br>75 percentile<br>West Bengal West Bengal<br>25 percentile<br>1991-1998 1999-2004 2005-2010<br>Debt/GSDP IP/RR<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Authors compilation using data from RBI reports Figure 6. Interest Burden in select States (percent) <!-- Start of picture text --> 50<br>Bihar<br>45<br>40 Gujarat<br>35 Kerala<br>30<br>Orissa<br>25<br>20 Punjab<br>15 Rajasthan<br>10<br>Uttar Pradesh<br>5<br>0 West Bengal<br>1991-1998 1999-2004 2005-2010 Median<br><!-- End of picture text --> Source: Authors compilation using data from RBI reports Analyzing vulnerability in terms of debt as a ratio of GSDP and interest burden (interest payments as a share of each state‘s revenue receipts) provides a useful indication of the susceptibility that states face. Table 3 is a matrix that presents the states that face more vulnerability. Gujarat, Himachal Pradesh, Kerala, Punjab, Rajasthan, and West Bengal reflect both debt levels of over 30 percent of GSDP and a high interest burden. The combined GSDP of these states accounts for over 12 percent of the national GDP. Their continued struggle with 19
following<br>equation was used: Log of household’s welfare = 0.151*Log of the wage<br>per hour + 0.237*Average education level in the household +<br>0.022*Average age in the household -0.057*Number of children in the<br>household+6.81|
|**Sources of livelihood**|Five dummy variables created for the following sources: wages/pensions<br>of others, remittances, social assistance, capital income and other income.<br>Wages/pensions of others are the reference category.|
|**Field of studies completed**|Six dummy variables created for the following fields: general programs,<br>humanities and arts, social sciences, STEM, agriculture and health care.<br>General programs used as the reference category.|
|**Skill-specific employment rate**|The employment rate by skill by period.|
|**Field-specific employment rate**|The employment rate by field by period.|
|**Urban**|1 if the person is residing in urban area, 0 otherwise.|
|_Source: Labor Force Survey._||

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results.<sup>79</sup>

**Lastly, we assumed that Gaza residents are not paying indirect taxes.** We could be underestimating the tax burden for Gaza residents if they do pay informal taxes to the _de facto_ authorities, or if indirect effects of taxation from using taxed production inputs from the West Bank affect Gaza residents.

# **9.2.3 Indirect subsidies**

**The main limitation to the estimation of the electricity subsidy is lack of administrative data on “average cost of supply” of electricity by DISCOs to the domestic consumers.** Data is also not available on distribution margin of the DISCOs. Additionally, the PECS only provides the information about the “electricity charges” paid by the households. It does not provide the information about the “type of meter” households have. The application of “incremental block Tariff” is based on the type of meter. In the absence of this information, it is assumed that electricity tariff is volumetric.

> 79 In a fiscal incidence analysis of Benin in 2018 the authors model customs using two different assumptions: in the baseline they assume that domestic and imported goods are differentiated enough (in terms of quality and type of good within IO sector) to prevent price convergence, while as a sensitivity analysis they assume that domestic and imported goods belong to the same IO subsector and are of similar levels of quality, such that the Law of One Price (LOOP) applies. (Goldman et al., 2022). They find little difference in the results of the two methods, with a slight increase in the Kakwani Index from 5.7 to 5.8 with the LOOP assumption, no change in the marginal contribution to inequality of 0.2, and a slightly greater marginal poverty increase decrease in the marginal contribution to poverty reduction from –1.9 to –2.0 (i.e., a larger poverty increase).

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|S/N|**Table A3 continued**<br>Data dimension|Data category|Representative indicators|Categorical disaggregation|Coverage element 1 scoring guidelines*|
|---|---|---|---|---|---|
|16|Economic statistics|Balance of Payments|(16.1) Current account;<br>(16.2) Capital and Financial<br>account.|(16.1) Goods and services,<br>income, and current transfers<br>(or secondary income);<br>(16.2) Direct investment or<br>international investment<br>position.|To receive full point: Must have (16.1) and (16.2) with one<br>disaggregation each.<br>To receive half point: Must have (16.1) or (16.2) with one<br>disaggregation.|
|17|Environmental statistics|Land Use|(17.1) Data on land use or land<br>cover.<br>(17.2) Data on protected lands.|(17.1) Urban and rural;<br>agricultural use (crop type);<br>environmental zones.<br>(17.2) None|To receive full point: Must have (17.1) with two<br>disaggregations, as well as (17.2). Land use data with one<br>disaggregation and land cover data with one disaggregation is<br>accepted.<br>To receive half point: Must have one indicator with one<br>disaggregation.|
|18||Resource Use|(18.1) Data on fishery harvests;<br>(18.2) Data on timber harvests<br>or deforestation;<br>(18.3)
All industry characteristics (ExFj, CapIntj, HumIntj) are computed solely from the US data and thus do not vary across the exporting countries. The US market is large, diversi...ed, well-functioning, and comparably frictionless. Industry characteristics based on the US data can thus be interpreted as exogenous technological characteristics that are not driven by various imperfections prevalent in many countries. This idea comes back to the seminal paper of Rajan and Zingales (1998).

We compute the export survival in the US market and the remaining productrelated variables from the BACI<sup>9</sup> dataset developed by the CEPII and described in Gaulier and Zignago (2009). The dataset provides harmonized bilateral trade ‡ows for more than 5,000 HS 6-digit products and 143 countries, over the 19882005 period. In the following, we focus on the 1995-2005 period due to the high number of missing values before 1994, and we consider only exports of manufactured products and tobacco to the USA.<sup>10</sup> Export ‡ows are reported annually in values (US dollars) and quantities. This highly detailed level of information is particularly suitable for survival analysis. Aggregation could introduce a considerable bias, essentially hiding individual export failures at the product level.

The product-related variables include the value of export to the US market in the initial year of the trade relationship t0 (initial_exportckt0), in log terms. This re‡ects the level of con...dence US importers have in the reliability of their trading partner. Additionally, we include the total export value of product k from country c to all countries in the initial year of the trade relationship (total_exportckt0), in log terms. This variable captures the experience the exporting country has in supplying the world market with product k. We also control for the degree of competition for a given product on the US market, incorporating the number of countries exporting product k to the USA in the initial year of the trade relationship (NSupplierskt0)
outward FDI flows due to hedging, and (iv) market clearing/exchange rate effect—more inflows appreciate the exchange rate which encourage outward FDI. In the end they conclude that these do not fully explain the high correlation between inflows and outflows of FDI or the strong correlation between FDI inflows and the US policy rate. They instead find the correlation between inflows and outflows to be negatively correlated to the corporate tax rate and positively to capital controls.

# 3. **Data**

We use quarterly data from the IMF’s Balance of Payments Statistics between 1990 Q1 and 2015 Q4 for 34 emerging countries.<sup>2</sup> The data are patchy for the earlier years; coverage improves over time, yielding an unbalanced panel. The capital flow data are in US dollars. We scale them by annual trend GDP for the purposes of analysis.

We analyze inflows and outflows separately. Data are available separately for FDI- and nonFDI flows. The latter are further decomposed into portfolio flows (and into portfolio equity and portfolio debt), versus what are labelled “other” flows. The “other” category includes flows through the banking sector (loans, deposits and banking capital), loans raised by the private sector, trade credits, official government flows, and other smaller residual components. We exclude flows to the general government and monetary authorities, retaining only private flows. The largest share of (private) other flows are made up of flows through the banking sector. Hence some researchers simply refer to them as “bank flows.”<sup>3</sup> We represent this taxonomy in a tree diagram in the Appendix.

# **4. Magnitude, Persistence, and Volatility of Capital Flows**

Figures 1-4 and Table 1 show that the average FDI and non-FDI inflows are roughly equal in magnitude. Median average annual flows are 2.6 percent and 2.4 percent of GDP annually.<sup>4</sup> Within non-FDI flows
compared to no growth in developed countries. Approximately one-half of world sugar imports are now by developing countries compared to less than one-third in 1980. These shifts have diminished the importance of developed country imports and the importance of EU and U.S. quota imports, which now account for only 7 percent of total world import quantities compared to 13 percent in 1982.

The world sugar market has also been affected by the increased consumption of alternative caloric sweetener, high-fructose corn syrups (HFCS). This alternative sweetener accounts for about 40 percent of total caloric sweeteners consumption in Japan and nearly half in the United States (Figure 8). However, some medical researchers are now questioning whether HFCS is absorbed differently than sugar by the body and whether this difference may contribute to obesity by stifling the body's ability to feel full and thereby encouraging eating more. If these concerns are substantiated by research finding, then it could shift consumer demand back towards sugar and would have some impact on world sugar imports and prices. At the same time, there is increasing concern over the health consequences of obesity in the United States and this could reduce consumption of all caloric sweeteners.

**Figure 8. U.S. Sugar and HFCS Consumption, 1970-2003**

<!-- Start of picture text --> U.S. Sugar and HFCS Consumption<br>12<br>10<br>Sugar<br>8<br>6<br>HFCS<br>4<br>2<br>0<br>1970 1975 1980 1985 1990 1995 2000<br>Source: USDA<br>Million Metric Tons<br><!-- End of picture text --> Some Caribbean sugar producers may benefit from higher world market sugar prices while losing the benefits of access to preferential markets. If sugar trade preferences erode as expected, it would have some impact on world sugar prices because sugar producers with lower preference prices would reduce output. World sugar prices would increase
country's territory lies within 20 degrees of the equator.|
|_Access to safe water_|Percent of population with access to safe water. From the World Bank's Social Indicators of<br>Development database (World Bank, 1997).|
|_Oil exporter_<br>_Oil exporter_|Dummy equal to one if the country primary export is fuels (mainly oil) as classified by the<br>World Bank's World Development Indicators (1996) plus Kuwait.|
|_Years independent_|The percentage of years since 1776 that a cotntry has been independent, as reported in Easterly<br>and Levine (1996).|
|_Defense spending_|Defense spending as a share of GDP, as reported in CIA (1994)|

# c) Data on health sector variables_

|_Health Expenditures_|Updates of health expenditures from Murray, Govindaraj, and<br>Musgrove (1995) which appear in World Bank (1993)|
|---|---|
|_Perrentage of national health expenditures_<br>_devoted to local health services_|As reported in the WHO's Health for All Database. The observation<br>closest to 1990 in the 1986-1993 period is used.|
|_Percentage of the population with local health_<br>_services, including availability of essential_<br>_drugs, within one hour's walk or travel_|As reported in the WHO's Health for All Database. The observation<br>closest to 1990 in the 1,986-1993 period is used.|

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Data on the use of mortgages show a large difference between income groups: in high-income economies 24 percent of adults report having an outstanding loan to purchase a home or apartment, while only 3 percent do in developing economies. Even within the European Union there is large variation in the use of mortgages, with very low rates of use in some of the new member states. For example, while 21 percent of adults in Germany have an outstanding mortgage, only 3 percent in Poland do (map 3.3). Such differences may in part reflect differences in housing finance systems across economies—such as in product diversity, types of lenders, mortgage funding, and the degree of government participation, all of which have been shown to affect <!-- Start of picture text --> Mortgages outstanding<br>Adults with an<br>outstanding loan<br>to purchase a home<br>or apartment (%)<br>0–5<br>6–10<br>11–20<br>21–30<br>31+<br>No data<br>Source: Demirguc-Kunt and Klapper 2012.<br>3.3<br>MAP<br><!-- End of picture text --> the availability of loans to individuals.<sup>4</sup> Collateral and bankruptcy laws that define legal rights of borrowers and lenders have also been shown to affect housing finance.<sup>5</sup> And to develop in the first place, a mortgage market requires formal property rights and an efficient framework to record ownership of property.<sup>6</sup>

1. Information is collected on the ownership of credit cards but not their use.

2. Djankov, McLiesh, and Shleifer 2007.

3. Data on the main purpose of outstanding loans were gathered only in developing economies because Gallup, Inc. enforces a time limit for phone interviews conducted in high-income economies,