rafmacalaba/data-use-mentions-extended huggingface dataset:
income returns to cadre households shown in the paper.  Unfortunately, since the RCRE survey was conducted at the household level and did not collect data on each family member and track each family member, we are not able to examine how being a cadre affects the incomes of other family members.  However, to the extent that the family member does not move out and form his/her own household, any of the impacts of being a cadre on his/her incomes will be contained in per capita income of the cadre household.

28
# 1. Introduction

EMDEs weathered the global recession of 2009 relatively well for three reasons. First, EMDEs were generally not as exposed to the financial sector fragilities that triggered the crisis in advanced economies. Second, many EMDEs had used the 2000s to reduce vulnerabilities and rebuild policy room to respond effectively when the crisis hit. Third, at the onset of the crisis, advanced economies and some large EMDEs provided unprecedented and coordinated monetary and fiscal policy stimulus, which helped shield global economic growth.

Nevertheless, the global recession slowed per capita growth in EMDEs to 0.4 percent in 2009 from an average over much of the preceding decade of close to 5 percent. The rebound in 2010-11 was initially strong but per capita growth never returned to its rates from before the global recession. Commodity exporters faced further headwinds when global commodity prices slid to multi-year lows in 2011-16 and forced commodity-exporting EMDEs to engage in procyclical fiscal tightening. Energy-exporting EMDEs were particularly hard hit by the collapse in oil prices in 2014-16.

Amid slowing growth, most EMDEs were not able to fully unwind the policy stimulus put in place in response to the crisis—fiscal deficits in the average EMDE were about as wide in 2018 as they were in 2010, and external, fiscal, and corporate vulnerabilities have increased since 2007. Several EMDEs are highly indebted, have elevated levels of foreigncurrency denominated debt, or rely on portfolio or bank flows to finance large current account deficits.

Since the global recession, structural factors have eroded potential growth. Around 2010, the share of the working-age population in EMDEs stabilized after more than four decades of rapid increases. This coincided with a prolonged period of weak investment. As a result, potential growth in EMDEs slowed by 1.2 percentage points after 2003-07, to 4.7 percent in 2013-18.

The years prior and during the global recession provided an initial encouragement to adopt business-friendly reforms, when most EMDEs improved their scores in the World Bank’s Doing Business survey. From DB2008 to DB2010, the number of business-friendly reforms
Given the sampling design discussed in the preceding paragraphs, i is the probability for selecting a host HH household ( ) of stratum , in mauza , and enumeration area . kk PP HH ii jj i kk i = ×<sup>1</sup> ×<sup>1</sup> HH HH mmii × nni i i kk HH HH PP kk kk Where: ∑nn nn ll is the number of mauzas selected in each stratum _i_ . iii is the number of host households according to the 2011 census in mauza of stratum . mm HH i is the number of segments/enumeration areas in mauza j of stratum . nn jj ii i is the number of listed households in stratum mauza , in enumeration area (the target was to nn ii HH select 13 households per segment).kk ll ii, jj kk The three terms on the right-hand side of the equation respectively represent the probability of selecting a mauza within a stratum in the first stage; the probability of selecting an enumeration area in a mauza in the second stage; and the probability of selecting a specific host household in the third stage. jj ii kk jj Where:

# <u>Listing methodology</u>

The main listing exercise was an important part of the preparation of the survey as existing population counts for the host population dated 2011. Analysis of the IPA count for the host population indicated that this exercise did not provide reliable updated population counts of the host population due to the partial coverage of areas, as well as the large estimation error coming from key informants reporting figures centrally. Rohingya counts in camps were updated more frequently, however, movements across areas or campsites could have happened between the IOM count and the data collection. Therefore, a listing exercise was conducted for both host and Rohingya displace in the selected enumeration areas. In this section we explain how the listing exercise was conducted and highlight some challenges that arose when implementing the listing and their solutions.

The resources available for refugee camps and host
# **7.3 Evidence on alternative mechanisms**

Although we cannot perform a direct test to rule out the possibility of a rebound, a number of analyses are inconsistent with a direct or indirect rebound in electricity consumption. For instance, in Figure 3 we provided an event-study graph of the impacts on electricity consumption by season. We found that following a winter spike, savings in electricity consumption returned to the expected amount in the spring.<sup>63</sup> If there were a rebound, a persistent change in behavior would be expected, and a reversal in the spring would have been unlikely. Furthermore, using our detailed baseline and follow-up survey data, we can test for impacts of treatment on appliance ownership and average monthly and daily use (results not shown).<sup>64</sup> We found no effects of treatment on lightbulb use (which would be the measure of a direct rebound) and only one significant effect of treatment on use of any household appliances (which would be a measure of an indirect rebound). In fact, treatment households were significantly less likely to report using electric heaters at follow-up. If anything, this finding is evidence counter to a rebound

# **7.4 External validity**

We implemented a randomized saturation design to study the impact of energy efficient lightbulbs on household electricity consumption and local electricity reliability in a lowermiddle-income developing country. A policy-relevant question that guides our thinking about external validity is to what extent our results are location-specific or technology-specific.

A feature of this study’s setting, the Kyrgyz Republic, is that essentially all households are electrified, formally connected to the network and metered individually. We believe that research in this context does not harm the external validity of our study, rather it just helps with the internal validity. It has the additional benefit of ensuring that we are not just studying the responses of the richest households. The Kyrgyz Republic also has an extensive electricity generation and distribution infrastructure. However, similar
>(0.174)<br>|0.226<br>(0.193)<br>|0.158<br>(0.163)|
|±0.02|270|8.474|8.707|0.234<sup>**</sup>|0.236<sup>**</sup>|0.109|
|||||(0.098)<br>|(0.101)<br>|(0.086)|
|±0.03|328|8.471|8.648|0.177<sup>**</sup><br>(0.082)<br>|0.165<sup>**</sup><br>(0.084)<br>|0.113<br>(0.074)|
|±0.04|395|8.478|8.604|0.126<sup>*</sup><br>(0.071)|0.136<sup>*</sup><br>(0.072)|0.082<br>(0.063)|
|_Controls_|||||||
|Socio-ec.||||No|Yes|Yes|
|background|||||||
|Educational<br>background||||No|No|Yes|

Note: Standard errors are between brackets. * indicates significance at the 10%-level, ** indicates significance at the 5%-level, and *** indicates significance at the 1%-level. Controls denoted by “socioeconomic background” include the tuition fee level, a dummy indicating whether a student receives a grant or not, family income, the socio-economic stratification index, a dummy indicating the student’
Notes: i) Authors’ estimation from VHLSSs using province FE models, ii) Robust standard errors in parentheses clustered at the province level, iii)*** p<0.01, ** p<0.05, * p<0.1.

**<u>Table A.6: Relation between income growth, poverty, and income inequality</u>**

|||D|ependent variable is the g|rowth of per capita incom|e||
|---|---|---|---|---|---|---|
|||FE|||GMM||
|Explanatory variables|||||||
||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
||(1)|(2)|(3)|(4)|(5)|(6)|
||-0.169***|-0.589***|-0.579***|-0.156***|-0.119|-0.456***|
|Lagged log of per capita income|(0.025)|(0.050)|(0.055)|(0.037)|(0.082)|(0.091)|
|Lagged Gini index|-0.298|-0.258|-0.224|-0.347**|-0.607**|-0.607***|
||(0.222)|(0.180)|(0.167)|(0.170)|(0.270)|(0.218)|
|Lagged poverty gap|-0.236***|-0.032|0
data on the original scale as a multiplicative error model with a changing variance function, this issue is often addressed by rewriting the model as an additive error model on the log-scale with constant variance. This is appropriate as long as the log transformation is appropriate to normalize the data. To assess whether the simple estimations presented here are prone to a strong bias, section 6.2.2 investigates the residual distribution and re-analyzes the data using a log-type power transformation from a family of functions that allows for zeros. The results highlight that when the data is appropriately scaled and multiple diagnostics confirm that the Normality assumption is in fact valid, PM2 _._ 5 is still a highly significant positive predictor of case densities. Earlier studies on the role of ambient fine particles in the transmission risk of airborne disease have instead relied on Poisson-type regressions using count data. While these regressions are not entirely appropriate as they do not account for the highly significant geographical relationships in the data, section 6.2.1 presents Poisson-type results that allow for over-dispersion to show that the main conclusions are also robust to this specification choice.

# **4.2 Nonlinear analysis of March 22 cases per 100,000 inhabitants**

Instead of working on a transformed scale to address some of the highlighted issues, it is also possible to tackle the problem nonlinearly on the original scale. This might also lead to interesting results on important thresholds in the data. In particular, one might expect particulate matter to only contribute to COVID-19 incidence after concentrations surpass a certain critical threshold, or expect pollution dependencies to vary with unobserved weather variables including humidity and temperature (Chen et al., 2017b,a; Peng et al., 2020). In a recent study, Sajadi et al. (2020) have already shown that there could be a relationship 9
2000 (and 88.8% of the stock in 1990). For the remaining 7.3 percent (11.2 percent in 1990), we usually have information on the country of origin but have to extrapolate the skill structure on the basis of available information. In the spirit of the Carrington and Detragiache or Adams studies, a method based on the US census requires extrapolating the skill structure on the basis of 42 percent of the total OECD stock (36 percent in 1990). Our paper is a strong improvement on earlier e¤orts.

Our method allows to distinguish about 190 sending countries in 2000 and 170 countries in 1990 (before the secession of the Soviet block, ex-Yugoslavia and exCzechoslovakia). Our measures can be used to examine the changes in the international distribution of migration rates, to test for the (push and pull) determinants per skill group, or to evaluate the macroeconomic consequences of migration on source and destination countries.

The remainder of this paper is organized as follows. Section 2 describes the methodology. Emigration rates for 1990 and 2000 are presented in Section 3. Section 4 presents the key indicators of skilled migration. Finally, Section 6 concludes.

# 2 Methodology

This section describes the methodology and the data sources used to compute emigration rates of skilled workers for a large set of sending countries. Building on Carrington and Detragiache, we de...ne the brain drain as the proportion of working age individuals (aged 25 and over) with at least tertiary educational attainment, born in a given country but living in another country, taking into account neither their occupation, nor where education took place, nor when they arrived. To build such indicators, we proceed in two steps. We ...rst compute emigration stocks by educational attainment from all countries of the world. Then, we evaluate these numbers as a proportion of the labor force born in the sending country. This gives emigration rates per educational attainment.

These two steps rely on various assumptions. We distinguish
misallocation episodes are sufficiently frequent to merit including effective risk-reduction mechanisms for both the banking system and the non-bank financial sector including macro-prudential measures among the relevant supporting institutions.

# _Empirics and Issues_

The relative merits of bank-based/RBFS and market-based/ALFS have been the subject of extensive empirical work.

Historical case studies, notably assessments of industrialization in late 19<sup>th</sup> century Western Europe have importantly shaped the evolving debate. A popular view – dating back to Alexander Gerschenkron’s (1962) analysis – argues that Britain, with already well developed capital markets and a fairly extensive and differentiated banking system – including country, national and internationally focused banks - was able to rely on a diverse set of financial structures to fund industrialization, reaping the maximum benefits from the two-sided feedback loop between financial and real development. Conditions on the continent in this view were less propitious. Conditional on the starting point, the emerging financial structure centered on banks with deep ties to their customers was however seen as an effective arrangement for funding the catch-up process, with the German three pillar system centered on the large universal banks complemented by savings and co-operative banks focused on the small business sector gaining particular attention. More recent empirical work including comparative studies however qualifies some of the central tenets of this dichotomous view in terms of both features and measurable outcomes.<sup>7</sup> The case studies permitting in depth exploration of national idiosyncrasies are complemented<sup>8</sup> by a recent literature exploring the linkages for broad cross-country datasets<sup>9</sup> documenting a strong link between financial development and economic growth, but tending to find less robust evidence for conditional systemic links between financial system structure and performance. Context dependence, with both RBFS and ALFS better suited for some but not other sets of (partly time-varying) circumstances provides a possible interpretation (Rajan and Zingales (2002)). Under this view, the ability of the financial system structure to adjust to changing circumstances, and
opportunity for children (when considering the potential effects on the physical and emotional development of the child). Furthermore, what is considered ethically acceptable or desirable conveniently depends on a society’s judgment. Another limitation is to the empirical application of measurements. The HOI is also sensitive to the set of circumstances selected as well as the set of opportunities considered. Quality issues are rarely included in the analysis because it is an open-ended conceptual issue (whether an opportunity is simply access to a service or, rather, access to a _quality_ service) and because datasets typically lack the information to systematically include quality considerations. Finally, from a policy point of view, HOI does not address the causes or motivations behind the distributive features of a policy or set of policies that cause the inequality of opportunities.

# **3. Choice of Opportunities and Circumstances for Côte d’Ivoire**

As with previous work on opportunities using HOI, the focus is on opportunities for children for two main reasons. First, intuitively there can be little disagreement about the set of circumstances that are beyond a child’s control, and the issue of ―effort‖ in achieving access to the opportunities can be considered irrelevant in the case of children. Second, and perhaps more important, ―opportunities‖ for children, which are defined as access to basic goods and services early in life, are generally accepted as necessary for an individual to progress to a productive adulthood and fulfill his or her potential as a member of society.

In selecting the final set of indicators among the list of basic opportunities (as discussed in the previous section) that can be analyzed as ―opportunities‖ for Côte d’Ivoire, two issues are of paramount importance:

- (i) **The indicators must be available from available data sources** . Fortunately, a series of household surveys for Côte d’Ivoire have been administered since 1985. The surveys are nationally representative and fairly comparable over time. In addition to the long time period covered
# **4.2 Estimating the Effects on Labor and Capital Demand**

Recent technological innovations have revamped an old concern about productivity-driven displacement of jobs and shifts of labor demand towards skilled workers. New theoretical models developed to understand the potential effects of automation on jobs, as well as the channels through which it operates, depart from the standard factor-augmenting technological change approach. Instead, they propose a new framework, where robots compete against workers, and thus machines replace tasks previously performed by humans (Acemoglu and Restrepo 2018, 2019b, 2020a; Autor and Salomons 2018). However, the displacementinduced contraction in wages dynamically creates incentives for the introduction of new tasks, in general equilibrium, where labor has a comparative advantage relative to technology, the so-called reinstatement effect. This new setup thus enables researchers to think about new forces at work, which have opposing effects on labor demand in general equilibrium.

Our estimation framework with enterprise panel data is, by definition, partial equilibrium. But the estimation framework is flexible and allows for the estimation of the effects of the choice variables on both TFPR and factor demand. As mentioned, the latter effect has two channels, the factor-augmenting or saving effect as well as a scale effect. This allows for a direct test of labor- (and capital-) saving hypothesis. Since our TFPR measure confounds both prices and efficiency, our productivity-driven effect is not fully comparable to the displacement effect cited in the literature (Acemoglu and Restrepo 2018, 2019a). This is because the price-related component of this effect could be labor-augmenting, as efficiency gains are passed-through onto product prices. That is, efficiency gains resulting in price reductions can increase product demand. The efficiency-related component of this effect could be labor-saving, just like the displacement effect cited in the literature. The scale effect, however, is unambiguously labor-augmenting. It is associated with an expansion in firms’ profits due to a reduction in marginal costs or the scale-up of demand for a firm’s output as digitization allows firms to reach
system, while the Order sub-component is an assessment<sup>of popular observance of the</sup> law. Data on bank concentration and foreign bank penetration<sup>are taken from the World</sup> Bank Financial Structure Database. Finally, we have<sup>taken data from Demirgui,-Kunt and</sup> Huizinga (1999) on the features of the deposit<sup>insurance scheme of a country, in</sup> particular whether insurance is implicit or explicit<sup>and how large the explicit insurance</sup> premiums are.

Our data set includes listed banks from 12<sup>countries, namely the four East Asian</sup> crisis countries (Indonesia, South Korea, Malaysia,<sup>and Thailand), four other countries in</sup> South East Asia (Hong Kong, Japan, Singapore, and<sup>Taiwan), and the four major</sup> Western countries to provide for a benchmark (France,<sup>Germany, UK and US). Across</sup> these 12 countries we have collected data on 137 listed<sup>banks during the period 1991-98.</sup> The banks in our sample include most of the major<sup>listed banks in the country4.The</sup> features of the deposit insurance schemes of the 12<sup>countries are presented in Table 2 of</sup> the Appendix.

To limit the large number of listed banks in Japan, we<sup>only look at the long-term</sup> credit banks (3 in total), the city banks (9 in total), and<sup>the trust banks (7 in total), thereby</sup> excluding the mostly smaller regional banks (127 in total).<sup>To limit the large number of</sup> listed banks in the US, we only include the 22 largest<sup>
# Introduction and context

Since the change of administration in August 2021, there has been a critical need for timely and frequently updated information on welfare in Afghanistan. While macroeconomic data shows a large economic contraction, the evolution of poverty levels over the last two years remains uncertain since no additional household expenditure survey has been conducted. The latest official estimates based on the 2019-2020 Expenditure and Labor Force Survey (IE-LFS) conducted by the National Statistics and Information Authority (NSIA) placed the national poverty rate at 47.1 percent, meaning that close to half of the country’s population was already living in poverty before 2021. Data collection for a new IE-LFS started in April 2021 (IE-LFS 2021-22) but fieldwork was suspended in June 2021 due to the deteriorating security situation. Poverty estimates based on first quarter data (spring season) of the IE-LFS 2021 showed that poverty for that period was 52 percent, very close to that reported for the same period in 2020.

The lack of comparable household budget survey data limits the ability to directly estimate the evolution of monetary poverty. We fill this knowledge gap by implementing a survey-to-survey imputation method using a SWIFT-plus approach. After the NSIA halted regular collection and reporting on key socioeconomic indicators in 2021, the World Bank continued welfare monitoring using phone surveys, and implemented the third round of the Afghanistan Welfare Monitoring Survey (AWMS) between April and June 2023. This round was expanded to include a module that allows estimating current monetary poverty levels through survey-to-survey imputation techniques.

We use the IE-LFS 2019-20 as it represents the last full-year household survey the NSIA conducted. Considering the strong seasonal dimension of poverty in Afghanistan and the timing of the AWMS R3 (April to June 2023), we trained the model using a quarter-representative sample of the IE-LFS 2019-20 corresponding to the same months of 2020. That is, we used data from the spring quarter of the IE-LFS 2019-20 to calibrate a model that predicts household expenditure and used this model to impute poverty in the AWMS R3. The SWIFT-plus methodology includes fast-changing consumption
|(0.002)|(0.002)||||(0.002)|(0.002)|(0.002)||||
|Share of refugees in 1928||||0.083|0.004|-0.014||||0.178**|0.098|0.057|
|||||(0.052)|(0.058)|(0.054)||||(0.069)|(0.072)|(0.060)|
|Share of refugees in 1928, square||||-0.099|-0.013|0.003||||-0.192**|-0.081|-0.038|
|||||(0.065)|(0.070)|(0.065)||||(0.078)|(0.078)|(0.066)|
|_R_<sup>2</sup>|0.189|0.307|0.372|0.181|0.307|0.373|0.356|0.448|0.546|0.341|0.447|0.550|
|Observations|275|275|275|275|275|275|275|275|275|275|275|275|
|Region FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Geography controls and Population 2011||Yes|Yes||Yes|Yes||Yes|Yes||Yes|Yes|
|Socio-Economic Development|||Y es|||Yes|||Yes|||Yes|
|Dep. var:||||Po|
# **Corruption and Country Size:**

# **Evidence Using Firm-Level Survey Data**

By: Mohammad Amin* and Yew Chong Soh** (World Bank) _Keywords_ : Corruption, Bribes, Country size, Population, Area _JEL Codes_ : D72, D73, K42, O17 ____________________ We would like to thank Jorge Luis Rodriguez Meza for providing useful comments.

* Corresponding Author. Senior Economist, Enterprise Analysis Unit, DECEA, World Bank Country Office in Kuala Lumpur, Malaysia, World Bank **.** Email: <u>mamin@worldbank.org</u>

****** Consultant **,** Enterprise Analysis Unit, DECEA, World Bank Country Office in Kuala Lumpur, Malaysia, World Bank **.** Email: <u>ysoh@worldbank.org</u>