rafmacalaba/datause-encoder-data huggingface dataset:
namely Cambodia (2003), China (2002, 2003), Indonesia (2003), Malaysia (2002), Philippines (2003), Republic of Korea (2005), Thailand (2004), and Vietnam (2005).

The information available in the _Enterprise Surveys_ has several advantages for analyzing this topic. First, the data are based on a common questionnaire across a large set of countries, yielding comparable information on several firm level characteristics. In particular, the survey collects information on age, size, geographical location, 3-digit International Standard Industrial Classification (ISIC) sector of activity, and foreign and public ownership. Most importantly, it collects information on whether the firm participates in international trade (captured by the import and export shares). Second, the surveys collect detailed and comparable information on the current skills of the workforce, both with the share of workers with secondary and upper education as well as with the share of skilled workers (proxied by managers, professionals and non-production workers). Finally, the surveys also collect detailed balance sheet information at the firm level including total sales and value added. Table 1 reports the summary statistics and table A.1. in the appendix defines the main variables used in the paper.

The original data cover 9,776 firms distributed across a wide range of sectors (Manufacturing 77.54 percent, Construction 1.37 percent, Services 20.09 percent and Agro-Industry 1 percent). Within the manufacturing sector numerous industries are covered: auto and auto components, beverages, chemicals, electronics, food, garments, leather, metals and machinery, non-metallic and plastic materials, paper, textiles, wood and furniture.

A major caveat in these surveys is the reduced representativeness of the nonmanufacturing sample in most countries. Services are only a significant part of the sample for Cambodia, China, Republic of Korea and, to a lesser extent, Vietnam. For all the other countries, non-manufacturing sectors are either not included or have a reduced sample. This naturally compromises the comparison of the main findings separately for manufacturing and services. We thus choose to report the main empirical findings of the paper only for the sample of manufacturing. This is a severe limitation of the paper since the shares of skilled labor have increased more in services than in manufacturing across most of the countries in the region (see Di Gropello and Sakellariou, 2008).

7
<mark>Nunn, Nathan (2007). “Relationship-Specificity, Incomplete Contracts, and the Pattern of Trade,”</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>122(2): 569–600.</mark> <mark>Olivetti, Claudia and Petrongolo, Barbara, (2014), Gender gaps across countries and skills: Demand, supply and the industry structure,</mark> _<mark>Review of Economic Dynamics</mark>_ <mark>, 17, issue 4, p. 842859. h</mark> <u>ttps://doi.org/10.1016/j.red.2014.03.001</u> <mark>Olivetti, Claudia, and Barbara Petrongolo. 2017. "The Economic Consequences of Family Policies: Lessons from a Century of Legislation in High-Income Countries."</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>, 31 (1): 205-30.DOI: 10.1257/jep.31.1.205</mark> <mark>Ouedraogo, Rasmane and Marlet, Elodie, (2018), Foreign Direct Investment and Women Empowerment: New Evidence on Developing Countries, No 2018/025, IMF Working Papers, International Monetary Fund.</mark> Rajan, R. G., and L. Zingales (1998). “Financial Dependence and Growth,” _American Economic Review_ 88(3): 559-586.

<mark>Rossin-Slater, Maya (2017), Maternity and Family Leave Policy, No 23069, NBER Working Papers, National Bureau of Economic Research.</mark> <mark>Ruhm, Christopher, (1998), The Economic Consequences of Parental Leave Mandates: Lessons from Europe,</mark> _<mark>The Quarterly Journal of Economics</mark>_ <mark>, 113, issue 1, p. 285-317</mark> <mark>Schmieder, Julia (2021), Fertility as a driver of maternal employment,</mark> _<mark>Labour Economics</mark>_ <mark>, 72(C): 1-16.</mark> <mark>Tunalı, İnsan, Murat Kırdar, and Metlem Dayioglu (2021), Down and up the “U” – A synthetic cohort (panel) analysis of female labor force participation in Turkey, 1988–2013,</mark> _<mark>World Development</mark>_ <mark>, 146(C): 1-17.</mark> <mark>Uribe, Ana Maria Tribin, Vargas, Carmina and Bustamante, Natalia Ramírez, (2019), Unintended consequences of maternity leave legislation: The case of Colombia,</mark> _<mark>World Development</mark>_ <mark>, 122, issue C, p. 218-232.</mark> <mark>Viollaz, M., and H. Winkler. 2020.</mark> _<mark>Does the Internet Reduce Gender Gaps?: The Case of Jordan</mark>_ <mark>. Washington, D.C., USA: The World Bank Working Paper.10.1596/1813-9450-9183</mark> <mark>Vo, Thang T. and Ha, Truong Thiet, (2021), Decomposition of gender bias in enterprise employment: Insights from Vietnam,</mark> _<mark>Economic Analysis and Policy</mark>_ <mark>, 70, issue C, p. 182-194.</mark> <u>https://doi.org/10.1016/j.eap.2021.02.007</u> World Bank (2011). World Development Report 2012: Gender Equality and Development. Washington, D.C.: World Bank.

46
- **13 -**

**coefficients** would thus lead to significantly Inaccurate assessments of the effect of credit on output supply.-

# **V. NPLICATIONS**

Based on the estimated coefficients, if every credit-constrained household In the sample is given an additional credit of 17.82 yuan (equal to 1 percent of the average level of liquidity of the credit-constrained households), the total output of these households may be projected to increase by 201.08 yuan, or approximately 0.04 percent of the total output. Thus, on - average, one additional yuan of liquidity (credit) would yield 201.08/(17.82 x 48) 0.235 yuan of additlonal gross value of output. These results suggest that for the area of China covered in the present study, a significant proportion of the short term credit provided by the rural credit cooperatives as production ci-edit" may actually be utilized for consumDtion and investment. Indeed, medium and long term formal credit is practically nil amongst the agricultural households In our study areas, and a similar picture is given by aggregate statistics. Rolledover short term credit is sometimes utilized to finance small scale investments. A recent study by Feder et al. (1990b) finds that the diversion of short-term credit for farm investment is about 40 percent for an average household in the study area. This, in turn, Implies that almost a third of the formal credit is utilized for consumption (whether of current goods or durables).

The results of the study highlight two important factors which should be considered when evaluating the likely impact of agricultural credit expansion: (i) Not all farmers, and sometimes only a minority, are constrained in their farming operations by inadequate credit; (ii) Expanded supplies of formal credit will be diverted in part to consumption, thus the likely output effect will be smaller than that which is expected when all funds are assumed to be used productively. These ideas have been propounded by the Ohio State school critics of credit supply-led development schemes. The present paper thus provides empirical verification of these views.
of early childhood for human capital investment (Almond, 2006; Cunha et al. 2006; Heckman 2008; Maccini and Yang, 2009; Cunha and Heckman 2010; Almond and Currie 2011). Our estimates suggest that this program is highly cost-effective.

Our paper contributes to four strands of literature. The first is the literature of the effect of safe drinking water programs. While the literature on water programs is not small (see Esrey et al., 1991; Tonglet et al., 1992; Jalan and Ravallion, 2003; Fewtrell et al. 2005; Galiani et al., 2005; Clarke et al., 2009; Maimaitwe and Siebert, 2009; Gamper-Rabindran et al., 2010; Kremer et al., 2011), there have been few studies that rigorously examine the effect of _water treatment and water quality_ , especially on final educational attainment. Kosec (2014) uses child-level data from 39 African countries and finds that private sector participation in the piped water sector decreases diarrhea among urban under-five children, and is associated with a 8 percentage point increase in school attendance of 7-17 year olds.<sup>3</sup> However, the water treatment program in Kosec (2014) concerns access to piped water. It does not deal with change in water quality, and nor does it examine the long-term effect on final schooling attainment. We differ from this literature in focusing on water treatment program of which changes in water quality is a key component, examining the long-term effects on children’s final educational attainment, along with how the effect differs by gender and exposure at different periods of the early life cycle. The second is the literature of human capital investment and the gender division of labor pioneered by Pitt et al. (2012). We add to this literature by providing a dramatic example of how a large-scale health intervention ends up improving rural girls’ education much more than rural boys’ education through the brawn channel. Indeed, the relative female gains over male is so big that the rural gender gap in education is completely eradicated—with all this being accomplished within a short span of two decades. The third contribution is to the literature of the long term effects of early childhood conditions (see Maccini and Yang, 2009; Cunha and Heckman, 2010; Almond and Currie, 2011). We show that exposure to treated water at very young age (i.e., 0-2 years of age) had much more pronounced effect on a person’s final educational attainment than exposure at later life stages.<sup>4</sup> Finally, there have been few studies that examine the cost effectiveness of

> 3 Galiani et al. (2005) also find strong benefits on child mortality of private sector provision of water, but they do not examine the impact on education.

> 4 Maccini and Yang (2009) find that Indonesian women experienced long-term gains in education (and other socioeconomic outcomes) when they had favorable weather shocks at their birth years.  No such effects exist for

5
informal labor. The low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.

It should be highlighted that these estimates represent the short-run impact of the inflow of refugees to Turkey. Until recently Turkey’s position had been that these refugees were temporary and would return to Syria soon. Going forward the key issue is how refugees will continue to integrate into the Turkish labor market and society. In particular, in January 2016 Turkey decide to grant Syrian refugees increased access to labor markets. The other major source of uncertainty is the large-scale onward migration of refugees to Western Europe, which likely not only affects the number but also the composition of refugees in Turkey.

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29
# I. INTRODUCTION

This report examines the relationship between reproductive behavior and child survival in Egypt. The relationship is of fundamental importance to an understanding of demographic dynamics in Egypt and for the formulation of population policies.

Various facets of the fertility - mortality relationship have been central concerns of demographic theory and research for many decades. Classical - demographic transition theory, as formulated in the 1930s 1950s, posits a lagged response of fertility to mortality: mortality declines, themselves a conseauence of exogenous improvements in standards of living and public health services, prompt corresponding declines in fertility, in order to prevent sustained rapid population growth and the stresses it causes to families at.d larger social groups. A review of the empirical evidence in the mid-1970s concluded that neither aggregate-level nor micro-level responses of fertility to infant and child mortality of the magnitude implied by classical demographic transition theory could be detected in historical European and contemporary Third World data (Preston, 1978). A more recent detailed micro-level investigation with World Fertility Survey (WFS) data from 25 developing countries (not including Egypt) suggests a somewhat stronger fertility response; nevertheless, on average the estimated response is one-half a birth for each child death, i.e. far from full fertility corpensation for child deaths (Cochrane and Zachariah, 1984). The fertility response can consist of voluntary and involuntary components; the mechanisms that may operate at the micro-level will be described in more detail in section II of this report.

1
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# **APPENDIX**

41
Since sparsity, rather than density, is the focus of the current body of work relating to the PICs, we focus on analyzing the inverse of this density measure: a dispersion-adjusted measure of population sparsity. It is defined as: The examples in Table 1 demonstrate ways in which the value of this indicator varies in different empirical settlings. Dispersion-adjusted density is higher in panel B than panel A, reflecting the greater concentration of the population in the most highly populated (dark blue) node. In contrast, a straightforward density estimate would not differentiate between these two settings (since their overall population, and overall area of terrain, is the same). Consider now Panel A and Panel E. The pattern of population distribution is the same across these two settings, and this is reflected in their common geographical scale of dispersion. However, dispersion-adjusted density is twice as high in panel E as in panel A, reflecting panel E’s larger population.

# 2. Evaluation for the PICs

In evaluating our indicators of local-area level remoteness, the geographical scale of dispersion, and dispersion-adjusted density for the PICs, we rely upon data from population and housing censuses, collected by country National Statistics Offices and made available through the Pacific Community (SPC) PopGIS platform (see Appendix A for detail). We access data for Fiji (2007,<sup>6</sup> by Tikina), Solomon Islands (2009, by Constituency), Vanuatu (2009, by Area Council), Kiribati (2010, by island/atoll grouping), FSM (2010, by Municipality), Tonga (2016, by District), RMI (2011, by atoll grouping) and Tuvalu (2012, by village). Note that some of these census data are quite dated, so the results (which reflect the distribution of the population at a particular point in time) presented in this paper should be interpreted accordingly.

The distribution of local-area remoteness measures, and the country-level geographical scale of dispersion are illustrated for each of our eight PIC case-study countries in Figure 1. Distances are indicated on the horizontal axis, and the population share is indicated on the vertical axis. Columns shows the share of the population living in local areas with remoteness measures within a particular distance interval. The geographical scale of dispersion—the population-weighted average remoteness—is marked with a vertical red line.

> 6 Note that a more recent census was administered in Fiji in 2017, but the associated data were not available through PopGIS at the time this paper was prepared in March 2019. See Appendix A for more detail on the source data.

7
2

# **1. Introduction**

There is wide agreement that achieving net-zero emissions globally requires a rapid increase in the pace and scale of adoption of green technologies. However, to date there has been relatively little empirical evidence on the drivers of green technology _adoption_ , with much of the literature focusing instead on green _innovation_ , measured by patenting (e.g. Popp, 2002, 2019; Aghion et al., 2016; Dechezleprˆetre, Glachant and M ́eni`ere, 2013; Acemoglu et al., 2012).<sup>1</sup> As a result, there remains an open debate over the importance of energy and carbon prices in influencing green technology adoption. Empirical papers on green technology adoption have typically focused on a specific technology in a specific setting (e.g cooling and heating equipment in the US by Newell, Jaffe and Stavins, 1999) and find contrasting results about the role of energy or carbon prices.<sup>2</sup> This paper aims to fill this gap by providing new empirical evidence on the diffusion of a comprehensive range of green technologies across establishments over several countries over time. We follow a growing literature that infers the spread of new technologies through their real-time footprint in the demand for new tasks or skills in the text of job postings (e.g. Acemoglu et al., 2022; Goldfarb, Taska and Teodoridis, 2023). We use a rich and high-frequency dataset of some 580 million online job postings in 16 advanced economies from Lightcast from 2014 to the end of 2022.<sup>3</sup> We create a list of keywords relating to green technologies from the European Patent Office (EPO) Y02 classification of patents related to ‘climate change mitigation technologies’ and classify job postings as green technology-related if they mention these keywords (or close synonyms). We then match the job postings data to firm-level data including balance sheet information and energy consumption from Factset and the Carbon Disclosure Project (CDP).

We first show that growth in openings related to green technologies was modest from 2014 to 2018, before increasing slightly in 2019 and then accelerating rapidly in 2022. The share of all job postings mentioning green technologies more than doubled between 2019 and 2022. Rapid growth in green technology-related hiring was strongest in Europe but occurred in three quarters

> 1See H ́emous and Olsen (2021) for a recent review.

> 2For example, while Newell, Jaffe and Stavins (1999), and Delarue, Ellerman and D’haeseleer (2010) find positive impacts of carbon prices on the adoption of different green technologies, Klemetsen, Rosendahl and Jakobsen (2020) and Calel (2020) do not find any such effects.

> 3Lightcast was formerly Burning Glass Technologies.