rafmacalaba/data-use-mentions-extended huggingface dataset:
The COVID-19 crisis has also led to a lower likelihood of being employed full-time, with men more adversely affected than women. Among those working, the pandemic has reduced the weekly hours of women and men by 2.3 and 2.6 hours, respectively, relative to the average total hours worked per week pre-pandemic. Moreover, we find that employed women and men earn 16 percent and 15 percent less in monthly earnings, respectively.

The contribution of this paper is twofold. First, findings from Indonesia add to the rapidly growing literature on the gendered impacts of the pandemic-induced crisis on labor markets. This paper employs the seasonality difference-in-differences approach to investigate gender differences in the impact of the pandemic on employment. However, it improves on earlier work by extending the baseline period to 2016 to capture more years of seasonal pre-pandemic trends. The paper also goes one step further than earlier studies, as it not only measures the overall employment impacts, but also explores how the pandemic has impacted the structure and composition of the labor market.

Second, the paper contributes to the literature on the impacts of the COVID-19 pandemic on employment in developing countries, as it also considers pre-pandemic employment dynamics in measuring the causal effect of the pandemic on employment. Due to social distancing regulations, regular face-to-face (F2F) data collection efforts were halted in many countries. In the context of low- and middle-income countries which predominantly relied on F2F surveys prior to the pandemic, incredible efforts were undertaken to quickly roll out remote data collection to measure the early impacts of the COVID-19 pandemic, such as with the World Bank’s HighFrequency Phone Surveys (HFPS) in more than 100 countries.<sup>3</sup> However, this new wave of data

> 3 Given the importance, albeit difficult circumstances, of monitoring the welfare impacts of the COVID-19 pandemic on households, the World Bank administered High Frequency Phone Surveys (HFPS) in more than 100 countries, including Indonesia (for more details: <u>https://www
more attractive for financial institutions. Finally, we find some limited support for the hypothesis of a “sectoral selection” as female ownership tend to be more prevalent in sectors that, on average across countries, tend to rely less on access to external finance. This is consistent with Gajigo and Hallward-Driemeier (2010) who find suggestive evidence, in four African countries, that there is a gender gap in capital at the start up. Although start-up capital gender difference is higher along sector than by gender, the median capital for male entrepreneurs is more than twice that of the female entrepreneurs. These selection biases might explain that once we focus on a sample of existing enterprises and control for firm size, sector and other firm characteristics we do not find any significant effect of female ownership participation.

The household analysis shows that while unconditional comparisons present a lower use of formal banking services by women, there is no significant gender difference once we control for other individual characteristics, including education, income, work status, geographic location, and education. While gender differences in the use of informal financial services vary across countries, women are not more or less likely to be excluded from any financial service than men, at least in our sample of nine countries. Lower income and education, a lower likelihood to be formally employed and their role within the household explain why, _prima facie_ , women are less likely to use formal financial services. These barriers that women face as individuals to access formal financial services might also explain the selection bias among female entrepreneurs mentioned above, though we cannot formally test for it. On the other hand, we find that women in several countries are more likely to use informal financial services.

Our paper contributes to the literature on access to finance along several dimensions. First, while most studies so far have been limited to one country, this is a cross-country exploration of gender differences in access to and use of financial services, both across 4
upon return. We use an alternative definition of undocumented migration. We define undocumented migrants as migrants who entered the destination country without having a visa or official document, for countries that required an entry visa, and as those who did not have a work contract for their employment abroad, for countries that had a visa waiver for Egyptians. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, conditional on return migration. The predicted logarithm of hourly wages for illegal and legal migrants are reported in the last row.

54
idiosyncratic distortions directly from the firm-level data and inferring the entry barrier so that the model’s average firm size for the Ghanaian equilibrium matches the average firm size in the formal-sector data. Given the idiosyncratic distortion

> 20 The average size in U.S. manufacturing is conditional on firms with 10 or more workers, to make it comparable with Ghana’s formal sector database

> 21 This is the database used by Cirera et al. (2019) to characterize misallocation in Sub-Saharan Africa.

34
A shadow price does not a market make. (Anonymous, quoted by Joskow [4])

# 1 Introduction

Over the last decade, network industries have undergone a dramatic transformation: competition has been introduced in industries that had long been viewed as text-book examples of natural monopolies, such as gas, electricity, and telecommunications. Production and transport have been unbundled to foster the introduction of competition: the capacity provider, i.e., the owner of the infrastructure, now often differs from the service provider. This situation raises new challenges for economists and policy makers. Chief among them is the design of institutions that will lead to "optimal" network expansion. Different arrangements have been suggested, ranging from indicative planning to decentralization of investment decisions through congestion pricing. Two questions lie at the core of the debate: is the infrastructure network still a natural monopoly? and what role could congestion pricing play? This article shows how simple economic theory sheds light on the argument, and informs the policy discussion.

In the early 80's, network industries in most countries were composed of vertically integrated utilities, subject to some form of governmental control. In the United Kingdom and in South America utilities were publicly owned. In the United States they were (for the most part) privately owned, and subject to regulatory oversight.

In many countries, today's industrial organization is dramatically different (see the survey by Klein [5]). First, utilities (or more exactly, their successor companies) are now privately owned. Second, and more importantly for this analysis, network industries are now competitive: production and transport have been unbundled, and vertical separation has been introduced. Usually, firms compete to provide service to customers, using the network infrastructure that often remains a monopoly. In the electricity industry, the transmission and distribution networks are sofar considered natural monopolies, while generation and retail sale to customers are competitive. In the United Kingdom, the monopolistic owner of the power transmission grid is not involved
This stands in contrast to the 2.8 plots per household reported by recall NPS and recall ALT households. This is only slightly larger than the number of plots reported in the three waves of the NPS (reporting plots in the 2008, 2010, and 2012 long rainy seasons) for rural farm households in the Mara or bordering regions, where the mean number of plots per household is 2.2. However, it is much smaller than even the smallest-ever number of plots reported by households in the weekly surveys.

The main reason households offer for making changes on the plot list obtained in the baseline is that some plots have been erroneously forgotten (table 8). Far fewer plots (91) in the weekly visit group are dropped over the course of the season; the chief reason given is that the plots were originally listed in error. The number of plots dropped over the course of the season is not sufficiently high to account for the discrepancy between the plots reported by recall and the plots reported in weekly interviews. Furthermore, households are more likely to add plots over the season than drop them. Even if we restrict the number of plots per household to those that faced no change over the season, that is, plots that were never dropped early or added late, on the premise that, perhaps, recall households only tended to remember to report those plots that were consistently present to be reported on, the weekly-recall plot gap remains (not reported).

Plot traits might reveal which plots are likelier to have been forgotten. We perform this reckoning by comparing the proportion of plots by characteristic across the four arms of the study. In table 9, we show, however, that, except perhaps for an overrepresentation of nearby plots in the recall surveys, plot characteristics are similar, suggesting that recall farmers do not systematically forget certain types of plots.<sup>22</sup> Instead, it may be possible that the plots that are forgotten are the prime responsibility or exclusive domain of
. _Capital requirements for agriculture in developing countries to 2050. In FAO Expert Meeting on “How to Feed the World in 2050.”._ Rome: FAO.

- Schultz, T. P. (2002). Wage gains associated with height as a form of health human capital. _Yale Economic Growth Center Discussion Paper_ (841).

- Schultz, T. P. (2009a). How Does Family Planning Promote Development? Evidence from a Social Experiment in Matlab, Bangladesh, 1977–1996. _Yale University, Economic Growth Center, New Haven, Conn_ .

- Schultz, T. P. (2009b). Population and Health Policies. _IZA Discussion Paper No. 4340_ .

- Simon, J. L. (1976). Population Growth May Be Good for LDCs in the Long Run: A Richer Simulation Model. _Economic Development and Cultural Change_ , _24_ (2), 309–337. https://doi.org/10.1086/450871

- Tuccio, M., & Paci, P. (2016). Why is growth less poverty reducing in Africa. _Jobs and Development Conference._ UN Women. (2016). _Leave No One Behind: A Call to Action for Gender Equality and Women’s Economic Empowerment._ Geneva: UN Women.

- UN Women. (2019). _Women Count: Liberia_ . Retrieved September 2020, from UN Women Data: https://data.unwomen.org/country/liberia

- UN Women. (2021). _Liberia: Country Gender Equality Profile._ Geneva: UN Women.

- United Nations. (2010). _World Population Prospects 2010, Online Edition. Rev. 1._ Department of Economic and Social Affairs, Population Division.

- United Nations. (2015). _World Fertility Patterns 2015 – Data Booklet (ST/ESA/ SER.A/370._ Geneva: Department of Economic and Social Affairs, Population Division.

- UNSD. (2020, August
importers still incur some search costs, these are typically brought down to a simple internet search. In any event, online search costs are not necessarily correlated with how remote markets are.

The heart of our paper is a dataset on cross-border transactions conducted over eBay, the world’s largest online marketplace. This dataset allows us to examine the effect of distance on international online trade. Our approach is similar to that of Horta ̧csu et al. (2009) who, using a sample of within-US eBay transactions, showed that the coefficient on distance on trade was much smaller online than offline. However, as noted by the authors, several caveats make their comparison with offline trade imperfect. One is that the products traded on eBay are mainly household durables, and thus comparison with total offline trade 2
# **11.  Summary and Conclusions**

In this paper we have investigated the determinants of indoor air pollution in Bangladesh, using monitoring data for a stratified sample of 236 households in the region of Dhaka. Extrapolating from our results, we have estimated indoor air pollution levels for a random sample of 600 rural, peri-urban and urban households in six regions: Rangpur, Sylhet, Rajshahi, Faridpur, Jessore and Cox’s Bazar.

We have used the results of our analysis to address several basic questions about air quality for poor households in Bangladesh: (1) Does air pollution from cooking primarily affect the women who cook and children who are with them in the kitchen? An appropriate answer to this question must consider both potential exposure (from pollution levels in kitchens and living areas) and actual exposure (from age/sex differences in time spent indoors, in kitchens and living areas. In a future paper, we will analyze the sources and consequences of age/sex differences in exposure time. In this paper, we can only summarize the implications of our results for potential exposure.

Overall, our results suggest that potential exposure is similar in kitchens and living areas. Pollution from cooking diffuses into living spaces rapidly and fairly completely in many cases, so that exposure is similar for all household members who are indoors during the same periods. Table 18 indicates that living-area PM10 is about 17% lower than kitchen PM10 on average, although regional differences in ventilation factors cause this percent difference to vary considerably. In absolute magnitude, all kitchen PM10 concentrations and most living-area levels are above 200 ug/m<sup>3</sup> in all rural areas. As we have noted in the paper, the sealing effect of mud-wall construction can pose a major 36
25 Trigonometric (also known as harmonic) regression models have been shown to address some of the limitations of the seasonal dummy and moving average deviation methods. They are parsimonious in the number of parameters to estimate and less prone to biased gap estimation, especially when the number of years from which to identify seasonal patterns is limited as is the case here (Ray et al. 2001; Kaminski, Christiaensen and Gilbert 2016; Bai et al. 2020; Kotu et al. 2019; Wassie, Kusakari and Sumimoto 2019). Gilbert et al. (2017) find the more parsimonious trigonometric method to be preferable for food price data. When applied to our least cost diet indicators, this translates into: where _C_ is the log diet cost observed, in nominal terms, for household (h) in year (y) and month (m). The cost in nominal terms is used since food expenditure comprises a large proportion of budget shares. Therefore, deflation factors (to domestic real or international PPP dollars) are sensitive to food prices and their use in seasonality analysis may understate the extent of seasonality (Gilbert et al. 2017). The seasonal factors can be computed as follows: The disadvantage of the trigonometric specification is that it imposes vertical and horizontal symmetry to the seasonality pattern. It will perform poorly if the time series is not well represented by that functional form. It is possible that the diet cost may not follow a symmetrical pattern if the timing of price fluctuations for nutritionally comparable food items are spread over longer periods or throughout the whole year; there could then be multiple local maxima and
facilitating women business leaders’ relative advantage. We thus expect that availability of internet would reduce women business leaders’ productivity performance disadvantage.

_The personal network hypothesis_ : The effects of female corporate leadership on productivity improvement would be especially negative where access to personal network is important, that is, where trade credit relationship is important, where access to electricity and to bank credit is scarce and important, but less negative and pronounced where the rule of law is stronger, and where the availability of internet is better _._

# **3. Data and Empirical Results**

The key data source is the World Bank’s Enterprise Surveys (WBES) in 579 cities of 103 countries. The WBES data are collected by the World Bank to benchmark the business climate in developing countries across the world and to understand the determinants of firm performance. In each country the survey is based on the universe of eligible firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the non-agricultural private economy in the country.<sup>11</sup> Stratification is based on two criteria: the sector of activity and firm size. Typically, the stratified sampling yields between 100 – 1,000 firms per country, with 108 firms for the median city. Industries range from manufacturing and construction to services and retail and wholesale trade.<sup>12</sup> We include data collected after 2006, although some WBES were conducted earlier. Prior to that year, there was considerable heterogeneity across countries in terms of the questionnaire format, sectors covered, and sampling methodology. Moreover, the samples for surveys conducted before 2006 were not generally representative. A complete list of variables and data sources is shown in Table 1; the summary statistics for our key variables are in Table 2. Our final sample consists of up to approximately 65,000 firms covering 579 cities in 103 countries.<sup>13</sup>

> 11 Thus, wholly
Policy Research Working Paper 9067

# **Abstract**

Embedding management and operational practices survey in a broader firm capabilities survey, this paper finds that an average firm in Croatia scores 0.532 on structured management practices, which is farther from the frontier (0.615 in the United States). This average, however, masks the wide heterogeneity in management practices among firms. Relative to advanced countries, a large share of firms in Croatia are badly managed. Management is particularly worse in services and more so in non-knowledge intensive services. Better managed firms show superior performance: improving the management score from the 10<sup>th</sup> decile to the 90<sup>th</sup> decile is expected to improve sales per employee by 36 percent, profits by 33 percent and the probability to innovate by 11 percent. Likewise, better managed firms more likely use sophisticated technologies and have a higher probability of accessing external finance. What drives firms to improve their management practices? As elsewhere in the world, global linkages of firms matter. However, unlike the evidence in advanced countries, management capabilities in Croatia is negatively associated with firm age, especially in services, indicating the possibility of allocative inefficiency, where learning and selection mechanism does not weed out the badly managed firms perhaps due to the lack of pro-competitive forces.

This paper is a product of the Finance, Competitiveness and Innovation 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/prwp. The authors may be contacted at agrover1@worldbank.org.

_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
taking Bhutan’s 2022 population as the base, this corresponds to about 45,100 people.<sup>7</sup> On the other hand, the probabilities reported in the off-diagonal refer to what we might call “mobile” patterns: people who experienced different poverty statuses in the two periods, either because they became poor or because they escaped poverty. Although the two trajectories are opposite in terms of social desirability, their combined probability provides an aggregate measure of mobility: overall, fewer than one in six people in Bhutan, or 15.7 percent, crossed the poverty line in either direction between 2017 and 2022, which indicates that overall poverty mobility was relatively low. However, most of it was upward mobility: only 3 percent of the population ended up falling into poverty over the period considered.

**Table 5. Poverty dynamics based on synthetic panel data, joint probabilities (percent)**

||Poor 2022|Non-poor 2022|
|---|---|---|
|Poor 2017|5.77***|12.71***|
|se|(0.47)|(0.60)|
|Non-poor 2017|3.00***|78.52***|
|se|(0.47)|(0.66)|

Note: *** p<.01, ** p<.05, * p<.1. Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

Table 6 offers a different set of measures of poverty mobility: conditional probabilities, _P_ ( _yi_ 2 ~ _z_ 2 | _yi_ 1 ~ _z_ 1 ), represent the likelihood of an individual’s status in the second period, _given_ their status in the first period.
overlapping quinquennial averages of data over the same two periods as for the time series model (although for a somewhat smaller sample of countries as dictated by data availability), and then generate forecasts for the remaining years in the sample which can be compared to actual outcomes. In order to benchmark the forecasts generated by these models against current practice, we also make some comparisons with long-term forecasts produced by the World Bank's Unified Survey in 1990. However, our primary interest is in the relative performance of the time series and growth models. **2** We assess the out-of-sample forecast performance of these models using standard summary statistics which capture their bias and mean squared error. These statistics suggest small median (across countries) differences in forecast performance of the alternative models, which vary with the forecast horizon. For example, there is some evidence -- consistent with our priors -- that the mean squared error of growth regression based forecasts is smaller at long forecast horizons (five years or more). However, these differences in median forecast performance are typically very small relative to the cross-country dispersion in forecast performance, casting doubt on the significance of observed "typical" differences. The relative performance of the altemative forecasting models is also very unstable over time within countries. We test for and do not reject the null hypothesis that the past relative performance of the growth model and the time series model in a particular country is independent of the future relative performance of the two models in that country.

These results indicate that neither forecasting model dominates, both across countries and within countries over time. Rather than attempt to choose a single "best" forecasting model, we instead ask whether there is value in combining the forecasts of alternative models. We implement forecast encompassing tests and find evidence that these approaches can "learn from each other", in the sense that the forecasts from both models are jointly significant in explaining actual outcomes. This is especially true at shorter horizons, and it suggests that there are potential benefits