rafmacalaba/data-use-mentions-extended huggingface dataset:
) approach),<br>women|0,31|1.17/1.52|0.41/0.78|
|**Biewen and Juhasz**<br>**(2017)*****|GSOEP, 1999–2009|Satisfaction with hh income|Individuals≥17 yo living in<br>households with less than 6<br>members|Nonlinear FE ordered logit (based on Baetschmann et al.<br>(2015))|0,35|0,13|0,13|
||||Individuals≥18 yo, one- or|Pooled ordered logit|0.31-0.36|0.18-0.29/<br>0.29-0.37|0.10-0.27/<br>0.16-0.30|
|**Borah et al. (2018)******|GSOEP, 1984-2013|Satisfaction with hh income|two-adult-households with or|Nonlinear least squares|0.31-0.25|0.12-0.31|0.12-0.31|
||||without minor children|FE ordered logit (Baetschmann et al. (2015) approach)|0.26-0.15|0.16-0.13/<br>0.24-0.17|0.10-0.12/<br>0.15-0.14|

**Note:** GSOEP - German Socio-Economic Panel; SHP - Swiss Household Panel; BHPS - British Household Panel Survey

*Equivalence weights reflect period-specific equivalence scales based on satisfaction with income in case when the first child is 12 years old and the second child is 6 years old.

**Equivalence weights refer to the model with control variables

***Equivalence weights refer to OECD-type scale

****Equivalence weights reflect the cases without and with reference effect measured with household Mincer equation

36
A1 in the Appendix shows descriptive statistics of the variables used in the empirical analysis.

## _Maternal social capital_

The YL survey contains a module to measure multiple aspects of maternal social capital. It is based on the _Short Adapted Social Capital Assessment Tool_ (SASCAT) which is a shortened version of the _Adapted Social Capital Assessment Tool_ (A-SCAT) by Harpham et al. (2002) developed specifically to measure social capital in low-income countries. The SASCAT tool measures both the individual structural social capital and cognitive social capital.

In the present analysis, the mother’s social participation is used to measure structural social capital. It is defined as a dummy equal to 1 if the mother in the previous 12 months was a member of at least one of the following community organizations: work-related groups or trade unions, religious groups, community associations, cooperatives for saving or housing/credit/funeral

> 12 See WHO Child Growth Standards report (www.who.int/childgrowth/en) for a detailed description on the methods used to construct the standards for height-for-age, weight-for-age and weight-for-height. 13 Notably, the term “risk factors” is adopted from epidemiologic literature and describe only associations and not causal relations. The “risk factors” analysis substantially differs from “analysis of determinants” which in turn investigates causal relationships.

8
***<br>[0.238]<br>4,879<br>tobit<br>no<br>none<br>263<br>at includes c<br>l primate co<br> all pixels in <br>k distance to<br>ual average E<br>rrors, cluster<br>in column 6|
|(1)<br>ln(light)|-0.289*<br>[0.154]<br><br>-0.490<br>[0.303]<br>4,879<br>tobit<br>no<br>none<br>263<br>egression th<br>in 15 coasta<br>med across <br>oad networ<br>lly the ann<br> standard e<br>ely. Splines|
||distance(Primate)*_Poil_<br>dist(Primate)*_ln_(_Poil_)<br>ln(dist(Primate))*_ln_(_Poil_)<br>distance(Primate)<br>primate*year<br>distance(Primate)*year<br>primate<br>Observations<br>model<br>city FEs<br>city trends<br>left censored cases<br>Each column is a separate r<br>annual panel of 287 cities <br>lights digital number, sum<br>Distance(Primate) is the r<br>is the price of oil (specifca<br>cutof is light=5.5. Robust<br>one percent level, respectiv<br>stage estimation.|

31
# TABLE OF CONTENTS

Introduction

- I. Findings based on data from China, the home country

- II. Findings based on data from host governments

- III. Findings based on feedback from companies

- IV. Analysis and policy implications

1. Policy implications for host countries’ industrialization

2. Policy implications for investment promotion 3. Policy implications on labor and immigration issues 4. Implications for China, its government and firms

- V. Conclusion

Bibliography 2
and men’s role as “breadwinner”, women would be hesitant to join the labor force in greater numbers as that could “evoke anxiety and discomfort in oneself” while males would see female participation in the labor force as a “threat” to their own male identity. To the degree these factors influence women’s participation decisions, they can help to explain why female labor force participation rates remain so low and at the same time why many women may not feel as distressed by this outcome: _“Similarly women’s assumed lower desire for labor force participation (as in Mincer and Polachek {1974}, Bulow and Summers {1986}, and Lazear and Rosen {1990}) can be understood as the result of their identity as homemakers.”_ (Akerlof and Kranton, 2000, p. 732).

Based on these premises, this paper will draw on a recent literature that explores how the inclusion of the reference group into measures of relative deprivation can help to explain the apparent mismatch between objective statuses of labor deprivation and subjective perceptions of these same statuses. This is an important issue to understand in designing economic policies that aim to integrate women in the labor market. The empirical analysis of this paper is based on the Morocco Household and Youth Survey (MHYS) 2009-2010.

The paper begins by examining spatial inequality and relative deprivation across households using standard welfare measures and focusing on the rural-urban divide, one of the salient features of the Moroccan society. Our results conform to previous studies finding that the urban-rural divide in Morocco is very large by world standards, with rural areas showing much lower income levels. This is a recurrent finding in North-Africa and the Middle-East but also one that is at odds with the fact that urban areas tend to be louder when it comes to social and economic claims.

3
level were negatively related to educational achievement because of the constraints imposed on municipal management.

22 A study of Cox and Gonzalez (1998) shows increasing overall SIMCE scores in the 1990-1996 time period and a decreasing gap between municipal and private schools. Additionally, there are also indications that within the same municipal sector, test scores became slightly more homogeneous across schools. Gallego (2002) shows convergence among private non-subsidized schools and subsidized schools in the 1988-1999 time period.

23 Sapelli and Vial (2002), using the new individual data available for the 1998 SIMCE test score, estimate the average treatment effect parameter and treatment on the treated for those students effectively choosing a private voucher school, controlling for the selection bias caused by school choice.

24 Gallego (2002), using roughly 5,000 Chilean schools in the 1994-97 period and econometric techniques robust to endogeneity, presents empirical estimations that show a positive and relevant effect of 24
data from the 1990s for firms in six emerging economies that experienced a financial crisis (Indonesia, Malaysia, Mexico, the Philippines, the Republic of Korea and Thailand), Love, Preve, and Sarria-Allende (2007) find that firms with high short-term debt levels, an indicator of financial vulnerability due to the increased interest rates and difficulties in rolling over debt during a crisis, reduced their provision of trade credit significantly more than other firms in response to the aggregate contraction in bank credit. In comparing the crisis episodes across countries, they found that the countries that experienced a sharper decline in bank credit also experienced a sharper decline in trade credit. Their interpretation is that firms with better access to capital will redistribute the credit they receive to less advantaged firms via trade credit only if they able to, and that under the extreme scenarios imposed by financial crises, all potential sources of funds including bank credit tend to dry up. Thus, trade credit cannot serve as a substitute for other types of formal credit during extreme financial distress because there are very limited funds that could be redistributed.

# **4. Trade Credit in Advanced versus Lower Income Economies**

In the discussion in section 3, an assumption was that underlying legal and institutional foundations were sufficient to support trade credit arrangements when business disputes arise (e.g., with respect to customer repayment or supplier product quality). Much of the evidence in that section therefore focused on advanced economies, though even there, smaller, lessestablished suppliers extend longer trade credit terms to buyers (Klapper, Laeven, and Rajan, 2012) and the growth-supporting effects of trade credit were not evident for start-up firms (Fisman and Love, 2003). In the absence of reliable supporting legal and institutional foundations, the issue of trust between supplier and customer becomes even more salient and thus less developed countries provide useful laboratories to investigate how this challenge is overcome in practice.

# **4a. Trust or institutions?**

Knowledge of, and trust in, each
we found that even women who do not live with their husbands live with other male relatives, including fathers, fathers- or brothers-in law, uncles, and sons. In all our fieldwork, we only encountered seventeen women who lived alone or without any older male relative. Of these seventeen women, all but six lived with their mothers- or sisters-in-law. The presence of these relatives in the house represents restrictions on the woman’s empowerment, even if she lives without her husband.

Several other characteristics, such as the number and age of daughters, the spousal education ratio, and the woman’s time to collect water, are not statistically different for participants and nonparticipants. Further, none of the wealth indicators, including number of rooms, electrification, improved toilet facilities, materials used in floor and wall construction, are different for these two groups, suggesting that poorer participants neither select into the program nor are they targeted based on indicators of wealth (number of rooms, electrification, access to improved toilet facilities, and nature of the construction materials used for the floor and walls of the house). Nonetheless, this table highlights the importance of controlling for selection in to the _Mahila Samakhya_ program. In addition to the variables listed above in the Survey Description, we also control for the imbalanced characteristics discussed here.

# **6 Empirical Analysis**

## **6.1 Methodology**

We use several methods to identify the effect of the program in the presence of self-selection. We first match women by their pre-existing bargaining outcomes and other characteristics across treated and untreated districts. We then control for potential issues of truncation in the matching. Next, we use an instrumental variables approach, where our instrument is the interaction between the time the program has been in the village and the years over 16 of the woman to capture the exposure of the woman to the program. We separately control for the age of the woman in both first and 14
Policy Research Working Paper 10064

# **Abstract**

There is limited research on the determinants of firms’ green investment strategies in developing regions despite their importance to meet global climate change targets. Understanding how changes in firm climate investment affect environmental performance is essential for policy makers and firms alike. Based on unique data from the joint European Bank for Reconstruction and Development–European Investment Bank–World Bank Group Enterprise Surveys, this paper empirically examines the role of access to finance and green management practices in firms’ green investment strategies. Based on logistic regressions, the econometric analysis finds a positive influence of green management practices on the number of mitigation measures implemented. By contrast, firms that are financially constrained are less likely to pursue many mitigation measures. Finally, the results do not show significant differences in the impact of financial constraints on the type of green investment, but indicate that better green management practices lead to a higher likelihood of investing in both capital- and non-capital-intensive green measures.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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 f.kalantzis@eib.org and schweigh@ebrd.com.

_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
quality of human capital has been a critical ingredient in the East Asian Miracles’ success in the world market.

- **<u>Are labor market rigidities preventing more adjustment?</u>** As noted above, what the IADB found surprising was the low level of intersectoral allocation of workers and, overall, the literature has found modest changes in rates of turnover. Again, a much broader agenda is required on the impact of regulation on technological adoption, that would, in all probability, increase both flows, and the ability of small firms to exploit economies of scale.

# **III. Protecting Workers**

The issue of the optimal design of worker protections is complicated and the region has made some small progress on the issue in recent years. This section discusses the importance of the phenomenon of informality, the deficiencies of extant employment protections, the impact of nonwage costs, the employment and poverty implications of minimum wages, and the impact of unions. Heckman and Pages (2004) have summarized much of the recently emerging literature and only select references will be mentioned here. A theme throughout this section is that the study labor legislation needs to move away from a traditional focus on segmentation and job creation in a static sense, toward a focus on the labor market as an integral part of the National Innovation System, the constellation of institutions and firms that are involved in dynamic productivity gains through technological progress. (See De Ferranti et al 2003).

# **a. Informality**<sup>**5**</sup>

The informal sector merits treatment first not only because it is ostensibly the largest mass of unprotected workers, but also because recent evolution in thinking has implications for the design of social protection systems.

**_The informal sector comprises both workers preferring formal sector employment (involuntarily informal) and those preferring the qualities of unregulated entrepreneurship._** In motivation surveys from Brazil and surveys of workers leaving formal employment in Mexico, 60-70%
Using data for 29 countries covering 19 years, we find that telecommunications reforms have been associated with significant changes in the European labor markets. More specifically, they have been accompanied by disproportionately higher employment creation and an increasing share of individuals working from home and part‐time among ICT‐intensive sectors. These results are robust to a number of alternative specifications. In contrast, we do not find evidence suggesting that the Internet has been associated with an increase in self‐employment, temporary work and second job holding.

We focus on Europe for several reasons. First, many European countries have witnessed a dramatic increase in alternative work arrangements, and while there is a large body of literature examining the drivers of these trends, there is no empirical evidence on the role played by the Internet. Second, while telecommunications reforms may be a noisy measure of policy‐driven Internet adoption when considering countries at very different levels of economic development, we expect that this concern would be attenuated when restricting the analysis to European economies. Finally, it allows us to use harmonized labor force surveys with detailed information on alternative work arrangements for 29 countries over a time span covering almost twenty years.

To our knowledge, this paper is the first to provide empirical evidence supporting a positive link between the expansion of the Internet and the rise of part‐time employment. The share of workers in part‐time jobs has dramatically increased in most European countries since the early 2000s, with more than 20 percent of workers having a part‐time job across many European countries in 2014 (see Figure 1). This trend has important welfare implications, as part‐time workers are more likely to be poor and a growing fraction of them find themselves in part‐time work involuntarily, as they cannot find a full‐time job (OECD [2016]; Horemans and Marx [2013]). Several hypotheses have been put forward to explain the rise of part‐time work in Europe, such as more flexible labor market regulations, an
<mark>We proceed with the construction of the skills measures applying the same steps described in section A of this appendix. In the first step, we c</mark> alculate the mean and standard deviation for each activity associated with any of the five types of skills across the sample of 899 occupations using as weights the share of each occupation at 2-digit ISCO 2008 level in total Indonesian employment in 2003 using information from Sakernas, i.e., previous to our period of analysis. Second, we add all the activities associated to each skill measure to obtain score measures. Third, we estimate the 1-digit occupation level average score measure using as weights the contribution of each occupation at 2-digit level into the 1-digit category using Sakernas 2003 data. Finally, we standardize each of the five scores by subtracting its mean and dividing by its standard deviation.

In Figure A1 we compare the value of the skills measures we obtained using the Philippines STEP survey and the U.S. O*Net database. For the non-routine analytical and non-routine manual skills measures we find the highest rank correlation coefficients –0.75 and 0.67 respectively. For the routine manual and interpersonal skills measures we find rank correlation coefficients of 0.49 and 0.57 respectively. For non-routine cognitive the rank correlation coefficient is negative. We believe this can explained by the different type of information we captured from STEP and O*Net to construct this measure. In the STEP survey, the variable we use captures the structured vs. unstructured nature of the job, while in the O*Net database the routine cognitive measure comes from the activity “documenting/recording information”.

56
related to poverty mapping.) For examples of surveyto-survey imputation for poverty analysis, see Stifel and Christiaensen (2007), Tarozzi (2007), Grosse et al (2009), and Douidich et al (2013).

A more formal presentation of the S2S model is as follows. There are surveys: in Survey 1 there are information on the income or consumption as well as the set of household characteristics ���. In the Survey 2, the same set of household characteristics are also observed, and those characteristics are comparable between the two surveys.

The (log) of per capita household income or consumption is modeled for the first survey as: ��� ��′������� where ��� is the per capita income or consumption of household _h_ residing in area _c_ , ��� are household and area/location characteristics, and ��� ��� ���� is the residual composed of the area component �� and the household component ���, which have zero expectations and are independent of each other.

In the second survey where there is no income or consumption information, a set of common variables ��� will be use to impute the income or consumption for each household in the second survey using the estimated points and their distributions estimated from the survey 1. Since each estimated point is fluctuated from an assumed normal distribution with its standard errors, the imputation is done through a number of simulation in order to preserve the error structure of the correlates.

33
smaller in Bolivia. The differences in cognitive skill endowments are significant in Bolivia, Vietnam, and Kenya but do not significantly explain the gender wage gaps in these countries over and above schooling and noncognitive skills. In contrast to these findings, Nordman et al. (2014) found that adding cognitive skills to the model reduces the unexplained portion of the wage gap by 2 percent in Bangladesh. The measure of cognitive skills used in their study is self-reported, and not a direct assessment of reading proficiency skills like the one available in the STEP data.

For our second measure of cognitive skills—complexity of computer use at work—we find that the estimated wage gap shrinks significantly in Bolivia and Georgia but increases in Armenia and Kenya when computer skills are added to the model. The addition of computer skills, along with schooling, noncognitive skills, and reading proficiency scores, explains an additional 3 percent of the unexplained wage gap in Bolivia but shows no effect in other countries.

23
this exercise is to measure the effect of size-dependent distortions in the presence of bribery and to compare it with the previous literature. In the second quantitative exercise, I calibrate model parameters including distortion rates and the fix cost of bribery by taking advantage of the bribery payment data for Türkiye from the WBES. The goal of this exercise is to measure the level of distortions as well as the role of bribery in an economy with distortions.

The U.S. exercise shows that bribery opportunities decrease the negative effects of sizedependent distortions on the mean plant size and on the output. Since bigger plants have more resources to spend on bribery, they are able to circumvent the size dependency of distortions. Hence both small and big plants are subject to similar distortion levels after bribes.

For the second quantitative exercise, I use the data on bribery payments of big and small plants, as well as the plant size distribution of Türkiye, in order to calibrate key parameters of the model including the size-dependent distortion rate and the return on the bribe parameter.

My results indicate that the inferred distortion levels are sizable: plants are subject to a distortion rate of 28.3% after the inclusion of bribery payments. Small plants pay almost all of their bribe for the associated fix costs since their estimated return on bribe is low. On the other hand, the return on bribes is high enough for big plants since they are subject to higher distortion rates.

Finally, removing size-dependent distortions, and so the incentives for bribing, results in substantial increases in output and mean plant size. Since the size-dependent distortions affect bigger plants more than small plants, bigger plants increase their input demand more than small plants by the removal of distortions. As a result, if distortions associated with being assigned to a corrupt official were removed, aggregate output and mean plant size would grow by 63.6% and 82.5% respectively.

The rest of the paper is organized as follows. The next section provides a concise literature review. Section 3 describes