rafmacalaba/datause-encoder-data huggingface dataset:
distribution of per capita income, and three poverty measures computed using the 6.85-USD-a-day line: the poverty headcount ratio, the poverty gap and the severity index (FGT(2)). In most panels there are three sets of indicators computed for (i) the group pf dropouts, (ii) the cohort aged 30 to 45 in the base year, and (iii) the whole population.

The first column (“original”) shows values for 2045 without the COVID-19 shock, which, by construction, are those for the year of the latest available household survey (2019). The second column displays the results for the dropout exercise. Since we randomly select the dropouts (within the groups), in column (ii) we show the mean results over 50 draws. The next two columns show the results for the exercise of instructional losses due to school closures under two extreme alternatives: assuming no adjustments to the loss of days of school ( _f_ =n=0 and  =1), and assuming parental and government adjustments ( _f_ and n >0, and  <1). Columns (v) and (vi) present the combined effects of the two exercises (dropouts and school closures). The second panel of the table -columns vii to xi- shows proportional changes in relation to the original situation for each indicator.

The impact of leaving school is strong over the group of dropouts. Mean income falls by 17.6%, the incidence of poverty climbs by 22.7% and the severity of poverty jumps by 38.5%. Since the proportion of dropouts is relatively small (around 1%) the size of the impact becomes small for the treated cohort 30-45, and tiny for the whole population. The poverty headcount ratio increases by 0.6% in the cohort 30-45 and by 0.2% in the population due to the drop-out effect.

The impacts are larger due to the generalized instructional losses caused by the school closures. If reactions to school closures were inexistent or fully ineffective (columns iii and viii), the COVID-19 shock would imply a fall by around 15% in earnings in the treatment group in 2045. The impact would be a decline by 10.5% of average household per capita income. These income changes have significant effects over the poverty indicators within this group: the poverty headcount ratio would increase by 19.3%, the poverty gap by 20.2% and the 20
# Figure II

Local expenditure share within products <!-- Start of picture text --> 80<br>77<br>09 15 60<br>90<br>19 91 35 6331 7332 7139144355 4465 925003 98 51 45 857016<br>33 249330 3472 42 7918 75 41<br>37<br>66 48 38 64 22<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>subnational governments<br>1<br>.9<br>.8<br>.7<br>.6<br>.5<br>.4<br>national governments .3<br>.2<br>.1<br>0<br><!-- End of picture text -->

**Notes:** Figure II shows the “within” component in equation 3 of the main text. In particular, it shows the term _Xrr_<sup>_j,n_</sup> � _Xr_<sup>_j,n_</sup> � for different _j_ ’s averaged across regions. Each observation corresponds to a two-digit product (division) of the CPV

in which we keep, for each product category, their actual _within_ component but set the _across_ component to their counterparts of national governments. The main result from running this counterfactual exercise is that subnational governments would still have systematically higher local expenditure shares than national governments. In the data, national governments spend around 29% on local firms, whereas this number is 56% for subnational governments. In the counterfactual, we equate the expenditure composition of subnational governments to that of national governments and we find that the local share of subnational governments would slightly decrease to 55%.<sup>16</sup>

# **4 Firm-level stylized facts on procurement market integration**

We next present some empirical regularities about firms’ participation in procurement markets, within and across countries, that will motivate our model. We first show that firms’ participation in procurement markets (defined by the destination region) follows the same overall patterns documented in the context of exporting markets in the private sector (e.g., Eaton, Kortum, and Kramarz (2011)). However, we show that the patterns related to participation in local regions significantly differ depending on whether the buyer is a subnational or national government.

Our dataset contains 192,155 establishments in total (defined as a firm at a specific origin region). Out of them, 66% (126,935) only sell to governments at home, i.e., the same region where they are located. This means that 34% of the firms in our sample “export” to governments out of their home region, either within the same country or across country borders.

> 16See Appendix D for details on how we perform this counterfactual.

15
might be opened, impose higher minimum balances to open accounts, require more documents to open checking or savings accounts, need more days to process loan applications, and are less likely to accept loan applications through non-traditional channels.

Overall, we find many country characteristics associated with barriers to banking services. Improvements in physical infrastructure and more efficient credit information and contract enforcement frameworks are associated with lower barriers. Barriers are also lower in countries with greater banking freedoms, greater competition and transparency. Although foreign banks themselves charge higher fees, banking systems with greater foreign entry have lower barriers in general. While government banks themselves do not seem to provide improved access, in banking systems dominated by state banks customers face lower fees but poorer quality of service (fewer locations that accept loan applications, longer loan processing times). These results have important policy implications for potential reforms to broaden access.

# **7. Conclusions**

This paper is the first effort to systematically document the existence of barriers to banking services. Using surveys of 193 banks in 58 countries, our data show significant variation in barriers to banking across countries. Though not without limitations, we think that this effort is important in identifying and understanding the channels through which financial exclusion works. Barriers like high minimum deposit balances, minimum loan amounts and fees can lead to exclusion by making these products unaffordable for large shares of the population. For example, in our sample high fees on checking and savings accounts effectively exclude more than 30 percent of the population from having a checking account in ten of our 58 countries. Also, strict documentation requirements and long processing times can exclude households and firms that cannot provide these documents or that depend on faster loan decisions. Similarly, 27
nationally and internationally-recognized institutions. This view coincides with that of other reviews<sup>(27)</sup> .

The quality scores for the studies done on the general population, and adolescents and youth, were found to be higher than the ones done on MSM and female sex workers. The difference between them is statistically significant. The most frequent limitations found in the studies done on the most-at-risk populations were inadequate sample size and participant selection bias; in most of these studies convenience sampling was utilized. These methodological obstacles are much more difficult to sort out when working with hard-to-reach populations, due to the stigma and discrimination frequently faced by its members. The use of new sampling strategies for hard-to-reach populations, such as TSS (time space sampling), represent a challenge for future investigations.

# **Ecuador**

In Ecuador, there was a heterogeneous set of documents, mostly made up of technical reports and scientific articles, but also including PowerPoint presentations and one poster presentation. Only 60% of these studies had been published at the time of data compilation. The lack of publication not only limits dissemination of the results, but also contributes to generating studies with methodological limitations. It is more desirable to promote the submission of manuscripts to journals that have a peer review process. Peer review can provide options and point out aspects that in the end will contribute to improving the quality of the study’s results.

Almost 60% of the studies were conducted in key HIV populations. Among the population groups considered at lower risk, the studies conducted on youth were the most frequent; studies on youth constituted nearly 20% of the total studies.

The methodological quality among all the studies is quite dispersed. Approximately half of the studies had high methodological quality scores, one quarter had intermediate quality, and the remaining quarter had limited quality. In the studies done on MSM and female sex workers, selection bias and inadequate sample size were the main causes of methodological weakness. The majority of studies done on people living with HIV had limited methodological quality. However, a single cause for the flaws within this group could not be identified, due to the fact that almost all of the assessed characteristics had some weakness.

An important number of KAP studies have been compiled through ASAP’s activities. Sixty percent of these studies had high methodological quality. However, this did not hold true for HIV serological studies—in addition to being done less frequently, only about 40% of such studies had high methodological quality. The analysis of syphilis and other STI studies found a more serious scenario, where only two studies were analyzed and one of them was of limited methodological quality. For this reason, the information on syphilis and STI prevalence represents a critical information gap. These issues must be included in 37
To update non-labor income, we use administrative data<sup>10</sup> on the observed expansion of the main national cash transfer programs (Takaful and Karama) to simulate the expansions in coverage and amounts observed between FY18 and FY20 in the household survey. This adjustment is very important, as the cash transfers were introduced to compensate for some of the large economic reforms that started in 2016. The allocation rule is based on characteristics measured in the HIECS, which helps with allocation of the cash transfers. In addition, international remittances are adjusted by their observed growth rate using World Development Indicators (WDI) estimates of remittance flows (2.9 percent increase in FY20 compared to FY18). Domestic remittances are adjusted using growth rates in labor incomes.<sup>11</sup> Finally, other sources of income are updated by inflation.

Poverty rates are calculated based on the international poverty lines using household income per capita. The total household income per capita consumption aggregate is adjusted using 2011 purchasing power parity (PPP). Income poverty is estimated based on the $3.65 per day poverty line in 2017 PPP. We prefer to analyze income poverty due to the complexities involved in modeling changes in consumption from changes in income due to COVID-19.

Table 2.a: Two-year transition matrix used to update HIECS (males)

|||Upd<br>|ated HIECS<br>|FY20(no CO<br>|VID)<br>|||
|---|---|---|---|---|---|---|---|
|HIECS 2018|Agriculture|Industry-<br>Informal|Industry-<br>Formal|Services-<br>Informal|Services-<br>Formal|Not<br>working|All|
|Agriculture|11.1|0.8|0.0|0.6|0.2|0.5|13.2|
|Industry - Informal|0.5|11.2|0.4|1.1|0.3|0.8|14.3|
|Industry - Formal|0.1|0.4|4.3|0.1|0.5|0.3|5.8|
|Services - Informal|0.3|1.1|0.3|12.9|1.3|0.8|16.6|
|Services-Formal|0.2|0.0|0.5|1.4|15.0|0.9|18.0|
|Not working|0.9|1.4|0.3|1.6|0.6|27.4|32.1|
|All|13.0|14.7|5.8|17.7|18.0|30.7|100.0|

Note: Unweighted percentages based on a total sample size of working-age males of 36,126.

Table 2.b: Two-year transition matrix used to update HIECS (females)

|||Updated<br>|HIECS FY20(no COVID)<br>||
|---|---|---|---|---|
|HIECS 2018|Informal|Formal|Not working|All|
|Informal|6|0|4|10|
|Formal|0|7|1|8|
|Not Working|2|1|79|82|
|All|8|8|83|100|

Note: Unweighted percentages based on a total sample size of working-age females of 35,750.

10 The World Bank worked with Egypt’s Ministry of Finance on a Commitment to Equity (CEQ) exercise to map taxes, transfers, and subsidies in the HIECS survey to understand the incidence of fiscal policies. This exercise used administrative data and allocation rules which allowed the authors to model the expansion in cash transfers over the period covered in this paper.

11 Very few households report income from remittances in HIECS, thus how we model remittances does not impact results. Yet it is important to keep in mind that remittance inflows were impacted by COVID-19, such that the income shock estimated in this paper may underestimate the total income loss.

8
14 Table 3

|Inflation equation|for domestic agric|ultural goods||
|---|---|---|---|
||(1)|(2)|(3)|
|constant|3.411|2.286|3.222|
||_(2.07)_|_(1.60)_|_(2.17)_|
|Ac|0.634|0.642|0.486|
||_(9.16)_|_(12.77)_|_(4.87)_|
|Ap*|0.203|0.230|-0.009|
||_(1.79)_|_(2.42)_|_(-0.06)_|
|y (a)|3.341|||
||_(0.50)_|||
|x|-3.019|0.362||
||_(-0.51)_|_(1.46)_||
|AGDP||-0.401||
|||(-3.10)||
|**[p43c..(41-)p*]**<br>X|-0.283|-0.189|-0.267|
||_(-2.07)_|_(-1.60)_|_(-2.16)_|
|_with_||||
|3 (b)|0.682|0.672|0.678|
||_(33.49)_|_(22.39)_|_(32.52)_|
||||0.005|
||||(1.97)|
|Dummy|0.195|0.150|0.158|
||(2.03)|(1.83)|(1.75)|
|_AdjR2_|0.816|0.867|0.865|
|DW|2.49|2.46|2.48|

Notes: t-statistics in parenthesis. (a) Instrumented by lagged industrial GDP (up to four quarters), and the variations in the lagged real exchange rate and the lagged nominal wage;(b)the coefficients on c-l and P*_1 are constrained to sum to one.

The long-run markup behavior in the agricultural sector is defined as follows: Equation 4 _pi,- ci,=_ 0.47 _(p*, - pi,) -_ 0.85 _Api,_
- 60 -

The investment deflator used above was constructed by using<sup>a weighted average of price indices</sup> that constitute investment costs. Since the weights<sup>based on the Shura Council data were only available</sup> for 1961-82, the shares of investment costs were assuned<sup>fixed before 1961 and after 1982." The price</sup> index for domestic capital goods was the WPI for machinery<sup>and equipment which only includes indigenous</sup> capital goods. The indices for construction and transport<sup>were also obtained from the disaggregated</sup><sup>**'**PI.</sup> The WPI was not available in disaggregated form before 1977,<sup>therefore the aggregate WPI had to be</sup> used. The only exception was the price index for construction<sup>for which a series was available from</sup> 1970.63 A price index for machinery exports of the<sup>major industrial countnes was used as an index of</sup> imported capital goods prices. This was multiplied by the nominal parallel market<sup>exchange rate to</sup> obtain an index of imported capital goods prices in Egypt.<sup>The parallel market exchange rate was used</sup> since it was the operative rate for much of the period<sup>and captures the opportunity cost of foreign</sup> exchange for the period of rationing pnor to 1974. The relative<sup>shares of domestic versus imported</sup> machinery is assumed to be fixed. The weights are based on estimates from the World Bank<sup>that</sup> imported machinery constitutes 80.6% of total machinery<sup>inputs.64 This is a fairly plausible figure given</sup> the small size of the domestic capital goods industry and the<sup>fact that indigenous and imported machinery</sup> are far from perfect substitutes.

The index of wages used to calculate the markup proxy is based<sup>on two sources. For 1960-1981,</sup> the series **for** average wages in the economy calculated<sup>from the Shura Council was used. However, after</sup> 1981, data on aggregate wages are scarce since the Central<sup>Authority for Public Mobilization and Statistics</sup> is several years behind in compiling the labour force surveys.<sup>Consequently, data on agricultural wages</sup> was used for 1981-1986 to construct the wage index.61 _This_ is not<sup>an implausible supposition since wages</sup> in the agricultural sector are fairly market-determined and<sup>would represent a close approximation to wages</sup> in the private sector.

62The weights used before 1961 were: machinery-0.35; building-0.41; transport-0.15; other-0.09. The weights used after 1982 were: machinery-0.40; building-0.38;<sup>transport-0.16; and other-0.05.</sup> h3 Assaad constructs a Paaschc index for labour, capital and mnaterials<sup>in the construction sector.</sup> Assaad, 1989.

"World Bank, 1980.

6 eCommander, 1987, p. 92.
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**assessment, including revenue, employment,** profits, profit margins, and TFP. Their **main conclusion is that divested firms experienced an increase in** kevenua. profits, profit margins, and a positive TFP, but so did the firms that continued undar public ownership.

While these two studies are interesting in their o--n right, they suffer from some methodological problems. By focusing on cross-section data for a number of f;rms in the telecom industry in Europe, Foreman-Peck and Manning were able to assess the effect of the variations in size and technology on performance, but were unable to link the variations in BT's performance with the change in the company's ownership. Similarly, by focusing on a comparison between a diverse group of divested firms (in the shipping, airline, gas, telecom, oil, and automobile industries) and another diverse group of enterprises under continued public ownership (in the coal, rail, steel, and postal sectors), Bishop's and Kay's results with regard to the effect of the change in ownership are masked by large variations in the underlying market structure and technology in the two groups of firms. Moreover, neither study analyzed the impact of divestiture on allocative efficiency and the budget. Despite these limitations, these are the only such rigorous studies of postdivestiture experience that could be found. No similar studies at the firm level could be found for LDCs.

There is, finally, the empirical literature comparing the performance of public and private enterprises in the same industries and/or across
16 extensive use of this variable to summarize financial development is because –unlike other measures – Private Credit is available for many countries over a long period of time. Indeed, while financial sector is often thought of as being particularly well documented by statistical data, systematic indicators of access to different financial services is not. Therefore, measuring access to finance, its determinants and its impact has been the focus of a recent research effort which we describe below (World Bank, 2007).

# **_4.1. Measuring Access_**

What do we mean by access? Access is different than use. Figure 3 illustrates these differences. Access is more difficult to measure since it is not observed as use. It is also likely to be wider since some may have access but may not wish to use these services. Specifically, there are important distinctions among non-users. On the one hand are those who do not use financial services because they do not see any need or for cultural and religious reasons. This includes households who prefer to deal in cash or enterprises without any promising investment projects. While this group has access, it does not use financial services. On the other hand, there are the involuntarily excluded who, in spite of demanding financial services, do not have access to them. Among the involuntarily excluded, we can also distinguish between different groups. First, there is a group of households and enterprises that do not have enough income or constitute too high a lending risk for using financial services. This group can be considered un-bankable by commercially oriented financial institutions and markets. Second, there might be discrimination against certain population groups based on social, religious or ethnic grounds. Third, the contractual and informational framework might prevent financial institutions to reach out to certain population groups since it is too costly to be commercially viable. Finally, the price of financial services or the product features might not be appropriate for certain population groups. While the
# **4.4 Post-Separation Transitions**

To further understand the potential drivers behind the higher separation rates of non-white workers following the loss of a non-white peer, we use the matched employer-employee data to track separated workers to their new jobs. For 57.4% of the incumbent workers (both non-white and white) who separate from their current job, we observe a new formal job in the same or the following calendar year. For these workers, we can compare the characteristics of the new and the old job. To investigate whether workers tend to switch to jobs with a higher non-white share, we re-estimate model (1) using the difference between the non-white share in the new job (at the time of joining) and the non-white share in the old job (at the time of separation) as dependent variable.<sup>22</sup> The results, presented in column (1) of Table 4, show that the loss of a non-white vs. white worker in the old job leads non-white workers to transition to a new job with a non-white share that is 3.4 percentage points higher than in the old job. In other words, they counteract the initial decline in the non-white coworker share by moving again to peer groups with a higher nonwhite share. These results align with the hypothesis that preferences for same-race coworkers shape the initial separations of non-white incumbents. Interestingly, we also find a significant positive effect on the change in the non-white share for white incumbents. Considering that these workers separated from their old jobs after experiencing the loss of a non-white vs. white worker, it is likely that we are capturing a subset of white workers who may have a preference for greater racial diversity and who thus also move to jobs with a higher non-white share.

In column (2) of Table 4, we also test whether transitioning workers are willing to accept wage penalties to work again in teams with a higher non-white share. The results show negative but small (and only for white incumbents marginally significant) effects on the change in log wages between the new and the old jobs. Thus, we do not find strong evidence for compensating wage differentials of working with more non-white peers.

# **4.5 Interaction with Gender Composition**

So far, we have seen that a decrease in the non-white share of the peer group negatively affects the retention of non-white incumbents. While we consider racial groups as a primary delineation that may drive heterogeneity in workplace peer effects, other peer characteristics – such as gender – may also influence employee retention decisions. Much of the workplace demography literature considers both race and gender as important factors in determining the effects of coworker composition (Elvira and Cohen, 2001; Hirsch _et al._ , 2020; Linos _et al._ , 2024). In Brazil,

> 22The racial composition is measured, as before, among all employees working in the same 4-digit occupation as the focal worker.

19