rafmacalaba/data-use-mentions-extended huggingface dataset:
**|-0.089***|0|-0.205***|-0.278***|0|
|Piped drinking water|-0.010**|-0.037***|0.0331|-0.091***|-0.118***|0.0331|
|Improved sanitation|-0.009|-0.067***|0|-0.146***|-0.203***|0|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u>

**Table A18: Identifying the $3.20 poor based on covariates of poverty**

_Yemen 2014_

|**Category**|**Per capita**<br>**poor only**|**Square root**<br>**poor only**|**Diff.**<br>**p-value**|**Per capita**<br>**poor**|**Square**<br>**root poor**|**Diff.**<br>**p-value**|
|---|---|---|---|---|---|---|
|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Years of schooling|0.000|-0.002**|0.0417|-0.019***|-0.021***|0.0417|
|Asset index|-0
located. Additionally, studies based on surveillance data in other areas of Kenya find large drops in malaria rates. O’Meara et al. (2008) find that in Kilifi, Kenya hospital admissions for malaria decreased from 18.43 per 1000 children in 2003 to 3.42 in 2007. Okiro et al. (2007) find a similar decline across coastal Kenya.

_Maize yields_ . The MV sites experienced striking increases in maize productivity, from 1.9 to 5.0 tons per hectare in Sauri, 0.8 to 3.5 tons per hectare in Pampaida, and 2.2 to 4.5 tons per hectare in Sauri. The data needed to track maize productivity trends for comparison areas is not generally available. However, the Tegemeo Institute in Kenya has an ongoing panel survey of rural agricultural households in Kenya that makes it possible to follow increases in productivity over time. Kibaara et al. (2008) report that overall maize yields in the Western Lowlands region (near Sauri) were about 1.25 tons per hectare in 2007,<sup>12</sup> which is much lower than the 2008 yield in Sauri. This suggests that Sauri started out, before the intervention, with abnormally high maize yields for its region. Kibaara et al. (2008) also show that maize yields more than doubled across the Western Lowlands region, outside the MV intervention site, between 2004 and 2007—due to roughly a doubling in fertilizer usage, high-yielding variety usage, and the number of varieties planted, all of which occurred outside the MV intervention site during the same period.

_Child mortality_ . Although the MVP protocol (MVP 2010) stipulates that under-five child mortality is the primary indicator for the evaluation, child mortality rates are not included in the mid-term evaluation report. Because they calculate these rates for five-year periods preceding the data of the survey, it is not yet possible to calculate these rates for a pure period of MVP interventions. The DHS data, however, can
# **Acknowledgments**

The authors would like to thank the Turkey Ministry of Science, Industry, and Technology for their collaboration on the data analysis and access to the Entrepreneurial Information System database. We would also like to thank Daria Taglioni, Claire Honore Hollweg, Pinar Yasar, and Eavan O'Halloran for their comments and guidance.

# **Acronyms**

- ACF Ackerberg, Caves, and Frazer (2015) EIS Enterpreneurial Information System database EU European Union FDI Foreign direct investment GDP Gross domestic product GVCs Global value chains ICT Information and communications technologies MoSIT Ministry of Science, Industry, and Technology (Turkey) NACE Nomenclature des Activités Économiques dans la Communauté Européenne (the European industry standard classification system)

- OECD Organisation for Economic Co‐operation and Development PMR Product Market Regulation database R&D Research and development STRI Services Trade Restrictiveness Index TFP Total factor productivity TiVA Trade in Value‐Added database TL Turkish Lira USD United States Dollars VA Value‐added WDI World Development Indicators (World Bank) WTO World Trade Organization

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countries, although Colombia, Costa Rica, Uruguay, and Argentina appear to fare relatively better than Mexico and Chile.<sup>9</sup> This tale of several groups of countries also characterizes the regional sanitation panorama, although the composition of these groups is slightly different, with Costa Rica, the Dominican Republic and Paraguay now the strong underperformers, Brazil, Mexico, and Uruguay in an intermediate and rather weak position, and Bolivia, Ecuador, Peru and Colombia performing well.

Finally, for electricity, the region displays overall a much smoother concave picture, with the notable exception of Peru, whose poor performance makes it an outlier.

In addition, the use of household survey micro data allows for an unprecedented window into the relative treatment of households at different levels of the income distribution within countries. The comparison of within-country premia for each decile tells us whether the current access situation is progressive, meaning that a given country does relatively better delivering infrastructure to the lower rather than the upper part of the distribution, or regressive (Figure 3).

For water, there is again a clear dichotomy, between countries that display a rather regressive pattern (El Salvador, the Dominican Republic, Peru, Bolivia, and Paraguay) and those that display the opposite pattern, especially Colombia, Costa Rica, Chile, and Argentina. Note that to a large extent, these two groups overlap with the ones that emerged from the comparison of per capita GDP and average premium in the preceding figure. What this means is that for water, given the convergence to high access levels at the top of the income distribution, overall underperformance mostly stems from a failure to address service shortfalls for the lowest income quantiles.

This situation contrasts with that of sewerage. Indeed, in this case, a few countries fare badly overall, as witnessed by their negative premium in figure 2 above and the fact that the entire set of decile-level premiums are negative in Figure 3, despite boasting a rather progressive pattern. This is the case of Paraguay
can change their social category and with it values that reward and penalize certain behaviors.

There are striking parallels between STYL and socialization into militaries, street culture, gangs and armed groups. Such groups use similar techniques (appearance change, practice, modeling) to shape young men’s self-image and behavior (Vigil, 2003; Wood, 2008; Maruna and Roy, 2007). NEPI designed STYL to reverse this process.

We surveyed the men beforehand, a few weeks after the interventions, and a year later. Most had no fixed address, phone, or even name, and they moved frequently. Despite this mobility, we re-interviewed 93%. We rely on self-reported data since (like most poor and fragile states) there are no administrative data or arrest records. We did not necessarily trust self-reports, and so we validated behaviors such as drug use and stealing in a subsample.

We approached roughly 1500 high-risk men, and 999 agreed to enter the study. Of those assigned to therapy, nearly all attended at least a day, and two thirds completed it. The higher risk men were the most likely to finish.

> 4e.g. Gottfredson and Hirschi, 1990; Borghans et al., 2008. As an example of an intervention, the famous Perry Preschool Program emphasized the ability of young children to plan tasks, execute their plans, and review their work in social groups (Almlund et al., 2011).

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covariance matrix, inverting the matrix, adding up the rows of the matrix, and weighting each variable with its corresponding entry in the summed inverted covariance matrix. As a last step, the index is normalized again by dividing by the sum of the weights.

6. _Women’s empowerment index._ A 3-dimension index of women’s empowerment including (i) decisionmaking, (ii) economic empowerment, and (iii) political and social involvement. The decision-making index includes 12 measures of women’s agency through questions of whether women’s views are taken into consideration and/or followed on decisions related to household finances and expenditures (food, home improvements, household finances, buying or selling property, other non-food expenditures); their children (school enrollment, health visits, and marriage), and themselves (how many children to have, taking a loan, opening a business, working outside home). The women’s economic opportunities index includes whether the primary woman participates in market work, whether she is the owner or manager of a self-employment enterprise other than livestock or agriculture, as well as effective access to inputs

> 15Some questions were adapted to the local context as in Bedoya et al. (2019).

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<br>20072008200920102011201220132014201520162017201820192020202120222023<br>Year<br>Percentage<br>RON<br><!-- End of picture text --> <!-- Start of picture text --> Gross Nominal Minimum Wage Level (Left axis) Real Gross Minimum Wage Growth (Right axis)<br><!-- End of picture text --> _Source: Own estimates based on the Romanian Ministry of Public Finance and National Statistics Institute. Note: Since 2019, three statutory minimum wage levels were established, so the estimate represents the lowest of minimum wage levels (basic minimum wage). Changes in 2018/2019 also capture the government's imposition of mandatory social contributions transfer from the employer to the employee regarding income from wages. Change in real terms calculated using HICP inflation._

> 3 Source: <u>https://ec.europa.eu/commission/presscorner/detail/en/STAT_07_85.</u>

> 4 The year 2018 stands out as an anomaly in the context of wage increases, including the minimum wage, due to the government's imposition of mandatory social contributions transfer from the employer to the employee in the case of income from wages. Therefore, changes between pre-2018 and 2018 wages are not fully comparable to changes in other years, as they partly captured this policy change. For example, the minimum wage's rise from RON 1450 in 2017 to RON 1,900 in 2018 represented a 31% increase, influenced significantly by the transfer of contributions from the employer to the employee, constituting 22.75% of the total increase.

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# **1. Introduction**

Cross-country and bank-level evidence has shown the poor performance of government-owned banks, especially in developing countries (La Porta et al, 2001, Dinc, 2005), so that privatization could be expected to improve performance and thus boost efficiency of financial intermediation. Evidence from individual countries that have undertaken large privatization programs, however, has been mixed (Cull, Clarke, and Shirley, 2005; Megginson, 2005). For example, in Mexico in the early and mid-1990s, privatization outcomes were bad enough to prompt re-nationalization of the banking sector in the wake of the Tequila crisis (Haber, 2005). Banking sector performance eventually improved, but only after a second round of privatization in the late 1990s in which foreign ownership participation was encouraged. Initial attempts at bank privatization in the Czech Republic, and to a lesser extent Poland, were also not fully successful, at least in part because the state maintained relatively large shareholdings in the privatized banks and discouraged ownership by foreign investors (Bonin, Hasan, and Wachtel, 2005).

Assessing the effects of privatization across countries is made difficult by countryspecific circumstances that are hard to control for.<sup>1</sup> Researchers have therefore turned to country-level studies, which offer natural experiments if data availability allows the performance assessment of privatized banks before and after privatization, relative to other banks in the financial system and controlling for other bank and country-level but time-variant characteristics.

1 Otchere (2005) examines the effects of share issue bank privatizations for twenty-one banks in nine developing countries using pooled econometric tests. In the case of direct sales to strategic investors, this type of crosscountry analysis is more difficult, because share prices cannot be used as performance indicators and listed banks are generally subject to lower disclosure standards.

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and Friedman (2000) study is that it abstracts from changes in the households’ pattern of consumption in the wake of the financial crisis. In terms of a household’s demand schedule for a given good, there are no movements along or shifts 3 A few of the studies in this general category are: Deaton (1989); Ravallion (1990); Ravallion and van de Walle (1991); Levinsohn, Berry, and Friedman (2000); Case (2000); Deaton (2003); Minot and Goletti (2000); Dercon (2001); McCuloch and Calandrino (2001); Fofack, Monga, and Tuluy (2001). Summaries are provided in the tables of Reimer (2002).

4 This finding is reinforced by the study of Suryahadi et al. (2000) who estimate that the poverty headcount in Indonesia rose by 152% in urban areas, vs. just 57% in rural areas, during the 1996-99 period.

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women who do not work. Despite having very little schooling and low socioeconomic status as well as residing under strict social norms, the vast majority of women reported at baseline that they have high aspirations for their daughters’ education (completed secondary), marriage (after the age of 18), and employment (as teachers, in the public sector, or in a high-skill profession).

Among the other adults residing in the households (mostly husbands), 60 percent are working at any given point in time. Of those working, about 60 percent are casual day laborers and one-third are wage/salaried workers. Very few male adults are self-employed. Overall, participating households are poor, spend half of their income on food, and report high food insecurity.<sup>13</sup> Based on the asset index modeled on the national 2017 EDAM

> 11 Ninety percent of households identified the beneficiary’s husband as the head. Among the remaining 10 percent, either the female beneficiary or her daughter is the head.

> 12 The imbalance in food expenditure is due to a few observations at the right tail of the distribution (ofper capita food expenditure) for the control group. This imbalance disappears, however, when we work with natural logs. In our analysis, we introduced a dummy to indicate that the household belongs to the 25<sup>th</sup> percentile of the per capita food expenditure distribution in order to control for the nature of the imbalance at baseline.

> 13 Forty percent of respondents reported some form of food insecurity across six domains, which are captured in baseline survey via the following six questions about food insecurity in the last 7 days: (1) Have you been worried about your household getting enough food? (2) Do you rely on consuming less popular and/or less expensive foods? (3) Do you need to limit portion sizes? (4) Do you reduce the number of meals your household consumes? (5) Do you
randomly selected respondents answer only for themselves) and a proxy respondent approach (where a given individual answers on behalf of themselves as well as other household members). On the level of data collection, modules were designed to either collect data for each parcel separately (parcel-level data), which allows for disaggregation of the indicators by type of land tenure, or for all land in aggregate (aggregate-level data). For surveys that do not already collect parcel-level data, which is often the case 2 Global SDG Indicators Database, accessed April 26, 2022: https://unstats.un.org/sdgs/indicators/database/ 2
level of productivity and the results indicate that in the period under study productivity decreases slightly tended to take place in parallel to increases (or relatively low decreases) in capital. This is indicated by the negative point estimates, which are however insignificant. Furthermore, with the exception of the results based on FE estimates of TFP, our estimates show that state-owned enterprise adjusted their productivity levels at a higher speed than private firms.

Taking into account the developments in aggregate production during the period considered, which have been marked by the effects of the global recession, our results indicate that the likely reason for the polarization in firm productivity between state-owned enterprises and private firms is related to a potential missallocation of resources at the firm level. The loss of productivity in state-owned enterprises can thus at least partly be explained by sub-optimal decisions when it comes to labor allocation and capital investments during the period 2005-2010.

# **6. Soft budget constraints and inefficient resource allocation**

The results from the previous section indicate the existence of inefficiencies in state-owned firms due to labor hoarding and/or unproductive investments. This section provides results using an alternative method to investigate this issue further. We focus explicitly on potentially prevailing soft budget constraints (SBCs) in the sample of firms. SBCs in state-owned enterprises might arise if the government favours these companies _vis-à-vis_ private competitors. Such SBCs may take the form of access to below cost energy inputs, preferential tax treatment, preferential access to financing, subsidy of interest rate payments or preferential access to procurement tenders.<sup>15</sup> In order to empirically analyze whether SBCs are observable in the Belarusian machine building industry, we apply a simple matching approach. State ownership cannot be credibly considered

> 15Kornai, Maskin and Roland (2003) provide an excellent survey on SBCs.

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# _Question 21: Senior management time spent on negotiations with officials_

This question was asked to evaluate an amount of the productive time managers were forced to spend negotiating with the government bureaucracy. The answers show that only about 2 percent of managers in developed countries spent more than 25 percent of their time in negotiating with government officials. Developed countries are followed by CEE region, South and South-East Asia and LAC. The situation is worst in MNA, Sub-Saharan Africa and in CIS. In CIS countries more than a quarter of senior managers' time in the surveyed firms is spent on negotiation with officials about changes and interpretations of laws and regulations.

<!-- Start of picture text --> 21. What percentage of 30<br>senior management's time<br>is spent on negotiation with 25<br>officials about changes and<br>interpretations of laws and<br>regulations? 20<br>is<br>(1) less than5% El<br>(2) 5%-15% '° **<br>(3) 15%-25% El<br>(4) 25% - 50% D 5<br>(5) 50% - 75% *<br>(6) more than 75% 0<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text -->

**Percentage of firms who ticked (4), (5), or (6) for question 21**

Questions 22 - 25 were added to the questionnaire with a different goal in mind. The following four questions aimed a gaining indicators of the quality of government services such as roads, health care, etc. They were added to the questionnaire in order to take advantage of the cross country survey and to gain additional information about service delivery. The quality of these services may only indirectly be related to the uncertainty of doing business.

#
# 1 Introduction

In 2007-2008, the world economy entered the deepest ...nancial crisis since World War II. Countries around the globe witnessed major declines in output, employment, and trade. GDP in industrial countries fell by 4.5 percent while average GDP growth in emerging economies dropped from 8.8 percent in 2007 to 0.4 percent. The unemployment rate rose to 9 percent across OECD economies, and reached double digits in a mix of industrial and developing nations. World trade plummeted by over 40 percent in the second half of 2008, collapsing at a rate that outpaced the fall of total output.

The severity of what has been labeled as the Global Financial Crisis led many economists to explore its macro patterns and causes. Rose and Spiegel (2010a, b), for example, investigate the potential causes for the di¤erential extent of the crisis across countries. Using a large country-level dataset, they do not ...nd international trade and ...nancial linkages and other major economic indicators to be clearly associated with incidences of the crisis. Eaton et al. (2009), Levchenko, Lewis and Tesar (2010), and Chor and Manova (2011), among others, examine the potential causes of the great trade collapse, a phenomenon that received particular attention, and ...nd, respectively, manufacturing demand, vertical specialization, and credit conditions to play important roles.<sup>1</sup> Less explored in this debate is the pattern of micro economic responses to the crisis.<sup>2</sup> In this paper, we examine the di¤erential performance of establishments during the global crisis with particular emphasis on the role of foreign ownership. We investigate how foreign ownership a¤ected establishments’resilience to the negative economic shocks using a worldwide establishment panel dataset that reports detailed operation, location, and industry information of over 12 million establishments in 2005-2008. We exploit how multinational corporation (MNC) subsidiaries around the world responded
list of countries with WW2 casualties and COVID-19 statistics are shown in the Appendix (Table A1).

Recall from Section 2 that our hypothesis about the response to past big shocks is conditional on the probability of such shocks occurring ( ), which one would expect to differ systematically across countries. There may also be differences in the welfare function ) at pp given values of and . For example, richer countries will presumably be in a better position to tt uu(ττ, ss protect their citizens through the health care system. We will test the predictions of our model tt ττ ss controlling for GDP per capita (in constant 2011 PPP $) drawing on the World Development Indicators (World Bank 2020). We will also allow for differences in voice and accountability, using data from the World Governance Indicators (WGI) database produced by the World Bank

> 4 For example, losses for Balkan countries are imputed based on the losses of Yugoslavia.

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In 2006, the female share of employment across different industries ranged anywhere from 1% to 75%. The most female-intensive industries are garments, social and cultural services, education, and banking and insurance. Importantly, industry contribution to export growth in the 2006-16 period is negatively correlated with initial female intensity. We plot these statistics in Figure 2.

<!-- Start of picture text --> 20%<br>Manufacture of textiles and textile products 75%<br>Social and cultural services 32%<br>Mfc of Mechanical and Electrical Products 29% 15%<br>Agriculture, hunting, and fisheries 28%<br>Manufacture of chemicals 22%<br>Manufacture of food products 20% 10%<br>Banking and Insurance 17%<br>Education and public administration 11%<br>5%<br>Other manufacture 11%<br>Public works and buildings 11%<br>Production and distribution of water 10% 0%<br>Mining 9%<br>Mfc of construction products, clay, and glass 8%<br>Transportation 7% -5%<br>Real estate and entrepreneurial services 5%<br>Hotels and Restaurants 1%<br>-10%<br>0% 20% 40% 60% 80% 100% 0% 20% 40% 60% 80% 100%<br>Female share in employment, 2006 Female share in employment, 2006<br>Export growth contribution, 2006-16<br><!-- End of picture text --> Figure 2: **Gender composition and contribution to export growth across industries** . The left-hand side panel shows the distribution of female intensity across industries at the beginning of the period (2006). The right-hand side panel shows that industries that contributed the most to aggregate export growth (2006-16) tended to be male-intensive in the base year (2006).

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