rafmacalaba/data-use-mentions-extended huggingface dataset:
Note that this yields a few, basic expectations. First, bribe extraction will increase with a business’s revenues—that is, bribery tracks with a business’s ability to pay (e.g. Kaufmann and Wei, 1999). Secondly, the greater the cost to formalize, so too, the increase in bribes. That is, if a business operator has opportunities outside informality, they will have little incentive to deal with corrupt officials. The associated costs also vary by location, as opportunity costs are likely to be localized. So, too, the likelihood of detection by a corrupt official will, of course, increase the chances of facing a bribe, but this is also contingent on the tolerance for corruption and local circumstances.

# V. Data: Collection and Methodology

One limitation to studying informal sector businesses is that reliable data are sparse. By most operating definitions, informal businesses remain outside the bounds of many data sources. Informal businesses do not appear on business registries nor other administrative data. Adequate sampling frames for a robust survey methodology are, thus, nearly impossible to come by. Methods linked to household and labor-force surveys (for instance, what is known as the 1-2-3 method) have offered more systematic measurement of informal business activity (such as the data used by Lavallée and Roubaud, 2018), however such data are based on initial sampling designed to be representative of residential enumeration areas, and ultimately households. These methods are implemented over multiple waves and are thus much more expensive and can lack a depth of information.

The analysis here uses a data set that was developed by the World Bank, which implemented a survey in Harare, Zimbabwe over April–May 2017. The survey methodology uses a method called “adaptive cluster sampling”, with stratification (SACS), which was developed in bio-statistics (Thompson 1990, 1991) to efficiently measure elusive and clustered populations: the former characteristic being mostly self-evident and the latter—that informality tends to be highly clustered— being well-established
# **8. Systemic Risk Supervision and Challenges**

There is substantial scope for improvements in systemic risk supervision in LAC. About 45 percent of respondents saw their ability to monitor systemic risk as good or excellent (including bank supervisors in 4 of the LAC 5 countries), and 40 percent indicated it needs strengthening (Figure 6). Two respondents indicated that effective consolidated supervision of conglomerates (including cross-border ones) would be necessary to improve systemic risk monitoring16. There are several key hindrances to the agencies’ capacity to conduct effective systemic risk supervision including (i) the need to develop proper staff skills, (ii) better coordination with other domestic supervisors, (iii) need to strengthen legal powers, protection and independence of supervisors and (iv) better use of market data (Figure 6). Issues related to the supervisory architecture appear relevant as well; only about a third thought that rethinking the organization of supervision was moderately or not important factor. More than 50 percent of respondents saw assessment of risk profiles, strategic directions and internal controls as extremely important and virtually all thought this was extremely or very important for systemic risk monitoring. About 85 percent of respondents thought that analysis and monitoring of compliance and trends observed from reported prudential reports was extremely or very important and 65 percent thought the same about reviewing the accuracy of reports of regulatory compliance.

The majority of respondents thought that there should be a fundamental redefinition of the role and functions of the supervisor and making it more proactive, at least to some extent. Only about 15 percent of respondents did not think any reform was necessary (Figure 6). Some supervisors emphasized the need to decisively change the traditional scope of supervision to become more risk-based. Several of them also indicated that supervisors should be more proactive and adopt pre-emptive measures to preserve financial instability when perceived risks are building up. Supervisory agencies should then clarify their strategies and mechanisms to deal with such risks. It was also noted that there may be excessive reliance on rating agencies and that it
follows: 0.121 LE per loaf for baladi bread, 1.24 LE per kilogram for baladi wheat flour, 3.0 LE per kilogram for edible oil and 1.00 LE per kilogram for sugar. Data on the way these "free market" prices were calculated are available from the author.

29 Tables 6 and 9 are expressed in terms of calorie availability (calorie supply) rather than actual calorie intake. Even so, the figures for calorie availability (supply) in these tables may seem high when compared with international standards. However, two points should be noted. First, when measured by household budget surveys, calorie availability in Egypt is usually quite high. Second, because of problems relating
OLS<br>Observations<br>529<br>Countries<br>96<br>R-Squared<br>0.87<br>Country FE<br>yes<br>Time FE<br>yes<br>Notes: Standard errors clustered at country le<br>Dependent variable,_Private Credit/GDP_is c<br>the external finance need of exports;_Log(Tra_<br>real per capita income from Penn World Tabl<br>need of exports constructed by first estimatin<br>and sector. All of the variables are five-year a<br>the text.|

31
00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6 6<br>6.00 6.00<br>4 4<br>4.00 4.00<br>2 2<br>2.00 2.00<br>0 0<br>0.00 0.00<br>log Periodogram-2 log Periodogram-2<br>-2.00 -2.00<br>-4 -4<br>-4.00 -4.00<br>-6 -6<br>-6.00 -6.00<br><!-- End of picture text --> 27
growth rates for China are less than the official rates and those used by the World Bank. It is then not surprising that when one superimposes estimates of national distributions on one or another set of GDIs per capita to generate global inequality, the choice of the GDI database will clearly influence the end result.<sup>27</sup>

> 26 World Bank’s 2006 World Development Report uses mean log deviation as the measure of global inequality. It finds that it has decreased between 1994 and 2000  from 0.87 to 0.82 (see World Bank 2005, p.64).

> 27 For example, Concept 2 inequality calculated using World Bank GDI per capita data from 138 countries shows a decrease of some 3 Gini points between 1985 and 2000. The same concept calculated using Maddison’s data over the same period for about 160 countries shows a decline of only 1 Gini point (author’s unpublished calculations).

15
Inflation, which typically does the greatest harm to poor people, fell significantly during the 1990s; at the same time, macroeconomic management improved, exchange rates were more stable, and trade barriers were reduced. But there was little progress in governance, controlling corruption, and quality of institutions worldwide—as measured by the governance component of the World Bank’s CPIA index—although there is significant variation across countries. On the other hand, adjustment was, by and large, a failure in terms of growth. As shown by Easterly (1985), none of the top 20 recipients of repeated adjustment lending over 1980-99 were able to achieve reasonable growth and contain all policy distortions. About half of the adjustment loan recipients showed severe macroeconomic distortions regardless of cumulative adjustment loans. Econometric tests fail to show that adjustment lending has any positive effect on policies or growth.<sup>51</sup> This is also a conclusion reached by several internal OPCS reports.<sup>52</sup> Thus, despite good intentions, the record of adjustment lending by IDA (and by IBRD and the IMF) in the 1980s and 1990s is mixed at best. To be sure, it includes a number of success stories. But it also includes cases where adjustment programs were misguided or not followed. First, heavy reliance on conditionality was usually ineffective, with the large number of conditions doing nothing to strengthen borrower ownership of the reforms. Second, while the focus on improving poor policies was understandable, the design of adjustment programs paid inadequate attention to governance and institutional problems, which were major constraints on the investment climate. Finally, there was too little emphasis on equity issues and mitigating the social costs of adjustment. This was probably because many of the poor policies that adjustment lending targeted were both inefficient and biased against poor people. Nevertheless, the lack of specific attention to poverty issues led to progress involving damage to poor people and understandably attracted heavy criticism.

Although conditionality can support policy changes, it cannot persuade
Marked declines in economic activity accompanied the postTiananmen economic retrenchment and the Asian Financial Crisis. Incomes actually fell between 19871988 and 1990-1991, but recovered significantly by 1995-96 so that over the period between 1987-88 and 1995-96, growth averaged 3.6 percent. Growth in the period ending in 1999-2000 was also lower than that ending in 1997-1998. Second, there was clear heterogeneity in the success of households, but the dispersion in growth rates narrowed steadily over time. At the top end, for example, the 90th percentile of growth rates fell from 11.6% for the period ending in 1995-1996 to “only” 8.5 percent by 2001-2002, while fewer households experienced negative growth rates. Finally, data in Table 1 also reveal a pronounced shift in the structure of incomes. In 1987-88, agriculture accounted for half of household income (55%), but by 2001-2002, this had fallen by more than a third to 36%. Offsetting this decline was the growing importance of wage income earned locally and from outside the village, which grew from only 22% of income to 34%, and a slight increase in the role of income from family businesses.

In our estimation of Equation (1) we wish to control for as many household covariates as possible, and these are described in Table 1. Of particular interest is household education, in this case the

> 18 The RCRE fixed point survey team has followed the definitions and protocols established as standards by the NBS Rural Household Survey unit since its inception in 1986. Further details and comparisons of the RCRE data source with other data from rural China are found in the main text and extensive appendices of Benjamin, Brandt, and Giles (2005)

_Inequality and Growth in Rural China_ , Page 12
be as T2 but additionally provide microfinance. This design was meant to unpack the impacts found in our earlier work on ELA in Uganda [Bandiera _et al._ 2018]. Given the crisis and imperfect program compliance with the design, we pool T1, T2 and T3 arms throughout. Common to all treatment arms is that ELA clubs provide alternative time uses and life skills to young girls and women.

A concern is whether ELA clubs were rolled out to any meaningful extent during the crisis. This is especially so given that BRAC’s core field activities in microfinance were curtailed during the epidemic.<sup>24</sup> To provide evidence on the matter, Figure 3 summarizes the findings from our survey to ELA club mentors, conducted in June/July 2015 when weekly cases had dropped far from their peak. Panel A provides time series evidence on ELA club openings. This shows: (i) 70% of clubs opened on time (by September 2014) and by January 2015 all clubs in treated villages had opened; (ii) the majority of clubs were continuously open through the crisis. Panel B details ELA 24Concerns were first raised in March 2014 on BRAC field activities, with microfinance activities being suspended in August 2014. They restarted in March 2015. BRAC found huge demand for microfinance in response to the Ebola shock, credit being considered critical for household recovery.

14
3|
|Sylhet|4|5.2|1.0|-|6.2|
|Total|64|73.7|10.6|15.7|100.0|

Source: Household Income and Expenditure Survey (2010) The sampling design for the HIES 2016/17 includes two different levels of stratification:

- (i) _<u>Division-Rural/Urban/CC</u>_ <u>: The eight administrative divisions in Bangladesh are initially</u> used to construct 16 sampling strata: one urban and one rural for each division. In addition, each urban sampling stratum in the four main divisions of Chittagong, Dhaka, Khulna and Rajshahi is further divided into two sampling strata: City Corporation and Other Urban. This brings the total number of sampling strata to 20: eight in rural divisions, eight in urban divisions, and four main CCs.

- (ii) _<u>District- Rural/Urban/CC:</u>_ As explained in detail below, PSUs in the HIES 2016/17 are allocated at the district level. Therefore, the sample is implicitly sub-stratified at the district level. Since there are a total of 64 districts in Bangladesh, the sample design includes a total of 132 sub-strata: 64 urban, 64 rural, and four main CCs.

10
# **6. Conclusion**

Motivated by the incapacity of existing models of grievances to predict and explain the recent wave of nonviolent uprisings (Chenoweth & Ulfelder, 2017), we test whether well-being data can explain variation in these uprisings above what is captured by traditional social and economic indicators. The aim is to contribute to the empirical literature on the effect of grievances on political conflict, by augmenting standard models of political conflict with measures of subjective well-being. Instead of focusing on armed conflict as is commonly done in the literature on grievances and conflict, we focus on nonviolent uprisings, a type of political conflict that often precedes violent conflict and can cause significant changes to a regime. We find evidence of an effect of grievances, measured using subjective well-being data, on nonviolent conflict, and a preliminary analysis suggests that this effect is not present for its violent counterpart.

The main concern when studying the relationship between subjective well-being and nonviolent resistance is reverse causality due to the possibility that nonviolent resistance affects public goods provision, which, in turn, might decrease subjective well-being. We address this in an instrumental variable analysis, where we instrument subjective well-being with deaths due to parasitic and infectious diseases. In a system GMM model, we complement this instrumental variable with the lags of our independent variables. Both the 2SLS and system GMM model confirm that a decrease in subjective well-being increases nonviolent uprisings.

In addition, we consider the interplay between subjective well-being and alternative theories of nonviolent conflict. There is no evidence that the effect of _suffering_ depends on the country’s stage of development, the political opportunities for rebellion or the ease of mobilization. This lack of evidence for the role of context in the relationship between _suffering_ and uprisings might be the result of data limitations, but it also implies the effect of a decrease in subjective well-being on nonviolent uprisings is relatively universal. In line with the idea that 35
# **Conclusions**

In this paper we propose a new framework to analyze aid effectiveness. Using World Bank firm survey data and OECD aid flow data we analyze whether aid targets areas which firms in developing countries have identified as obstacles for their growth and whether aid actually helps improve firms’ perceptions of those areas.

We find that aid, for the most part, does target the areas which firms have identified as obstacles; particularly aid funding _trade_ related projects is effective in targeting the right countries. Unfortunately, we find that aid does not do much to improve firms’ perceptions of those areas which aid was set out to improve, except for aid that targets the _business environment_ of the country. We also find that for each target area, smaller aid disbursements seem to be more effective at improving firm perceptions than larger disbursements.

As mentioned previously, there is room for further research. It would be interesting to look at the composition of the type of firms that find each area as an obstacle and its links to aid disbursements. More foreign firms than local firms might find the trade area as an obstacle for their growth and thus the close positive relation between firm perceptions and aid disbursement in the trade area. It would also be interesting to take a closer look at the type of donors. Does aid from multilateral donors match better to firm perceptions than aid from bilateral donors? We leave these questions for future research.

Since firms are the drivers of economic growth it is imperative that we understand the needs of firms in developing countries. If donor countries and agencies want to have an impact on growth, they need to make firms’ needs a priority in their aid agenda.

21
Table D.24. Effects of Guerrilla Territorial Control on Crimes during the War Period

||Total War Events|Total War Victims|Has a War Event|Has War Victims|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Guerrilla control|0.00660|-0.258|0.00180|0.00322|
||(0.0894)|(0.490)|(0.00264)|(0.00287)|
|Observations|3,652|3,652|3,652|3,652|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|0.0410|0.2130|0.00100|0.00200|

_Note:_ The table presents the results of estimating equation 1 for our outcomes related to crimes committed in the war period. Columns 1 and 3 report the total of events related to war and its probability, respectively. A war event can be a massacre, combat, bombing, or any other war event that caused victims from war. Columns 2 and 4 show the total number of victims and the probability of the census tract to have war victims. The unit of observation in all Columns is the canton level. The information was recovered from the registry of victims. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrillacontrolled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Robust standard errors in parentheses. *** p _<_ 0.01, ** p
the importance of teacher quality and demonstrate the external validity of the value-added findings to a low-income environment where the average quality of teachers is low but also the variation in measured teacher quality is likely much greater.

Our work complements the literature in two additional ways. First, we estimate the causal effect of one key component of teacher quality; namely the extent to which teachers master the knowledge of the subject they are teaching. Second, we propose an alternative empirical approach that allows us to estimate the impact of teacher content knowledge using cross-sectional data. We identify structural impacts building on Dee (2005; 2007) who uses

> 4 The effect size is the impact on standard deviations of student performance from a one standard deviation improvement in teacher value added.

> 5 Singh (2019) presents grade- (but not teacher-) value added estimates for Ethiopia, India, Peru and Vietnam. Buhl-Wiggers et al. (2018) present teacher value added estimates in the context of a randomized evaluation in Northern Uganda. They find that a 1 SD increase in teacher effectiveness increases student learning by 0.09 SD.

4
Specifically, for the observational data, we estimate where yit is the outcome variable for individual or household i at time t. Tit is a dummy variable that takes the value 1 for individuals/households who were surveyed using a weekly recall format at time t; γj represent coefficients on 12 seasonal dummies, one for each month of the year, and μi represents individual/household effects that are time-invariant but may be correlated with the format of the survey. The estimator β captures the effect of the recall period in the observational data under certain assumptions on the temporal autocorrelation of the outcome measures and the linear separability of recall from seasonal effects. In the case of the experimental data, we estimate a standard difference estimator with the treatment effect estimated as the interaction between the treatment group and the 2<sup>nd</sup> month of the survey, when the randomization into weekly and monthly recall was implemented. In all specifications, the lagged outcome variable increases the efficiency of the recall estimate and subsumes the individual fixed-effect. Given the nature of the experiment, 10
0.091|0.218|0.180|0.623|0.150|0.410|

_Source:_ 2010 Yemen RIC Survey; _Notes:_ Sampling weights are used; standard errors in parentheses. *, **, and *** denote coefficient significant levels at the 10, 5, and 1% respectively; the dependent variable for all models are revenue per full time employee equivalent in log form; according to Breusch-Pagan/Cook-Weisberg test for heteroskedasticity, robust results show that there is no heteroskedasticity problem in the data set.

In analyzing a household nonfarm enterprise’s performance, a gender issue is also taken into consideration. There is total of 291 male managers and 33 female managers in nonfarm 15
# **Child Stature, Maternal Education, and Early Childhood Development**

In an ideal world, recommendations for social policies aimed at improving welfare and especially maternal and child outcomes would be based on well-established evidence on the causal relation between the variables of interest. In reality, however, this is not feasible. The increasing number of randomized controlled trials (RCTs) that are useful for establishing causal inferences, combined with the limited ability of RCTs to address every policy relevant question, has not been able to satisfy the increasing demand for evidence-based policy advice. At the same time, the higher standards for the internal and external validity of the empirical evidence generated have increased the urgency to extract credible causal relations, rather than just partial correlations, between variables of interest based on the already available and accessible cross-sectional surveys.

Yet, studies with credible causal inferences based on regression analysis using non-experimental household cross-sectional data are not common, or even purposefully avoided by young researchers in social sciences aspiring to establish their publication record in their respective fields in the context of a proliferation of RCTs as the standard for causal inferences. Instead, the common practice in fields such as maternal and child health is to present estimates from simple regression models with few chosen controls for confounding factors acknowledging the potential role of other confounding factors left out of the model and publish with the qualifying statement that such estimates establish significant partial correlations but not causation between the variables of interest (Miller et al, 2015). Frequently the acknowledged relative weaknesses in the internal validity of these studies is compensated by efforts to strengthen the external validity of the findings by carrying out the same regression (or partial correlation) analysis across different countries with the same type of survey (e.g., Li et al., 2020; Jeong et al., 2019; and Kang et al., 2018).

This paper applies recently developed methods to address threats to internal validity using individual child data from two countries, the Republic of Congo and São Tomé and Príncipe,