rafmacalaba/datause-encoder-data huggingface dataset:
———. 2009. ―Scale, Technique, and Composition Effects in Manufacturing SO2 Emissions.‖ _Environmental and Resource Economics_ 43 (2, June): 257–74.

Grossman, Gene, and Alan B. Krueger. 1992. ―Environmental Impacts of a North American Free Trade Agreement.‖ CEPR Discussion Paper 644, Centre for Economic Policy Research, London.

———. 1995. ―Economic Growth and the Environment.‖ _Quarterly Journal of Economics_ 110 (2, May): 353–77.

Halkos, George E. 2003. ―Environmental Kuznets Curve for Sulfur: Evidence Using GMM Estimation and Random Coefficient Panel Data Models.‖ _Environment and Development Economics_ , Cambridge University Press, vol. 8(04), pages 581-601, October Hettige, Hemamala, Muthukumara Mani, and David Wheeler. 2000. ―Industrial Pollution in Economic Development: Kuznets Revisited.‖ _Journal of Development Economics_ 62 (2, August): 445–76.

Kahn, Matthew E., and Yutaka Yoshino. 2004. ―Testing for Pollution Havens Inside and Outside of Regional Trading Blocs.‖ _B. E. Journal of Economic Analysis and Policy_ 4 (2): art, 4.

López, Ramón. 1994. ―The Environment as a Factor of Production: The Effects of Economic Growth and Trade Liberalization.‖ _Journal of Environmental Economics and Management_ 27 (2, September): 163–84.

Mani, Muthukumara S. 1996. ―Environmental Tariffs on Polluting Imports: An Empirical Study.‖ _Environmental and Resource Economics_ 7 (4): 391–411.

Mani, Muthukumara S., and Shreyasi Jha. 2006. ―Trade Liberalization and the Environment in Vietnam.‖ Policy Research Working Paper 3879,World Bank, April.

Mani, Muthukumara, and David Wheeler. 1998. ―In Search of Pollution Havens? Dirty Industry in the World Economy, 1960–1999.‖ _Journal of Environment and Development_ 7 (3, September): 215–47.

Markandya,Anil,Alexander Golub, and Suzette Pedroso-Galinato. 2006.―Empirical Analysis of National Income and SO2 Emissions in Selected European Countries.‖ _Environmental and Resource Economics_ 35 (3, November): 221–57.

Matthew E. Kahn & Yutaka Yoshino, (2004). "Testing for Pollution Havens Inside and Outside of Regional Trading Blocs," _The B.E. Journal of Economic Analysis & Policy_ , Berkeley Electronic Press, vol. 0(2).

Péridy, Nicolas. 2006. ―Pollution Effects of Free Trade Areas: Simulations from a General Equilibrium Model.‖ _International Economic Journal_ 20 (1, March): 37–62.

Sarkar, S. 2009. ―A Study of the Environmental Issues Associated with the Dominican Republic–Central American Free Trade Agreement (DR-CAFTA).‖ _International Business and Economics Research Journal_ 8 (1, January): 113–88.

22
We do not find a similar association for services or overall job turnover. When limiting the sample to women only, we find that women in households with children under 10 are more likely to experience job turnover when employed in manufacturing. This relationship is reversed for services and overall job turnover. Again, we find that preschool availability is associated with a lower likelihood of job turnover for women in households with young children overall, and in manufacturing, but not in services.

5.3. Does increased supply of female workers lower wages?

A classical supply and demand framework in a perfectly competitive labor market predicts that a positive labor supply shock due to childcare provision may trigger a reduction in wages. In contrast, an increase in wages would be consistent with both channels tested in the previous section. Pissarides (2000) shows that in an imperfect labor market with firm-employee matching, frictional search and job posting, a positive labor supply shock may enable firms to match workers to jobs more effectively. Such improved matching can increase wages. Similarly, lower turnover may lengthen the average tenure of workers in the firm, which would also increase average wages given the positive relation between tenure and wages (Brown, 1989).

In table 12 we use the SI to test the impact of preschool expansion on worker wages by estimating Equation (1) with worker income as the dependent variable. We indeed find strong impacts on wages: an additional preschool per 1,000 children raises worker wages by approximately 1.5 percent in a plant with an average fraction of female employees (Table 12). The estimates are significant at the 1% level and of the same magnitude regardless of whether we use the full sample of plants (column 1) or limit the sample to plants for which the measures of TFPR and TFPQ are available (column 2).

To the extent that preschool expansion relaxes firms’ skill availability constraint, these impacts should be larger precisely for those plants that are initially most constrained. To test for this differential effect, we exploit a question in the 2006 SI data that asks if the lack of workers’ skill is the primary reason not to expand. Column (3) shows that those plants which answered ‘yes’ to this question increased wages as a result of preschool expansion significantly more than 19
# **2 An empirical macro-economic framework for T ̈urkiye**

The underlying model is designed for data-driven climate policy analysis in T ̈urkiye.<sup>4</sup> It is calibrated and estimated using quarterly and annual data covering the period from 2000 to 2022. It comprises a high-skilled household sector, a low-skilled household sector, a core good sector, an energy sector, a public sector, and the Rest of the World (RW). Households use energy for consumption (on average around 48% of total final energy consumption) and firms for non-energy production (around 52%), which can be fuel, heat, or electricity. The energy sector consists of renewable energy (with a sample average of 0.9% of GDP and 17% of total energy consumption) and fossil energy (6.2% of GDP and 83% of total energy consumption). A carbon mining sector (3.4% of GDP) extracts and imports carbon (such as coal, crude oil, and gas) which is an input to fossil energy production. CO2 emissions arise from the combustion of fossil fuels and have fluctuated around 0.46 kilogram (kg) per 1 constant 2015 USD of GDP since the early 2000s.<sup>5</sup> Households, firms, the government, and the RW demand and supply finance, respectively. The model features various financial instruments. Households are assumed to be net creditors holding their wealth as bonds, deposits, and private equity. Firms are net debtors financing investments by borrowing from households and issuing equity. The government finances its deficit by issuing bonds. The RW is a net creditor from T ̈urkiye’s perspective. The public, corporate, and RW’s net debt-GDP ratios are moderate on average: around 36%, 57%, and -35% of GDP, respectively. Yet, these debt levels have increased sharply in recent years (OECD 2022). Despite a low net debt vis-a-vis the RW, T ̈urkiye imports around 87% of its total primary energy supply.

## **2.1 High- and low-skilled labor households**

Capturing to some extent how the low-carbon transition affects households differently, the model features _high-skilled labor_ H-households (50% of the labor force) and liquidity-constrained _low-skilled labor_ L-households (50% of the labor force). We assume that L-households hold no financial wealth. In T ̈urkiye, the 50% top-income households account for two-thirds of all consumption expenditures. Hence, we assume H-households consume twice as much as L-households while providing the same labor hours. The former receive capital income as well as a higher real wage. The overall wage share in total income (household income and retained earnings) is around 50%. L-households also receive transfer payments from the government (around 7.3% of GDP).

H-households own the firms and domestic carbon mines (oil, coal, and natural gas), provide high-skilled labor services, and save. Because a specific household’s labor variety is unique, each H-household has market power. Wage adjustment costs allow the model to capture the persistence in high-skilled wage contracts as observed in empirical data (Taylor

> 4Appendix A reports the model in detail.

> 5Appendix B reports the model calibration and lists the respective data sources and references.

6
# **1 Introduction**

Women’s entrepreneurship can be a powerful force for women’s empowerment and economic growth (Duflo, 2012). Yet a gender gap in entrepreneurial activity and firm performance persists across countries worldwide, with women-owned firms being less profitable and smaller than male-owned firms (Hardy and Kagy, 2018; World Bank, 2019; Delecourt and Ng, 2020; Fang et al., 2022). A key contributing factor is that women entrepreneurs are often more credit-constrained (De Mel et al., 2009). In particular, despite gender parity in access to microfinance, and many group lending schemes favoring women, women face more difficulty than men in getting loans of larger size, longer duration, individual liability and more flexible terms (Agier and Szafarz, 2013). Such features are important for growth of some micro-, small and medium enterprises (MSMEs) (Field et al., 2013; Kersten et al., 2017). A major constraint to accessing larger loans is that women are less likely to own high-value assets such as land or housing that are often required as collateral, especially in contexts where well-functioning credit bureaus are lacking (Morsy and Youssef, 2017; World Bank, 2019). Absent a large shift in women’s ownership of collateralizable assets, innovative solutions are thus needed to expand access to capital for growth-oriented women-owned firms.

In this paper, we test an innovative solution to providing larger loans to women who lack collateral: psychometric credit scoring. Psychometrics (literally “measurement of the mind”) uses questions designed to measure traits such as the “big five”. Such traits, and models based on such traits, have been shown to be predictive of default risk for MSMEs (Klinger et al., 2013a,b; Arr ́aiz et al., 2017). We partnered with a microfinance institution (MFI) in Ethiopia that, alongside group-based loans, also offers larger individual-liability loans to business owners who can provide collateral. The MFI was seeking to expand its client base to include new female entrepreneurs who lacked access to collateral. We conducted a randomized controlled trial (RCT) to test the impact of introducing psychometric credit scoring as a substitute for collateral requirements during the due diligence process. We 2
54

# **Table 31. Mexico: emission standards for gasoline-powered motor vehicles**

|Remarks|Emission|Standards in|g/mile|
|---|---|---|---|
||HC|CO|NOx|
|'89 Automobiles, no trucks|3.20|35.2|3.68|
|'90 Automobiles|2.88|28.8|3.20|
|GVW up to 6012 lbs'|3.20|35.2|3.68|
|GVW 6013 - 6614 lbsb|4.80|56.0|5.60|
|'91 Automobile|1.12|11.2|2.24|
|GVW up to 6012 lbs'|3.20|35.2|3.68|
|GVW6013 - 6614 lbsb|4.80|56.0|5.60|
|'92 Automobiles|1.12|11.2|2.24|
|GVW up to 6012 lbs<sup>1</sup>|3.20|35.2|3.68|
|GVW 6013- 6614lbsb|3.20|35.2|3.68|
|'93 Automobiles|0.40|3.4|1.00|
|GVW up to 6012 lbs|3.20|35.2|3.68|
|GVW6013 - 6614 lbsb|3.20|35.2|3.68|
|'94 Automobiles|0.40|3.4|1.00|
|GVW up to 6012 lbs'|1.00|14.0|1.00|
|GVW 6013 - 6614 lbsb|1.00|14.0|1.00|

a) Commercial vehicles (i.e., van & combis) b) Light duty trucks Source: [Walsh June, 1988]
Table 1: Poverty and Inequality in Bangladesh, 2000-2010

||Poverty<br>Head Count R|atio|Inequal<br>Gini Coeff|ity<br>icient|
|---|---|---|---|---|
||2000|2010|2000|2010|
|National|49|32|0.312|0.308|
|Urban|35|21|0.374|0.336|
|Rural|52|35|0.274|0.282|

Source: Bangladesh Poverty Assessments, different reports Table 2: Spatial Disparity and Geo-climatic conditions

|||2000|||2010||
|---|---|---|---|---|---|---|
||All|Urban|Rural|All|Urban|Rural|
|**Base Case**|||||||
|Neighborhood Correlation (r)|0.363***|0.385***|0.218***|0.266***|0.298***|0.199***|
||(19.929)|(13.271)|(11.820)|(20.405)|(12.998)|(14.158)|
|Controlling for 30 years average rainfall|||||||
|Neighborhood Correlation (r*)|0.360***|0.384***|0.218***|0.266***|0.289***|0.199***|
||(19.814)|(13.216)|(11.803)|(20.367)|(12.734)|(14.155)|
|Contribution of rainfall (r-r*)|0.002|0.001|0.000|0.001|0.009|0.000|
|% Decrease[((r-r*)/r)*100]|0.66|0.23|-0.10|0.20|3.17|-0.22|
|Controlling for standard deviation of rainfall|||||||
|Neighborhood Correlation (r*)|0.359***|0.386***|0.214***|0.266***|0.296***|0.196***|
||(19.726)|(13.284)|(11.700)|(20.367)|(12.899)|(14.043)|
|Contribution of rainfall SD (r-r*)|0.004|-0.001|0.004|0.001|0.003|0.003|
|% Decrease[((r-r*)/r)*100]|1.12|-0.38|1.75|0.20|0.97|1.51|
|Controlling for monsoon rainfall coefficient of|variation||||||
|Neighborhood Correlation (r*)|0.363***|0.379***|0.209***|0.260***|0.299***|0.188***|
||(19.901)|(13.135)|(11.578)|(20.146)|(12.975)|(13.776)|
|Contribution of rainfall COV (r-r*)|0.000|0.005|0.009|0.006|0.000|0.011|
|% Decrease[((r-r*)/r)*100]|-0.05|1.39|3.91|2.26|-0.08|5.60|
|Controlling for Agro-ecological zones|||||||
|Neighborhood Correlation (r*)|0.316***|0.354***|0.191***|0.237***|0.278***|0.164***|
||(17.834)|(11.953)|(10.764)|(18.965)|(12.024)|(12.732)|
|Contribution of Agro-eco zones (r-r*)|0.046|0.031|0.027|0.029|0.020|0.034|
|% Decrease[((r-r*)/r)*100]|12.75|7.96|12.20|11.06|6.74|17.32|
|Controlling for Agro-ecological zones and mo|nsoon rain C|OV|||||
|Neighborhood Correlation (r*)|0.316***|0.350***|0.192***|0.236***|0.279***|0.162***|
||(17.830)|(11.828)|(10.761)|(18.905)|(12.020)|(12.615)|
|Contribution of all geo-climate (r-r*)|0.046|0.035|0.026|0.031|0.019|0.037|
|% Decrease [((r-r*)/r)*100]|12.74|9.09|11.87|11.48|6.39|18.79|

Robust z statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%
# **The Relative Income and Relative Deprivation Hypotheses**

_Paolo Verme_<sup>_2_</sup>

**Keywords:** Literature Review **,** Inequality, relative deprivation, relative income

**JEL:** I3, Z1

**Sector board:** Poverty Reduction (POV)

> 1 Paper prepared for the Handbook of Research on Economic and Social Well-being, D’Ambrosio, C. (Ed.), Edward Elgar Publishing Limited, forthcoming.

> 2 World Bank.
Policy Research Working Paper 10672

# **Abstract**

Various challenges are thought to render female-headed households (FHHs) vulnerable to poverty in the Arab region. Yet, previous studies have had mixed results and the absence of household panel survey data hinders analysis of poverty dynamics. This paper addresses these challenges by proposing a novel typology of FHHs and analyzes synthetic panels constructed from 20 rounds of repeated cross-sectional surveys spanning the past two decades from the Arab Republic of Egypt, Iraq, Jordan, Mauritania, the West Bank and Gaza, and Tunisia. The paper finds that the definition of FHHs matters for measuring poverty levels and dynamics. Most types of FHHs are less poor than non– FHHs on average, but FHHs with a major share of female adults are generally poorer. FHHs are more likely to escape poverty than households on average, but FHHs without children are the most likely to do so. While more children are generally associated with more poverty for FHHs, there is heterogeneity across countries in addition to heterogeneity across measures of FHHs. The findings provide useful inputs for social protection and employment programs aiming at reducing gender inequalities and poverty in the Arab region.

This paper is a product of the Development Data Group, Development Economics. 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 hdang@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 carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Likewise, a bias favoring men is also identified (Figure 7). On average, for the whole period studied, the passive coverage among men is 50% higher than the one recorded for women. These results show that women face greater challenge for maintaining a contribution volume that would allow them to be entitled to the old age benefit. This suggests that just as it happens in most countries in the region, women face a high rate of transition between formal and informal work and unemployment.

Figure 7. Percentage of Older people over 65 Years of Age Receiving a Contributory Benefit per Gender 2003 - 2016 <!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>20032004200520062007200820092010201120122013201420152016<br>Women Men<br><!-- End of picture text --> Source: author elaboration based on ENEMDU

# 3.2 Non-contributory system coverage

The scope of the Ecuadorian pension system discussed above shows one of the greatest problems faced by any contributory system: the coverage level is defined outside the social security system itself, while the dynamics and performance of the labor market trigger worker inclusion and exclusion. The contributory coverage reproduces the labor market patterns given that workers in intermediate age groups, having the highest physical productivity, with higher educational level, employed by large companies or in fields characterized by labor stability, have higher chances of contributing (Apella, 2007).

In order to reduce the coverage gap on passive population, in year 2006, the authorities introduced a non-contributory pension, managed apart from the contributory scheme, as a particular kind of transfer of the Human Development Bonus. Originally, this scheme was focused on older people in 13
Evidence on informal sector earnings is scarce. However, the available information appears to indicate that average earnings in the informal sector changed in the same direction as real minimum wages. Vivancos [1989), for example, found a

**Table** 2: Poverty in Latin America

(Percentage population under poverty line)<sup>12</sup> <!-- Start of picture text --> 1980/81 1986/87<br>Argentina 8.0 13.0<br>Brazil 17.7 23.3<br>Chile 14.4 19.2 (c)<br>Colombia 43.0 38.0<br>Costa Rica 29.0 (a) 24.0<br>Ecuador 53.0 55.0<br>Mexico 48.5 59.0 (d)<br>Peru 49.0 52.0<br>Venezuela 12.0 (b) 17.0<br><!-- End of picture text -->

**(a) 1977,** (b) 1982, Cc) 1985, (d) 1988

12The definitions used are different for each country. Thus, the numbers are not comparable across countries. The information for Argentina, Colombia and Pert were obtained from CEPAL (1985] and CEPAL (1989bJ; for Costa Rica and Venezuela from World Bank [l190]; for Brazil from Fox and Morley (1990]; for Chile from Pollack and Uthoff (19893 for Ecuador from Santos (1989]; and for Mexico from Lustig (1990].

10
- 10 -

Northwest. In the case of plant seedling auctions, Paarsch (1992) indicates that although the timber seedling auctions are likely to be characterized as the common value paradigm, the expected winning bid would decline until the number of participants reaches roughly 5 to 10.

By contrast, in the Internet-based auction, eBay, for a personal digital assistants device, the observed bid price tends to continue increasing with the number of bidders beyond the above-mentioned levels; the incremental effect would taper off only when more than 14 bidders participate. Under the experimental setting, Kagel and Levin (1986) show that bidders tend to submit relatively aggressive bids in auctions with 6–7 participants, compared with the case with 3–4 bidders.

<u>Table 1. Competition Effects in Various Auctions</u>

|Study|Type of contract|Est. optimal<br>no. of bidders|No. of<br>observations|
|---|---|---|---|
|Brannman_et al_.<br>(1987)|U.S. municipal bond underwriters spreads<br>in 1959-67.|5–8|9,420|
||U.S. offshore oil lease auctions from 1954<br>to 1975.|7–9|2,211|
||U.S. National Forest Service timber<br>auctions in 1977.|4–5|639|
|Paarsch (1992)|British Columbia's plant seedling contract<br>auctions between 1985-88.|About 5|144|
|Gupta (2002)|Highway construction procurement auctions<br>in Florida from 1981 to 1986.|6–8|1,937|
|Rezende (2005)|Palm III personal digital assistants auctions<br>at eBay in Oct-Nov, 2000.|About<br>more than<br>14|2,299|
|Iimi (2006)|Large-scale official development projects,<br>largely in the infrastructure sector, assisted<br>byJapan for1999-2005.|8|9<br>22|

Source: Author’s calculations from the original studies.

As far as infrastructure projects are concerned, the observed level of competition is less than the general norm—e.g., eight. Iimi (2006) indicates that in Japan’s ODA procurement, the average number of bidders is about six. In the U.K. PFI projects, half of them attracted at most two or three bids prior to 2004 (NOA, 2007). In 2005-06, this share increased to as high as 80 percent; more than 30 percent of the PFI projects were competed for by only two bidders. Similarly, PPI database reveals that no more than five firms participate in bidding competition for PPP transactions in developing countries (Figure 6).
Table 1

# Size Structure of Colombian Manufacturing. 1970-90

|Year|Estab<br>lish-<br>|-<br>Emplo<br>ment<br>'|y-<br>Paid<br>Employ<br>|-|Emplo|yment<br>by S<br>(Nu|ize of E<br>mber<br>of W|stablishment<br>orkers)|
|---|---|---|---|---|---|---|---|---|
||ment|000|ment||||||
||||<br>( 000)|<10|10-49|50-99|100-199|<br>200-499 500 and up|
|1970|7459|347.1|338.8|18.126|71.877|45,659|49r300|---<br>162.197---|
|1975|6354|456.8|450.5|4.230|90.074|60.456|71.458|--- 230.605---|
|1979|6763|516.7|509.2|5.588|90.929|681/3|78.475|--- 273.537----|
|1980|6850|508.5|||||||
|1981|6792|493.5|||||||
|1982|7067|481.1|||||||
|1983|" 6249|465.1|||||||
|1988|7244|483.0|475.3|2.553|107.152|67.672|77.559|101.363 126.663|
|1989|7586|492.7|484.9|3.827|108.B5|7 68.445|79.804|107.885 123.854|
|1990|7533|496.2|488.8|4.765|104.731|71.332|79.159|108.865 126.941|
|199<br>(per|0-Cens<br>manent<br>wo|us<br>530.0<br> <br>rkers<br>on|'<br>ly)|na|164,4|70<br>72.60|9<br>81.68|1<br>102.134 134.579|
|199<br>(All<br>w|0-Cens<br> <br>orker|us<br>622.0<br>s)|a|na|181.363|68.445|94.534|121.451 156.326|

a) Less than the sum of the other figures in the row since we have adjusted that total down by about 28.000 (permanent workers row) or 31.000 (total workers row) to exclude plants that would have more than 10 workers in total (i.e.including the temiporary ones) but less than 10 permanent workers. This figure excludes all workers in plants of less than 10 workers.

b) The sharp drop in the number of establishments and most or all of the decline in the number of workers reported in 1983 is due to exclusion of a group of previously included firms--those with less than 10 workers but having had 10 or more the previous year.

32