rafmacalaba/data-use-mentions-extended huggingface dataset:
Thus, at least for this sample of countries, employment impacts of the pandemic in upper-middle and highincome countries appear to be associated with gender and education level of workers, with women and lowskilled workers being most affected. In low and low-middle income countries, location (urban vs rural) and gender (to some extent) are the key contributors to inequality.

These patterns raise the question whether micro and small enterprises, which are more likely to employ less-skilled workers, have been disproportionately affected by the pandemic. Estimates based on harmonized data from Business Pulse Surveys (BPS) – phone surveys of firms – conducted in 51 countries by the World Bank reveal that the COVID-19 pandemic has affected smaller firms to a greater degree. In particular, larger firms have a lower probability of falling into arrears and can cover their costs with cash at hand for a longer period than small and micro firms (Apedo-Amah et al., 2020). Data from World Bank Enterprise Survey (WBES) COVID-19 Follow-up surveys across 35,496 firms in 40 countries similarly reveal a higher incidence of permanent firm closures among firms of smaller size (Karalashvili and Viganola, 2021).

# 4.2. Unequal impact on food security

Food insecurity after the pandemic was higher for countries with lower per capita income, and particularly high in some low-income countries, with more than 30 percent of households in some countries in SubSaharan Africa reporting going without food for at least one full day in the last month due to lack of resources.<sup>13</sup> Since food insecurity is pervasive in poorer countries even in normal times, its observed incidence in the data cannot be attributed directly to the pandemic, unlike work stoppages and income loss, which are based on questions in HFPS that link these losses to the pandemic. Instead, it is possible to explore whether pandemic-induced jobs and income losses are associated with worsening food security, which would suggest that the pandemic has affected food security via its economic impacts on households (Bundervoet et al., 2021
# **Figure 8: Use of public education and health services in Mongolia (2016)**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>1 2 3 4 5 6 7 8 9 10<br>deciles (1 is poorest, 10 is richest)<br>preschool primary secondary tertiary health<br>percentage of population attending public school<br><!-- End of picture text --> <u>Source: Authors elaboration using HSES 2016</u>

# **Figure 9: Use of electricity, heating and mortgages in Mongolia (2016)**

<!-- Start of picture text --> Connected to Central Heating Connected to Electricity mortgage loan<br>100.0%<br>90.0%<br>80.0%<br>70.0%<br>60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>1 2 3 4 5 6 7 8 9 10<br>deciles (1 is poorest, 10 is richest)<br>percentage of population receiving health care<br><!-- End of picture text --> <u>Source: Authors elaboration using HSES 2016</u> 26 MONGOLIA Distributional impact of taxes and transfers / November 2018
/sup> As restrictions were scaled back, the average share of food-insecure population in 16 countries fell from 13 percent to 9 percent.<sup>37</sup> Job recovery was associated with rising food security (Figure 17). For households across the socioeconomic spectrum, food insecurity became less of a concern by August-September, but improvements were more pronounced for households with college-educated members.<sup>38</sup> Extending the time window of the analysis to January 2021 for a smaller set of 8 countries, we observe continuing signs of uneven recovery in labor markets, along with some signs of stalling.<sup>39</sup> Women and those living in urban areas continued to experience a slower recovery in employment towards the end of 2020 and beginning of 2021 as a proportion of the initial losses suffered — while employment among men reached 95 percent of its pre-pandemic level by January 2021, women’s employment was only at 89 percent. Employment recovery also continued lagging for urban areas (Annex. Figure A-7). Moreover, overall recovery in employment seemed to have stalled for the 8 countries since September 2020, even though 32 Furthermore, Batana et al. (2021) find, based on city-level panel data from Ethiopia and from Kinshasa, DRC, that households from large, densely populated cities (and, within cities, vulnerable households residing in remote areas), suffered greater COVID-19 related losses and recovered at a slower rate.

33 This analysis could be done for a subset of 6 countries from the original sample of 20 countries, because it requires data on all the characteristics of workers and households, namely age, gender, location and education.

34 These 7 countries are: Argentina, Costa Rica, Ethiopia, Guatemala, Mongolia, Paraguay, and El Salvador.

35 Data on respondents’ urban/rural status is not available for Guatemala and El Salvador.

36 While public safety nets were scaled up, they were largely inadequate in lower income countries. Average per capita spending
reputable public market.

Two recent international surveys have examined the division of responsibilities between government regulators and SROs. In 2004 the World Federation of Exchanges (WFE 2005) surveyed its members on their regulation responsibilities. The survey showed that a large majority of exchanges perform at least some market regulation. Many regulate trading members and supervise clearing and settlement activities. Many also perform a listings role and have some responsibility for listed company disclosure and corporate governance. The results show that each of the regulatory roles is usually shared with the government regulator.

In 2006, the International Council of Securities Associations (ICSA 2006b) conducted a survey of 10 major exchange and independent SROs. All the SROs surveyed regulate member firms. Almost all regulate markets, either exchange or OTC. The exchange SROs also regulated issuers.

32
both young|-0.031|-0.007|-0.025|-0.040|
||(0.066)|(0.062)|(0.017)|(0.053)|
|Enumerator and respondent both old|0.067|-0.013|-0.055**|-0.029|
||(0.043)|(0.055)|(0.022)|(0.044)|
|Respondent marital status (single)|0.054|0.087|-0.034|0.045|
||(0.059)|(0.058)|(0.026)|(0.060)|
|Enumerator from urban area|0.500***|0.672***|-0.066|0.490***|
||(0.131)|(0.115)|(0.041)|(0.095)|
|Work experience as enumerator|0.007***|0.007***|0.000|0.006***|
||(0.001)|(0.001)|(0.000)|(0.001)|
|Observations|6,245|6,332|6,552|6,319|
|R-squared|0.495|0.474|0.241|0.486|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

_Notes_ : This table presents OLS regression results for equation (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely DP, FDC, NRM, and UPC
Elvidge et al. (2017), is employed. In addition, settlement layers have been used to remove nightlight cells with zero population to reduce the impact that “empty” cells could have on the analysis.

Five countries in Southeast Asia have been chosen as case studies due to their general lack of local level economic data and prevalence of different natural hazards: Indonesia, Myanmar, the Philippines, Thailand and Vietnam. These countries represent a large portion of the total GDP, population and area of the region. In addition, they experience earthquakes, typhoons and floods to a varying degree.

The VIIRS data have a number of advantages over the widely used, but now discontinued, Defense Meteorological Satellite Program (DMSP). Firstly, they have a higher resolution, 450m by 450m compared to 1km by 1km for the DMSP. Furthermore, the VIIRS data are released monthly compared to the annual DMSP data, making it possible to compare month‐ on‐month effects of natural disasters. Finally, the DMSP data are normalized and capped at 63, whereas the VIIRS data have no upper limit. However, the VIIRS data exhibit a lot more noise than what is found in the DMSP data. Part of this is due to stray light corrections that can lead to negative light values for some months. Another issue, prevalent in both data sets, occurs due to the many days with cloud cover that happens in the tropics. As a matter of fact, several months have no days with a measurement. Importantly, both the DMSP and the VIIRS data have shown strong correlation with local GDP (World Bank 2017) (Henderson, Storeygard 2
16 have disproportionately benefitted agriculture (e.g., extending voting rights to small farmers and tenants in the early twentieth century) have induced an increase in protectionism. In contrast, other electoral changes have not affected agricultural protection because they increased the voting rights of both those in favor and those against protection.

Olper, Falkowski, and Swinnen (2011) employ both difference-in-differences regressions and semi-parametric matching methods, exploiting the time-series and crosssectional variation in the World Bank’s data showing that democratization causes an increase in RRAs (that is, democratization tends to reduce agricultural taxation and/or increase agricultural subsidization). However, they do not find any support for a reduction in positive RRAs. In other words, a country’s transition to democracy may change the distribution of policy rents but need not lead to more-efficient policies. Moreover, the study also finds that the reverse political transition (from democracy to autocracy) does not affect agricultural protection.

Some researchers have empirically assessed the impact of rulers’ preferences. It appears that agricultural interests have been protected mostly by right-wing governments. Olper (2001) found that in OECD countries, on average, right-wing governments are more protectionist in the case of agriculture than are left-wing governments. This is consistent with other empirical analyses, such as that of Bates (1983), who argues that socialist rulers in Africa taxed farmers (by imposing low commodity prices). Similarly, Tracy (1989) finds that right-wing governments in Europe (such as those dominated by Catholic parties and conservative parties, including the Nazi party in Germany) tended to support farm interests and protectionism. Although, on average, left-wing governments support agriculture less, they tend to support farmers more in unequal societies (Olper 2007). For example, for more than a century in France, large farms and landowners have been associated with right-wing political parties and small farms with left-wing parties (Swinnen 2010). This empirical result also holds more generally: right-wing dictators are more inclined to support agriculture if the sector is
**8. Summary**

The public sector is a diverse environment in which to work. Although there are some general conclusions that can be drawn, staff perform better or worse in different agencies for highly diverse reasons. The Anna Karenina principle applies: "all well-performing agencies are alike; there are so many preconditions for effective performance that every dysfunctional agency is dysfunctional in its own way"." To perform well, public officials need to be confident about the future - not to the point of smugness, but certainly to the point that they can see the relationship between their efforts and any eventual outcome. The institutional environment within which they are working shapes these expectations. If the rules are not credible, with little prospect of enforcement, if they expect policies to be contradicted or resources to flow unpredictably, then they cannot envision any relationship between their effort and public sector performance. Rationally, there is little point in working purposefully as the results are so uncertain.

This paper sets out an analytical framework that was used in designing a series of surveys of the views of public officials concerning their institutional environment and in analyzing the information that was generated. It describes how the survey results help to map the strengths and weaknesses of a public sector, and offers an approach for identifying potential pay-offs from reform interventions. Above all, the framework emphasizes the heterogeneity of incentives and institutional arrangements _within_ the public sector - and the need for policy makers to have information that moves beyond generalities and indicates what is most likely to work and where.

In building on the premise that public officials' actions - and hence the performance of their organizations - depend upon the institutional environment in which they find themselves, this framework avoids any simplistic anti-government positions, without attempting a defensive justification for poor performance. Although there is every reason to be concerned about poor performance and rent-seeking by some public officials, there are as many reasons to be impressed by the selflessness and determination of others working under conditions of extreme difficulty. This framework offers an approach
treatment and control schools was found. The school readiness intervention was less costly than the CLRW intervention; however, only for one-subpopulation can we conclude that the school readiness intervention was more cost effective.

To estimate the effect of the school readiness intervention on reading scores, we relied on a mapping of students between communities and schools defined at the school level. This provided a probability that a student in a particular school was from a school readiness treatment community. The resulting estimator is unbiased under several conditions. First, if the proportion of students in a school from a treatment community is uncorrelated with the unobserved school effect. This is a reasonable assumption because the assignment of communities was randomized. Second, the probability of a student being from a treatment community is estimated based on a sub-sample of students matched between the TEHCI and TEGRA surveys. Uncertainty of this estimate creates the well-known error-in-regressors bias. If it acts as attenuation bias as in a univariate regression model, then this results in an underestimate of the effect size.

# <u>6. Conclusion</u>

This study evaluates two quite different approaches to improve early educational outcomes in Tonga. The school readiness intervention targets the home environment and was implemented by communities; the CLRW intervention targets schools and was implemented more directly by the education ministry. Positive effects for the school readiness intervention were found on children of mothers without a high-school education on the overall school readiness score for girls and the literacy score for both boys and girls of the same mothers. It also affected some specific measures of home activities as well. The CLRW positively affected observed teaching practices in the classroom as well as reading skills for both boys and girls and most domains of reading skill. These findings suggest that expansion of both the CLRW and school readiness interventions is warranted.

Because two cohorts were potentially exposed to both interventions, we are able to compare the effects of these two very different interventions on common measures of learning outcomes for 41
host countries rises by 10 percent. In fact, the extent of gravity in FDI is comparable to the extent of gravity in trade that has been found in the literature (see, for example, Head and Mayer, 2013).

Yeaple (2003a) extends earlier work by exploring an interaction between country and industry determinants of FDI and offers empirical support to both market access and comparative advantage motives. Specifically, he uses U.S. affiliate sales data in 39 countries and 50 manufacturing industries from the BEA Benchmark Survey of 1994 and finds that U.S. multinational firms from unskilledlabor intensive industries tend to invest in unskilled-labor abundant countries, a result consistent with the hypothesis that countries' factor endowment differences lead to vertical FDI. Unlike in Brainard (1997), the role of transportation cost is found to be negative and statistically insignificant, departing from the expected sign in the context of horizontal FDI.

Irarrazabal, Moxnes and Opromolla (2013) exploit the source of gravity in FDI by introducing intrafirm trade into the framework of Helpman, Melitz and Yeaple (2004) and generating gravity relationships for both exports and multinational production. The model rationalizes gravity in FDI by assuming that the headquarters produce a firm-specific tangible or intangible input that is required for production in any location and trade in such input is subject to trade costs including transportation costs or tariffs in the case of physical inputs and cultural and geographic remoteness from the headquarters in the case of headquarters services. The results suggest that intra-firm trade appears to play a crucial role in shaping the geography of multinational production; to justify the gravity observed, the affiliate's cost share related to input purchases from the headquarters must be about 90 percent. As suggested in the paper, this result may have captured other mechanisms that are dampening firms' multinational production as trade costs increase, such as imperfect transmission of technology between parents and affiliates either because of imperfect codifiability as discussed in Keller and Yeaple (2013) or because of higher frictions in the match between firms and workers.

Addressing
(2) The main controls included in **_Xit_** are indicator variables to identify microenterprises (fewer than nine employees), age, sectors, and a female-leadership. We also control for country and time-fixed effects, the severity of the lockdown (using the Google mobility indicator),<sup>5</sup> and a dummy variable indicating if the firm is a panel firm to control for recall biases in the case of retrospective questions. **_Zit_** are additional controls to account for the size of the shock. For example, when we analyze uncertainty, we control for the level of change in sales reported against the level of 2019 because a big shock can lead to higher uncertainty. In the case of investment in digital technologies, we control for the score of digital readiness, as used in previous papers (Cirera et al., 2021; Avalos et al., 2023; Grover and Pereira-López, forthcoming) based on the BPS, because this is a factor that could condition the need to invest in digital technologies.

The following are the firm outcomes, _yit_ : : _Financial fragility_ This is measured based on whether the responding firm is already in arrears or expects to fall into arrears in any of its outstanding liabilities (Apedo-Amah et al., 2020; Cirera et al., 2021). On average, over the three waves of data collection, 48% of the observations have a value of one in this dichotomous variable (Table B1), with some variation over time.<sup>6</sup> In addition, we measure liquidity constraints with self-reported data on the number of weeks that the firm can cover its operating costs. The average in our sample is around 14 weeks (3.5 months) of costs covered, and half of the observations exhibit less than one month of liquidity.<sup>7</sup> _Uncertainty_ : Following Altig et al. (2022), we rely on a measure of uncertainty computed using
be directly linked to policy recommendations for West Bengal. In light of the widespread incidence of share cropping globally, it will also be of interest beyond the case at hand. We discuss implications, noting that some features of our setting, especially the long-term nature of the relationship, would imply that what we obtain are conservative estimates.

Our empirical analysis relies on a 2008/9 survey of some 9,000 parcels, owned by about 2,000 ownercum tenants who had benefited from tenancy reforms in 1978-82, in 200 villages from 10 West Bengal districts. Household-level listing data on production and investment for all 40,000 producers in the survey villages suggest that owner-cum tenants’ levels of productivity and investment are below those of pure tenants as predicted by theory, increasing confidence in the external validity of the results. Plot-wise data 1
# **Distortions to world trade: impacts on agricultural markets and farm incomes**

To what extent are government trade and subsidy policies still distorting agricultural markets and farmers’ incentives around the world? Nearly two decades ago a major World Bank study addressed that question for 18 developing countries (Krueger, Schiff and Valdes 1988), and found that farmers in those countries were discriminated against by their governments’ policies, albeit less so directly than indirectly (via restrictions on imports of industrial products and overvalued exchange rates). Another study at that time focused on time series data for developed and newly industrializing countries, and found a tendency for national governments to gradually change from taxing to subsidizing agricultural relative to industrial production (and from subsidizing to taxing food consumers) in the course of their economic development, and at an earlier stage the weaker an economy’s comparative advantage in agriculture (Anderson and Hayami 1986).

Meanwhile the OECD Secretariat has been estimating direct producer supports by its member governments, and finds that they all support their farmers and that the aggregate degree of support is high and has not fallen over the past fifteen years (Legg 2003, OECD 2005) – having risen substantially over earlier post-World War II decades when manufacturing protection rates began falling to what are now very low levels in those countries.

That is, developing countries’ policies of the past had caused international prices of farm products to be above what they otherwise would have been, while the policies of high-income countries had the opposite effect. A partial equilibrium attempt to measure the net effect of those
Therefore, these rounds do not necessarily correspond to waves 1 and 2 for all countries but can also correspond to waves 1 and 3.

> 29 Countries are Argentina Burkina Faso, Colombia, Costa Rica, Dominican Republic, Ethiopia, Guatemala, Honduras, Kenya, Cambodia, Mali, Mongolia, Mozambique, Malawi, Nigeria, Paraguay, El Salvador, Tajikistan, Uganda, and Uzbekistan.  Please see Annex A for further details. Chile and Mexico witnessed a rise in the Oxford Stringency index in this period and are hence omitted for our analysis. We exclude St. Lucia because of lack data on policy stringency. 30 The Oxford stringency index is also highly correlated with Google mobility data, which is another commonly used indicator of the impact of COVID related restrictions. For further details, see Bundervoet et al (2021).

23