rafmacalaba/data-use-mentions-extended huggingface dataset:
levels (columns 4 and 5), and on median plant performance, measured as labor productivity and TFP (columns 6 and 7). By comparison, for mature and older cohorts we observe either no significant positive or adverse effects in Panels B and C.<sup>10</sup> The results suggest that the positive aggregate responses to the GQ observed by Ghani et al. (2016a, 2016b) were largely driven by the young cohort of plants.<sup>11</sup> Because of the imprecision of the estimates for the older plants, it is difficult to reject equivalence in the coefficients in most cases, with only three test rejecting

> 9 We construct the demographic dividend measure as the ratio of working age population to non-working age population using 2001 population census counts.

> 10 Effects in terms of employment for non-nodal districts within 0-10 kilometers of GQ are consistently positive and insignificant across all age classes. Thus, there is overall a minimal effect on labor demand among any age group, although these estimates are noisily measured. These results are in line with Sedláček and Sterk (2017) who estimate a general equilibrium firm dynamics model that suggests that aggregate conditions at birth, rather than post-entry choices, drive the majority of cohort-level employment variation.

> 11 Notice that the results on new establishments (or activity cropping along the GQ network) in Ghani et al. (2016) are distinct from our results on the average effects being driven by the young cohort of plants because: (i) these plants are not young by the time of completion of GQ; (ii) Unlike Ghani et al. they _do not_ represent new activity as our dataset drops all new plants that were born after the GQ upgrades began.

5
# 1. Introduction

Despite rapid increases in access to health services over the past two decades, maternal and child mortality remain high in many settings. Improving health outcomes requires not only that the subpopulations that most urgently need care get it, but also adequate quality of care (Das and Hammer 2014; Kruk et al. 2018). The Democratic Republic of Congo (DRC) almost perfectly illustrates the often weak link between health service utilization and health outcomes: although more than 80 percent of women receive antenatal care and deliver in facilities (Demographic and Health Survey 2014), the country remains among the ten with the highest maternal and infant mortality rates globally.<sup>4</sup> Equally striking is the steep socioeconomic gradient in health outcomes: under‐5 mortality in the bottom quintile is 54 percent higher than in the top quintile.<sup>5</sup> Although gaps in utilization between the poor and rich exist, they are substantially smaller in magnitude relative to the gaps in health outcomes.<sup>6</sup> This suggests that the poor do not receive the same quality of services or these services do not correspond as well to their needs. This paper focuses on the first hypothesis. We use recently collected data on care seeking and treatment quality to assess the relationship between wealth and quality of health services in the context of antenatal care in the DRC.

There are several advantages to focusing on antenatal care in studying clinical quality. First, antenatal care reduces maternal and neonatal mortality (Adam et al. 2005; Hollowell et al. 2011). Second, the content and quality of antenatal care varies widely (Hodgins and D’Agostino 2014) and can be assessed objectively based on the essential procedures list outlined in the World Health Organization (WHO) guidelines and officially followed by all providers in the country. Most components of an antenatal care consultation are not dependent on the condition or medical history of the patient, facilitating the construction of a relatively large sample of
.5|102.0|103.3|101.5|101.5|99.7|90.5|95.3|105.3|102.2|99.4|103.7|
|1 child|100.0|94.6|91.5|94.9|99.7|99.7|97.8|96.5|94.1|94.1|84.6|86.4|100.3|93.9|87.9|95.0|
|2 children<br>3 and more|100.0|117.0|109.6|110.5|105.7|101.1|102.7|102.5|110.9|102.6|92.2|102.5|111.3|113.0|116.4|116.3|
|children|100.0|132.4|132.2|126.3|123.3|120.2|147.1|135.2|123.6|131.8|128.6|136.9|121.4|126.4|125.1|125.0|
|singleparent|100.0|106.7|103.9|96.9|106.3|103.8|108.9|102.2|105.8|106.6|99.4|98.5|111.1|105.5|98.4|100.6|

Source: Authors’ estimates based on HSS data.

40
all urban growth in Africa during the 1960s and 1970s and about 25% of urban growth in the 1980s and 1990s (Brockerhoff, 1995). In Brazil, at the peak of its urbanization process, it is estimated that over 20 million people moved from rural to urban areas between the 1950s and the 1970s. In comparison, 20.5 million people in India (30% of national urban growth) moved from rural to urban areas in the 1990s (Census of India, 2005).

> 1 In this survey, we do not focus on amenity-based migration which explains the migration of students or of retired people. Nor do we focus on forced migration (due to war or civil unrest).

3
between urban and rural areas.<sup>16</sup> Freguglia and Menezes-Filho (2012) use data from RAIS to investigate the decrease in inter-regional wage differentials and show that nearly 2/3 of the differences in regional wage premia are explained by differences in composition, including unobserved ability. Hence, we should also expect that at least part of the convergence in regional gap be the result of a convergence in the distribution of skills across regions.

Trade liberalization may also have played a role in reducing gaps among minorities, a possibility investigated by Hirata and Soares (2016) with respect to the black-white wage gap. The prediction of taste-based discrimination models is that racial gaps should decline with increased competition (Becker, 1955). Hirata and Soares (2016) find that regions that were more exposed to foreign competition after trade liberalization in the early 1990s are also the regions with larger declines in the conditional racial wage gap between 1991 and 2000. Racial gaps may also have fallen as a consequence of decreased interfirm heterogeneity in wages. Gerard et al. (2018) show that 20% of the whitenonwhite wage gap is explained by nonwhites being less likely to work in firms with higher wages, and at least one third of this under-representation is due to unexplained preference

> 16We thank Liliana Sousa for suggesting this channel.

19
little support for it. Alternatively, heat may have negative impacts on legal sources of income (for instance, by reducing crop yields) and so increase the benefits of committing crime.

The hypothesis that heat increases criminal activity by reducing psychological control also has a long history. Work in psychology has documented the role of heat in increasing aggression for decades (Baron and Bell, 1976), succinctly summarized by Boyanowsky (1999): “aggression in heat is mediated by emotions, cognitions, and stress from affectivethermoregulatory conflict that produces violently aggressive behavior.” These results have been supplemented by more recent work in experimental economics showing the same (Alm ̊as et al., 2019). Observational work using billions of data points from Twitter has shown that heat increases negative sentiment (Baylis, 2020) in countries around the world. Arguments for a psychological mechanism linking heat and crime combine the observation that heat can increase irritability, anger, and hostility (Anderson, 1989; Anderson et al., 2000; Denissen et al., 2008; Larrick et al., 2011) with the evidence that a large share of crime, especially violent crime, is due to a non-rational response to stimuli (Heller et al., 2017).

Heat, thus, makes individuals more likely to respond to a given stimulus with violence.<sup>6</sup> This is particularly consistent with the evidence that heat has much larger impacts on violent crimes, or crimes of passion, than property crimes (Ranson, 2014; Heilmann and Kahn, 2019; Blakeslee et al., 2018; Mukherjee and Sanders, 2021). Heilmann and Kahn (2019) suggests that the psychological mechanism can also explain observed poverty and income gradients in the impact of heat on crime due to lower levels of investment in the development of noncognitive skills in childhood in higher poverty neighborhoods (Fletcher and Wolfe, 2016).

> 6Cohen et al. (2018) suggests that increased consumption of alcohol on hot days drives some of
in the next section.

# 3. Data and Measurement

## 3.1. NBS Data

The data used in the analysis have been collected by the China National Bureau of Statistics (NBS) at the fi rm level and then aggregated up to 600 industries by province, based on the 4 digit Chinese Industrial Classi fi cation. Before releasing this data to us, the NBS removed all ‘sensitive industries’ from the sample, and then we excluded agriculture, extractive industries and services in order to focus on the manufacturing sector. The information available includes the number of foreign fi rms, the value of output of foreign fi rms and the value of output of domestic fi rms. All variables vary by province, sector and time. Our sample covers the 1998-2001 period. It was not possible to include earlier years in the sample as data on the number of foreign fi rms were unavailable.

The fi gures indicate that a vast majority of foreign entry in 2001 occurred in coastal provinces (see Appendix). Seven out of twelve provinces in the coastal region saw the number of foreign investment projects rising by more than a hundred. Guangdong and Zhejiang were the most successful provinces increasing the number of foreign investment enterprises (FIEs) by about 600 each. Although midland provinces received much less net foreign investment, some provinces like Hunan recorded a net entry as high as 40, similar to some of the coastal provinces such as Beijing.

In terms of distribution of net entry across industries, both sectors producing consumer goods and industrial parts and components appeared to be attractive to foreign investors.

11
|0.197|0.197|0.152|0.0152|
|_R_<sup>2</sup>|0.066|0.082|0.045|0.049|0.011|
|Randomization inference p-value|0.072|0.21|0.096|0.022|0.71|
|Sharpened q-value|0.15|0.19|0.15|0.12|0.35|

Notes: Outcome 1 takes the value of 1 if the WEDP business was operating at the time of the endline survey (September 2021). Outcome 2 takes the value of 1 if respondent stated that they closed their original business, and their main economic activity is not another business they own. Outcome 3 takes the value of 1 if respondent reported starting a new business regardless of their WEDP business’s operational status. Outcome 4 takes the value of 1 if respondent stated their new business is in a different sector from their original WEDP business. Outcome 5 takes the value of 1 if respondent stated their new business is in the same sector as their original WEDP business. Psychometric scores in the IE sample ranged from 370 to 452, and the median score used a cut-off is 383 (note that 370 was the qualifying score to receive an uncollateralized loan). Includes a control for number of children in the households that was imbalanced between treatment and control at baseline. Observations represent number of individuals. Robust standard errors are in parentheses. * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001
establishments in the unorganized sector account for a small share of total manufacturing activity: in 2005, they represent 36% of establishments, 19% of employment, and 1% of output. Most of this activity is household based. For example, 90% of employment in women-owned establishments was in household-based operations in 2005.

While being a small share of total activity, women-owned establishments have experienced much more rapid growth during the 1994-2005 period than the manufacturing sector

> 10 The ASI sampling frame is based on business registers rather than the Economic Census.

12
8 approach (discussed below) can provide a better perspective on the degree to which lower levels of government are fiscally dependent upon the actions of the granting government, the latter indicator suggests the willingness of the granting government to give up at least some control over the actual utilization of funds. The changing role of grants over time and in response to changing external economic and financial conditions can also provide some indication of the importance accorded to lower levels of goverrment by the higher level.

While there are obviously weaknesses in the data due to nonuniformity 'n the meaning of grants across countries and due to the previously-noted problems associated with tax sharing arrangements, one reasonably consistent source of data is the Government Finance Statistics Yearbook of the International Monetary Fund. Table 1 displays the relative size of transfers (current plus capital) in total central government expenditures for both 1974 and 1984 in a nonrandom sample of 15 Third World countries. Shown also are the median percentages for each year- and an indication of whether the percentages rose or fell during the period.

Drawing broad-based generalizable conclusions from such data is unwarranted due to the arbitrary choice of this small sample; it is also difficult because of the variability in the entries shown. The ratios range from 0.8-24.0 percent in 1974 and from 1.3-28.7 percent in 1984. Although the median percentages declined slightly, six of the individual countries reveal some increase in the relative importance of transfer programs.
31

**Table 1. Summary of Rural Income Data From Egypt and Jordan, 1997**

||Rural|Egypt|Rural J|ordan|
|---|---|---|---|---|
|Source of<br>income|Mean annual<br>per capita<br>household<br>income(LE)(')|Percent of total<br>per capita<br>household<br>income from<br>source|Mean annual<br>per capita<br>household<br>income(JD)(b)|Percent of<br>total per<br>capita<br>household<br>income from<br>source|
|Nonfarm|414.1<br>(626.1)|42.2|323.4<br>(3,702.6)|50.6|
|Agricultural|241.3<br>(1,161.3)|24.7|54.5<br>(1,799.5)|8.5|
|Transfer|150.9<br>(360.8)|15.4|174.3<br>(3,510.8)|27.3|
|Livestock|92.6<br>(268.7)|9.4|-|-|
|Rental|81.3<br>(311.6)|8.3|87.2<br>(1,926.1)|13.6|
|Total|980.2<br>(1,480.7)|100.0|639.5<br>(5,988.6)|100.0|

- Sources: Egypt: Egypt Integrated Household Survey, 1997. Jordan: Jordan Household Income and Expenditure Survey, 1997
Indeed, empirical work on spousal disaccord (e.g. Annan et al. 2021; Twyman, Useche and Deere 2015) has found spousal response discrepancies to be correlated with other factors within and outside the household, suggesting that random measurement error, though important, is not fully responsible for the discrepancies consistently seen in spousal surveys. Below, we consider in turn the three main explanations for systematic measurement error advanced in the literature: asymmetric interpretation, asymmetric information, and asymmetric response to enumerator characteristics.

# <u>2.1. Asymmetric Interpretation</u>

A major source of measurement error may be differing interpretations of survey questions, which are asymmetric if interpretations vary by gender or other respondent characteristics. Scanzoni (1965) suggests that husbands and wives may be interpreting family situations differently based on their gender, and thus relaying different information in surveys. This sentiment is echoed by Granbois and Willet (1970), who suggest that survey questions should be made more specific to avoid disaccord stemming from differing interpretations. Ambler et al. (2021) find that wives are significantly more likely to report their own involvement in decisionmaking than their husbands, a scenario that may suggest different gendered interpretations of questions are partially responsible for response discrepancies. Using an item-response model for the responses of spouses in India, Pakistan, Malaysia, Philippines, and Thailand, Ghuman, Lee & Smith (2006) find that husbands and wives give systematically different responses to decision-making questions, which may suggest different interpretations of the questions. However, they find that these differences vary across communities and cultures in ways that make comparing survey results across countries or contexts difficult or counterproductive. In a qualitative study of spousal survey responses in Malawi, Miller et al. (2001) find that spouses may have differing definitions of the decision-making process; one spouse may consider a very short discussion before making a decision to be a true conversation, while the other may not.

Although the possibility of asymmetric interpretations of decision-making is well-described in the literature, few
Policy Research Working Paper 8776

# **Abstract**

Country-level census data are typically collected once every 10 years. However, conflict, migration, urbanization, and natural disasters can cause rapid shifts in local population patterns. This study uses Sri Lankan data to demonstrate the feasibility of a bottom-up method that combines household survey data with contemporaneous satellite imagery to track frequent changes in local population density. A Poisson regression model based on indicators derived from satellite data, selected using the least absolute shrinkage and selection operator, accurately predicts village-level population density. The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey to obtain out-of-sample density predictions in the nonsurveyed villages. The predictions approximate the 2012 census density well and are more accurate than other bottom-up studies based on lower-resolution satellite data. The predictions are also more accurate than most publicly available population products, which rely on areal interpolation of census data to redistribute population at the local level. The accuracies are similar when estimated using a random forest model, and when density estimates are expressed in terms of population counts. The collective evidence suggests that combining surveys with satellite data is a cost-effective method to track local population changes at more frequent intervals.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at vidhyasrajan@iimb.ac.in.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the
cell that has buildings on it. A little over 3.8 million cells are needed to cover the entire area of Ghana.<sup>1</sup> The variable _yit_ exist for four separate years: 1975, 1990, 2000, and 2014. Cells for each of these years have been carefully aligned using detailed GPS coordinates so as to form a panel dataset.<sup>2</sup> Variable _yit_ takes values between 0 and 1, with 0 meaning no building detected in the cell, and 1 meaning the cell is entirely occupied by buildings – i.e., as in a city center.<sup>3</sup> As shown in Table 1, most cells (98.5%) in 1975 are predicted to be unbuilt, which is hardly surprising given that Ghana at the time only had a national population density of 43.9 per square km. Builtup area grows over time as population increases to 119.4 per sqkm in 2014 and Ghana becomes more urbanized (urban population share increasing from 30% in 1975 to 53% in 2014). In 2014, 93.8% of cells were unbuilt.

For each cell _i_ in year _t_ we construct two measures of buildup in the cells surrounding _i_ . The first of these measures is denoted as _yit_<sup>_n_1andistheaveragebuilt-upareainthecellsimmediately</sup> adjacent to _i_ . Since the data is divided into a square grid, each cell on that grid in principle has 8 immediate neighbors, which constitute the set _n_ 1. We thus have: where by an abuse of notation _n_ 1 represents both the set of immediate neighbors and their number. Together with _yit_ , this variable captures the proportion of built-up area in an approximate cell 675 meters wide by 825 meters high (approximately 2025 by 2475 feet).<sup>4</sup> We also construct a variable _yit_<sup>_n_2thatcapturestheaveragebuildupincellsimmediatelyadjacentto</sup><sup>_y_</sup> _it_<sup>_n_1
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.94*** 0.92*** 0.68***<br>H<br>Kendall tau-b 0.81*** 0.78*** 0.53***<br>Spearman 0.92*** 0.78*** 0.74***<br>A<br>Kendall tau-b 0.81*** 0.63*** 0.64***<br>Spearman 0.93*** 0.92*** 0.68***<br>M0<br>Kendall tau-b 0.79*** 0.77*** 0.54***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C13.1. FOSD: Counting Vector C13.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C13.3. SOSD: Social Sustainability Index C13.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k
Table 11 presents the results of a simplified analysis of potential fuel cost savings from substituting HFO for diesel in the reference year for the five countries listed above.<sup>8</sup> The results indicate there could be significant cost savings for those countries with a high dependence on diesel, with the QFD being reduced by 25 percent in Rwanda to as much as 63 percent in Liberia. These results are intended to be illustrative of the short term opportunity to reduce cost of generation.

**Table 11: Simplified analysis of cost savings from switching from diesel to HFO based generation**

||**% of electricity dispatched**<br>**to grid generated using**<br>**diesel**|**Potential fuel**<br>**savings as % of**<br>**GDP**|**Base-case**<br>**QFD as % of**<br>**GDP**|**Potential fuel**<br>**savings as % of QFD**|
|---|---|---|---|---|
|Liberia|100%|0.22%|0.36%|63%|
|Burkina Faso|57%|0.45%|0.99%|46%|
|São Tomé and Príncipe|91%|1.77%|6.08%|29%|
|Comoros|100%|1.10%|4.13%|27%|
|Rwanda|50%|0.26%|1.04%|25%|

_Source_ : World Bank staff calculations based on utility data.

In the medium to long term, there are further opportunities to reduce the cost of service. While a quick and practical solution in many contexts, HFO-based generation still represents a relatively expensive source of electricity,