rafmacalaba/data-use-mentions-extended huggingface dataset:
effects that are due to EU preferences. Finally, we then re-calculate our estimate of EU preference erosion after adjusting for the administrative cost threshold of 4 percent identified in the previous section. This has a substantial impact on the estimated scope for preference erosion.

Our estimates of the dollar impact of full preference erosion on real national income are shown in Table 6. We have included the impact on the LDCs in Sub-Saharan Africa, as well as other low-income countries in our sample (using the World Bank classification of countries by income). The tables reveal that EU preferences are very important, as a bilateral measure, for Sub-Saharan African countries. Given the current trade policy landscape, we estimate EU preferences to be potentially worth some $460 million annually to African LDCs. Asian countries benefit less, with the exception of Bangladesh ($100 million). These are therefore countries that stand to lose—all other things equal—from a move by the EU to lower MFN trade protection. Other developing country groups stand to gain—these are the “less preferred” in the overall hierarchy of preferences.<sup>24</sup> Although we are not convinced that the potential preference rents all actually accrue to the exporting countries (again, see Olarreaga and Özden 2005), our estimates provide a measure of what is at stake.<sup>25</sup> Our welfare estimates cannot be compared directly to the results obtained in recent partial equilibrium-based analyses such as IMF (2003) and Alexandraki and Lankes (2004) as these focus solely on trade effects. The IMF estimates the potential export revenue loss from preference erosion resulting from a 40 percent cut in protection by Canada, the EU, Japan and the US at some $530 million. This assumes full utilization of preferences and that developing countries get all of the associated rents. Alexandraki and Lankes (2004), focusing on middle-income countries only, conclude that potential erosion impacts are less than 2 percent of total exports for the countries that are most
**Figure 2. More than two thirds of developing countries are on track or close to being on track**

<!-- Start of picture text --> Distribution of countries by level of progress toward<br>MDGs<br>On target Close to target Far from target<br>80%<br>89 82<br>70%<br>60% 47<br>55 66<br>50% 25 58<br>40% 18 38 5253 4648<br>30% 21 36 30 39 3537<br>20% 17 12 19 25 23 27<br>15<br>13<br>10%<br>0%<br>Source: Authors’ calculations based on data from the World Development Indicators database.<br>Note: A country is ―close to the target‖ if its distance to getting on target (that is, its gap of trajectory) is smaller than the average<br>gap of all lagging countries. Otherwise, it is ―far from the target‖ (that is, its distance is greater than the average gap).<br>Proportion of developing countries<br><!-- End of picture text -->

**_How the country pattern differs from the aggregate picture._** The reference unit matters. Simple country averages that give equal importance to each country qualify the global story, which uses weighted averages that give more importance to countries with large populations. This pattern can go in both directions, for examples:

- The progress in reducing world poverty and in meeting the goal is essentially the result of rapid advances by China and India, with the absolute number of poor people falling rapidly in China. Despite the progress on the poverty goal, the average shortfall of lagging countries at 39 percent is still the biggest among the MDGs. Among lagging countries in the bottom half, extreme poverty also has the largest average gap at 67
# **An Analysis of Clinical Knowledge, Absenteeism, and Availability of Resources for Maternal and Child Health: A Cross-Sectional Quality of Care Study in 10 African Countries**

Laura Di Giorgio<sup>1</sup> , David K. Evans<sup>2</sup> , Magnus Lindelow<sup>1</sup> , Son Nam Nguyen<sup>1</sup> , Jakob Svensson<sup>3</sup> , Waly Wane<sup>1</sup> , and Anna Welander Tärneberg<sup>4</sup> _This paper is forthcoming in the journal BMJ Global Health._ JEL codes: I15, I18, O15 Keywords: health, service delivery, quality of care, Sub-Saharan Africa

- 1 World Bank, Washington DC, USA.

- 2 Center for Global Development, Washington, DC, USA.

- 3 Institute for International Economic Studies, Stockholm University, Stockholm, Sweden.

- 4 Center for Economic Demography and Department of Economic History, Lund University School of Economics and Management, Lund, Sweden.
<sup>2</sup>||0.801||
|Hansen J statistic<br>(overidentificationtest)|1.770|||
|(p-value)|(.880)|||
|Average of Partial R<sup>2</sup>|.477|||
|Average of Partial F's|129.47|||

*** significant at 1% level; ** significant at 5% level; * significant at 10% level.

- a. The instruments are semi-arid area dummy, port dummy, illiteracy rate (1970), ln(industry capital per worker, 1970), ln(distance to state capital)*ln(market pot. agric. land availability, 1970), ln(humidity), ln(avg. temperature), ln(rural pop. supply, 1970), ln(rural income opportunities, 1970), ln(market potential, 1970), and state capital and time dummies.

- b. GMM estimates are from the two-step efficient GMM in the presence of arbitrary heteroskedasticity and arbitrary intra-group (within-state) correlation.

- c. OLS regressions are with robust cluster standard errors. We assume the observations may be correlated within states, but would be independent between states.

- d. Average of Partial R<sup>2</sup> and Partial F’s are for average schooling and Ln(inter-city transport costs). Market potential and gravity measures are almost completely correlated with those in 1970 (Partial R<sup>2</sup> ’s are around .99).

33
# **1 Introduction**

_“A rebel with a cause is someone who breaks rules that should be broken. They break rules that hold them and others back, and their way of rule breaking is constructive rather than destructive. It creates positive change.” Gino (2018)_ Gender norms are shared expectations about how women and men should behave in a social group or culture. Restrictive or discriminatory gender norms creates social pressure that can adversely affect women and girl’s decisions around education, labor supply, marriage and fertility which are critical for women’s empowerment (Jayachandran, 2021). Shifting gender attitudes and norms to encourage greater female engagement in education and incomegenerating activity and male involvement in household production is necessary for reducing gender inequalities and to promote economic growth and development (Duflo, 2012).

Recently economic research has put greater emphasis on understanding how gender norms are formed (Alesina et al., 2013; Jayachandran, 2021), measured (Bernhardt et al., 2018; Bursztyn et al., 2023), and changed (Dhar et al., 2022; Bursztyn et al., 2020). Programs that directly challenge restrictive gender norms have also begun to be rigorously evaluated in some low- and middle-income country contexts. For example, interventions designed to change gender attitudes and norms (e.g. evidence from India by Dhar et al., 2022) or change how existing norms are perceived (e.g. in Saudi Arabia by Bursztyn et al., 2020) have been found to increase gender equitable attitudes and support women’s work. Similarly, programs that engage men in sessions of critical reflection on fatherhood and caregiving have led to higher levels of male participation in childcare and household tasks (e.g. in the Democratic Republic of Congo by Vaillant et al., 2020).

Our interest in this paper is to advance this nascent literature. We devise innovative behavioral games to examine social dynamics to supplement the survey data from a randomized
To the best of our knowledge, this study is the first to examine the role of management practices as a potential mediator of firm-specific responses to the current pandemic. This is despite a large literature on the role of management practices in steady-state firm performance (see for example (Bloom et al., 2016)), and suggestive evidence from earlier studies of firm responses to crisis (Pal et al., 2014; Aghion et al., 2020). The World Bank Enterprise Survey (WBES) collected panel data on firms before and after the onset of the pandemic in 16 countries, where the pre-COVID-19 panel includes a module on management practices. Although the full data set includes over 8,000 matched observations of firms, the management module in the case of manufacturing sector was restricted to firms with over 20 employees. Arguably, this is reasonable and consistent with the size threshold used in Grover and Torre (2019) and Grover et al. (2019), although it limits the subset of firms in the manufacturing sector. We use the subset of the sample that were administered the management module to examine the role of structured practices as a potential mediator of firms’ pandemic responses. In addition, we examine whether these effects are influenced by specific types of practices related to operational efficiency, incentives, targeting, and monitoring. A unique feature of our data set is that it spans both manufacturing and services firms, allowing us to examine how management’s role varies across these sectors.

We report four main findings. _First_ , structured management practices are associated with more limited downside impacts of crisis on firm performance in manufacturing but not in services. Better managed manufacturing firms, on average, experience a smaller reduction in sales. This effect is most pronounced among firms experiencing an above-median decline in sales. They are also less likely to close temporarily or permanently. Services firms, however, are more exposed to lockdown conditions and demand shocks such that management practices may have less of an opportunity to influence changes in sales
respond, and women in households with more children display a less negative response (although neither difference is statistically significant). Panel D does provide some evidence that the labor supply response to illness shocks among male workers is driven primarily by married workers.

# **3.3 Evidence of the main mechanism: Workers earn more when they work more**

A key assumption behind the mechanism that workers increase labor supply to compensate for income losses due to the illness of other household members is that workers actually earn more when they work more hours. For self-employed workers, this assumption is a priori believable, and we can also check this in our data by regressing weekly earnings on hours of work, conditional on worker fixed effects. Table A12 confirms this relationship: a selfemployed worker earns 2.5 additional cedis for every hour that she or he works, compared to an average total earning of self-employed workers of 116 cedis.

For wage workers, we must assume this relationship; recall that section 2.4 explains that we did not directly ask wage workers their earnings in the high frequency survey. So, if wage workers earn a fixed salary regardless of their hours, the premise that they work more to make up the lost income from other workers would not apply. However, many wage workers are in customer-facing jobs – 65% percent of wage workers are in services and 14% of wage workers are in retail – where we believe it is plausible that they can work and earn more if they desire.

Moreover, wage workers do work more in response to the illness of other workers in their household: the coefficient on Other Worker Ill���� �Self Employed��� in table 6 is positive, but small in magnitude. If wage workers do not actually earn more when they work more, a key question is why they do actually work more. While we argue that the ability to increase takehome pay is the most plausible reason for increased labor supply among wage workers in 29
**|
|LR test vs.logistic reg.|P=0.0025|-|-|P = 0.1146|P = 0.0082|P = 0.0000|P = 0.0000|

_Notes:_ East-1: CZ, PL, SK, EE, LV, LT, BG, RU, UA; East-2: HU, RO, SI; North: DK, FI, NO, SE; West-1: UK, IE, NL, and AT; West-2: DE, FR, BE, and CH; South-1: ES, PT, and IL; South-2: IT, GR, CY. The models include region-level random effects. For East-2 and North, these effects are integrated over the sample, i.e. population-averaged models estimated by the GEE method are presented. ―Regions‖ are NUTS level 1 for DE, ES, FR and UK; NUTS level 2 for the Nordic countries, IE, CH, CZ, HU, PL, SK, RO, PT, GR, and IL; NUTS level 3 for the Baltic countries, NL, BG and SI; 10 federal regions for Russia, and 26 [ _oblast_ level] regions for Ukraine. Marginal effects are based on fixed parts of the models and conditional on working as employee (i.e. not corrected for selection into paid employment). (*), *, **, * -** marginal e **f** fects significantly different from zero at 12%, 10%, 5%, 1% level, respectively. _Source:_ Calculation with ESS data.

56
wider set of countries than was previously possible.<sup>11</sup> This is not a concern for country-specific national surveys.

Another concern with self-reported data is that it is difficult to assess whether respondents are being accurate and truthful. In the WMS there are several strategies to elicit truthful information

> 11In the 2012 PISA the dataset included a PISA-built ‘leadership and management’ measure. This is distinctively different from ours, as it was based off a section of the questionnaire that was titled ‘management’ and contained only a small subset of questions. This index fails to take advantage of the full questionnaire and the information available elsewhere that also speaks to managerial practices used in the schools. More pertinently, PISA’s measure does not compare well to the (empirically robust) management index derived from the World Management Survey (see Liberto et al. [2015]).

8
Turning to the role of trust for the choice of saving instruments, we again control for risk aversion, expectations and experience of economic crises to lessen the probability that trust is a proxy for other determinants of saving instrument choice (Guiso et al., 2008). We find that those who experienced a financial loss during transition are more likely to have formal savings, in particular contractual savings (3 percentage points compared to a sample mean of 24%) and capital market savings (4 percentage points compared to a sample mean of 8%). The effect on bank savings is much smaller at 4 percentage points or 5% of the sample mean. This “preference” for non-bank formal savings is likely related to the experience of banking crises during transition. Experience of economic crises exerts the strongest effect on the propensity to hold a diversified saving portfolio at 6 percentage points compared. Those who think that the economic situation of their country will improve are 3 percentage points more likely to hold formal savings – at banks or in pension funds / life insurance.

In analyzing the role of trust for the choice of saving instrument, we distinguish between trust in non-financial and financial institutions. Trust in government does not affect the choice of saving instruments; however, those who trust the EU are 2 percentage points more likely to invest in capital markets, a sizeable effect compared to the sample average of 8%. Stix (2013) shows that mistrust in the banking system due to the experience of economic crises during transition is one of the main factors driving informal savings in cash. Figure 5 presents descriptive results on the four indicators of trust in financial institutions included in the Euro Survey. Trust in both foreign and domestic banks is low compared to a non-transition economy.<sup>6</sup> In seven of 10 countries, trust in domestic banks is higher than trust in foreign banks. However, trust in the safety of deposits is higher than trust in banks and trust in the overall financial system is higher than
Second, we examine whether there is heterogeneity within each exporter by product types – initially high vs low quality, complex vs non-complex and new vs existing exported products. Initially low- and high-quality products are measured using their initial quality gap – the distance between their quality and the quality of the frontier (the top 10% highest quality) in the first year. The quality gaps are calculated within each HS 10-digit product and for each exporter of those products.<sup>18</sup> Product complexity is defined following Levchenko (2007) and reflects the variety of different inputs used to produce one unit of the good. Specifically, we calculate the Herfindahl index of intermediate input use using US Input-Output tables for 1997 using 496 NAICS industries. We measure complexity as the negative of the Herfindahl index, so that higher values of complexity reflect more diverse inputs (as in Levchenko, 2007).<sup>19</sup> New exported products reflect those products a country first exports after 2000, and not exported at any point during 19901999 before our sample.<sup>20</sup> We estimate product heterogeneity by interacting our robot measure with a quality gap or complex product or new product variable.

We also consider whether certain robot types matter more for quality than others. To assess this we rely on a relatively unexploited aspect of the IFR data which collects information on robot applications at the country level. Following the engineering literature, we focus on several applications which are perceived to be used mainly for improving the quality of production, notably those equipped with laser guided devices. In particular, we examine heterogeneity in quality effects of laser guided robots to non-laser guided robots within each broad application categories from the IFR data (cutting, handling and welding). Because robot applications data are at the country-level, we first construct country-level robot stocks for each application, exactly as we describe in section 3 for (overall) robot data at the country-industry level. We approximate robot applications at the country-industry-level, by
Bottom Up._ Basingstoke, UK, and Washington, DC: Palgrave Macmillan and World Bank.

Turk, C., A. Mason, and P. Petesch. 2010. “Impacts of the Economic Crisis on Vulnerable Population Groups: Midcourse Findings from a Qualitative Monitoring Project in 12 Developing Countries.” World Bank, processed.

World Bank, Thailand. 2010. _Thailand Economic Monitor_ (November). Bangkok.

30
this indicator is also reflective of the prenatal, birth and early neonatal conditions, with earlier neonatal deaths being more closely associated with the pregnancy environment.<sup>28</sup> The long span and high frequency of the data allows for the variability in the exposure to violence during gestation. In this study, the gestation period starts at the conception date and ends at birth. To estimate the beginning of the gestation period -- the date of conception-- I subtract the pregnancy length from the date of birth.<sup>29</sup> The pregnancy length is reported in the Colombian DHS and corresponds to the time elapsed between the first day of the last normal menstrual period of the woman and the delivery date.<sup>30</sup> Since conception usually occurs about two weeks after the last menstrual period, I subtract two further weeks from the reported pregnancy length to more accurately capture the gestation period.<sup>31</sup> This period is divided into trimesters, which are calculated by sequentially adding three-month intervals from the estimated conception date until birth.<sup>32</sup> The last trimester can thus be shorter than three months.

Exposure to violence in utero is reported in Table 1. Of pregnancies ending in a live birth, 3.29% were exposed to a violent attack against civilians at any point during gestation. In each trimester about 1% of fetuses were exposed to an attack, with a slightly higher share exposed in the first trimester (1.31%). Most pregnancies were exposed only once; less than 7% of the exposed pregnancies experienced two attacks and none were exposed more than twice. Because of this, in the analysis I use a dummy variable of exposure.<sup>33</sup>

# [Table 1 here]

# **C. Specification**

I exploit the geographic — by municipality — and temporal variation — by date of occurrence and gestational age — of violent
m<sup>3</sup>||**215**|120.74|. – .|

_Source:_ Derived from the AICD national and utility database (www.infrastructureafrica.org/aicd/tools/data).

Progress in providing improved sanitation accelerated after 2003. According to the JMP, less than 20 percent of the Burkinabè population had access to improved sanitation in 2003, while close to two-thirds of the population engaged in open defecation practices. By 2009 close to 50 percent of the population were using latrines and, a small percentage, flush toilets (figure 12b). Yet it is widely accepted that Burkina is running short of meeting the MDG in sanitation.

22
earners in Egypt (USD, nominal, 2009 prices) We focus on the total compensation of senior executives, who represent 2 percent of survey participants and have the highest reported median compensation among survey participants.<sup>16</sup> Therefore, in principle, these households should be in the top 1% of households in Egypt’s household survey. However, since the median senior executive income is closer to the maximum income than to the median income of the richest 1 percent in the household survey, and the maximum income earned by senior executives is much higher than the maximum income in 14 We assume that household income reflects mainly the income of the household head and that the top households save at the average rate. The source for the average savings rate is the World Bank’s World Development Indicators (WDI).

> 15Household income is imputed based on information on household expenditures in Egypt’s 2009 household survey and the average saving rate in Egypt in 2009. The total pay of senior executives in Egypt is obtained from a global database of salaries and compensation for 2015. If anything we expect the data on executive pay to be on the conservative side. The values in the table are deflated and converted from EGP into USD using annual average inflation and exchange rate data from the World Bank’s World Development Indicators.

> 16The senior executives surveyed by Payscale are either chief executive officers (CEOs) or chief financial officers (CFOs) in Egyptian firms.

15
tractive investment destinations, which is not surprising, since as we discussed earlier, coastal regions have been the primary recipients of FDI in China. The negative coefficient in this fi rst differenced equation suggests that distance to ports has become more important over time.

The ‘total foreign fi rms’ variable is de fi ned as the total number of foreign fi rms in all industries in a province, rather than a particular industry as is the case with the dependent variable, and it enters as a one period lag. The coefficient is positive and signi fi cant. Provinces with a large number of foreign fi rms are more attractive to new entrants either due to agglomeration bene fi ts or due to a better investment climate that attracted the other fi rms. It seems that the competition for resources or congestion externalities have not yet outweighed the bene fi ts of being in a province with many other foreign fi rms present. Controlling for distance to ports and the lagged number of foreign fi rms reduces the market access effects slightly but leaves the supplier access coefficients unchanged.

# 5. Conclusion

This study examines factors driving entry of foreign fi rms in China, using a comprehensive dataset covering nearly all manufacturing industries at the provincial level during the period 1998-2001. The analysis is based on a new economic geography model, and thus focuses on the importance of market and supplier access effects both within and outside the province of entry, relative to production costs.

The fi ndings suggest that access to customers and suppliers of intermediate inputs are the key determinants of FDI in fl ows. The analysis also highlights the importance of taking 21