rafmacalaba/data-use-mentions-extended huggingface dataset:
_HOTTER PLANET, HOTTER FACTORIES_ _5_ outdoor workplace setting of data collection is different from most production activities in non-agriculture sectors. Our study builds on and contributes to this literature.

This paper makes two main contributions to the literature. First, it employs data from a global, standardized, and comparable survey of firms rather than an individual country. This presents opportunities to understand the potentially heterogeneous impacts of temperature change on firm productivity and allows the estimation of impacts across regions, climate zones, industries, and country income groups. To account for the potentially heterogeneous impacts of changes in temperature in colder and hotter climate zones, we estimate a nonlinear model using a binning approach in which we group together firms located in the same categories of temperatures. This may be the only study that estimates the impact of temperature change on productivity using a representative sample of firms in more than 150 countries, providing the most comprehensive study in the literature. The large sample allows for additional heterogeneity analysis by firm characteristics, including firm size and industry classification, country income group, and region of the world. This analysis can be used in the determination and distribution of costs and investments associated with actions to mitigate and adapt to climate change in global negotiations.

Second, this study uses high-resolution climate data capturing localized climate hazard impacts, which are the most relevant because the nature and extent of exposure and damage vary within a few thousand meters.<sup>5</sup> The study combines gridded historical climate data with WBES firm-level data, allowing better identification of impact within a relatively highly geographically specified location. Thus, we can estimate important heterogeneity, controlling for both withincountry and cross-country variations, going beyond cross-country to the level of subnational variations. In addition, the study contributes to the relatively scarce literature on the impacts of climate change on the non-agriculture sectors, especially at the firm level. Despite the relatively rich literature on the impacts of climate change on agriculture, studies on the impacts on the non-agriculture sectors are relatively scarce.

We document that the
Walle and Mu (2011). Their study controls for time-invariant unobserved heterogeneity, as well as potential time-varying selection bias due to differences in initial observable characteristics by combining double-difference and propensity score matching using data from project and control communes over three periods: a baseline in 1997, as well as 2001 and 2003. Highlighting the importance of comparing short- and long-term impacts, their study finds that most outcomes are realized at different stages over the period. Short-term effects included number of secondary schools and availability of food-related goods. Primary school completion reflected sustained growth between 1997 and 2003, while such outcomes as expansion of markets and availability of non-food related goods took a longer time to emerge.

# **3. Dynamics of roads and household welfare**

Estimating short-term versus long-term effects of a rural road development project requires an understanding of the underlying relationships between providing an improved road in a community and household-level outcomes (e.g., farm and non-farm production, marketing and transport costs, and income and non-income gains). Households in communities without a connection to a road network are likely to depend entirely on subsistence farming, in which case the hurdles and cost of marketing farm production are enormous.

With the introduction of a paved road, households typically enjoy reduced transport costs of goods and services, with a potential for substantial farm and non-farm income gains as a result of higher prices of agricultural output and reduced costs of inputs purchased from the market. They experience higher incomes and hence consumption by diversifying crop production, as well as undertaking off-farm income opportunities. Income and consumption gains may be substantial for those households who depend on the wage market because of increased wages induced by higher farm and non-farm production and labor demand. Furthermore, better roads can lead to higher 6
assess the contribution of each fiscal intervention to poverty and inequality. We can analyze an intervention in terms of its progressivity and marginal contribution to poverty and inequality. To do this, we use the Kakwani index (Kakwani 1977) to analyze whether a tax or transfer is progressive.<sup>18</sup> A Kakwani index for taxes will be positive (negative) if a tax is globally progressive (regressive), and a Kakwani index is positive if a transfer is progressive in relative terms. A tax is considered progressive if the burden proportionally increases with income. For each tax, the Kakwani index is defined as the difference between the concentration coefficient of the tax and the Gini coefficient of _market income_ . Similarly, a transfer is considered progressive if the entitlement proportionally decreases with income. In the case of transfers, the Kakwani index is the difference between the Gini coefficient of _market income_ and the concentration coefficient of the transfer.

To analyze whether a tax or transfer is equalizing, we use the marginal contribution to income inequality measured by the Gini coefficient. The marginal contribution measures the additional change in inequality due to a tax or transfer. The marginal contribution is the difference between the Gini without the fiscal intervention and the Gini coefficient of all income components combined. An intervention is equalizing when the marginal contribution is positive. When there is only a single intervention in the fiscal system, the Kakwani index would have been sufficient to determine whether the intervention is unambiguously equalizing. When there are multiple fiscal interventions, the relationship between inequality outcome and

> 18 Progressivity can also be assessed using Lorenz curves and concentration curves.

21
# **1 Introduction**

Female entrepreneurship is an important vehicle for women’s empowerment and economic growth (Duflo, 2012). Yet, women only represent 34% of all business owners worldwide (World Bank, 2020), and a gender gap in firm performance has been shown across a number of country contexts (De Mel, McKenzie and Woodruff, 2009; Fafchamps et al., 2014; Berge, Bjorvatn and Tungodden, 2015; Hardy and Kagy, 2018; World Bank, 2019). Labor economists who study the gender wage gap often highlight a childcare constraint and motherhood penalty to explain remaining gender inequalities in labor market outcomes (Goldin, 2021; Bertrand, 2020). However, in entrepreneurship, there is less evidence linking childcare and gender inequalities in firm performance, with a few exceptions (see for example, Delecourt and Fitzpatrick (2021)).

This paper examines childcare as a constraint to women’s entrepreneurial success. Specifically, we examine the impact of national school closure policies related to COVID-19 on business outcomes, disaggregated by the gender of the firm owner. We consider a governmentmandated COVID-19 school closure policy within a country as a shock to the domestic and childcare-related activities for households.<sup>1</sup> Overall, the COVID-19 pandemic has led to increased scrutiny on the public policies around childcare and norms around unpaid domestic and care work. For example, in the US there were frequent media reports of a greater exodus of women from the labor market, largely due to the increase in the domestic work burden as a result of the closures of schools and childcare facilities (Pitts, 2021).

The paper asks whether COVID-19 related school closure policies had a differential impact on the time spent on care and domestic responsibilities and business performance outcomes for male and female entrepreneurs. To shed light on this, the paper links entrepreneurlevel and firm-level data collected from business owners across 50 countries in 2020 and 2021 1According to UNESCO, more than 1.5 billion children were out
expenditure on inputs and thereby raise the production and exports, market potential, trade intensity, and agglomeration forces or co‐location effects (see Hummels and Hillberry, 2002).

The data for the main variable of interest ( GisTime�� ) were described in detail in the previous section. Other variables that are used in the econometric analysis come from usual sources. Export flows are collected from Comtrade at the hs6 level for the year 2013. Information on the gravity variables (common language or border or past colonial relationship and the strength of market penetration) comes from BACI data set produced at Center for Prospective Studies and International Information. Product‐level information on preferential and Most Favored Nation tariffs imposed by countries comes from the new ITC‐World Bank data set on preferential tariffs (Espitia et al, 2018). Data on the depth of preferential trade agreements come from the new World Bank database on the content of preferential trade agreements (Hofmann, Osnago and Ruta, 2017). Table A. 2 in the appendix presents the correlation between all the variables that are used in the estimations.

ii. Results Table 3 presents the results of the estimation of equation 1 for a set of 71 countries and 5,039 HS‐6 products in 2013. Regressions are estimated both using a linear model (OLS) and a Poisson pseudo maximum likelihood model (PPML) to control for the presence of zero trade flows. The results confirm a negative relationship between trading time and exports. The coefficient of the GisTime�� variable represents the percentage change in exports to a one hour increase in trading times. Results from the PPML model suggest that a one‐day increase in trading times decreases exports by 5.2 (0.00217*100*24hrs) percent on average. Our results are in line with what has been found in the literature on the impact of trading times on exports. Papers such as Djankov, Freund and Pham (2010) find that, on average, each additional day that a
0.86 kg, all things being equal. The -0.86 kg crowding out estimate obtained in this study is higher than the -0.22 kg estimated by Ricker-Gilbert et al. (2011). Because this study followed the same methodological approach as Ricker-Gilbert et al. (2011), the higher crowding out estimate found in this study suggests that crowding out has increased substantially over time. The increase in crowding out could have resulted from an increase in demand for commercial fertilizer that has grown in Malawi over the past ten years, and is now quit high (Ricker-Gilbert and Jayne 2017; Sheahan and Barrett 2014).

_Effect of Poverty Status on Crowding Out,_ ∆C nnpp,pp Table 7 presents estimates of crowding out (unconditional APEs) across the various poverty groups; and the difference in the crowding-out estimates across the various poverty groups are presented in table 8. Generally, the crowding out estimates for non-poor households is significantly higher than it is for poor and ultra-poor households. This is expected because non-poor farmers, as the descriptive statistics indicated, purchase significantly larger quantities of commercial fertilizer than poor households. Using the two-category consumption poverty classification, the estimates indicate that crowding out is 0.045 kg (5.4%) higher among non-poor households than it is among poor households; and using the three-category consumption poverty classification, crowding out among non-poor household is 0.034 kg (4.5%) and 0.069 kg (8.6%) greater than the estimates among poor and ultra-poor households respectively. The estimates also show that crowding out among poor households is 0.035 kg (4.3%) greater than ultra-poor households.

The variation in crowding out across asset poverty groups follows in a similar but more pronounced pattern. Based on the two-category asset poverty classification, crowding out is 0.130 kg (16.6%) higher for non-poor households than it is for poor households. In terms of the three26
expect in the absence of manipulation (48.80 percent - 49.26 percent); and it shows no tendency to increase in the post-ultrasound period. This contrasts sharply with the time profile of the sex ratio for higher order births preceded by a girl.<sup>23</sup> Second, Table 2 shows that families with firstborn boys and firstborn girls are similar along a number of observables.<sup>24</sup>

Third, we combine these two stylized descriptive patterns in a regression framework—see Table A.3, where firstborn sex is the dependent variable. Column 1 shows that the share of firstborn girls (as opposed to boys) does not change post-ultrasound, conditional on state fixed effects, linear time trends, and indicators of SES. In column 2, the specification is enriched to allow interactions between every SES indicator and the post-ultrasound indicator. This is relevant because previous work shows that the proclivity to commit sex-selective abortion varies systematically with SES (Bhalotra and Cochrane (2010)). The interaction terms are jointly insignificant, indicating no the costs of ultrasound and abortion were not prohibitive even for relatively poor households.

> 23Figure 3 reproduces Figures 1-4 from Bhalotra and Cochrane (2010).

> 24Table A.4 reports the corresponding summary statistics for the REDS sample. The definition of preultrasound and early diffusion period is the same as in Table 2 but the late diffusion period is shorter (1995-1999) because we use the 1999 survey. The sample sizes in Table 2 are smaller than in other tables because there is only one first birth per woman, but multiple births or multiple years per woman in the other tables. The reason we have more firstborn-girl women in more recent years in the data is that we construct a retrospective woman-year panel from three cross-sections and firstborn-girl mothers have higher mortality as shown in Milazzo (2018), causing fewer of them to appear in the sample when we project backwards from cross-sections of surviving women.

9
percent of their internal trade was free of tariffs). Trade data is the average of 1993, 1994 and 1995 whereas production data correspond to the industrial census of 1985 in Argentina and Brazil

> 1<sup>7</sup> If not we would not observe variation in tariff levels within industries.

> 18The test consists in verifying whether the error term of the weighted regression is correlated with the number of tariff lines in each industry. If this is the case, then one estimates consistently the variance of the common and individual error components by regressing the error of the weighted estimate on a constant and on l/nj, where **nj** is the number of tariff lines in industry j. To obtain asymptoticlly efficient estimates, one re-weighs the observations at the industry level using these variance estimates.

20
sup>2</sup> to over 13 kmph,<sup>3</sup> comparable to East Africa; in the parts less travelled and with poorer-quality roads, velocity is consistent with central African countries

**Table 3. Time and costs associated with transport within Sudan**

|Route|Mode of transport<br>Time (days)|Implicit velocity|
|---|---|---|
|Khartoum–<br>Malakal|Road and river<br>7|4.7|
||Road<br>5|6.7|
|Khartoum–<br>Juba|Road and river<br>13.5|6.3|
||Road<br>7.5|11.4|
|_Source:_AICD c<br>and FAO (2005|alculations based on data from Yoshino and oth<br>), and Keer- MISC (2007).|ers (2009), UNJLC|

(table 2). Overall, freight in Sudan moves at the pace of a horse and buggy.

Poor road transport connectivity between Sudan and South Sudan requires the use of multimodal transport during half the year and is associated with lengthy transit times. Roads, particularly during the

> 2 This is the estimated velocity of traffic between Khartoum to Kosti.

> 3 This is the estimated velocity of traffic between Khartoum and Port Sudan.

> <sup>10</sup>
We observe large heterogeneity in the share of self-employed individuals at home that remain self-employed at destination, ranging from 20% in the US over the period 2001-17 to 73% in Egypt in 1998-2006. Pooling together all the countries, 40% of individuals who are self-employed at home maintain this status after migrating. Thus, the majority of the self-employed do not stay self-employed after migrating. Nevertheless, their self-employment rate at destination is substantially higher than that of migrants who were wage workers at origin (10% become self-employed at destination), or who were not working at origin (only 6% become self-employed at destination).

# _Are the self-employed more or less likely to migrate?_

We begin with simple unconditional comparisons that tell us whether the average selfworker is more or less to than the worker or employed likely migrate average wage individual not working at origin. Figure 2 shows that, for the majority of the countryperiods considered, the self-employed are less likely to migrate than wage workers, and even more so than individuals not employed. Indeed, on average, 8% of selfemployed respondents migrates internally or internationally, as opposed to about 12% of wage workers, and 13% of the individuals with no job. We draw similar conclusions when we look separately at internal migration rates (figure 3) and international migration rates (figure 4), although tracking people who move abroad is a particularly difficult task, making international migration a rare outcome in the survey data available.<sup>11</sup> The considerable heterogeneity in country migration rates emerging from each of the previous figures is partly coming from place- and period-specific differences, but it is also due to the fact that our migration definition varies across surveys. For example, while in the Chinese panel data (CFPS) we can only measure migration as changes in home province or county, in the Egyptian panel data (ELMPS) we additionally account for moves between ”sheyakha”, a relatively small administrative division. We return to this more in detail
]|0.01|(0.10)|1,422|
|IP Assistance, Last 12 Months (=1)|0.08**|(0.03)|[0.14]|0.04|(0.19)|1,422|
|IP Assistance, Last 12 Months (USD PPP)|128.25***|(46.50)|[0.10]|28.74|(198.87)|1,422|
|**Panel B: Follow-up**|||||||
|Child SDQ score, std|-0.56***|(0.17)|[0.01]|-0.01|(1.00)|859|

_Notes:_ The table shows the regression results on statistically significant results from the endline survey. Each row is its own dependent variable. IP refers to the implementing partner. Monetary values are in USD PPP and are winsorized at the top 1% of values in order to limit the influence of outliers. The regressions also have month-by-year fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to various families in the pre-analysis plan. Focus Respondent Childcare & Chores (Hours) was pre-specified in two outcome families; the table reports the lower of the two q-values; the higher q-value is 0.02 .

46
Transition Economy: The Case of Rural China.” _Canadian Journal of Economics_ 35.

- Benjamin, Dwayne, Loren Brandt, and John Giles. 2005. “The Evolution of Income Inequality in Rural China.” _Economic Development and Cultural Change_ , 53(4): 769-824.

- Benjamin, Dwayne, Loren Brandt, and John Giles. 2006. “Inequality and Growth in Rural China: Does Higher Inequality Impede Growth,” University of Toronto Working Paper #237, and Institute for the Study of Labor (IZA) Discussion Paper 2344.

- Besley, Timothy and Robin Burgess. 2000. “Land Reform, Poverty Reduction, and Growth: Evidence from India.” _Quarterly Journal of Economics_ , 115(2): 389-430.

- Besley, Timothy, Jessica Leight, Rohini Pande and Vijayendra Rao. 2010. “Land Reform and Inequality in Southern India: A Natural Experiment,” Paper Presented at NEUDC 2010, November 6, 2010.

- Buck, Pearl S. 1933. _All Men are Brothers (translation of Shi and Luo, Shui Hu Zhuan [The Water Margin])_ . New York: J. Day Co.

- Chong, Alberto and Mark Gradstein. 2007a. “Inequality and Institutions,” _Review of Economics and Statistics_ , 89(3): 454-65.

- Chong, Alberto and Mark Gradstein. 2007b. “Inequality and Informality,” _Journal of Public Economics,_ 91(1-2): 159-79.

- Dayton-Johnson, Jeff. 2000. “Determinants of Collective Action on the Local Commons: A Model with Evidence from Mexico.” _Journal of Development Economics_ , 62(1): 181-208.

- Deaton, Angus. 2003. “Health, Inequality, and Economic Development.” _Journal of Economic Literature_ , 41(1): 113-58.

- Deaton, Angus. 1985. “Panel Data from Time Series of Cross-Sections.” _Journal of Econometrics_ , 30(12): 109-26

- Devereux, Paul. 2007. “Improved Errors-in-Variables Estimators for
ZAMBIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Zambia’s infrastructure situation is more hopeful than that of many other African countries. Infrastructure spending needs—though large—are not beyond the realm of possibility, and Zambia’s resource wealth and relatively well-off population provide a more solid financing basis than is available to many other countries. Zambia’s infrastructure funding gap—though substantial—can be dramatically reduced through measures to stem inefficiencies and lower costs.

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in some 40 Sub-Saharan countries, including Zambia. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, and WSS—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for Zambia, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that Zambia’s economy is dependent on mining, it will be benchmarked against other resource-rich economies in Africa, as well as against other African low-income countries. Detailed comparisons will also be made with immediate regional neighbors in the Southern Africa Development Community (SADC).

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the five years 2003–07, Zambia’s economic performance was relatively strong; by 2007 it