rafmacalaba/data-use-mentions-extended huggingface dataset:
|||||0|**1**|**1**|**1**<br>0|0|0.2|98.9|**1**|0|0|0|0|0|0.2|99.1|
|22|||||||||||0|0|**1**|**1**<br>0|0|0.2|99.1|||||||||

_Source_ : World Bank staff calculations using the Ghana Living Standards Survey.

_Note_ : There are 35 distinct combinations of energy sources in urban areas, 43 in rural areas, and 48 nationally. The combinations are listed in order of decreasing frequency for the first 99 percent of all households in each area. Other energy sources not captured in this table cited by respondents were sawdust, animal waste, and other fuels. Elec = electricity; kero = kerosene; char = charcoal; res = crop residues; % = percentage of households; cum % = cumulative percentage of households.
developed countries have also focused on domestic banking reforms. Using establishment level data from India, we study the impact on gender-based outcomes and segmentation in both manufacturing and services emanating from a range of pro-competitive reforms including not only India’s mostly exogenous trade liberalization episode, but also industry-specific domestic reforms and infrastructural reforms.

We are not aware of any such studies for developing countries in Africa and/or South Asia. Yet India is a very important laboratory for studying these effects. First, India has a history of discrimination, broadly speaking, be it by gender, caste, or otherwise. Moreover, this matters for India’s economic growth and development. For instance, Khera (2016) finds that an improvement in public provisions (such as better water facilities, sanitation development, and access to electricity) which increases female labor participation by 1.5% would lead to a 1.4% gain in GDP. Lawson (2008) estimates that India’s per capita income could be 10% to 13% higher than under the baseline scenario of unchanged gender inequality in 2020 if the gender gap decreases by 50% from its 2008 value. The widely followed crimes against women in India and the growth of female political set-asides speak to this important struggle of mainstreaming gender parity in India’s growth narrative.

3
shortages of food and energy are likely unless production gaps are compensated by increased production elsewhere (Lin _et al._ 2023). One concern has been that countries could use the crisis as a pretext for adopting protectionist policies (Ben Hassen & El Bilali 2022) that, while possibly yielding political benefits in the short term, could create negative externalities that would make achieving global food security more difficult (Glauben _et al._ 2022).

The results of macro models depend significantly on the quality and granularity of the data used which are often derived from satellite imagery or household surveys. Remotely sensed imagery allows to identify changes in crop cover over time and separate direct conflict effects due to active fighting, mining, explosion of ordnance, heavy vehicle movement, and deliberate burning of agricultural fields from indirect ones at a granular level. Kussul _et al._ 2023 infer direct effects for 2-week intervals since the start of the war using satellite imagery and interpretation by means of machine learning as well as manual classification.<sup>7</sup> Using this information together with a 4-year panel (2019-2022) of cropped area for 10,125 village councils aggregated up from the field level, points to an area reduction of slightly less than 1 mn. ha (or 10%), well

> 5 Ukraine accounts for 10% of world trade in wheat, 42% trade in sunflower oil, and 16% of maize.

> 6 In March 2023, 3.5 million tons of grain and oilseed crops were exported through the Black Sea, adding to 2.8 million tons exported via rail, road, and river, bringing the total amount of crops exported in the 2022/23 marketing year to 44.7 million tons about 70% of the potential of 64 million tons (84% grain and 16% oilseeds), comprising the 2022 harvest as well as stocks that had been in storage in February 2022.

> 7 High resolution imagery is needed to detect craters from ordnance explosion, a measure
no impact on weight.

> 13 For the expected effect on infected children, we use TMSDG’s random effects estimate of 0.75 kg (95% CI: 0.24, 1.26). Note that these calculations use estimated prevalence for two studies in TMSDG's sample that do not report prevalence within their study – Alderman et al. (2006) and Awasthi et al. (2008). See Section 4 for more details. 14 In particular, suppose that our beliefs about the average treatment effect on infected children are informed by TMSDG’s results and are characterized by ). Under the assumptions of this exercise, in an MDA setting with prevalence , our beliefs about the MDA treatment effect would be characterized by ). The hypothetical data we consider, then, are NNP NNSSNN(0.75, 0.26<sup>2</sup> consistent with our beliefs about MDA treatment effects. Results we obtain using these hypothetical pp data are results that we consider reasonable to expect given TMSDG’s results on infected children.NNP NNSSNN(0.75pp, 0.26<sup>2</sup> pp<sup>2</sup>

13
** is monthly profit across all businesses operated by the entrepreneur, winsorized at the 1st and 99th percentiles, and coded as 0 for those without businesses; **All Profits conditonal on survival** is real profit in all businesses conditional on the firm operating; **Real sales in main business** and **Real sales in all businesses** are both last month's sales, winsorized at the 99th percentile, and coded as 0 for those without businesses. Sales in other firms not collected in earlier rounds. **Sales in main business conditional on survival** is real sales in the main business conditional on the firm operating; **Main business profit and sales index** is the average of standardized z-scores of the main profits and main sales variables; **Total labor income** is winsorized real monthly profit in all businesses added to real income from wages and other work in the last month, also winsorized at the 99th percentile.

21
India).

A longstanding concern in the literature has been whether the observed correlations are primarily mechanical, driven largely by genetic transmissions from parents to children (see, for example, the discussion by Black and Devereux (2011)). We address this issue in two ways. First, we develop a simple but plausible approach to check whether the estimated intergenerational persistence could be due solely to genetic correlations. The approach combines the recent evidence on intergenerational correlation in cognitive ability from the behavioral genetics and economics literature with the Altonji, Elder, and Taber (2005) biprobit sensitivity analysis (henceforth called augmented AET (AAET) analysis). Second, the theoretical foundation for the empirical specification allows us to use economic mechanisms as a test for the importance of parental economic choices.

The substantive conclusions of this paper can be summarized as follows. Intergenerational educational mobility was substantially lower for sons in rural India compared to sons in rural China for the cohorts that went to school in the 1970s-1990s. The point estimates of intergenerational persistence are larger in nonfarm households in both countries. The difference between farm and nonfarm households is statistically significant in rural India, but not in rural China, and this conclusion applies to both the slope and the intercept of the intergenerational educational persistence regression. The evidence suggests that while parent’s education and nonfarm occupation were complementary in determining a son’s education in rural India, they were separable in rural China.<sup>14</sup> The long-term variance in schooling was significantly higher for the sons born into nonfarm households in rural India, and structural change from agriculture to the nonfarm sector contributed to educational inequality. The evidence from the augmented AET sensitivity analysis shows that the observed persistence in rural India cannot be accounted for by genetic correlation alone, while the persistence in China can be explained away by an ability correlation of plausible magnitude.<sup>15</sup> An important advantage of this approach is that the conclusions refer to the whole population of interest, rather than a
Rather than estimating emissions equations across many sectors in one country, we analyze explicitly-observed abatement activity in one sector -- pulp and paper -- across several countries. We also distinguish between possible Pigouvian and Coasion interpretations of abatement. In Section 2 of the paper, we review the Pigouvian and Coasian approaches and introduce the distinction between 'formal' and 'informal' regulation. Section 3 presents our analysis of the plant-level data. Section 4 summarizes the paper.

# 2. PIGOUVIAN **vs. COASIAN REGULATION**

The Pigouvian approach places responsibility for an externality on its generator (the polluter) and imposes a pollution tax (effluent fee) as the policy prescription. Its optimality rests on a set of restrictive neoclassical assumptions.10 Most importantly, the Pigouvian approach does not address the reciprocal nature of externalities. Nor does it analyze the abatement behavior that will be induced by the Pigouvian tax.

In contrast, the Coasian approach assumes that an externality is reciprocal (i.e., both polluter and pollutee cause

> **10** See Baumol [1972]. This optimality of the Pigouvian **tax holds whether** participants merge or operate independently. Buchanan [1969) and Davis and Whinston [19731 question this optimality in the case of monopoly and/or oligopoly. However, for a workably large number of participants, the Pigouvian results are unassailable.

8
Policy Research Working Paper 10861

# **Abstract**

This study examined whether psychosocial stimulation, parental distress, and enrollment in pre- primary education had different associations with early child development outcomes for boys and girls in Pakistan. Using data from a nationally representative phone survey in Pakistan, it assessed these relationships for two cohorts of children— those aged 0–35 months and those aged 36–72 months. The study found that among very young children (0–35 months), lower parental distress and higher psychosocial stimulation were strongly associated with better child development for both boys and girls. Girls were more sensitive to higher levels of parental distress and lower levels of psychosocial stimulation than boys. On average, girls in the sample fared worse compared to boys in their developmental outcomes in the context of low levels of stimulation and high levels of parental distress. Among the older age cohort (36–72 months), lower psychosocial stimulation and higher parental distress were each similarly associated with lower child development outcomes, regardless of child gender. Access to early childhood education was associated with better child development outcomes for both genders. The results confirm existing evidence that early learning opportunities in the first six years of life are important supports for promoting early child development for all children and suggest that girls aged 0–35 months in this sample may be uniquely sensitive to psychosocial stimulation and parental distress.

This paper is a product of the Education 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 ahasan1@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

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58

# **Appendix A: Generation of Aggregate Capital Stock**

Annual values of the country specific capital stock were generated from gross investment data using the perpetual inventory method: where _Kit_ is the level of the aggregate capital stock for country _i_ in year _t_

- δ is the depreciation rate, which is assumed to be have the same value for all countries for all time periods

_Iit_ is the level of gross investment for country _i_ in year _t_ Data on gross investment were obtained from the World Bank SIMA database as gross capital formation<sup>50</sup> in constant local currency units. We assumed a depreciation rate of 5 percent for all countries and all time periods, which implies that δ was set equal to 0.05. Note that before any capital stock series can be constructed, the initial value of the capital stock is required. To obtain these initial capital stock values, we employed the constant initial investment growth specification, detailed in Young (1995). Specifically, assume the period _t_ is the first period in which gross investment data for a country is available and also that _g_ is the value of the average growth rate of gross investment for the next 4 periods, that is periods _t_ +1 through _t_ +4. If we further assume that the capital stock had been growing at the same growth rate _g_ for infinite number of periods before period _t_ , then we are able to use the mathematical result for the sum of an infinite geometric series and thus it can be shown that the value of the initial capital stock (value of the capital stock in period 1) is given by: 1 − δ Note that < 1 is necessary in order to ensure that the convergence of the capital 1 + _g_ stock (infinite geometric) series

> 50 Gross capital formation (formerly gross domestic investment) consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories.  Fixed assets include land improvements (fences, ditches, drains,
|Formal Salaried|36.9|35.5|
|---|---|---|
|Informal Salaried|23.9|25.4|
|Formal Self-employed|3.1|2.2|
|Informal Self-employed|34.9|35.5|
|Unpaid Worker|1.2|1.3|

Source: Authors’ estimates from Fedesarrollo (2008, 2009) FSLS. Notes: Official poverty lines are COP 269,362 and COP 281,384 per person in 2008 and 2009, respectively, according to MESEP (2010).

**Table 2: Socioeconomic and Labor Characteristics of Formal and Informal Workers in Colombia**

|||||**2008**|**Informal**||||**2009**|**Informal**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||**Formal**<br>**Salaried**|**Informal**<br>**Salaried**|**Fromal Self-**<br>**employed**|<br>**Self-**<br>**employed**|**Unpaid**<br>**Worker**|**Formal**<br>**Salaried**|**Informal**<br>**Salaried**|**Fromal Self-**<br>**employed**|<br>**Self-**<br>**employed**|**Unpaid**<br>**Worker**|
|**Gender**|**Male*
female employment or total employment. (v) Finally, we exclude countries with fewer than 100 manufacturing firms after applying the previous steps.

The procedure above results in more than 29,000 manufacturing firms in 64 countries. The list of countries, year of the most recent survey and number of firms by country can be found in Appendix 1. The distribution of firms across ISIC sectors is shown in Appendix 2.

We combine the firm‐level data with Global Employment Sex‐disaggregated Statistics (GESS) at the sector level from the World Bank Household Surveys and other public resources. GESS uses harmonized classifications of economic activities and occupation categories. It fills an important information gap in global sex statistics by providing more detailed accounts on education, employment levels, wages, labor income, and employment status at very disaggregated economic activity levels and occupation categories than is usually available. The data are available for 50 countries at the ISIC Rev. 3.1 level, but only 30 countries overlap with the Enterprise Survey data sample. These 30 countries are highlighted bold in the country list of Appendix 1.

# **2.3 Measures**

This study focuses on two measures of _female labor participation_ :

- Female labor share, _fem_sh_ = number of permanent full‐time female employees as % of total number of permanent full‐time employees. The variable is computed for permanent production workers, non‐production workers and total workers (sum of both). Non‐production workers include managers, administration and sales.

- Female wage share, _S_<sup>_F_</sup> = total compensation of female employees as % of total compensation of all employees. Since the Enterprise Survey dataset has only information on average wage rates,<sup>10</sup> we approximate female wage rates at the firm level by assuming the same ratio of female‐to‐ average wages in a given sector as in the gender‐disaggregated wage data from the World Bank Household Surveys. Total female compensation is then obtained
in India has grown by 6 percent between 2001 and 2011 census rounds (ORGI, 2011) as villages evolved into towns, resulting in a divergence in the urban-rural classification between the two surveys. From a poverty measurement perspective this could matter because growth of smaller towns has an impact on rural poverty (Gibson et al., 2017). Second, larger villages and towns are more likely to be selected in the NSS, whereas differently sized villages have an equal probability of being sampled into the CPHS. More specifically, the NSS draws FSU locations based on population size. In comparison, the CPHS selects rural villages from the rural strata using simple random sampling; for urban areas, CPHS stratifies cities into four groups based on their population and then draws urban FSUs using simple random sampling. Within the FSUs from the CPHS, households have unequal sampling probabilities as households on the main street may have a higher likelihood of selection into the sample relative to other households (see Pais and Rawal, 2021; Dreze and Somanchi, 2021 for details).

Third, the NSS-2011 survey implemented a second stage stratification process, selecting a greater fraction of households in state-regions that had a higher proportion of non-agricultural occupations in rural areas and urban households with mean per capita consumption expenditure between the 1<sup>st</sup> and 6<sup>th</sup> decile based on the NSS’ 2009-10 expenditure survey. The CPHS in contrast, randomly selects households in rural and urban areas without second-stage stratification, with higher urban draws compared to rural. Despite comparatively larger urban samples, the absence of a second stage stratification in the CPHS means that representation of households from both ends of the income distribution is left to chance. In the NSS, representation of urban households from the 1<sup>st</sup> to 6<sup>th</sup> deciles of the distribution is embedded into the sampling design.

Fourth, the CPHS defines households as the physical unit where a group of individual members
, simple averages will be used, thus replicating the DB rankings (however, the analysis departs from the published rankings by consistently excluding the ‘Employing Workers’ indicator).

Annual changes in the subcomponents of the individual indicators are calculated in accordance with the _Doing Business_ ‘change scores ‘ (World Bank, 2011:112): first absolute annual changes on each subcomponent are calculated and these changes are, again, normalized to vary between zero and one. Then, the indicators are rescaled, such that a higher value always corresponds to an improvement in the investment climate.<sup>8</sup> The indicators are rescaled once more to ensure that a value of zero indicates that no change was made, whilst negative values indicate a worsening and positive values an improving regulatory environment.

The DBIs are developed from surveys which are sent out to experts and lawyers in each country included in the ranking around April of each year. The results are then summarized in Doing Business Reports in the next year. Thus, for example, data for the Doing Business Report 2011 were obtained in 2010. Accordingly, for the analysis all DB data are matched with the years they were collected in.

The choice of control variables is based on Djankov _et al._ (2006) but with a view to maximize country coverage. Key controls will thus include: the deviation from the average GDP deflator and initial log GDP per capita (this follows Djankov _et al.,_ 2006 who in turn build on Barro, 1991). These data were obtained from the _World Development Indicators._ In addition, a variable for civil conflict will be included. Data are

> 8 For example, increasing the number of procedures to start a business is associated with a _worse_ investment climate whilst larger credit bureau coverage is associated with a _better_ investment climate. The normalized index is adjusted in a way that a larger value always implies an improvement.

8
. Princeton University Press.</mark> Crafts, N. 1997. Economic Growth in East Asia and Western Europe Since 1950: Implications for Living Standards. _National Institute Economic Review, 162_ , 75-84. doi:10.1177/002795019716200106 Department of Statistics, Malaysia (DOSM). 2020. “Household Income and Basic Amenities Survey Report, 2019.” Putrajaya, Malaysia.

Devarajan, S. and E. Ianchovichina. 2018. A Broken Social Contract, Not High Inequality, Led to the Arab Spring. _Review of Income and Wealth_ , 64: S5-S25.

Dollar, D., Kraay, A. Growth is Good for the Poor. _Journal of Economic Growth_ **7** , 195– 225 (2002).

Duesenberry, James S. 1949. _Income, Savings and the Theory of Consumer Behavior_ , Harvard University Press, Cambridge, MA.

52
or equal to _ω_<sup>_′_</sup> . A fraction (1 _− δ_ ) [1 _− qt_ ( _ω_<sup>_′_</sup> + ∆)] of the mass of firms with productivity between _ω_<sup>_′_</sup> and ( _ω_<sup>_′_</sup> + ∆) survives the exit shock and jumps downward to have productivity less than or equal to _ω_<sup>_′_</sup> . There is also an inflow of new firms into this group, which is given by the mass of entrants, a fraction Γ ( _ω_<sup>_′_</sup> ) of which will feature productivity less than or equal to _ω_<sup>_′_</sup> . The endogenous exit will be driven by the mass of firms that transition downwards from the productivity cutoff, (1 _− δ_ ) _qt_ <u>(</u> _<u>ω</u>_ + ∆) _Mt_ <u>(</u> _<u>ω</u>_ + ∆).

# **6 Quantitative Analysis**

We turn now to the quantitative analysis of the role of entry barriers and idiosyncratic distortions in the formal sector in accounting for the extent of informal production and the shape of the size distribution. We first discuss the calibration strategy and then move on to quantifying the counterfactuals.

## **6.1 Calibration**

Our calibration strategy proceeds in two steps. First, we calibrate structural parameters in the model, such as those governing the elasticity of substitution, the entry costs, and the innovation cost function, to match salient macro and firm-level properties of the United States. Then, we estimate idiosyncratic distortions from Ghanaian firm-level data and feed alternative estimates of entry barriers from the World Bank’s Doing Business Indicators and from (Fattal-Jaef,
|Schools in 2006|CQ24|3.5|5.04|3.51|4.48|-0.01|(0.36)|713|
|Clinics in 2006|CQ25|0.32|0.84|0.29|0.51|0.03|(0.05)|718|
|Churches in 2006|CQ26|2.18|2.5|2.55|2.67|-0.37*|(0.19)|716|
|Halls in 2006|CQ27|0.04|0.3|0.04|0.21|0.01|(0.02)|709|
|IDPs in 2006|CQ136|2.8|12.64|4.86|20.07|-2.06|(1.47)|533|
|IDPs returned in 2006|CQ137|5.4|24.45|3.91|16.04|1.49|(1.8)|518|
|Refugees in 2006|CQ138|0.57|3.78|0.97|7.29|-0.41|(0.5)|557|
|Refugees repatriated in 2006|CQ139|0.76|12.13|0.25|3.21|0.52|(0.72)|575|
|Share of male respondents|QF7|0.5|0.5|0.5|0.5|0|(0.01)|23567|
|Average age respondents|QF9|20.12|16.82|20.17|17.02|-0.06|(0.23)|22536|

Note: Question number responds to 2010-2011 survey (Humphreys et al. 2012). Tests of difference based on simple OLS regressions. * p