rafmacalaba/data-use-mentions-extended huggingface dataset:
sampling weights for the HIES 2016/17 yield an urban share of 28 percent, and the sum of the sampling weights by _zila_ match the official BBS 2016 population statistics.

> 30 <mark>The BBS official population projections are available at the following link:</mark> <u><mark>http://www.bbs.gov.bd/WebTestApplication/userfiles/Image/PopMonographs/PopulationProjection.pdf</mark></u> 31 To convert the adjusted number of households to adjusted population counts for estimating the final poststratification factor by zila, we use the official household size reported in the 2011 Census for Population and – Housing for rural, urban, and City Corporations areas 4.49 (rural), 4.45 (urban), 3.98 (City Corporation). 35
# **6 Main Results**

The empirical analysis proceeds in two steps. I first examine the impact of debt relief on the financial position of beneficiary households and the real effect of the bailout on households’ investment decisions and productivity. In the second part of the analysis, I explore potential mechanisms that can explain the observed economic behavior of beneficiary households after the program, and focus specifically on the impact of debt relief on borrower expectations.

## **6.1 The Level of Household Debt**

Table 5 presents RD estimates of the change in total household indebtedness. Panel A reports baseline estimates using the sample of all surveyed households and employing four different RD specifications: a basic discontinuity specification without controls and bank*district fixed effects, a linear control function specification with a complete set of bank*district fixed effects _φbd_ , with and without additional loan and household controls, and a quadratic control function specification with bank*district fixed effects. To validate these results, Table 5, Panel B, replicates the estimates for two restricted samples: (i) an audited robustness sample consisting of all households with audited and matching electronic land records, and (ii) a reduced bandwidth sample that omits the top and bottom quartile of the land distribution to either side of the program threshold. As in subsequent tables, the reported coefficients are local average treatment effect (LATE) estimates, measuring the effect of debt relief on households benefiting from 100% unconditional debt relief, relative to borrowers qualifying for only 25% conditional debt relief.

The point estimates in Table 5 indicate that debt relief leads to a significant and persistent reduction in the overall indebtedness of beneficiary households. The economic magnitude of this effect is substantial: on average, the total indebtedness of households that had their outstanding balance cancelled entirely declined by between Rs 24,000 (US$ 470) and Rs 26,000 (US$ 508), or approximately 30% of the pre-treatment mean of overall household debt.<sup>18<
example. This last effect has straightforward implications for the hypothesis explored in this paper. If firms disclose more information when issuing ADRs, they reduce the cost of information for investors and thus become less prone to informational frictions. Hence, these firms should become more attractive to global fund managers, the relatively uninformed investor when compared to specialized funds.

With the ADR data collected, we can distinguish between these two effects. ADRs can be basically classified into two types: private issues with no additional requirement on information disclosures (Levels I and IV, which are OTC and private placements), and public issues that are required to comply with U.S. disclosure rules (Levels II and III). Notice that both types of ADRs involve reductions in the direct barriers to foreign investors, but only the “public” type is associated with a better informational environment. We thus expect public ADRs to have a stronger effect on the portfolio choice of global funds than on the portfolio of specialized funds. But no such heterogeneity is expected from private issues.

Our second set of controls is related to direct measures of restrictions to foreign investments, namely investability. This is a firm-level variable from EMDB capturing the legal availability of a firm’s stocks to foreigners. Hence, a larger number of available shares to foreigners would allow greater investments by mutual funds. Given that this measures a direct barrier to foreign investment, we expect no heterogeneity of its effect on mutual funds with different mandates.

The estimations are reported in Tables 7A and 7B for the logit and negative binomial models, respectively. Our hypothesis that global funds prefer to invest in firms where they are 33
# Figure 8: ECA: Investment prospects

# A. Foreign direct investment liabilities, by source, 2019-20

<!-- Start of picture text --> Percent of total FDI liabilitie s<br>Russian Federation<br>75<br>EU<br>50<br>25<br>0<br>Türkiye<br>ECA excl. RUS and UKR Central Asia Central Europe Eastern Europe South Caucasus Western Balkans<br><!-- End of picture text --> C. Investment growth, 2010-21, and 2022 forecasts for 2022-27 <!-- Start of picture text --> Percent<br>ECA Minimum-maximum range<br>30<br>0<br>-30<br>-60<br>E. ECA countries’ dependence on imports<br>from<br>Russia<br>Percent of partner's total imports<br>60<br>Interquartile range<br>Average<br>40<br>20<br>0<br>Total Fuels Wood Metals Chemicals<br>2010 2012 2014 2016 2018 2020 2022 2025 2027<br><!-- End of picture text -->

# B. Political risk in 15 ECA countries and policy

<!-- Start of picture text --> uncertainty in Russia and Poland<br>Index ICRG Index<br>500 Economic policy uncertainty 67<br>Political risk (RHS)<br>66<br>400<br>65<br>300 64<br>200 63<br>62<br>100<br>61<br>0 60<br>D. ‘Well-governed transition’ indicator<br>(EBRD<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br><!-- End of picture text --> assessment) <!-- Start of picture text --> Score<br>2016 2021
. The full sample consists of 16,372 plots, 26% of them managed by females.<sup>25</sup> Table 1 clearly demonstrates the (unadjusted) gender gap: the 20 The lead author was the point person for the World Bank technical assistance towards the design and implementation of the IHS3 under the LSMS-ISA initiative, which is a household survey program established by a grant from the Bill and Melinda Gates Foundation to provide financial and technical support to governments in subSaharan Africa in the design and implementation of nationally-representative multi-topic panel household surveys - with a strong focus on agriculture (www.worldbank.org/lsms <u>isa). The IHS3 data and documentation are publicly</u> available through the LSMS website (www.worldbank.org/lsms).

21 A plot was defined as a continuous piece of land on which a unique crop or a mixture of crops is grown, under a uniform, consistent crop management system, not split by a path of more than one meter in width. Plot boundaries were defined in accordance with the crops grown and the operator.

22 For each plot, the following question was asked to identify the primary decision maker/manager: “Who in this household makes the decisions concerning crops to be planted, input use and the timing of cropping activities on this plot?” The questionnaire allowed for identification of one manager per plot, on whom individual-level information could be recovered from the Household Questionnaire.

23 For each plot, the following question was asked to identify the plot owners: “Who owns this plot?” The question allowed up to 2 household members to be specified as owners.

24 81 percent of the plots in our sample are reported to be owned. Among the owned plots, 15 percent have joint ownership, of which the predominant form is male-female. The remaining 38 percent and 47 percent of the owned plots are under sole male ownership and sole female ownership, respectively.

25 The IHS3 identified 18,917 plots that
the equity and long-term viability of many operational reforms, including privatization; the design of auction and procurement rules in a sector where in competitive markets low-balling by bidders with limited implementation skills or in much less competitive markets high bids with bidders with strong ex-post negotiation skills; and the coordination between the institutions created or restructured as part of reforms. Effective regulation, procurement rules and institutions are needed to increase transparency and accountability and minimize the effects of corruption on the level, quality, and price of public services being delivered, often by public and private monopolies, to the world’s poorest people.<sup>34</sup> Much more is known from quantitative research on the infrastructure needs of the investment climate and long-term growth than on the needs of residential users, particularly the poor users and on the costs to the public sector of meeting the needs of all users.

Ensuring a fair balance in the monitoring of the winners and losers of reforms (or lack of reforms) will require much better coordination between donors. Indeed, the economic literature surveyed and donors’ policy statements point to a wide diversity of concerns and approaches in the infrastructure agenda for developing countries with little joint accountability for outcomes. While every donor is sovereign in its strategies, the incidence of coordination failures is on the countries, not on the agencies. Just as in the case of the MDGs, some basic quantitative agreement

> 32 The original baseline poverty estimates used for the MDGs were based on income and consumption distribution surveys for 44 countries containing 71 percent of the population in 1990 (Ghaiha 2003). These countries accounted for about a quarter of those considered to be developing. Ghaiha also documents the many sources of data and their inconsistencies.

> 33 These data and instruments can also be used to assess the regressivity of the use of infrastructure sectors as tax handles which seems to also be an emerging problem from the literature surveyed here.

> 34 At the same time, it must be recognized that in the context of privatization, when pushed
2 their earnings from supplying agricultural labour. Sah and Stiglitz contend it to be "plausible" that the food price elasticity of the agricultural wage rate is close to unity, implying that a... a movement in the terms of trade against (in favour of) agriculture hurts (helps) everyone in the sector, whether rich or poor" (p.111).2 On the other hand, de Janvry and Subbarao (1984, 1986) assume that the nominal wage rate in agriculture is exogenously fixed, implying zero food price elasticity, and, hence, quite adverse effects on the rural poor of higher food prices. Neither study presents any empirical evidence to support their assumptions.

This paper exaziines the rural welfare distributional effects of changes in food prices under induced wage responses for rural Bangladesh. The approach falls short of a fully fledged general equilibrium analysis of welfare effects with flexible prices; rather, attention is focused on the more tractable (and arguably most important) issue of the labour market responses to changes emanating from food markets. An effort is made here to base the analysis on a consistent model of farm-household behaviour for which crucial parameters can be estimated with readily available data. Section 2 outlines the theoretical conditions for a vector of small price changes to be welfare improving for a stylized agricultural household. This permits identification of the critical value of the elasticity of the agricultural wage rate with respect to the price of food necessary for an increase in food price to be welfare improving. The critical value of that elasticity depends solely on variables which can be measureu from any standard income-expenditure survey. Section 3 applies the theoretical results to available income-expenditure data and econometric estimates of the wage-price elasticity for Bangladesh and discusses the implications for the welfare distributional effects of foodgrain price changes. Finally, section 4 offers some conclusions.
9

# 4. CHANGES OVER THE LAST DECADE

In 1983, Peter Heller and Alan Tait produced an international comparison of government employment and wages covering a sample of about 45-50 countries. Table 3 and Figure 5 compare some of their findings with our data for the same countries (hence different data from our larger sample). Furthermore, for comparability, it is necessary to limit the sample to central government and wages only. Nevertheless, the trends are clear. The following points emerge:

- A large contraction in both central government employment (relative to population) and the relative wage bill is evident in all regions, with the relative size of central govermnent shrinking by about one-third when measured by employment and onefourth when measured by the wage bill;

- Over the last 10-15 years, central government employment decreased in relative terms across the board, from about 2.5% of population to 1.5%;

Table 3. Government Employment and Wages, early 1980s and early 1990s

|||_Central Go_<br>_percent of_|_vernmenf as_<br>_population_||_Central Gov_<br>_bill as perc_|_ernment wage_<br>_ent of GDP_||_Central G_<br>_average wag_<br>_of per ca_|_overnment_<br>_es as multiple_<br>_pita GDP_|
|---|---|---|---|---|---|---|---|---|---|
||_N_|_Early 1980s_|_Early 1990s_|_4'_|_Early 1980s_|_Early 1990s_||_Early 1980s_|_Early 1990s_|
|Africa||1.8|1.1|g|10.0|7.9|X|6.1|4.8|
|Asia||2.6|1.1||7.5<br>|4.9||2.9|3.8|
reported in the SINAN data are thus considered less severe than the cases reported in the hospitalization data. In an additional exploration of the SINAN data, the clinical reports and the hospital emergency room reports were separated during the study to determine if the analysis might differ according to whether the facilities treat less or more severe cases.

_The limitations of violence data_ . The use of the variables as proxies for IPV is grounded on a conceptualization of the relationship of IPV to the overall universe of incidents of violence against women (UNODC 2019). The concept is expressed by using concentric circles to indicate that IPV is at the core of violence against women, which then broadens to include violence by other family members, femicide by nonfamily members, and then any incident that has resulted in female homicide. The variables are thus meant to be proxies for IPV, the center of the model. Because these are proxy variables, the limitations of the data must be considered. In most cases, it is not possible to determine where within the concentric circle model the incident lies. One may not know if the perpetrators are intimate partners or strangers, although one may control for the general level of violence relative to the incidence of violence against men. However, the characteristics of the victims (age, education) and the events (time of day, in the home or not) are similar in reports in which the perpetrator is identified as a partner or ex-partner and in the case of the hospitalizations and homicides on which this information is not available (Reynolds 2020).

The issue of whether these data exclude some incidents, as is so frequently a concern in surveys, also arises. Homicide data are generally considered the most accurate because there are few reporting issues. Data on hospitalizations because of violence rely mainly on the reporting of physicians on injuries they believe result from aggression. Because these are typically serious injuries involving overnight hospitalization, the likelihood of misclassification is smaller than in the case of more minor injuries that are the subject of such reports
---|
|**Inte**|**llectual property protection**|•<br>Ease of doing business index (IFC, WTI 3.1, WDI)|
|•|Improvement of IP regime and administration to comply with trade<br>agreements, to include patents, authors’ rights, geographical indications,<br>etc.|•<br>World governance indicators – corruption, rule of law,<br>government effectiveness, regulatory quality, political<br>stability (WTI 3.2)|
|•|Improvement of enforcement mechanisms and practices|•<br>Enabling Trade and Global competitiveness indexes|
|•|Promotion of IPRs and related training or technical assistance|–<br>Regulatory environment (WEF ETI, 8.01-08)<br>–<br>institutions: property rights, ethics and corruption,|
|**Com**|**petition, including privatizations and concessions**|undue influence, government inefficiency, security|
|•|Privatizations, concessions, and other forms of opening of sectors to<br>competition|(WEF GCI 1.01-1.16)<br>–<br>labor market efficiency (WEF GCI 7.01-7.09);|
|•|Elaboration and implementation of a competition framework, including<br>competition law, competition authority (e.g. independence, resources,<br>etc.), competition law enforcement (e.g. investigations, sanctions, etc.) and<br>related training or technical assistance|–<br>goods market efficiency (WEF GCI 6.01-6.16)<br>–<br>business sophistication: state of cluster development<br>(WEF GCI 11.03)<br>•<br>Enterprise ownership (government, private foreign, private<br>domestic) (ADI)|
|**Gov**|*
,399|34.7|1.5|3.7|12.8|
|Non-metalic minerals|21,430|176|0.8|0.4|9.7|12.0|
|Iron and steel|6,364|4,218|66.3|0.1|0.7|5.7|
|Non-ferrous metals|640,473|251,514|39.3|11.2|3.5|10.0|
|Fabricated metal products|7,671|2,166|28.2|0.1|2.3|8.5|
|Motor vehicles and parts|3,120|143|4.6|0.1|6.8|22.0|
|Other transport equipment|587,053|78|0.0|10.3|1.4|15.0|
|Electrical machinery|16,766|15,003|89.5|0.3|0.6|14.0|
|Other machinery|41,592|9,546|23.0|0.7|1.6|8.0|
|Other manufactures|32,025|15,258|47.6|0.6|1.4|9.0|
|TOTAL|5,720,632|394,087|6.9|100.0|9.0|22.0|

source: WTO integrated database 29
particular discount more than a month in advance.

The experimental design incorporated a six-month pause in all discounts beginning January 2017, one year into the price experiment. The purpose of the pause was to allow a reduced-form test of demand persistence, exploiting the fact the each of the three groups had been exposed to different price discounts over the previous year yet were currently facing the same price. We discuss this test in section 2.4.

> 8While the baseline survey collected data on 1,488 households, 3 were dropped due to consumption exceeding 50 m<sup>3</sup> for 5 or more months during the experiment; they were most likely operating a business out of their homes. We dropped a further 23 households due to non-participation in the quarterly monitoring surveys (so that no time-varying demographic information could be collected).

> 9As mentioned earlier, a universal 50 percent increase in the base price was implemented in the whole province of Bac Ninh in June 2017. This price increase was also passed on to the experimental sample; total subsidies are reflected accordingly in the lower panel of Figure 1, which indicates that 50 (resp. 75) percent rebates translate into a price equal to 75 (resp. 37.5) percent of the July 2015 price.

7
to include as the perpetrator of this crime whomever, for reasons of hatred, contempt or abuse due to gender, kills a woman, not only when it is her spouse or partner. The "Justice for Antonia" bill seeks to criminalize the incitement of suicide, also known as femicide-suicide. The National Service for Women and Gender Equity (SernamEG) reports that 43 completed femicides</mark><sup>8</sup> <mark>and 151 attempted femicides</mark><sup>9</sup> <mark>occurred during 2020.</mark> The COVID-19 pandemic has further exposed women's vulnerability to intimate partner violence. A recent UN Women report on the impact of Covid-19 on violence against women in Latin America and the Caribbean found that cases of violence increased considerably since the onset of the pandemic and the establishment of quarantine measures. The number of calls to helplines rose fivefold in some countries (UN Women, 2020). In Chile, the number of calls to the SernamEG hotline for guidance on violence against women nearly tripled between March and July 2020, according to information provided by the Ministry of Women and Gender Equity.<sup>10</sup> This situation demonstrates that for many women, the home is not a safe place.

> 3 The 2020 ENVIF-VCM is representative of the population of women aged 15 and over in urban areas at the national and regional levels.

> 4 The intrafamily violence indicator includes women aged 15 to 65 in urban areas.

5It is difficult to determine the extent to which this increase owes to rising violence rates, particularly psychological violence; and how much is due to the fact that violence is no longer normalized and, as such, women are not only more aware that they are or have been victims of violence but are more willing to report if it occurs. In recent years, the government, nongovernmental organizations, and feminist movements have increased their outreach campaigns to raise awareness of genderbased violence and inequality, which
measured or monitored to the same extent, organizations tend to have worse outcomes because practices related to incentives/monitoring might be poorly designed and not be holistic enough to encourage all forms of e↵ort required for successful project completion.

# **4.2 Intrinsic Motivation**

A long established literature suggests those that self-select into public service might be relatively more _intrinsically motivated_ than those working in the private sector [Crowley and Smith 2014]. Performance incentives or monitoring might then be detrimental if such practices crowd out such intrinsic motivation. To measure civil servant’s intrinsic motivation, As part of our civil servants survey, we obtained individual measures of the public service motivation (PSM) of bureaucrats, using an abbreviated version of the standard Perry scale [Perry 1996]. This includes four sub-indices related to motivations related to compassion, public interest, policy making and sacrifice. We then examine whether the negative impacts of management practices related to incentives/monitoring

> 16An established macroeconomic literature documents a negative correlation between societal diversity and economy-wide outcomes [Easterly and Levine 1997, Alesina and La Ferrara 2005]. In Kenyan private sector settings, Hjort [2014] and Macchiavello and Morjaria [2015] show ethnic divisions impact productivity due to worker discrimination.

18
reduce the variation in commercial premium rates within a state.

# _Probable Yields_

# <u>Description of step</u>

First, the yield data to be used in calculating the probable yield must be determined according to the rules of mNAIS. For the worked example of cotton in Gujarat, it is assumed that the probable yield is to be calculated using the seven years of yield data between 2004 and 2007 inclusive, after detrending by 50 kg/ha/year (columns [2] to [8] of Table 4).

Second, calculate the average detrended yield for each insurance unit (column [9] of Table 4).

> 4 There are much broader questions of political economy as to the degree to which the value of a mNAIS insurance product should be the same for different farmers growing different crops in different states. These broader questions are not addressed by this objective.  Instead, the focus is on the technical issue of how products could be designed and priced so that, for example, the mNAIS cotton product sold in one insurance unit in one district offers the same expected claim payment to farmers as the cotton product sold in another insurance unit in the same district.

11
Figure 6: Births per 1000 district population using the 2001 Nepal population census <!-- Start of picture text --> 1966 1971 1976 1981 1986 1991 1996<br>Birth year<br>High conflict Low conflict 95% CI<br>40<br>30<br>20<br>Births per thousand<br>10<br>0<br><!-- End of picture text --> Note: High conict areas are dened as districts that experienced above median level of conict intensity (months of war). Birth rates are calculated using individuals observed in the 2001 Nepal population census and their year of birth.

38