rafmacalaba/datause-encoder-data huggingface dataset:
**Table A.2: Mapping, sampling and completion in the dual practice sample**

|**Facilities**|**(1)**<br>Number of<br>facilities|**(2)**<br>Percentage|**Providers**|**(3)**<br>Number of<br>providers|**(4)**<br> <br>Percentage|
|---|---|---|---|---|---|
|**Panel A: Mapping**||||||
|Total|200||Total|216||
|without doctors|40|20.0%|without private facilities|84|38.9%|
|with doctors|160|80.0%|withprivate facilities|132|61.1%|
|**Panel B: Sampling**||||||
|Total|143||Total|143||
||||without private facilities<br>withprivate facilties|49<br>94|34.3%<br>65.7%|
|**Panel C: Completed(at**|**least one case)**|||||
|Total|102||Total|118||
||||without private facilities<br>withprivate facilities|30<br>88|25.4%<br>74.6%|

Notes: Reasons for not completing SP surveys include transferred and provider not found. In almost all cases our field staff made at least three attempts to complete a case. During fieldwork we replaced five sampled providers with other providers. In two cases, it was because the provider was on sick leave, two cases because provider had been transferred and one case because provider had gone on training.
**Figure 2. Women are disproportionally less likely to participate in firm ownership and management**<sup>7</sup>

<!-- Start of picture text --> 35% 33%<br>30% 28%<br>25%<br>20% 20%<br>19%<br>20% 18%<br>16%<br>15%<br>15% 13% 13%<br>11%<br>10%<br>10%<br>5%<br>3%<br>5%<br>0%<br>All countries East Asia & Latin America & Sub‐Saharan Europe & South Asia Middle East &<br>Pacific Caribbean Africa Central Asia North Africa<br>Percent of firms with majority female ownership Percent of firms with a female top manager<br><!-- End of picture text --> Providing work opportunities for women is also good for companies. There is evidence that firms with greater gender diversity in senior management and on boards enjoy better performance. Greater gender diversity allows companies to take full advantage of the talent pool, along with the wider range of perspectives and experience that come with it.<sup>8</sup> Although the topic of gender equality in the private sector is widely covered by the development knowledge agenda, there is much less said on the particular role that investment incentives can play in changing firm and investor behavior. The research on when and how incentives can support gender equality is both limited and scattered, especially in the context of developing countries and for the purposes of evaluating the effectiveness of such instruments. Drawing on empirical evidence and international experiences, this working paper explores the potential role of investment incentives in advancing goals around gender equality, specifically, increasing female labor force participation, growing the number of women in leadership roles, and encouraging female entrepreneurship.

This paper is organized along three key sections. This opening section continues with the identification of supply‐ and demand‐side barriers that can impede progress toward gender equality in the workplace, and then introduces incentive instruments that may be considered to address these barriers. Section two elaborates on when and how different incentive instruments can help promote gender equality, discussing their respective advantages and disadvantages, and providing design considerations for implementation. Finally, the paper concludes with a summary of key findings and suggestions on areas for further research.

# **1.2 Identifying key barriers to gender equality in the workplace**

To understand which types of investment incentives can be effective in supporting gender equality goals, a first step involves discussing the barriers they aim to address. These can be categorized as “demand‐ side” barriers, which are barriers inhibiting firms and investors from hiring or investing in women‐led

> 7 World Bank Group, Enterprise Surveys, Latest available year.

> 8 Elborgh‐Woytek et al.

3
The fact that contractual arrangements respond to exogenous legal constraints rather than being chosen by contracting parties reduces concerns about endogeneity of contract choice that affect most studies in this area. It makes our setting well suited to provide robust estimates of the contemporaneous and long-term productivity effects of share cropping. This is policy relevant because, in the absence of other binding constraints, the productivity losses from share tenancy provide an upper bound for the gains possibly to be realized from eliminating the conditions (legal restrictions or credit market imperfections) causing such arrangements in the first place.

To frame the empirical analysis, we consider how West Bengal’s policies may affect productivity and investment. Rent ceilings and the award of permanent use rights to their plots will increase tenants’ wealth and bargaining power vis-à-vis landlords, consistent with the observation that tenant registration led to a drop in the proportion of contracts with a landlord share of half and a concomitant increase of those with a 75/25% sharing rule (Banerjee, Gertler, and Ghatak 2002). The implied increase in investment incentives is likely to outweigh any potential negative impacts arising from the reforminduced elimination of eviction threats as a device to increase tenants’ incentives for effort supply that may reduce productivity for short-term contracts (Banerjee and Ghatak 2004). Also, the long-term nature of contracts will transform tenant-landlord bargaining into a repeated game, thus increasing the incentive to apply optimum levels of inputs and efficiency of production in any given period. It implies that our results are likely to constitute a conservative estimate of the contemporaneous productivity-impact of share tenancy.

Regarding longer term investment incentives, two factors have to be considered. First, the reform-induced wealth transfer will increase tenants’ ability to undertake indivisible investments. At the same time, as output has to be shared, neither party to the contract can reap the full marginal benefits from such investment. Thus, unless parties reach an implicit and enforceable agreement on how to share benefits, investment levels will be below what would be expected under full ownership. The long-term nature of contractual relationships in West Bengal implies that any effects will be lower than what one would expect in a setting where sharecropping contracts are renewed annually and contractual parameters are less constrained.

# **2.4 Estimation strategy**

To quantify the effect of sharecropping on agricultural production revenues and the probability of making long-term land-attached investments, we follow the literature (Bell 1977; Shaban 1987) and use plot level data on production and investment from our sample of owner-cum tenants to estimate 6
50

# TABLE 4 SIZE STRUCTURE OF THE GARMENT INDUSTRY, 1981.89'

|||Slz|e of Estab|lishment(Nu|mberof W|orkers)||
|---|---|---|---|---|---|---|---|
||<50|51-100|101-500|501-1000|>1000|All|Total<br>Number|
|Distribution of||||||||
|Establishments:<br>1981|55.4|19.1|19.1|51.0|1.3|100.0|157b|
|1985<br>.|74.0|15.8|17.4|3.3|1.7|100.0|5 7 5 <sup>b</sup>|
|1988|61.8|13.2|18.9|18.8|18.8|100.0||
|Distribution of||||||||
|Output:||||||||
|1g81|11.6|10.8|36.6|28.9|12.2|100.0|16.40|
|<br>1985|11.7|10.0|46.5|17.7|14.0|100.0|121.60|
|1987|13.3|6.7|33.8|31.0|18.9|100.0|333.50|
|1989|7.2|6.0|39.6|29.5|17.7|100.0|446.20|
|Distrfbution of||||||||
|Employees:||||||||
|1981|11.4|10.4|41.7|26.2|10.3|100.0|20.2d|
|1985|17.4|8.9|33.7|18.9|21.2|100.0|69.7"|
|1987|13.3|6.7|33.3|22.6|24.2|100.0|107.Sd|
|1966|11.0|6.4|32.6|27.0|-22.9|100.0||

Source: Unpublished data of BPS, based on the annual manuhcturing survey.

* Thee data ae highly inoomplete wifth respec **t** o the smallest size category. The number of establishments in large catoegories that may be missed is not clear, but it is probably not too many. **b** Valu-added at mwket prices, billion rupiah. Number of establishments. aNumber of employee., thousands.
Ethiopia, Malawi, Nigeria, and Sierra Leone. Zimbabwe is also included in the sample when using the household business definition of a household enterprise.

**The poorest households were most likely to report enterprise income declines.** The households in the top three income groups were less likely to report a loss in enterprise income since the pandemic began relative to the poorest income group (Table 7). This result is observed both for households owning farm and non-farm enterprises and for both definitions of household enterprise. That said, none of the coefficients are statistically significant.

**Female-owned enterprises were more likely to experience income loss than those owned by men.** When controlling for welfare level, male household heads, who are a proxy for male owners of non-farm enterprises, were 10 percentage points less likely than women to lose income (Table 7). While urban male farm enterprise owners are 3.6 percentage points less likely than women to experience business income loss, the difference is not statistically significant.

**Income loss by age differed by firm type, but the estimates are not statistically significant.** Using the sample based on the household head’s job definition, we see that self-employed heads that are adults (age 25 or older) were nearly 3 percentage points less likely to suffer a decline in farm income than young owners (18 to 24 years old). Yet they are 8 percent more likely to experience a decline in non-farm enterprise income (Table 7). Neither estimate is statistically significant.

**Table 7. Correlates of reported household enterprise income decline**

||**Household He**|**ad Definition**|**Household Bu**|**siness Definition**|
|---|---|---|---|---|
|Sample|agricultural self-<br>employed|non-agricultural<br>self-employed|Own farm<br>enterprise|Own non-farm<br>enterprise|
|Dependent variable|Farm income<br>decline|Nonfarm income<br>decline|Farm income<br>decline|Nonfarm income<br>decline|
|Between $2.15 and $3.65|-0.402|-0.215|-0.040|-0.101|
||[0.209]*|[0.115]*|[0.406]|[0.079]|
|Between $3.65 and $6.85|0.064|-0.085|-0.135|-0.024|
||[0.091]|[0.083]|[0.401]|[0.084]|
|Above $6.85|-0.247|-0.076|-0.211|-0.056|
||[0.135]*|[0.091]|[0.414]|[0.080]|
|Adult|-0.029|0.084|||
||[0.194]|[0.112]|||
|male|-0.036|-0.103|||
||[0.051]|[0.046]**|||
|Constant|1.135|1.071|0.666|1.054|
||[0.166]***|[0.112]***|[0.402]|[0.077]***|
|Observations|105|595|447|1,131|
|R-squared|0.330|0.338|0.288|0.238|
|Cti|ETH,MWI,|ETH,MWI,NGA,|ETH,MWI,|ETH,MWI,NGA,|
|ounres|NGA,SLE|SLE,ZWE|NGA, SLE|SLE,ZWE|

Source: HFPS-Wave 1.

18
# **-** **<u>Table A.14. Probabilities of Being Poor for Combination of Main Female Headed Household Types, Linear Probability Model</u>**

||**Specific**|**ation 1**|**Specif**|**ication 2**|
|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|
||-0037***||-0033***||
|Overlap of self-reported FHHs, potential FHHs, and most-educated-female-adult FHHs|.<br>(0.00)<br>||.<br>(0.00)<br>||
|Overlap of self-reported FHHs, potential FHHs, and most-educated-female-adult FHHs # Number of children age 0-14|0.005**<br>(0.00)||0.007***<br>(0.00)||
|Overlap of self-reported FHHs, potential FHHs, majority of females and most-educated-female-adult FHHs||-0.037***<br>(0.00)||-0.033***<br>(0.00)|
|Overlap of self-reported FHHs, potential FHHs, majority of females and most-educated-female-adult FHHs # Number of children age 0-14||0.005**<br>(0.00)||0.007***<br>(0.00)|
|_Household head`s characteristics_|||||
|`|||-0.001***|-0.001***|
|Heads age|||(0.00)|(0.00)|
|Highest education level is primary|||-0.064***<br>(0.00)|-0.064***<br>(0.00)|
||||-0.121***|-0.121***|
|Highest education level is secondary|||(0.00)|(0.00)|
||||-0.176***|-0.176***|
|Highest education level is tertiary|||(0.00)|(0.00)|
|Head is married|||0.001<br>|0.001<br>|
||||(0.00)<br>|(0.00)<br>|
||||-0.033***|-0.033***|
|Head is employed|||<br>(0.00)|<br>(0.00)|
|_Household characteristics_|||||
|Household size|||0.023***|0.023***|
||||(0.00)|(0.00)|
|Nb f hild 014|0.078***|0.078***|0.050***|0.050***|
|umer o cren age -|(0.00)|(0.00)|(0.00)|(0.00)|
|Sh f hhld b 1524|||-0.009***|-0.009***|
|are o ouseo memers age -|||(0.00)|(0.00)|
|Share of household members ae 60 and older|||-0.021***|-0.021***|
|g|||(0.00)|(0.00)|
||||-0.084***|-0.084***|
|Urban|||(0.00)|(0.00)|
||-0.150***|-0.150***|-0.181***|-0.181***|
|Iraq|(0.00)|(0.00)|(0.00)|(0.00)|
||-0.145***|-0.145***|-0.093***|-0.093***|
|Jordan|(0.01)|(0.01)|(0.01)|(0.01)|
||-0.150***|-0.150***|-0.209***|-0.209***|
|Mauritania|(0.01)|(0.01)|(0.01)|(0.01)|
||0.180***|0.180***|0.184***|0.184***|
|West Bank and Gaza|<br>(0.01)|<br>(0.01)|<br>(0.01)|<br>(0.01)|
||-0.061***|-0.061***|-0.054***|-0.054***|
|Tunisia|(0.00)|(0.00)|(0.00)|(0.00)|
|_cons|0.129***|0.129***|0.283***|0.283***|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Adjuster R2|0.16|0.16|0.21|0.21|
|Number of observations|214931|214931|211069|211069|

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Robust standard errors are in parentheses.

58
# TABLE _5_

## **IMPACr** OF OPENNESS IN SSAs A SYNTHESIS

## (1967-87)

<!-- Start of picture text --> Fixed Effect Cross Section<br>Estimation Estimation<br>Openness<br>Variable Level Difference L.evel Difference<br>1. Trade Shares >0" >O" <0 <0<br>2. Black Marketb >OW >0!00 <0 >0<br>Premium<br>3. Trade >0o >0 >0 >0<br>Liberalization<br>Index (HT Index)<br>4. Outward >o >0 >0!<br>Orientation<br>Index (D $ Index)<br><!-- End of picture text --> Indicates significant at 1 percent level; ** indicates significant at _5_ percent level; e indicates significant at 10 percent leveL Notes:

- a. Pooled annual data regressions include country dummies.

b. Since '>0" implies more openness (less distortion) has a positive effect on growth, therefore for black market premium the table shows W70s when the estimates show that a higher level of premium or distortion affects performance negatively.

29
Trade among countries due to technology differences is a core principle in international economics. Countries with heterogeneous technologies focus on producing goods in which they have comparative advantages; subsequent exchanges afford higher standards of living than are possible in isolation. This Ricardian finding is the first lesson in most undergraduate courses on trade, and it undergirds many modeling frameworks on which recent theoretical advances build (e.g., Dornbusch et al. 1977, Eaton and Kortum 2002, Costinot et al. 2012). In response to Stanislaw Ulam’s challenge to name a true and nontrivial theory in social sciences, Paul Samuelson chose this principle of comparative advantage due to technology differences.

While empirical tests date back to David Ricardo (1817), quantifying technology differences across countries and industries is extremely difficult. Even when observable proxies for latent technology differences are developed (e.g., labor productivity, industrial specialization), cross-sectional analyses risk confounding heterogeneous technologies with other country-industry determinants of trade. Panel data models can further remove time-invariant characteristics (e.g., distances, colonial histories) and afford explicit controls of time-varying determinants (e.g., factor accumulation, economic development, trading blocs). Quantifying the dynamics of uneven technology advancement across countries is an even more challenging task, however, and whether identified relationships represent causal linkages remains a concern. These limitations are particularly acute for developing and emerging economies. This is unfortunate as nonOECD economies have experienced some of the more dramatic changes in technology sets and manufacturing trade over the last thirty years, providing a useful laboratory for quantifying Ricardian effects.

This study contributes to the empirical trade literature on Ricardian advantages in three ways. First, it utilizes a panel dataset that includes many countries at various development stages (e.g., Bolivia, France, South Africa), a large group of focused manufacturing industries, and an extended time frame. The 1975–2000 World Trade Flows (WTF) database provides export data for each bilateral route (exporter-importerindustry-year), and data from the United Nations Industrial Development Organization (UNIDO) provide labor productivity estimates. The developed data platform includes
- Dube, A. (2019, October). Minimum wages and the distribution of family incomes. _American Economic Journal: Applied Economics 11_ (4), 268–304.

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- Fazel-Zarandi, M. M., J. S. Feinstein, and E. H. Kaplan (2018, 09). The number of undocumented immigrants in the united states: Estimates based on demographic modeling with data from 1990 to 2016. _PLOS ONE 13_ (9), 1–11.

- INEGI (2021). National survey of occupation and employment (enoe), population aged 15 years and older. Technical report, Instituto National de Estatistica y Geographia.

- Lokshin, M. and M. Ravallion (2022). A market for work permits. _Economic Policy 37_ (111), 471–499.

- McKenzie, D., S. Stillman, and J. Gibson (2010, 06). How Important is Selection? Experimental vs. Non-Experimental Measures of the Income Gains from Migration. _Journal of the European Economic Association 8_ (4), 913–945.

- National Academies of Sciences, Engineering, and Medicine (2017). _The Economic and Fiscal Consequences of Immigration_ . Washington, DC: The National Academies Press.

- Neumark, D., M. Schweitzer, and W. Wascher (2005). The effects of minimum wages on the distribution of family incomes. _Journal of Human Resources XL_ (4), 867–894.

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20
|31, 2005)|||
|---|---|---|
|Std. error|(20.5)|—|
|No. of obs. (window length in<br>weeks)|2281 (13)|—|
|Blue Line second extension<br>(Nov 11, 2006)|-2.3|-12.2|
|Std. error|(7.6)|(10.5)|
|No. of obs. (window length in<br>weeks)|2397 (13)|2397 (13)|
|Siri Fort|||
|Blue Line second extension<br>(Nov 11, 2006)|-28.4|9.2|
|Std. error|(29.4)|(16.2)|
|No. of obs. (window length in<br>weeks)|2392 (13)|2315 (13)|

Source: Authors’ analysis based on data described in the text. Missing observations in each included week do not exceed 20 percent. Each estimate is calculated from a separate regression (equation (1)), where the dependent variable is the natural logarithm of hourly pollutant. Controls are the same as used in table 4. Std. errors are clustered at one week. * indicates significantly different from zero at 10 percent level, ** at 5 percent level and *** at 1 percent level.

**Table 7. Effect of Delhi Metro on CO at ITO: Entire Series, 2004–2006**

|Metro Extension|CO at ITO|
|---|---|
|Yellow Line First Extension (Jul 03, 2005)|-33.5**|

47
with and without children. Importantly, the common finding in these few studies is that FHHs with children are generally poorer than FHHs without children.

Furthermore, a related economics literature on equivalence scales suggests that scale adjustments (for different numbers of adults and children) could have substantial effects on poverty and profiles of the poor for various countries at different income levels (Lanjouw and Ravallion, 1995; Newhouse _et al._ , 2017; Abanokova _et al._ , 2022). This is especially relevant for FHHs; for example, FHHs tend to have lower numbers of household members, but higher dependency ratios (World Bank, 2011; Klasen _et al._ , 2015). In our sample (Appendix B, Table B.7), across all years and countries, the average size of FHHs is 5.7, while that of MHHs is 7.4. The average age of female heads is much higher than that of male heads (56 for FHHs vs. 48 for MHHs). Female heads are also mostly widowed (70.7%, compared to 1.3% for male heads).

For widowed FHHs, their offspring are typically already-grown, independent adults who might be contributing to household expenses from their own earnings. The current welfare of these female heads is likely a function of their accumulated earnings, or more likely those of their deceased or living spouses and adult children, and thus are not strictly comparable to (male or female) heads with young children who rely on current labor market earnings to support themselves and their families. This distinction is especially pertinent to dynamic analysis. If the full sample of female or male heads were treated as a single group, this would unduly bias the results in favor of the elderly, widowed female heads without children, and against the much younger working male heads with children. Consequently, it is important to examine poverty incidence and dynamics among FHHs with or without children.

In summary, our new typology consists of four main types of FHHs and their associated five sub-types (variants). We investigate poverty trends and dynamics of these four types of FHHs for 12
||**Log [Se**<br>|**lf-reported**<br>|**Productivity (X**<br>|**OF/ha)]**<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||All crops|All crops|Export crops|Food crops|
||(0.009)|(0.009)|(0.012)|(0.011)|
|Hired Labor Use (Number)|-0.009|-0.008|-0.029|-0.003|
||(0.015)|(0.015)|(0.022)|(0.017)|
|Number of Fields Managed by the household|-0.079|-0.072|-0.688**|0.043|
||(0.138)|(0.138)|(0.277)|(0.168)|
|Total number of crops produced|0.103|0.111|0.461|-0.065|
||(0.150)|(0.151)|(0.294)|(0.176)|
|Household used any purchased seed|0.021|0.020|0.023|0.005|
||(0.015)|(0.015)|(0.024)|(0.017)|
|Plots that Use Chemical Fertilizer (% of total)|0.013|0.012|0.008|0.002|
||(0.010)|(0.010)|(0.018)|(0.010)|
|Plots that Use Pesticide (% of total)|-0.016|-0.012|-0.015|-0.005|
||(0.014)|(0.014)|(0.030)|(0.015)|
|Export crops dummy (1 if Household produced any||0.019|||
|export crop)||(0.058)|||
|Constant|0.215|0.126|1.014*|0.969***|
||(0.402)|(0.382)|(0.561)|(0.359)|
|Observations|5428|5428|3588|4601|

_Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively._ Next, we conduct the decomposition for each decile of the productivity distribution, using the Recentered Influence Function (RIF) method. Note that agricultural productivity has a positive relationship, significant at the 1 percent level, to both total consumption and per capita consumption in our data. It is thus a proxy for wealth, meaning that households with higher agricultural productivity are also richer.

Results of the decomposition are shown in Table 6. First, we see that the unconditional gender gap is fairly stable across productivity deciles, ranging from 18 percent in the 60<sup>th</sup> and 70<sup>th</sup> deciles to 25 percent in the 30<sup>th</sup> decile. A closer examination of the endowment effect shows that the inverse farm-size productivity relationship primarily works to the advantage of the lowest- and highest-productivity female-headed households. Female household head’s older age does not reduce the gap for the lowest-productivity households, while higher household female labor use does not work to close the gap for the highestproductivity households.

The negative effect of lower household male labor use and pesticide use is most pronounced for the lowestproductivity female heads, while lower application of chemical fertilizer does not emerge as a determinant for them. Interestingly, lower planting of export crops appears to exhibit a U-shaped relationship and is a driver of the gender gap both for the lowest-productivity and highest-productivity households.

13