rafmacalaba/datause-encoder-data huggingface dataset:
# 1 Introduction

An important topic in economics is understanding the contribution of agglomeration economies and competition for local growth. Even though this is an old debate, most of the empirical work on this topic is fairly recent. Since productivity growth is not directly observed, economists have tried to find proxies for it. Most of the empirical literature used employment growth to proxy for productivity growth at the local level even though this requires strong assumptions. In this paper I examine and then relax some of these assumptions to derive an alternative measure of productivity growth based on regional wage growth. I then apply it to the study of the sources of local growth using data from a matched employer-employee data set for Portugal. With the regional adjusted wage growth I find evidence of MAR externalities in some sectors and no evidence of Jacobs or Porter externalities in most of the manufacturing sectors. These results are at odds with those for employment based regressions where concentration and region size has a negative and significant effect in most of the sectors.

The importance of scale economies in explaining the agglomeration of economic activity was first recognized by Marshall (1890). The idea behind this concept is that it would be beneficial to locate where other firms in the same sector already produce, for reasons such as the availability of intermediate goods, of specialized labor force, the existence of a large product demand or the diffusion of knowledge in the local environment. Recently, the literature on endogenous growth models emphasizes the role of dynamic information externalities as a driving force for technological innovations and, hence, for economic growth. According to this literature, the accumulation of knowledge in the geographical area, is a key driving factor for current productivity growth. Marshall (1890), Arrow (1962) and Romer (1986) argue that the most important externality derives from a buildup of knowledge associated with communications among local firms in the same sector (MAR externalities). Sector concentration in a region helps knowledge spillovers between firms and, therefore, that sector’s growth in the region. This theory also predicts that local monopoly benefits growth as it restricts the flow of ideas to others and allows externalities to be internalized by the innovator. Alternative theories such as Jacobs (1969) have focused on the importance of the cross-fertilization of ideas across different sectors to promote innovation and growth (Jacobs externalities). According to this theory, more diversity in the region causes higher sector growth. Porter (1990) argues that local competition speeds up the adoption of technology. Several firms located together will intensely compete to innovate since the alternative to innovation is demise 2
homogeneous product. To better understand this point, let us take a passenger car, a typical differentiated product, for example. Obviously, Ford Escort and Porche are quite differentiated from each other, and consumers do care about the product differentiation. Although the price of the latter is substantially higher than that of the former, some consumers buy the expensive Porche. However, if we define a product with sufficient disaggregation as, say, "new 1997 white 2-door Ford Escort LX with automatic transmission, power steering, air conditioning, etc., which has complete 3-year bumper-to-bumper warranty by Ford Motor Company, and which is delivered to New Haven, Connecticut on April 10, 1997," then, it can be regarded as an almost homogeneous product. In this case, consumers do not care about which particular unit of the product they are buying, and would buy any unit which is cheaper than other units.

Similar things can be said about agricultural products. When we look at trade flow data at the SITC 1-digit, 2-digit, or 3-digit level, as most studies do, we are looking, for example, at the product of "food and live animals (SITC 1-digit)," "fruits and vegetables (SITC 2-digit)," or "fresh fruits and fresh or dried nuts (SITC 3-digit)." In other words, in any of the above three classifications, "apples" and "oranges" fall in the same product category. It is only when we define the product at SITC 4-digit level that apples and oranges become different products. Even when we disaggregate the product in such detail, a certain variety of apple can command a much higher price than other apples, because consumers do care which variety of apples they are buying. In fact, even at the SITC 5-digit level, long-grain Indica rice is treated as the same product as short-grain Japonica rice. The same applies to other agricultural products.

Thus, the popular belief that manufacturing goods are differentiated but agricultural goods are homogeneous is fairly misleading. Both goods are more or less differentiated. The 9
# Annex Table 1: Estimated coefficients used to evaluate the elasticities reported in table 3.

|||OLS fixed-ef<br>|fects results||Instrum<br>|ental variable<br>|s fixed effects|results<br>|
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|t-score|P>|t||Coef.|Std. Err.|t-score|P>|t||
|_Inputs_|||||||||
|Fertilizer use (kg/ha)<sup>*</sup>|48.51|8.97|5.41|0.00|245.71|117.60|2.09|0.04|
|Manure use (kg/ha)|0.07|0.11|0.61|0.54|0.16|0.17|0.92|0.36|
|Family labor (days/ha)|80.39|11.46|7.02|0.00|91.09|15.47|5.89|0.00|
|Hired labor (days/ha)|14.65|1.96|7.46|0.00|23.74|10.79|2.20|0.03|
|Maize area|-27.65|8.91|-3.10|0.00|-19.56|10.87|-1.80|0.07|
|_Risk management_|||||||||
|Share of maize area in pure stands|-43.17|65.13|-0.66|0.51|-137.58|88.30|-1.56|0.12|
|Maize area/ plots managed|-70.62|34.29|-2.06|0.04|-97.45|41.03|-2.38|0.02|
|Number of crops managed|63.79|12.79|4.99|0.00|45.49|16.02|2.84|0.01|
|_Farmer characteristics_|||||||||
|Female head of household|-78.71|39.95|-1.97|0.05|-35.32|58.65|-0.60|0.55|
|Age of household head|-0.85|1.20|-0.71|0.48|0.58|1.56|0.37|0.71|
|_Weather effects_|||||||||
|Difference from average rainfall (mm)|-0.77|2.82|-0.27|0.79|-1.63|3.89|-0.42|0.68|
|Late start for season 1 rains (weeks)|-78.72|23.88|-3.30|0.00|-115.32|32.22|-3.58|0.00|
|Late start for season 2 rains (weeks)|-41.04|36.07|-1.14|0.26|-58.84|43.16|-1.36|0.17|

Note: Both regressions included 215 fixed location effects, which were jointly significant at the 0.01 threshold. The number of observations for the OLS and IV regressions were 1662 and 1617 respectively.

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criteria (villages that had at least 100 children ages 0–5 and were located in a rural commune where the poverty rate exceeded 30 percent) were randomly selected to participate in the first year of the program. Each of the 450 eligible villages was allocated to one of three groups: 150 would receive a CBP, 150 would receive an HBP, and 150 would serve as the control group. Comparisons across these three groups would allow impacts to be identified. The evaluation relied on data collected in 32 villages randomly selected for CBPs, 32 villages randomly selected for HBPs, and 32 control villages (3,807 households, henceforth referred to as the “informal sample”).

The baseline data were collected in control and treatment communities for CBPs and HBPs beginning in May 2008 and for FPSs in December 2008. In each village, up to 40 households with at least one child between the ages of 2 and 4 at baseline (24–59 months) were sampled. If a village had more than 40 such households, households were randomly selected to be included in the baseline. Follow-up surveys were conducted in January 2011 for the informal sample and in June 2011 for the formal sample.

Both at baseline and at follow-up, a household survey, a mother/caregiver survey, and a childspecific survey instrument were implemented. Detailed data on different aspects of early childhood cognitive and non-cognitive development were collected for the targeted children, including a measure of the overall development of the child (the Ages and Stages Questionnaire—ASQ,<sup>3</sup> including measures of cognitive, socio-emotional, linguistic, gross motor, and fine motor skills) as well as more specific tests assessing receptive vocabulary (Peabody Vocabulary test—TVIP), associative memory (Woodcock Johnson Memory for Names—WJ) and behavior (Strengths and Difficulties Questionnaire—SDQ). In addition, the Early Development Instrument (EDI) was implemented in first grade of primary school.<sup>4</sup> Together, these tests provide a good indication of a child’s school readiness. The evaluation also collected information on other outcomes in early childhood, such as measures of anthropometric status and child health. Finally, the evaluation collected information on a number of possible intervening variables, including household socioeconomic characteristics (including adults’ education), household labor supply (including maternal), maternal mental health and cognitive ability, and parenting “quality.” The quantitative data collection was

> 3Ages & Stages Questionnaires® (ASQ), Second Edition: A Parent-Completed, Child-Monitoring System, by Diane Bricker and Jane Squires. Copyright © 1999 by Paul H. Brookes Publishing Co., Inc. www.agesandstages.com. Used with permission of the publisher.

> 4 As the children for whom the EDI was conducted could not be matched directly to the children in the sample, the EDI is not considered in this report.

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# **Short- and Long-Term Effects of a Child-Labor Ban**

Caio Piza Caio Piza André Portela Souza The World Bank Development Research Group Sao Paulo School of Economics (EESP-FGV) <u>caiopiza@worldbank.org andre.portela.souza@fgv.br</u>

**JEL Codes:** C21, J08, J22, J24, K31

**Keywords:** Child-labor ban, labor-market experience, school attainment, regression discontinuity design, long-term effects.
centers outside the education system (Psacharopolous, 1986). Some authorities have further raised the possibility of a "new educational vision" for the public secondary schools that would expand the experiential content of schooling to better prepare students for adult life (Wilms, 1987).

Is there evidence in the record of World Bank experience to suggest that educational autho;ities in developing countries, as well as in the World Bank, have moved away from investments in secondary vocational schools? What does the record say about the patterns through which relatively more effective training systems evolve in developing countries? Is there any evidence .that a "new educational vision" is emerging for secondary education?

The paper is based mainly on reviews c -rently underway of World Bank project appraisals and evaluations. include a comprehensive analysis of the patterns of Bank arL ,overnment investments in the 320 World Bank education sector loans and tredits from 1963-1986, and a more detailed study of the plans and evaluations for a representative sample of 121 components in 76 projects in 34 countries that supported training for industrial employment. Of particular interest in the project review have been countries where the Bank and the Government have collaborated in the development of national training systems over a relatively long period (10-20 years) through a sequence of investments.l/ Data for the study of projects is limited, particularly in documentation of the employment outcomes and equity effects of training institutions supported.2/ Thus our ability to assess the productivity
provided by an inter‐agency partnership that was led by the World Bank Living Standards Measurement Study (LSMS).

# **2.1. Sampling Design**

In Round I, the MAPS fieldwork was conducted during the first rainy season of 2015, from April to October 2015, in Eastern Uganda, the top maize‐producing region of the country. A sample of 75 enumeration areas (EAs) were selected from the 2014 Population and Household Census (PHC) EA frame, with probability proportional to EA‐level household counts. The sampled EAs were distributed across 3 strata, namely (1) Sironko district (15 EAs), (2) Serere district (15 EAs), and (3) a 400 square kilometer remote sensing tasking area spanning Iganga and Mayuge districts (45 EAs).

In each sampled EA, the original intention had been to select, at random, 6 households from each of the pure stand and intercropped universes of households of an EA, and ensure an even sample split by maize cultivation status. Within the remote sensing tasking area of interest, the MAPS I fieldwork started out with 540 households, of which 249 were pure stand (46 percent) and 291 (54 percent) were intercropped.<sup>3</sup> In each MAPS household, 1 maize plot, matching the household cultivation status, was selected at random by the Survey Solutions CAPI application for crop cutting, soil sampling, and variety identification components.

In MAPS II, the fieldwork was conducted during the first rainy season of 2016, from June to October 2016. The field teams attempted to track and re‐interview 540 households that had been interviewed in 2015 within the 400 square kilometer remote sensing tasking area. Figure 1 provides an overview of the study region in MAPS II. Overall, 489 of the 540 households were successfully re‐interviewed.<sup>4</sup> As in MAPS I, 1 maize plot was selected from each household for crop cutting and variety identification components. Whenever possible, the plot was selected among those that were matching the household cultivation status in MAPS I. Preference was given such that the plot would be selected from the same parcel that had contained the plot selected in Round I. If multiple plots matched the household cultivation status, the CAPI application selected one plot at random.<sup>5</sup>

> 3 The uneven split by cultivation status was due to the low incidence of pure stand households, and the cases in which pure stand households would switch to intercropping status between the household listing and the first interview.

> 4 34 out of 51 households that we did not interview in MAPS II were due to the fact that they were not cultivating maize in the first season of 2016. The remaining 17 households can be broken down as follows: 5 households could not be tracked or were outside of the tracking area defined as the Iganga and Mayuge districts (5); 4 households had suffered total crop loss prior to post‐planting interview; 7 households had already harvested their maize by the post‐ planting interview; and 1 household refused. Gourlay et al. (2017) report that attrition bias is not a concern.

> 5 Refer to Gourlay et al. (2017) for MAPS II household tracking and plot selection protocols.

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are now accustomed to see in (most) low-income countries. Organizations such as the United Nations High Commissioner for Refugees (UNHCR) and the World Bank are now fully committed to bridging this data gap, but past experiences with measuring poverty in low-income countries suggest that this is going to be a long-term process. For example, the UNHCR has attempted to collect consumption data for the Syrian refugees in Jordan using large-scale surveys that interview as many as 5,000 households per month (or 60,000 households per year). In other refugee contexts, where resources and logistical challenges exist, such large-scale surveys may not be feasible or sustainable.<sup>2</sup> In the meantime, the development of various methodologies designed to estimate poverty in contexts where income or consumption data are not available can provide a useful alternative to producing reliable poverty figures for displaced communities.

This paper contributes to the poverty measurement literature by applying recent advances in cross-survey imputations to measure poverty among Syrian refugees living in Jordan. All individuals seeking protection, assistance and refugee status are expected to register with the host government or the UNHCR and, for this purpose, the UNHCR maintains a profile Global Registration System (proGres). This system contains biometric and socio-economic information on asylum seekers and refugees and serves the purpose of identifying the persons most in need and determining the type of protection and assistance they require. ProGres does not offer information on income, consumption or expenditure but contains a rich list of variables that are potentially closely associated with these monetary indicators. In addition, the UNHCR and partner organizations occasionally collect information on household well-being by means of sample surveys designed to address specific issues, such as measuring food security or determining various types of vulnerabilities. These surveys may contain information on income, consumption

> 2 Over the past five years, these two organizations have sharply increased their cooperation and they recently announced the establishment of a joint data center with the objective of addressing this data challenge.

3
In theory, the structure of the BOP dataset should allow separating the flows by institutional sector, but the requisite data is sometimes missing. It is difficult to determine if missing data is truly missing, or if it is zero. Data on outflows are generally more sparse than data on inflows. Further, the time coverage of the data varies greatly across countries. Especially for variables with sectoral breakdown, the coverage is weighted heavily towards recent years.

# **A.2.1 Types of Flows**

Capital flows in the Financial Account of the BOP are disaggregated first by type of flow. The main types are direct investment, portfolio equity, portfolio debt, other investment, financial derivatives, and reserves. For each of these flow types, the BOP reports asset flows and liability flows. We describe each type of flow and how it can be broken down into the various institutional sectors.<sup>48</sup> We focus on the debt portions of capital flows (portfolio debt, other investment debt, reserves, and sometimes direct investment debt) in our dataset, but we describe all components of capital flows here.

**Direct Investment:** Direct investment, commonly called FDI, captures investment inat least 10% It is meant to investment based on volving ownership. reflect relationships control and influence. In addition to equity investment, it also captures other investments under a controlling relationship, including debt and reverse investment.

Direct investment is not broken down by sector. Unlike the BPM5 version of the data, the BPM6 data does have splits according to liability and asset flows for direct investment

> 48See Appendix 9 of the Balance of Payments Manual for a list of all the components of the Financial Account with their structure in the BOP data.

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line-up method, which gives elasticities that are significantly higher in magnitude than previously observed. The model estimates plausible poverty rates at higher poverty lines, and predicts greater poverty reduction in faster growing states. The validation exercises show that in 2011-12, the imputation model fared slightly worse than the typical method of applying HFCE growth to older survey data. When predicting back to 2004-05, however, the imputation model performed dramatically better. This evidence, though limited, suggests that a survey to survey approach generates more reliable estimates than the current line-up method.

The 2015 poverty rates reported for urban and rural India were calculated by applying a fractional “pass-through rate” to growth in Household Final Consumption Expenditure, with pass-through rates selected separately for urban and rural areas to match the model’s estimated $1.90 poverty rates. Therefore, the 2015 poverty rate was calculated by scaling up the 2011-12 survey data in a distributionally neutral way, rather than treating the predicted welfare distribution as if it were a new survey. Three considerations influenced this decision. First, using a pass-through rate to line up the estimates more clearly signals that the 2014-15 estimates are model-based predictions rather than actual survey data on welfare. The alternative of lining up the predicted 2014-15 welfare distribution to 2015 could create confusion by blurring the line between predicted and actual data. Second, applying a partial pass-through builds on and improves the existing practice of assuming a full pass-through to line up the estimates. Finally, no study to our knowledge has convincingly shown that survey to survey imputation methods can accurately predict welfare at the very top of the welfare distribution. As a result, it is not clear that this methodology can be used to accurately predict mean welfare and inequality, which unlike poverty are sensitive to the accuracy of the predictions for the richest households. Maintaining a distributionally neutral adjustment provides a way to use a survey to survey imputation for poverty without entering the uncharted territory of using imputed values to estimate mean welfare and inequality. Better understanding whether and how imputation methods can be used to accurately predict changes in mean welfare and inequality is an important topic for future research.

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governance segments was 70 percent of the new stocks offerings (Santana 2008). Similar patterns occurred during 2008–10. Santana (2008) has also argued that the Novo Mercado has allowed Brazilian companies, and particularly new entrants, to access foreign capital without having to cross-list on international stock markets. For example, among Bovespa’s 27 initial public offerings between 2004 and the first half of 2006, only two companies were listed simultaneously on the New York Stock Exchange.

# **6. Alternative Markets and Products**

In recent years, LAC countries have seen the development of less traditional forms of financing; for example, factoring has deepened along with derivative markets and credit by retailers. Quantifying these new developments is, however, not an easy task as cross-country data are typically not available. Therefore, we focus instead on specific country studies or particular datasets that allow us to shed some light on recent trends in these nontraditional markets.

# **6.1. Derivatives Market**

Since the late 1990s, trading of exchange rate derivatives in LAC7 countries has grown in dollar terms and as a percentage of GDP, particularly in Mexico.<sup>10</sup> Trading of interest rate contracts in LAC7 more than doubled as a percentage of GDP in the 2000s compared to the 1990s. Nevertheless, derivatives remain relatively illiquid in most emerging markets: turnover rates remain very small in comparison with those in developed countries. For example, the turnover in exchange rate contracts stands at about 1.1 percent of GDP in LAC countries, whereas the turnover in G-7 countries stands at 7.3 percent of GDP. Turnover figures also suggest that foreign exchange derivatives are largely concentrated in U.S. dollar contracts across developing countries, with U.S. dollar contracts representing about 98 percent of the turnover in LAC7.

# **6.2. Factoring**

Factoring is a financial transaction in which accounts receivable (that is, invoices) are sold at a discount to a third party.<sup>11</sup> Invoices are typically short term (less than 90 days), so that a market for invoice trading would be equivalent to a high-yield commercial paper market. This is a particularly important market for SME financing. Smaller firms are typically more opaque

> 10 The Bank for International Settlements publishes the "Triennial Central Bank Survey of Foreign Exchange and Derivatives Market Activity,” which provides comprehensive and internationally consistent information on turnover in foreign exchange and interest rate derivative markets for over 50 countries. 11 See de la Torre, Gozzi, and Schmukler (2007c) and Klapper (2006) for a detailed discussion of factoring per se, as well as for a few case studies around the world.

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