rafmacalaba/data-use-mentions-extended huggingface dataset:
poverty in a poor area that happens to contain a highway, if the high level of night-time lights associated with highways makes the area look less poor from the sky than it actually is. However, at least when predicting poverty rates at higher levels such as subdistricts, the evidence so far indicates that modelbased estimates based on geospatial indicators are more accurate than direct survey estimates. This implies that the benefits of reducing sampling error by supplementing survey data with model-based predictions derived from geospatial indicators outweighs the introduction of model bias.

c. Geospatial data are a second-best option when recent census data are unavailable Although geospatial data are strongly correlated with welfare across space, recent census data remain the gold standard for auxiliary data for small area estimation. Unfortunately, in many cases census data are old or unavailable, which creates two problems. First, because survey and census data cannot typically be linked at the household level to preserve confidentiality, it is standard when estimating a unit-level model to assume that the predictors follow the same distribution in the survey and the census data. This assumption becomes less tenable as the temporal gap between the census and survey increases. If the census and survey distributions are sufficiently different, estimating a linked model with primary-sampling-unit-level (PSU) aggregates from the census is preferable to assuming a common distribution (Lange, Pape, and Putz, 2018). A second and more important problem is that old census data, even if they are linked directly to the survey data, cannot reflect any changes that affect wealth or poverty that occurred since the census. Current geospatial data may be more likely to reflect current conditions than old census data, especially when including geospatial indicators such as precipitation and vegetation that better predict short-run shocks.

8
approach and thus do not consider behavioral responses, second-round effects, dynamic issues, or macroeconomic balances.

Using information from the _Continuous Household Survey_ (Encuesta Continua de Hogares - _ECH_ ) from 2017, the study documents that both income inequality and poverty decline as a result of Uruguay’s fiscal policy, with the highest reduction of the Gini coefficient due to in-kind transfers (i.e. the monetary valuation of public spending in health and education). In addition, the paper finds that the reduction in inequality from fiscal policies seen in 2017 is similar to the one from 2009 for most policies, although in-kind transfers have a stronger effect in 2009.

Immervoll et al. (2006) analyze the redistributive properties of tax benefit systems in countries of the EU-15 and find that the different sizes and structures of the systems are major determinants of differences in income inequality among countries. Higher reductions in inequality are achieved through non means-tested benefits (unemployment benefits, pensions to non-elderly, and universal benefits) and taxes. Newer work (De Agostini et al., 2016; Euromod, 2019) compares the effects of policy changes in several European states, with varying results depending on policies, periods and countries. Fiscal policy studies in Latin America highlight the importance of cash and in-kind transfers for reducing inequality, and of both transfers and taxes for reducing poverty (Arancibia Romero et al., 2019; Goñi et al., 2008; Lustig et al., 2014).

Uruguay stands out in regional comparisons as among the most egalitarian economies, and fiscal policies contribute to that result as it is the most redistributive country of those analyzed by Arancibia Romero et al. (2019) and among the top performers according to Bucheli, et al. (2013). The latter also find that the country ́s taxes and transfers reduce inequality and poverty when considered as a whole. In addition, direct taxes are progressive while indirect taxes are regressive, and social spending is quite progressive. Nevertheless, in-kind transfers _via_ higher education are
levels of income hiding within the experiment are associated with lower rates of skilled and formal employment and lower wages, and are also associated with substantial reductions in the probability of using fertilizer.

Such cross-community results do not merit a causal interpretation. Nonetheless, these findings are of interest for two reasons. First, results are consistent with the view that pressure to share income may act as a drag on savings and investment, and may therefore slow development. An alternative, though not mutually exclusive, explanation is that in the poorest areas, enforced sharing norms remain an essential tool for handling risk, and thus the pressure to share is the most acute. In either case, the evidence suggests that social pressure to share income is greatest in the worst off areas, and should be viewed as an important social factor in models of poor, rural village economies. More generally, these results go some way toward addressing the concern that individual choices in lab experiments may not be meaningfully correlated with decisions and outcomes outside the lab (cf. Levitt and List 2007); in this case, they clearly are.

# **4.2 The Willingness-to-Pay to Hide Income**

We now examine the willingness-to-pay to avoid the public announcement using data from the price treatments. Subjects assigned to the price treatments were offered the option of paying a randomly chosen price — 10, 20, 30, 40, 50, or 60 shillings — to avoid announcing their investment income to the other subjects.<sup>35</sup> Of 690 subjects assigned to the price treatments, 627 subjects could afford to pay _p_ to avoid the public announcement, and 190 (30.3 percent of those able to pay) chose to do so. Subjects who chose to buy out paid an average price of 29.3 shillings — equivalent to an average of 15.3 percent of their gross earnings. Figure 6 plots the proportion of subjects paying to avoid the public announcement, broken down by gender, endowment size, and exit price
Policy Research Working Paper 11044

# **Abstract**

Despite far-reaching banking sector reforms over the past few years, financial intermediation in Tajikistan continues to lag behind structural peers and the Caucasus and Central Asia region. At the same time, bank interest rate spreads, a standard measure of financial intermediation costs, have remained significantly higher than those of peers, ranking among the highest in the world. This paper examines the determinants of interest rate spreads in Tajikistan using a bank-level panel data set for the first quarter of 2011 to the fourth quarter of 2022. The findings show that bank-specific factors, particularly income diversification, loan size, risk aversion, market power, credit risk, and the macroeconomic and institutional environment within which banks operate, explain a large proportion of cross-bank, crosstime variation in spreads. The results suggest that there is ample room to promote economies of scale and enhance competition in the banking system while strengthening the operating environment.

This paper is a product of the Finance, Competitiveness and Innovation Global Department. 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 author may be contacted at pcalice@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Annex Survey Methodology

In 2011 the World Bank – with funding from the Bill & Melinda Gates Foundation – launched the Global Findex database, the world’s most comprehensive data set on how adults save, borrow, make payments, and manage risk. Drawing on survey data collected in collaboration with Gallup, Inc., the Global Findex database covers almost 150,000 people in 144 economies—representing more than 97 percent of the world’s population.<sup>2</sup> The initial survey round was followed by a second one in 2014 and by a third in 2017.

The 2017 survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has conducted annual surveys of approximately 1,000 people in each of more than 160 economies, in more than 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and older.

## _Interview Procedure_

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where doing so is the customary methodology. In most economies, fieldwork is completed in two to four weeks.

In economies where face‐to‐face surveys are conducted, the first stage of sampling is the identification of the primary sampling units. These units are stratified by population size, geography, or both; clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used.

Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used.

Respondents
the country. We aggregate data at the city-month level. By limiting crime reports to Venezuelans, we attempt to capture the effect of the regularization on the propensity of Venezuelan migrants to report crimes.

2. _Crimes perpetrated by migrants._ To examine changes in crime perpetrated by migrants we use data from the _Fiscal ́ıa General de la Naci ́on_ , Colombia’s Office of the Attorney General. The data set includes all criminal cases in Colombia that are in trial or have yielded convictions, including information on the year the crime occurred, the type of crime, and the gender, age, and nationality of the indicted individual between 2017 and 2021.<sup>9</sup> In the data, 97.11 percent of crimes (178 _,_ 483) were committed by Colombian nationals and 2.8 percent (5 _,_ 312) were committed by Venezuelan migrants.

3. _Registro Administrativo de Migrantes Venezolanos (RAMV)_ . We use administrative data sourced from the RAMV survey to compute the number of people eligible for the PEP migratory status and the actual number of individuals who applied for (and received) the PEP migratory status. Using individual records, we compute the allocated time window each individual had to register in the PEP (based on the registration number), which we use for our identification strategy.

4. _Municipality Controls_ . We control for a rich set of baseline municipal covariates including night light density, variables related to internal conflict, GDP municipal composition, distance to the Venezuelan border, and number of Venezuelans with previous rollouts of PEP migratory status (particularly the PEP1 and PEP2), as well as proxies for government activity. Administrative information at the municipal level comes from the CEDE municipal panel, the Ministry of Defense, the National Planning Department, and DANE (Colombia’s national statistical agency). Night

> 9The original data set had more information that we do not use in our effort to measure actual crimes. Thus, we exclude cases that
0.006||
|Mean of dep. var. for NB in NL vil.|0.958|8.92|2.71|18.33|0.136|4.13|
|P-val of B = NB in L villages|_0.749_|_0.304_|_0.620_|_0.961_|_0.311_|_0.883_|
|Model|OLS|Tobit|Tobit|Tobit|OLS|Tobit|
|Percentage of censured observations||0.041|0.884|0.618||0.138|
|Number of plots||414|414|414|414|414|
|Number of households|330|330|330|330|330|330|

Note: This table reports the estimation of the following specifications: _y ijk = α ijk + β 1 Beneficiary ij + β 2 Lottery j + β 3 Lottery j *Beneficiary ij + ε ijk_ , where _i_ indexes the individual (household), _j_ indexes the village and _k_ indexes plots. _Beneficiary ij_ is a dummy that takes value of 1 if the individual _i_ in village _j_ is a beneficiary of the NAIVS voucher program in 2010/11. _Lottery j_ is a dummy that takes value of 1 if beneficiaries in village _j_ were chosen using a lottery. The models presented here are not adjusted for any baseline covariates. Dependent variables are constructed based on questions asked on follow-up household survey. Data are restricted to baseline respondents in both panels. Panel A reports results using full baseline sample of households. In Panel B the sample is restricted to households in non-lottery who are similar in observable characteristics based on a nearest neighbor propensity score matching algorithm. The sample in Panel B also include households in lottery village who share the propensity score distribution support of households in no lottery villages. The dependent variable in Column 1 is a dummy that indicates whether or not the HH grew maize in 2010/11. Column 2 reports area planted.
the headcount. When countries have significantly different levels of GDP, the same absolute line would cut-off from very small to very large proportions of the population, which are difficult to disaggregate and compare in a meaningful way.

For the multivariate analysis we took note of the current debate over welfare regressions and binary poverty regressions as the main analytic tool for poverty research, but we argued that in transition economies both are needed as they serve different purposes. While welfare regresssions utilize the maximum available statistical information on the dependent variable, they ignore measurement errors of the type typically present in transition economy databases. In our results, the two models provided qualitatively consistent answers on the significant determinants of poverty and welfare, but underlined that some variables, such as education and productive assets, play different roles in escape from poverty as opposed to determining position on the nonpoor segment of the welfare distribution.

Likewise, our Tobit-based estimation of the poverty gap indicated that parameters measuring the impact of household characteristics on welfare are often not the same for the poor and nonpoor. Many human and physical capital assets had higher returns for the poor. Locational disadvantages were also often larger for the poor than the population at large. Our results call for caution when relying on one single multivariate model to study the correlates of poverty. We would argue that the three models used here (OLS Welfare Model, Probit Poverty Model, and Tobit Poverty Gap Model) constitute a useful. minimal set to investigate the determinants of poverty.

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Concerning labor market characteristics, public sector workers are relatively more likely to be employed as regular wage workers. This status applies to 94.1 percent of public sector workers but to only 16.5 percent of private sector workers. Conversely, public sector employees are substantially less likely to be contract or piece paid workers or, unsurprisingly, own-account workers. Sociodemographic variables also show notable differences between the two sectors. In particular, statistically significant differences exist with respect to workers’ average location, gender and experience. For instance, 67.9 percent of public sector workers live in urban parts of Bhutan but only 27.0 percent of private sector workers are city dwellers. Moreover, public sector workers are less likely to be female and on average have less potential experience. At the same time, while according to the BLFS 2014 the proportion of Bhutanese nationals is very high in both sectors it does not significantly differ between them.<sup>8</sup> The average educational attainment of workers differs markedly between the two sectors. Whereas less than a quarter (22.7 percent) of public sector employees have no or no formal education this is the case for almost two-thirds of private sector workers (64.8 percent). On the other hand, 3.5 percent of public sector workers but only 0.5 percent of private sector workers have higher tertiary education, defined here as the completion of at least a Master’s degree. Finally, industrial structures also differ between the two sectors. For instance, public sector workers are dramatically less likely to work in agriculture, hunting and forestry ( _nil_ vs. 46.7 percent) but much more likely to work in public administration and defense (52.5 vs. _nil_ percent). Again, these differences are statistically significant at the 1 percent level. In general, the picture that emerges from Table 2 is that in Bhutan wages, jobs and workers all differ considerably between the public and the private sector.

Fringe benefits that
Jaiswal and Wald (2008). The data provide the share of 99 different building types within a country separately for urban and rural areas. For Indonesia the building type information was compiled from a World Housing Encyclopedia (WHE) survey. The WHE survey uses fraction of population who lives or works in buildings of different types as their definition of how the building mass is split up. Without any other information available, we use this as an indication of the distribution of building types and mass, but, necessarily, assume that the distribution is homogenous within urban and rural areas.

Damage curves by building type are derived from the curves constructed by the Global Earthquake Safety Initiative (GESI) project; see (International and Regional Development 2001). More specifically, buildings are first divided into 9 different types across a wide range of building materials from wood and steel through to lightweight shacks. Each building type itself is then rated according to the quality of the design, the quality of construction, and the quality of materials. Total quality is measured on a scale of zero to seven, depending on the total accumulated points from all three categories. According to the type of building and the total points acquired through the quality classification, each building is then assigned one of nine damage curves which provides estimates of the percentage of building damage for a set of 28 peak ground acceleration intervals.

In order to use these damage curves, we first allocated each of the 99 building types given in the USGS building inventory to one of the 9 more aggregate categories of the GESI building classification. However, to assign a building type its quality‐specific damage curve we would further need to determine its quality in terms of design, construction, and materials, an aspect for which we unfortunately have no further information. We instead assume that building quality is homogenous across building type and construct eight different damage curves with different quality rating scenarios (ranging from 0 to 7). Initial analysis showed some effect based on the quality assumption, but lacking
7 the Taylor expansion) were rather muted: 2.3% of the direct effect in Nicaragua, 3.5% in Peru, 7% in Honduras, and 12.5% in Guatemala.<sup>4</sup> The second shortcoming of the first-order approximation based on net food purchases is, according to Deaton (1997), “a more serious deficiency... its neglect of repercussions in the labor market. Changes in the price of the basic staple will affect both supply and demand for labor, and these effects can cause first-order modifications to the results.” (p.187). Estimates that attempt to incorporate simulated wage responses, based on behavioral models, are available for Bangladesh (Ravallion, 1990) and for Indonesia (Ravallion and van de Walle, 1991), but we are not aware of studies of the recent 2007-2008 food price shock (prior to ours) that attempt to incorporate estimates of labor market effects.

Labor market effects, as well as substitution effects, are absent, for example, from the influential study by Ivanic and Martin (2008), which was based on pre-shock household survey data for ten low-income countries containing information on both purchases and sales of a few key staple commodities. Assuming 100 percent pass-through of international to domestic prices, they estimated the first-order effects of these price increases across the expenditure distributions. Even accounting for the existence of net food sellers among the poor, the authors conclude that poverty is likely to have increased in most countries in their sample.

As the authors note, however, they had no access to data on the evolution of the domestic food prices actually faced by consumers in each of the countries they analyzed. Although the assumption of 100 percent pass-through is a sensible one under these circumstances, it is clearly strong. In a recent survey of commodity price impacts, Brambilla and Porto (2009) argue that the pass-through from international food prices is generally well short of 100 percent. In addition, spatial heterogeneity in
>3500<br>Indigenous +  20<br>2500<br>North-South +<br>1500 South-South<br>10<br>500<br>1995 1998 2001 2004 2007 2010<br>0<br>1995 1998 2001 2004 2007 2010<br>Source: USPTO grants in PATSTAT<br><!-- End of picture text -->

# **2.4 Potential for expanding green production and trade**

The patent data suggest that there is little capacity for _frontier_ green innovation in most developing countries. However, there could be enormous capacity for _catch-up_ green -up innovation through new-tothe-firm adoption and adaptation of existing green technologies, and through indigenous base-of-pyramid

> 8 Guellec and Potterie (2001) use patent co-invention to measure international collaboration. Details of our estimation are given in the data annex.

> 9 The incidence of South-South collaboration among _all_ patents granted to developing country inventors is 0.3%.

7
<!-- Start of picture text --> 1000<br>Apparel, textiles<br>900<br>Footw ear, leather<br>800 products<br>Furniture, w ood,<br>700 manufact. n.e.s.<br>Food products<br>600<br>Electronics, machinery,<br>500 transport equipment<br>Metal, non-metallic<br>400 mineral products<br>Basic manufacturing<br>300<br>Chemical, rubber,<br>200 plastic products<br>Mining<br>100<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Figure 5. Evolution of Vietnam’s Industrial Employment by the VSIC Sub-categories (1,000s). Source: the Enterprise Survey data (GSO)._ <!-- Start of picture text --> 1200<br>1000<br>South East<br>800<br>HCMC<br>Red River Delta<br>Mekong River Delta<br>North East<br>600<br>Hanoi<br>South Central Coast<br>North Central Coast<br>Central Highlands<br>400<br>North West<br>200<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Figure 6. Vietnam’s Industrial Employment by Regions (1,000s). Source: the Enterprise Survey data (GSO)._ 41