rafmacalaba/data-use-mentions-extended huggingface dataset:
which reflects perceptions of the quality of public services, the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment (WB-WGI); availability of latest technology, which captures the perception of survey respondents of the extent to which latest technology are available in the country (WEF); a proxy for the quality of human capital, built as the average years at school weighted by an assumed rate of return to education (Penn World tables); CBR working time regulation index, which measures the extent of regulation of working time (Cambridge CBR); and trade-weighted average applied tariff rate (WEF). Variables on the left of the y axis indicate areas where Serbia has passed the frontier: number of hours to start a business (Doing Business); top marginal income and payroll tax rate (Fraser). Variables not included because of relatively small effects are energy use per capita (WDI) and Internet use (WEF). The EU accession dummy is also not included. The analysis relies on the supply-side methodology, which allows to decompose GDP growth as the sum of separable and independent components (capital, labor, and productivity) and to estimate the impact of key structural reforms for each input channel. The underlying linearity assumption may lead however to under/over estimate the importance of the reforms.

Better educational achievements, hence higher human capital quality, are associated to higher growth through a better use of labor and higher overall efficiency. Even though Serbia’s performance is relatively close to Germany’s, the beneficial effect of investing in raising the quality of human capital is particularly high. The quality of human capital measured as the average years at school weighted by an assumed rate of return to education (Penn World table) is in fact relatively high. Similar findings emerge from the new World Bank Human Capital Index (HCI) report. The HCI for Serbia is higher than the average for its region and income group and what
, & Lapuente, V. (2017). Careers, Connections, and Corruption Risks: Investigating the impact of bureaucratic meritocracy on public procurement processes. Journal of Politics, 79(1), 89–103. https://doi.org/10.1086/687209 Chong, E., Klien, M., & Saussier, S. (2015). The Quality of Governance and the Use of Negotiated Procurement Procedures: Evidence from the European Union (No. 2015–3; EPPP DP).

Collier, P., Kirchberger, M., & Söderbrom, M. (2015). The cost of road infrastructure in low and middle income countries.

Coviello, D., & Gagliarducci, S. (2017). Tenure in Office and Public Procurement. American Economic Journal: Economic Policy, 9(3), 59–105.

Coviello, D., Guglielmo, A., & Spagnolo, G. (2018). The Effect of Discretion on Procurement Performance. Management Science, 64(2), 715–738.

Coviello, D., & Mariniello, M. (2014). Publicity requirements in public procurement: Evidence from a regression discontinuity design. Journal of Public Economics, 109, 76–100.

Croom, S. R., & Brandon-Jones, A. (2005). Key Issues in E-Procurement: Procurement Implementation and Operation in the Public Sector. Journal of Public Procurement, 5(3), 367–387.

Dávid-Barrett, E., & Fazekas, M. (2020). Anti-corruption in aid-funded procurement: Is corruption reduced or merely displaced? World Development, 132, 105000.

http://www.sciencedirect.com/science/article/pii/S0305750X20301261 38
A note of caution is warranted here: the principal components procedure normalizes the mean of the index to zero for each country. Therefore, when comparing the "poor" in Kenya to the "poor" in Turkey or India it is important to keep in mind that the measure is relative, and 40 percent of the individuals are defined as living in "poor households" in every country. This paper does not attempt to generate an absolute poverty measure based on the asset index approach.<sup>9</sup> As a rough benchmark, Table 2 reports the percentage of the population living below the national poverty line, the dollar-a-day and the two-dollar-a-day poverty lines for the countries analyzed here as reported in the World Bank's World Development Indicators database (World Bank, 1999). The percent who live below a dollar a day clearly varies tremendously across countries, from below two percent in Morocco to almost 90 percent in Haiti. In an (unweighted) average across these countries the percentage living below this internationally comparable poverty line is about 40 percent - the percentage defined as the "poorest group" in the analysis in this paper. National poverty lines produce a much more stable proportion of each country defined as poor: again, the cross-country (unweighted) average is again about 40 percent.

What to take from this? Although using an asset index approach does not provide an internationally comparable cutoff (in the sense thait a dollar-a-day day does) it does identify a group of individuals in each country whose size is comparable to other breakdowns that are frequently made. In particular, using the 40 percent cutoff in this paper corresponds approximately to the percentage of people living below the national poverty line in many countries (Cameroon, Bangladesh, India, Nepal, Kenya, Philippines) or the percentage living under a dollar-a-day in Zimbabwe two-dollars-a-day in Brazil.1<sup>0</sup>

**9** An attemp **t** o do this would require benchmarking the index derived in
in some specifications). Despite the consumption boom induced by demobilization cash transfers, we find that the DDR program had no significant indirect effect on shopkeepers. This surprising result may be due to an increasingly competitive environment following the return of demobilized combatants. Table S.9 in the appendix indeed shows that shop ownership increased for FNL households between 2006 and 2010, from 12% to 45% ( _p_ -value = .12). In contrast, we find that shop ownership of CNDD+ households declined between 2006 and 2010, from 56% to 30% ( _p_ -value = .29). These changes go in the direction consistent with our interpretation but are not significant at conventional levels. Increased competition could also explain why we observe a reduction in the price of bottled beers in collines characterized by a high density of demobilized FNL returns and a positive evolution in the price of bottled beers in collines with a high density of CNDD+ returns (table S.8 in the appendix). Finally, the impact on public sector employees is striking and should be emphasized. The relative situation of public employees has sharply improved in collines with a high proportion of CNDD+ returns. On the contrary, public employees living in collines with numerous FNL ex-combatants are relatively worse off. This is not surprising if we remember that the actual president of Burundi, Pierre Nkurunziza, is the former leader of the CNDD-FDD rebel group; public employees working in collines with numerous CNDD+ ex-combatants may have been favored following his election in 2005.

An alternative explanation for our findings could be that collines hosting FNLs and CNDD+ ex-combatants were following different trends: a positive trend for FNL collines and a negative trend for CNDD+ collines. This explanation is unlikely for a few reasons. First, there is no evidence of strategic relocation of ex-combatants. The registers include data on village of origin and return. Comparing these for each of the ex-combatants, we find that around 88% have come back to their colline of origin, and 92
# **Contents**

|1.<br>**Introduction .......................................................**<br>I|
|---|
|2.<br>What do we know about the distributional impact of the crisis<br>_._<br>_3_|
|3.<br>Data**.6**|
|_3.1. 1998APIS survey: an opportunity?_<br>_.6_|
|_3.2._<br>_Direct self-reported measures of shock_<br>_.7_|
|_3.3._<br>_Alternative specifications of the shock variable_<br>_.8_|
|_4._<br>**Methodology..9**|
|**_5._**<br>**Results.14**|
|_5.1._<br>_What madefor lesser or greater impact?._<br>_15_|
|_5.2._<br>_Impact on poverty and inequality .16_|
|_5.3._<br>_El-Nino or El-Peso?.21_|
|_5.4._<br>_Income vs. consumption impact .21_|
|6.<br>Conclusion.24|
|Table 1: Macro-economic indicators, by quarter: 1997-1998 .27|
|Table 2: The incidence of crisis-related economic shocks .27|
|Table 3: The estimated consumption and income models (1998 APIS) .28|
|Table 4: Impact of the crisis on_consumption_poverty and inequality .31|
|Table 5: Impact of the crisis on_income_poverty and inequality .32|
|Figure 1: Some key macro indicators
with 8 questions from the NEO-5 factor personality inventory (Costa and McCrae, 1997). After one year, the therapy plus cash group reported 0.17 standard deviations lower post traumatic stress and 0.15 standard deviations lower neuroticism (though both are not significant).

**Self efficacy and esteem** The therapy did not treat these traits directly, and we have no theory to suppose a direct effect, but both could improve with self control and image change, and both could reinforce reductions in antisocial behaviors.<sup>47</sup> We asked eight questions from a standard locus of control questionnaire, which aims to measure the extent to which 46We used the five symptoms with the highest factor loadings in surveys of ex-combatant mental health by Blattman and Annan (2015).

> 47Negative self image has been linked with many aspects of negative behavior and counterproductive or extreme risk-seeking behavior (Coopersmith, 1967).

28
the _emigration life cycle_ (Hatton and Williamson 1994).<sup>6</sup> In other words, the propensity to migrate may increase, not decrease, as countries develop.

> 1 The poverty rate is based on the rate of $1.90 per capita per day. Poverty calculations are based on data from the World Bank Poverty & Inequality Platform: <u>https://pip.worldbank.org/home.</u>

> 2 COVID-19 is expected have an adverse impact on poverty, pushing some 100 million people into poverty in 2020 alone (World Bank 2020). 3 Unless otherwise mentioned, this paper will measure incomes in terms of GDP per capita in constant 2017 US dollars in purchasing power parity terms (2017 PPP$) as established by the Penn World Tables (PWT) 10.0 release (Feenstra, Inklaar, and Timmer 2015).

> 4 <u>https://ec.europa.eu/trustfundforafrica/index_en</u>

> 5 Even after adjusting for the population of the countries, low-income countries have about the same migration rate as middle-income countries. 6 See, for instance, Clemens (2014); Dao et al. (2018); Djajić, Kirdar, and Vinogradova (2016) for studies on the cross-sectional relationship between migration and development.

2
sure, there are high quality studies that analyze particular combinations.  See, for example, Cao and Lumineau (2015) on contractual and relational governance, which is the focus of many studies in the business economics literature. Hendley et al. (2000), Hendley and Murrell (2003), and Mike and Kiss (2019) are the only country-wide studies of which we are aware that use data sets to which the current paper's methods could be applicable. These data sets are all on single countries. The World Bank's Regional Project on Enterprise Development (RPED) collected cross-country comparable data on, inter alia, firms' attempts to solve transactional problems in seven African countries beginning in the early 1990s (see Fafchamps 2004). However, the questions used in the RPED surveys are not in a form that would make the responses suitable for estimation of governance structures. Unlike the current paper, none of the studies cited in this footnote use a sampling design and survey weights so that estimates are representative of a whole economy.

4
capita income across countries, slightly declining with per capita income across low and middle income levels and the rising rapidly from middle to higher income levels. This feature of utilization rates implies that UHCIs are low in the poorest countries, where

> 3 This definition of “better employment” came out of discussions with Michael Weber and Kathleen Beegle, and I thank them for both their constructive criticisms of the basic measure and helpful suggestions for refinements.

3
|(0.660)|(0.378)|(0.480)|(0.269)|(0.503)|(0.853)|(2.114)|
|Observations|1539|385|1539|385|1539|385|1170|271|
|R square|0.13|0.08|0.12|0.06|0.03|0.08|0.08|0.25|
|Control Mean|6.96|3.75|4.81|1.97|2.16|1.78|9.57|14.59|
|Std Dev|8.44|8.44|7.39|7.39|5.24|5.24|16.07|16.07|
|Sample|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|Techn.|Gener.|

**Panel B: Monthly income (pesos)**

||For|mal|Perma|nent|Temp|orary|Info|rmal|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Incentive|237.8<sup>_∗∗_</sup>|-78.4|284.2<sup>_∗∗∗_</sup>|-13.5|-22.4|-29.3|-2.3|413.9|
||(105.9)|(152.7)|(89.2)|(99.
12. We estimate that this drop in FLFP has occurred mainly due to the withdrawal of rural females. Most importantly, approximately 53 percent of the total drop transpired among rural women within the age group of 15 to 24 years.

The trends suggest that the drop in the FLFP rate has occurred across all socioeconomic and demographic groups. Patterns and trends in the relationship with consumption expenditure deciles indicate the existence of a negative association between the FLFP rate and income levels, although the decline in the FLFP rate over the years was higher for the less affluent. Analysis of the combined participation rates (CPR) shows that within the age group of 15 to 24 years, the decline in FLFP can be fully accounted for by a rise in educational participation. This full compensation results from that fact that there has been a larger response to income changes among the poor, rather than the wealthy, by sending children to school. For working age women as a whole, in the age group of 15 years and above, educational participation only partially accounted for the decline.

When considering the effects of marital status and membership in certain social groups on the FLFP rate, it is interesting to note that irrespecitve of their social status (SC, ST, OBC or general), the FLFP rate of married females is higher than unmarried females in rural areas. Marital status has differential effects across rural and urban settings. In comparison to currently unmarried women, the participation rate of married women is visibly higher in rural areas, while it is lower in urban areas. In terms of a combined effect, we find that rural married young women have dropped out of the labor force proportionately more than their urban counterparts. However, among unmarried women, such differences are not observed across age groups.

Overall, the labor force participation rate among all social groups dropped between 2004-05 and 2011-12. However, the drop in FLFP rates across all social groups is significantly more 33
not have a stock market may not be particularly concerned about the ‘Protecting Investors Index’ which measures the strength of legal protection of minor shareholders. Accordingly, it is important to bear in mind that a country’s ranking on a specific indicator may be worse compared to other countries because the issues it captures do not rank particularly high among the priorities of the business community.

To provide a more ‘subjective’ dimension to its more objective DB reports, the World Bank Group conducts large-scale surveys, the ‘Enterprise Surveys’, to identify country-specific obstacles to doing business.<sup>5</sup> These surveys are conducted irregularly, often two to four years apart, and ask business people to evaluate the sereneness of obstacles resulting from: the tax system (tax rates and administration), business licenses, and customs and trade regulations, access to land and finance, labor (regulation of the labor market and education of the work force), corruption, crime, theft and disorder, and the judicial system.

The Enterprise Surveys are not a perfect complement to the DB reports. For example, there is no DBI that captures the degree corruption or crime; on the other hand, the Enterprise Surveys have no close equivalent to the DB’s ‘Protecting Investors’ indicator. However, even though these surveys are conducted irregularly and do not always directly correspond to the DBIs, looking at the priorities of the local business community can help governments identify key reform areas. Thus unleashing the potential of the private sector is then likely to translate into increased economic activity and more jobs.

A more direct, but less country-specific, way of identifying reforms that maximize impact is to conduct a statistical analysis of the DBIs on key economic performance indicators. Djankov _et al._ (2006) put the DBIs to the test by examining the effect of countries’ DB rankings on economic performance.<sup>6</sup> They show that, indeed, a better investment climate results in stronger economic growth
The upper poverty line determines the nonfood adjustment on the basis of the consumption pattern of households whose _food expenditures_ are equal to the food poverty line. Their spending on nonfood consumption is added to the food poverty line, as shown by segment B in Figure 1.

# Data

We use two main data sources for our estimation of a poverty line for Brazil: the 2017/18 Household Budget Survey (Pesquisa de Orçamentos Familares; POF) and the Brazilian Table of Food Composition (Tabela Brasileira de Composição de Alimentos; TBCA). POF is a nationally representative semiregular survey on income and expenditures in Brazil, conducted every six to nine years. The sample of the 2017/18 round includes over 58,000 households in the whole country, comprising about 178,000 individuals. Data was collected between July 11, 2017, and July 9, 2018.

The survey collects very detailed data on the household budget composition and on the living conditions of the population, including the subjective perception of quality of life and information on the nutritional profile. It consists of seven questionnaires that collect information on demographics, work and income, quality-of-life perceptions, expenditures, and food consumption (at the household and individual level). For the estimation of the poverty line, we use data from the three expenditure questionnaires (POF 2-4), in addition to key demographic data collected in the general household questionnaire (POF 1). The expenditure questionnaires collect information on monetary consumption expenses as well as the value of nonmonetary consumption.<sup>6</sup> In addition, we need information on the caloric value of the food consumption expenditures incurred by households. This information can be obtained from the TBCA. It has nutritional values per 100 grams, including calorie intake, for an extensive list of meals and food items typically consumed in Brazil.<sup>7</sup> 3. Estimating the poverty line for Brazil We now describe the two main stages of our poverty line estimation: the food poverty line and the total
# Figure 12. Corporate vulnerabilities in EMDEs

# A. Nonfinancial corporate debt

# B. Nonfinancial corporate debt

<!-- Start of picture text --> Percent of GDP Number of economies<br>Latest available 2007<br>30<br>50 Latest available 2007<br>25<br>40<br>20<br>30<br>15<br>20<br>10<br>10 5<br>0 0<br>EMDEs Commodity- Commodity- 0-25 25-50 50+<br>exporting EMDEs importing EMDEs Percent of GDP<br>Source: Bank for International Settlements, International Monetary Fund.<br>Notes: Based on data for 48 EMDEs. Latest available datapoint is 2019Q1 for Argentina, Brazil, Chile, China,<br>Colombia, Hungary, India, Indonesia, Malaysia, Mexico, Poland, Russia,<br>Saudi Arabia, South Africa, Thailand, and Turkey; 2016 for Algeria, Malaysia, Peru, and Sri Lanka; and 2017 for<br>the rest. 2008 data are used for South Africa and the United Arab Emirates.<br>A. Unweighted average of nonfinancial corporate debt in 27 EMDE commodity exporters and 21 EMDE<br>commodity importers.<br><!-- End of picture text -->
defined. Therefore, the more weight authorities intend to put on the latter objective (the ―leaning‖), the more discretion likely is needed.

Be it principally rule-based or discretionary, a countercyclical regime would require the authorities to select a set of indicators and establish continuous monitoring in order to identify systemic risk and gauge the timing and appropriateness of policy actions. Potential indicators<sup>12</sup> may include general economic conditions (such as GDP growth); credit conditions (such as credit growth, credit-to-GDP ratio, and lending standards); asset prices (such as property prices); banking sector performance and soundness indicators (such as bank profits, loan losses, and capital adequacy); credit and liquidity spreads; leverage of financial institutions, corporations, and households; and banking balance sheet structure (liquidity and currency mismatches). The selection of indicators will need to take account of the specific national context. Obviously, countries prone to sudden capital movements would need to monitor capital flows more closely, and those with heavy reliance on commodity trade would monitor commodity prices. Also, many developing countries are experiencing financial deepening and structural changes, which means that the interpretation of data may be different at different stages of development and that sectoral statistics may need to be examined in addition to aggregate statistics.

Some of the above-mentioned indicators tend to be lagging indicators, which makes them less useful in identifying systemic risk. Data capacity constraints, such as data gaps and reporting lags, make the monitoring even more difficult. Data collection and analytical capacity need to be upgraded in most developing countries as a precondition for effective implementation of countercyclical measures. Sometimes authorities in developing countries may have to continue to rely on market intelligence and expert opinions to supplement official data collection.

The focus on systemic risk implies that policy coordination and governance arrangements are of paramount importance in effective implementation of countercyclical measures.<sup>13</sup> On the one hand, most measures are an extension of traditional microprudential measures for macroprudential