rafmacalaba/data-use-mentions-extended huggingface dataset:
through higher destination wealth in cities, which we know, from Table 3, is 0.69 in favor of cities (0.038*0.69=2.6). The first panel (‘ALL’) in Figure 4 visualizes this horse race between distance and wealth for urban migrants. We can also conduct this exercise using all migrant-destination pairs in Table 4. Here towns have a 6.5 percentage point higher likelihood of being chosen (0.025*2.58) and the wealth effect does little in favor of cities reducing that number by only 0.4 percentage points (0.006*0.69). Clearly, the pull effect of higher wealth in cities is much lower than the discouragement effect of the corresponding increase in distance. We conjecture that that is the reason many people prefer towns over cities.

Who does end up going to the city? Our heterogeneity analysis in Table 5 shows how wealth at destination has a larger pull effect for the non-poor and the educated and how the negative effect of distance on destination choice is attenuated by schooling, but exacerbated by poverty. Our simulations depicted in Figure 4 show that the distance effect becomes very attenuated for high levels of education. The deterrent effect of distance is over five times stronger for those without education compared to those with higher secondary education. In fact only for this latter group is the distance effect is so attenuated that the wealth effect (which increases with education, although much more modestly) becomes stronger and cities become more attractive than towns. As the regression results do not simultaneously interact with other individual characteristics, we do not conclude from this analysis that raising education levels would increase migration to cities. Rather the results indicate that there exists a type of respondent for whom the trade-off between distance and wealth no longer favors towns as a destination choice. Such differential frictions would lead to the kind of sorting documented by Hamory et al. (2020) and Young (2013). This may result from
When the supply of casual manual labor is positive, it is decreasing in both αi and C0i, and increasing in Wi. Equation (3.1) can also be written as: αi = Wi/Ci. The intuition here is that Wi/Ci reveals the preference of the household for this type of work: a household with a high disutility attached to doing this type of work will ask for a high wage rate.

We do not assume that everyone has the same preferences. There are clearly idiosyncratic differences in the disutility attached to doing casual labor, which may reflect personal labor histories, caste, gender and demographic characteristics. A natural choice for the common preference parameter in making inter-personal comparisons of welfare is α�= Q50 Wi , the � Ci � median of the observed ratio of the wage rate to consumption per capita. However, we will test sensitivity to this choice.

The above formulation assumes that the individual is free to supply any amount of labor to casual work—that there is no involuntary unemployment. This assumption can be questioned; some workers may be unable to get as much work as they would like at the going wage rate. We will use a specially designed survey that allows us to identify a sub-sample of those who are not rationed, to test if this alters our main results.

# **3. Data**

We use two household data sets. The first is the 66th round (July 2009-June 2012) of the Employment Schedule (“Schedule 10”) of the Government of India’s National Sample Survey (NSS). This dataset includes household characteristics (including demographics, NREGS participation, social groups) and information on household members’ education, principal and subsidiary activity and time disposition during the week ended (block 5.3). The questionnaire also includes a block on monthly household expenditures during the last 30 days (block 9). We observe the weekly supply of casual manual work and also the daily wage rate for this type of work
, MA. Appleton, S., and A. Balihuta. 1996. “Education and Agricultural Productivity: Evidence from Uganda.” _Journal of International Development_ 8 (3): 415–44.

Asadullah, M. N., and S. Rahman. 2009. “Farm Productivity and Efficiency in Rural Bangladesh: The Role of Education Revisited.” _Applied Economics_ 41 (1): 17–33.

Annual Status of Education Report. 2005. Published by ASER Center, New-Delhi.

Annual Status of Education Report. 2014. Published by ASER Center, New-Delhi.

Brock, W. A., and S. N. Durlauf. 2001. “Interactions-Based Models.” In _Handbook of Econometrics_ , vol. 5, edited by J. J. Heckman and E. E. Leamer, 3297–380. Amsterdam: Elsevier.

Case, A. 1992. “Neighborhood Influence and Technological Change.” _Regional Science and Urban Economics_ 22 (3): 491–508.

Census of India. 2011, http://censusindia.gov.in/2011-Common/CensusData2011.html.

Conley, T. G., and C. R. Udry. 2010. “Learning About a New Technology: Pineapple in Ghana.” _American Economic Review_ 100 (1): 35–69.

Damodaran, H. 2008. _India’s New Capitalists: Caste, Business, and Industry in a Modern Mation_ . London: Palgrave Macmillan.

Deliège, R. 2004. _Les Castes en Inde Aujourd’hui_ . Paris: Presses Universitaires de France. Foster, A. D., and M. R. Rosenzweig. 1995. “Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture.” _Journal of Political Economy_ 103 (6): 1176–209.

Gille, V. 2012. “Education Spillovers: Empirical Evidence in Rural India.” _Indian Growth and Development Review_ 5 (1): 4–24.

Jackson, M. O
11 80 percent of children aged 6-14 attend formal school in our sample. No differences are observed for school enrollment between boys and girls.

12 The use of plough shaft, ploughshare, plow, beam, and animal force is used for these activities.

13 The Coefficient of Variability (%CV) for the self-reported measures of child labor across three seasons is estimated as _CV_ = ( _SD_ / _Mean_ ) × 100. The lower the %CV, the less inter-variability.

14 Corresponding Tobit specifications were also implemented at the intensive margin as the dependent variable has limited support with a mass point of zero. Qualitative findings do not change and thus we do not report these estimates.

15 Descriptive statistics show that boys and girls spend the same number of hours in household chores and school activities.

16 We did not collect information on household chores and school enrollment in the first household survey (main rainy season) due to time constraints as this multi-topic survey was extensive.

17 This result does not change if we include a triple interaction of treatment indicator and child’s gender and age.

18 Questions about Fairtrade knowledge were asked in the first survey round (main rainy season). Households with response of “don’t know” were treated as missing observations. For ease of interpretation, we normalized the estimated indices by using the mean and standard deviation of the control group 19 We set significance level at α=0.05, power 1-β=0.8, and evenly split samples for treatment and control groups.

20 A detailed breakdown of the budget data for this survey design intervention reveals that the share of training costs, transportation costs, field supervisors’ and local senior researchers’ honorariums, and institutional fee is mostly independent of self- or proxy-reporting survey, while it is 32
# 1. Introduction

Preferential trade agreements (PTAs) are a prominent feature of current globalization. Over the last decades, country participation in PTAs has become widespread, with all members of the World Trade Organization (WTO) having signed an average of 10 PTAs, up from 3 PTAs in 1990. Most importantly, the proliferation of PTAs was accompanied by a significant deepening of their scope. Their content spans diverse non-trade related disciplines such as _investment_ , _intellectual property rights_ , _visa and asylum_ , _labor market regulations_ and _environmental laws_ . Meanwhile, the expansion of the fragmentation of production has shifted the composition of trade towards more flows of differentiated intermediate products and less of homogenous goods. This resulted into a rise in global value chains (GVC), reinforcing the need to concurrently regulate the exchange of goods and services with foreign direct investments (FDI) under a common framework (Antràs & Staiger, 2012).

Moreover, bilateral investment treaties (BITs) that have traditionally regulated foreign investments are losing ground as more investment related disciplines are incorporated in PTAs (Figure 1). By removing economic uncertainty regarding market access rules and behind-the-border business conditions, PTAs are increasingly relevant in the context of cross-border trade and investment. Their prevalence has revived interest in the literature on regionalism, as researchers examine the role of this new generation of deep trade agreements in shaping the exchange of goods and services as well as investment. Understanding the economic effects of PTAs is essential to design and implement them efficiently.

This paper contributes to the debate on the economic impact of deep trade agreements by empirically investigating the key disciplines that promote FDI. We use a new data set on the content of PTAs developed by the World Bank and bilateral cross-border data on greenfield investments run by the Financial Times. The latter provides information on the sector and business activity associated with an FDI 2
with sector-specific implications. Using the numerical model parameterized to US data, we find important quantitative differences between the policy approaches. Notably, with the same carbon price, abatement- and intensity-based rebating policies lead to two to three times

> 8A caveat, which we note in a later section of the paper, is that when firms are heterogeneous, IBER does entail additional distortions, due to the mechanism of leveraging emissions payments to incentivize additional reductions. Firms with lower baseline emissions payments get smaller rebates, and firms with higher-than-average baseline emission intensity have a harder time meeting the rebate criteria. As a result, marginal abatement costs will not be equalized across firms.

5
, poverty is lower in the group of larger cities than in other urban settings.

It is possible, of course, that poverty is both more widespread and deeper in smaller towns, but that population is so concentrated in large cities that the bulk of the poor live there. If this were the case, greater attention to (and resources for) metropolitan poverty might be justified on the basis that the _share of poverty_ is greatest there. But Table 2 shows that this is nowhere the case. In fact, the share of the poor is lower than the population share in every country that has a metropolitan area: the difference is relatively small in Kenya, but very substantial elsewhere. In Brazil, although 22% of the population live in cities greater than 1 million, only 9% of the country’s poor do. In Kazakhstan, 14% of the population lives in Almaty, but only 3% of the poor. In Mexico, 27% of the population resides in Mexico City and the other very large metropolitan areas of the 12 Again with the exception of Kenya, for which we do not have the disaggregated poverty mapping data.
Page 57

# **Lessons for Moving to Scale: Creating Momentum with Robust and Reliable Procedures**

Those implementing the SNP found that the pace of community organization and community financial contributions followed a simple, compelling logic. **Seeing is believing** . Once one slum had been upgraded, CBOs formed more quickly in neighboring areas. Communities made their cost contributions readily once they saw work crews in the neighborhood. Tangible results offer more credibility than the most finely crafted incentive schemes or communications strategies.

This suggests the following measures for accelerating the pace of a reinvigorated upgradation program in Ahmedabad. The proposed measures could reduce the time needed to improve one neighborhood from 24 to 15 months.

- Start working in priority neighborhoods quickly to demonstrate commitment to the program rather than mapping a process for the entire city up front. Working neighborhood by neighborhood offers natural project risk management. Any mistakes made in early upgradation projects can be corrected in later projects. Gains from learning on the job can easily be applied in later neighborhood upgradations.

- **Unlike large projects with major interdependencies across different components, such as a metro or airport, neighborhood upgrading does not need intricate advance planning, or socio economic surveys of the entire population living in substandard neighborhoods.** The overall cost and financing envelope should be understood with some accuracy to ensure that project can be sustained to completion. The basic principles of beneficiary contributions and responsibilities as well as commitments from the government should be understood up front.  But beyond that, it is unlikely that detailed project planning is worth the cost of the delays inherent in obtaining more precise knowledge. Informal housing in slums, chawls and pavements is the solution when all else fails. These solutions are inherently dynamic and subject to rapid change. Precise advance engineering of city-wide solutions for populations such as these is an illusion—and a very costly one for the households in these areas who remain chronically underserved while the global problem is studied in unnecessary detail. Neighborhood by neighborhood, these questions should be studied as
Policy Research Working Paper 10864

# **Abstract**

Laws and regulations represent a central tool for governments to achieve policy objectives, and they also represent a fundamental condition for making desirable individual and business decisions. While laws and regulations regularly have to be adapted to changing circumstances, frequent and sudden modifications indicate legislative unpredictability and are expected to impose considerable costs on citizens and businesses. Legislative predictability is assumed to be the consequence of high-quality laws, and existing evidence shows that regulatory management systems indeed impact legislative predictability. This paper proposes and implements an innovative legislative big data analytics approach to measuring legislative predictability in the Kingdom of Jordan and selected global comparator countries. It also maps out the feasibility of such an approach for the wider Middle East and North Africa region. Legislative data gathered from official government sources point to the high frequency of modifications in Jordan compared to a wide range of countries where data are available Around 10 to 15 percent of the original laws have been modified within 24 months of enactment over the past 20 years. In addition to prevalent modifications of new laws, even older laws face a comparatively high risk of modification in Jordan. Additional data collection following the template outlined in this paper could deliver a comparative data set, enabling a better understanding of the drivers and trends of legislative predictability and hence better evidence-based policies.

This paper is a product of the Governance 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 FazekasM@ceu.edu, BrennerD@ceu.edu and pladegaard@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
higher than the 32 percent growth rate experienced in the median of the distribution. In contrast, the increase in regional labor income inequality during the 1990s was supported by a reduction in real earnings at the bottom of the distribution, while the rest showed null or slightly positive growth in real earnings.

Our second set of results highlights a steady decline in the education premium in Latin America since the early 2000s that has been driven by larger growth in labor earnings among less well educated workers relative

> task. Moreover, most of these studies only explain the trend in the 2000s, which leads to a bias in their findings because they do not observe a period with different trends—the 1990s—to test their hypotheses. See, for instance, Paz and Urrutia (2015) on Peru; Ariztizabal et al. (2015) on Bolivia, Colombia, and Ecuador. Among these studies, Messina and Fernandez (2016) is the only one that analyzes the long-term trends in Argentina, Brazil, and Chile.

> 10 See, for instance, World Bank (2011) and Cord et al. (2016), which document that labor earnings represent about 80 percent of total household income in most Latin American countries.

> 11 Both the weighted and unweighted regional averages converge qualitatively in the fall of labor income inequality, albeit they report some differences in terms of the trend during the 1990s.

> 12 Our analysis also reaches this conclusion based on the Theil index, the mean log deviation, the Atkinson index, and the 90/10 ratio.

> 13 Labor earnings and wages represent different concepts. Our analysis focuses on labor earnings, which consider the earnings of both wage employees and self-employed workers. Even if we may sometimes use the term wage or wages, our results always refer to labor earnings.

3
**Figure 5. Comparing Egypt’s total exports from Enterprise Survey and UN COMTRADE, 2016**

ES vs COMTRADE: 2013, 2016, 2020 <!-- Start of picture text --> 34 12725524<br>20 3419136 92929282 3 2 312913 1 2218 1827 2 26176 2 2161 254 1 7 2 5272581741518 18<br>3334 2 36191 23<br>2 0 20223 322 2322 36<br>32<br>35 16<br>0 5 10 15 20 25<br>ln(COMTRADE ROW Imports)<br>2020 2016<br>2013 45-degree line<br>25<br>20<br>15<br>10<br>ln(ES Exports)<br>5<br>0<br><!-- End of picture text --> Notes: Only Manufacturing sectors are included. The vertical axis measures log export sales in value from the Enterprise Surveys while the horizontal axis measures the log imports by the rest of the world from Egypt.<sup>13</sup> Sectors groupings are in ISIC rev 3.1: 15 = Manufacture of food products and beverages; 16 = Manufacture of tobacco products; 17 = Manufacture of textiles; 18 = Manufacture of wearing apparel; dressing and dying of fur; 19 = Tanning and dressing of leather; manufacture of luggage, handbags, saddlery, harness and footwear; 20 = Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials; 21 = Manufacture of paper and paper products; 24 = Manufacture of chemicals and chemical products; 26 = Manufacture of other non-metallic mineral products; 28 = Manufacture of fabricated metal products, except machinery and equipment; 31 = Manufacture of electrical machinery and apparatus n.e.c.; 34 = Manufacture of motor vehicles, trailers and semi-trailers; 36 = Manufacture of furniture; manufacturing n.e.c. Source: Authors’
case of developing countries. Using a cross-section of firm-level survey data for private firms in 53 developing countries, the study finds

> 1 <u>http://fortune.com/2015/02/01/paternity-leave/</u>

> 2 <u>http://www.nytimes.com/2014/11/09/upshot/paternity-leave-the-rewards-and-the-remainingstigma.html?_r=0</u>

2
Dakar as predictors.<sup>13</sup> The humidity and temperature data for Dakar are collected by Visual Crossing (2024). We run the model separately for each monitor as well as combining the data from both monitors. We also run the model separately for hourly observations and for daily observations. We implement the calibrations at both the hourly and daily levels.

Table 1: Calibration Coefficients comparing Regulatory and nearby Purple Air monitors (Hourly)

||Monitor a|t Bel-Air|Monitor a|t Pikine|Bel-air &|Pikine|
|---|---|---|---|---|---|---|
||(1)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(2)<br>Count<br>>2.5|(3)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(4)<br>Count<br>>2.5|(5)<br>Mass<br>PM2.5<br>(_μg/m_<sup>3</sup>)|(6)<br>Count<br>>2.5|
|Mass Concentration PM2.5|2.378||0.762||1.588||
|Count concentration >2.5||1.486||1.780||1.715|
|Humidity|-0.606|-0.674|-1.766|-1.564|-1.178|-0.860|
|Temperature|-1.072|-0.504|-2.423|-1.228|-1.583|0.819|
|Constant
0.420***|0.417***|0.141***|0.126***|
||(0.0325)|(0.0382)|(0.0291)|(0.0353)|(0.0176)|(0.0202)|
|GDP importer (log)|0.249***|0.242***|0.396***|0.365***|-0.146***|-0.123***|
||(0.0326)|(0.0385)|(0.0292)|(0.0356)|(0.0177)|(0.0206)|
|Observations|81,311|81,031|81,311|81,031|81,311|81,031|
|R-squared|0.887|0.885|0.757|0.755|0.887|0.883|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

Standard errors in parentheses are clustered at the country-pair. *** p<0.01, ** p<0.05, * p<0.1

# (b) Alternative Measure of the Internet

An additional concern about the above results is that the regressions do not control for the quality of the Internet access, resulting in an omitted variable bias. The level of Internet adoption may be the same in two countries but one country may have faster broadband Internet that allows them to communicate more and transfer information faster compared to the other country with slower fixed line connection. We control for the quality of Internet access in a country in the regression by using data on download speeds. The data is obtained from the Net Index dataset based
||||||energy, IT, building<br>materials|
|---|---|---|---|---|---|
|True North<br>Associates|T.N.A. Invest Pvt.<br>Ltd.|2016|5.0<sup>3</sup>|SMEs, growth<br>capital|Generalist with focus on e-<br>commerce, education,<br>marketing, IT services,<br>energy|

# **B. Main features of PE/VC firms in Nepal**

Based on investor surveys and extensive interviews, the team has analyzed the key features of the funds investing in Nepal. The detailed discussion is followed by Table 2, which provides a summary of these features.

# <u>Investment strategy</u>

The prevailing investment strategy is minority, growth-equity investments in SMEs and midcaps. This is instrumental to servicing the capital needs of a growing economy, in infrastructure as well as the corporate sector. Average ticket size across funds ranges from less than $300k to $2m – although bigger investments were made in the hydropower sector (by Dolma in particular). Funds usually acquire a stake ranging from 20% to 49% and, despite not having equity control, try to have a proactive role in company management. Majority deals are extremely unlikely, due to a culture of long-term ownership and control that is typical of Nepalese entrepreneurs, and indeed most SMEs across developing countries.

While all funds expressly claim to focus on SMEs, only one provides clear parameters in terms of portfolio company size. BO2 defines as SME any company that meets at least two of the following three criteria: fewer than 50 employees, less than $3 million turnover, less than $3 million assets. This definition seems consistent with the typical profile of a portfolio company for the other funds interviewed. Hydropower investments, as