rafmacalaba/datause-encoder-data huggingface dataset:
# _3.2 Literature search_

Several strategies were used to find relevant studies. We first reviewed all titles and abstracts of search results in the following electronic databases: JSTOR, ERIC, WEB OF SCIENCE and the Pathways to College Online Library.<sup>1</sup> We also searched the websites of organizations working on higher education policies, most notably the Institute of Education Sciences (IES), the policy research organization MDRC, the National Center for Postsecondary Research (NCPR), the non‐profit organization ACT and The National Bureau of Economic Research (NBER). Once we had reached a starting set of papers matching all our inclusion criteria, we systematically reviewed all their references and identified and checked all the studies citing them. We limited the search to articles or reports in English and published by May 2018. Overall, we reviewed titles and abstracts of thousands of academic articles, working papers and policy reports. This yielded an initial set of 296 studies which we carefully read and systematically reviewed on our inclusion criteria, leaving us with 87 studies which met all the inclusion criteria. However, 12 studies which evaluate interventions for which the (quasi‐)experimental evidence is currently too scarce to be discussed in a literature review are not presented here. We thus further focus on the findings of 75 studies which specifically evaluate outreach programs, financial aid policies or a combination of the two. The list of the selected studies is presented in Table A.1 in the Appendix.

Figure 1 shows the distribution of the type of publications, the interventions evaluated, the (quasi‐)experimental designs, and the countries where the interventions were evaluated among these 75 studies. Randomized experiments are the most common methodology implemented, followed by regression discontinuity and difference‐in‐differences design. In addition, the (quasi‐)experimental literature on outreach and financial aid comes overwhelmingly from North America and no less than 60 studies evaluate an intervention from the United States. The lack of diversity in the educational contexts where interventions or policies are tested is already an important result from this review and should be kept in mind when interpreting the results of these studies.

> 1 The following search terms were used: (College OR “Higher Education” OR “Tertiary Education” OR University) AND (Inequality OR Stratification OR Access OR Drop‐out OR Retention OR Persistence) AND (Experiment OR RCT OR Policy OR Intervention OR Reform OR Effect OR Impact).

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economies of scale and scope. This relationship between production structure and economic performance has been found in a number of macro-level pieces of work: countries that specialize in products with dense connections to other goods grow faster (Kali et al. 2013; Hidalgo et al. 2007).

The analysis we propose here is micro level. We exploit Chinese firm-level data to determine whether the product space also matters at the level of individual exporters. To the best of our knowledge, this is the first analysis of the efficiency gains from product spillovers on firm export performance. Analogously to the country-level effect, we expect link density between products to give rise to a greater export performance for products that are close to those in the local export basket. The underlying idea is that they will share similar requirements in terms of institutions, infrastructure, resources, technology, or some combination thereof. The exports of products with denser connections to the local productive structure should grow faster because they can capitalize on existing local capabilities.

We focus on export growth to measure economic performance. Our analysis thus connects the macro literature on the links between productive structure and development to the micro literature on firm-level export performance. Our work follows recent efforts to understand the drivers of firm product mix (Bernard et al. 2010) and is consistent with models of endogenous within-firm (between products) activity reallocation. We focus on the role of product spillovers in the dynamics of product-level exports. By doing so, we confirm the findings of Bernard et al. (2010) that the within-firm allocation of export activity between products reflects not only firm- and product-level determinants but also factors combining these firm and product dimensions.

Our analysis differs from the existing cross-country work in a number of dimensions. First, 3
|Interaction: 2016*Textile and Apparel||-0.0908***<br>(0.00952)||
|---|---|---|---|
|Interaction: 2005*Female|||-0.569***<br>(0.0130)|
|Interaction: 2010*Female|||-0.0408***<br>(0.0113)|
|Interaction: 2016*Female|||-0.0739***<br>(0.00843)|
|Constant|6.811***|6.810***|6.781***|
||(0.0118)|(0.0118)|(0.0119)|
|N|159316|159316|159316|
|F-test|2684.25|2471.24|2555.56|
|AdjR-square|0.3572|0.3582|0.366|

Note: The grand mean effects of the industries are calculated; additional controls include age squared, weekly hours worked, and industry dummies. Standard errors are in parentheses. Statistically significant coefficients at the 10,5, and 1 percent level are indicated by one, two, and three asterisks respectively. Source: Author's estimates using data from Bangladesh LFS.

# 6. Conclusions

Recent developing country research finds evidence that trade shocks affect local labor market outcomes including earnings, informal and formal employment levels, and skill premium. These impacts vary depending on the type of shock being considered. On one hand, evidence shows that rising exports to industrial countries reduced poverty and spurred reallocation of labor from informal to formal jobs in several developing countries like India, Vietnam, and China (McCaig, 2011; McCaig and Pavcnik, 2018; Artuc et al., 2019; and Erten and Leight, 2017). On the other hand, trade liberalization measures slowed the pace of poverty reduction and triggered a persistent declining trend in wages and employment in few developing countries like India and Brazil (Topalova, 2010; Dix-Carneiro, 2017; and Artuc et al., 2019).

Despite this burgeoning literature shedding light on the heterogeneous effects of trade shocks across regions, evidence on local labor market effects from “positive demand shocks” is only nascent, and transitional dynamics driving labor market effects from shocks have not been widely studied.

Using detailed data from Bangladesh, we extend the existing literature by estimating not only the short run effects but also dissipation and long-run general equilibrium effects linked to neoclassical 31
Kenyan government and multinational corporations. Govemmenit participation in such ventures provided the foreign partners with low cost access to land, with preferential and subsidized access to public services (eg. roads, rail cargo, electricity), and with reduced financial risks.

13. Since the early 1970s, the horticultural export sub-sector has developed into an important component of the Kenyan economy, providing a major and growing source of foreign exchange earnings as well as substantial employment and farm income opportunities. During a period in which Kenya's exports of many of its traditional commodities have stagnated or declined (eg. sisal, pyrethrum, cotton, meat products), or fluctuated considerably from year to year (coffee, maize), the aggregate volume and value of horticultural exports has increased substantially and virtually continuously."4 As Table 1 and the Appendix 1 graph indicate, there has been double-digit growth on an annual basis in horticultural exports since 1973. By 1988, horticultural exports were valued at approximately 100 million Kenyan Pounds (eg. U '. $105 million), representing 17% of the countrv's total agricultural exports. If current trends continue, horticultural products may surpass both coffee and tea during the 1990 to become Kenya's leading commodity export sub-sector.

> '' The development of Kenya's traditional and non-traditional agricultural export sub-sectors is **analyzed in** Jaffee (1990).

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informality and legal informality, respectively (figures 8, panels a and b). Overall, the JQM is correlated with informality, as shown by the linear fit line. At the same time, the panels show that countries with comparable shares of informal workers have, in fact, different levels of job quality and vice versa. Among countries with similar informality shares, such as Ecuador (ECU), Honduras (HON), Mexico (MEX), and Paraguay (PRY), job quality varies from 0.55 to 0.8 (figure 8, panel a). In addition, countries with similar levels of job quality classify different shares of wage workers as informal. Thus, using the legal informality definition, wage workers in Bolivia (BOL), the Dominican Republic (DOM), and Ecuador have the same level of job quality (figure 8, panel b); yet, Bolivia counts 60 percent of wage workers as informal, while the Dominican Republic classifies only 20 percent as informal.

Figure 8. Comparison of the JQM with the Productivity and the Legal Definition of Informality <!-- Start of picture text --> a. Comparison with the productivity definition of b. Comparison with the legal definition of<br>informality, country level informality, country level<br>0.95 BOL 0.95 BOL<br>0.9 BRA 0.9 BRA<br>0.85 COL 0.85 COL<br>0.8 CRI 0.8 CRI<br>0.75 DOM 0.75 DOM<br>0.7 ECU 0.7 ECU<br>0.65 HND 0.65<br>HND<br>0.6 MEX 0.6<br>MEX<br>0.55 PAN 0.55<br>PER<br>0.5 PER 0.5<br>0.10 0.20 0.30 0.40 PRY 0.00 0.20 0.40 0.60 0.80 1.00 PRY<br>Productive Informality URY Legal Informality URY<br>c. Comparison with the productivity definition of d. Comparison with the legal definition of<br>informality, country-economic sector level informality, country-economic sector level<br>1 BOL 1 BOL<br>0.9 BRA 0.9 BRA<br>0.8 COL 0.8 COL<br>CRI CRI<br>0.7 0.7<br>DOM DOM<br>0.6 0.6<br>ECU ECU<br>0.5 HND 0.5 HND<br>0.4 MEX 0.4 MEX<br>0.3 PAN 0.3 PER<br>0.2 PER 0.2 PRY<br>0.00 0.20 0.40 0.60 0.80 1.00 PRY 0.00 0.20 0.40 0.60 0.80 1.00 URY<br>Productive Informality URY Legal Informality<br>Job Quality Index Job Quality Index<br>Job Quality Index Job Quality Index<br><!-- End of picture text --> _Source:_ Estimates based on data of SEDLAC (Socio-Economic Database for Latin America and the Caribbean), Center for Distributive, Labor, and Social Studies, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, La Plata, Argentina, and Equity Lab, Team for Statistical Development, World Bank, Washington, DC, https://www.cedlas.econo.unlp.edu.ar/wp/estadisticas/sedlac/.

Similar patterns emerge in measuring job quality and informality across countries and sectors. Figure 8, panels c and d show the average JQM and informality shares of wage workers across 10 sectors of the economy using productive and legal definitions of informality. Thus, in Brazil (BRA), shown in orange, using 24
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# IV. SIMULATING TARIFF POLICY FOR NIGERIA

In order to perform simulations with the above models of the firms' behavior, we have to first do two preparatory tasks. One is to calibrate the firm's production function using data from the survey of Nigerian manufacturers. The second task is to decide on an appropriate price setting behavior on the part of NEPA.

# A. Calibration Procedure

As explained in the earlier section, the degree of scale economies in producing endogenous power were assumed to be the same for each firm and the coefficient a = 0.55407 was estimated from a Cobb-Douglas specification of the embedded cost function. Then, the scale factor G, was set for each firm in such a way that the firm's reported cost of endogenous electricity generation was replicated.

Following this, the essential substitutes model was calibrated as follows. aij, the exponent of labor in the ith firm's production function, was set as aLu = sLJsy, where sL, is labor's share in the firm's total production cost and sy = 0.88172 is the degree of scale economies in primary production estimated from a Cobb-Douglas specification of the cost function. Once aL4 was thus calibrated, equations (7a)-(7c) were used with the observed prices and input levels to calculate am, a. and aNi, the remaining exponents in the firm's production function. Then, using the observed output and input levels and the calibrated exponents, the scale
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# Table 3: Description of Explanatory Variables

|Telephone mainlines per<br>person|"Telephone mainlines are telephone lines connecting a customer's equipmen**t**o the public<br>switched telephone network." Data are the averages of available years over the period 1971-<br>1985.|
|---|---|
|Infrastructure Quality|Assessment of the "facilities" for and ease of communication between headquarters and the<br>operation, and within the country," as well as the quality of the transportation. Average data<br>for the years 1972 to 1995. Scale from 0 to 10 with higher scores for superior quality.<br>Source:_BERI's Operation Risk Index_as used in La Porta et al [19991.|
|Transfers and subsidies/GDP|Total govermnen**t**ransfers and subsidies as a percentage of expenditure multiplied by<br>government consumption as a percentage of GDP. "Subsidies and other current transfers<br>include all unrequited, nonrepayable transfers on current accoun**t**o private and public<br>enterprises, and the cost of covering the cash operating deficits of departmental enterprise<br>sales to the public. Data are shown for central govermment<br>only. General government<br>consumption includes all current expenditures for purchases of goods and services by all<br>levels of government, excluding most government enterprises. It also includes capital<br>expenditure on national defense and security." Data are the average of available years over<br>the period 1971-1985.|
|Log GDP per capita|Logarithm of PPP GDP per capita measured in 1985 dollars. Data are the averages over the<br>period 1971-1985. Source: Penn World Tables (Mark 5.6).|
|GDP Growth|"Annual percentage growth rate of GDP at market prices based on constant local currency."<br>Data are the averages for available years over the period 1971-1985.|
|Lending Interest Rate|"Lending interest rate is the rate charged by banks on loans to prime customers." Real<br>lending rate is computed using GDP deflator. Data are the average over all available years in<br>the period 1971-1985.|
|Imports/GDP|"Imports of goods and services represent the value of all goods and other market services<br>provided to the world. Included is the value of merchandise, freight, insurance, travel, and<br>other nonfactor services. Factor and property income (formerly called factor services), such as<br>investment income, interest, and labor income, is excluded." Data are the averages for<br>available years over the period 1971-1985.|

Data source for explanatory variables is the 1999 _World Development Indicators_ on CD-ROM unless otherwise noted.

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# **4.4 Implications for the Cost of Delivering Training**

Consider a government or NGO deciding on how to best price training in order to train a target number of firms at the lowest possible cost. For example, if JBDC wants to train 1,000 firms like those in the BDM sample, what price should it charge to minimize its costs of doing so? The answer depends not just on the cost of training, but also on the costs of recruitment and training demonstrations, and on the impact of price on having people show up and attend training. It costs US$25,117 to recruit 831 firms to attend demonstration sessions. This includes the costs of telemarketers to call firms, advertising costs, venue hire, JBDC staff travel costs, and paying the trainers to conduct the demonstration sessions.<sup>24</sup> Taken together, this works out to be US$30 per firm that takes part in the training demonstration, or approximately 20 percent of the cost of training.

Table 10 then combines this cost data with the impact of prices on demand (measured here as attending at least one training session) in order to determine the cost of training 1,000 firms at different prices. For example, when training is free, only 1,316 firms need to get recruited to get 1,000 firms to attend training, but the full US$150 cost of training each one needs to be paid, for a total cost of US$189,474 for firms like those in the BDM sample. Increasing the price to J$3,000 (US$22.5) increases recruitment costs, since 1,587 firms need to be recruited to have 1,000 firms attend training, but these firms then cover 15% of the cost of their training, for a total cost of US$175,119. However, when training is priced at full cost, it is very expensive to recruit firms, and so even though they pay for their training, it is more expensive in total to get 1,000 trained than if a lower price was charged. We see that for the BDM sample, the J$3,000 price is optimal, but that prices of J$5,000 and J$10,000 (one-quarter to one-half the cost) also do well, compared to the extremes of free training or full-price.

In contrast, with the TIOLI sample, the wealthier nature of this sample means that attendance does not fall as rapidly with price, and charging J$15,000 is more cost-effective than lower prices. In practice, policymakers are likely to be concerned about both equity and efficiency, and so will want to charge lower prices when trying to reach poorer firm owners like those in the BDM sample, and higher prices if their goal is to train larger firms.

# **4.5 Effects of Business Training**

Our initial goal in designing this experiment was also to test whether the impact of training is higher for entrepreneurs with higher willingness to pay. Conditional on WTP, the ran-

> 24We do not include here costs associated with the impact evaluation, such as survey costs.

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than they actually employ because they want to show they have done what the training instructed. The final concern is that a systematic bias arises because more able business owners are better at knowing what the correct answer to these practices should be, and also do better in business.

We employ several methods to address these concerns. The first consists of _audit visits_ . We selected a random sample of 135 firms in Nigeria and 200 firms in Sri Lanka that we had interviewed one to two months earlier. These businesses were visited by a business mentor – individuals experienced in business and/or business training – hired by us. The visits were framed to firm owners as a chance to receive individualized feedback on their business. The mentors were given a set of open-ended questions to ask (e.g. “What methods do you use to ensure your customers are satisfied with your products?”, “Tell me about any advertising you have done lately”, “Can you tell me how you keep track of inventory?”, “What are your targets for the business for the coming year?”, etc.), and typically spent 2-3 hours with the business owner. At the end of the interview, they provided the owner with suggestions for areas where she/he could improve practices and provided us with an independent assessment of whether or not the firm owner was using each of the 26 business practices.

When we compare the business practice score based on this audit with the survey responses, we find a Pearson (Spearman) correlation of 0.74 (0.73). This is higher than Bloom et al. (2012) report for their repeat interviews by different surveyors using double-blind surveying. Figure 3 plots the audited score against the survey score, along with a 45 degree line and line of best fit. Note that the fitted line has slope less of less than one, implying that it is more common for the audit visit to suggest a lower score than the survey than vice versa. This could reflect some over-reporting by firm owners, or could reflect that mentors are not able to always tell whether a practice is being implemented on the basis of the visit. Nevertheless, the strong positive correlation provides us with some confidence that the reported scores contain a strong signal of actual business practice.

In Sri Lanka, 90 of the 200 firms audited had been assigned to a business training course. The Sri Lankan audit sample can therefore also be used to assess the extent to which training biases the self-reported responses. In Appendix Table A1 we show the results of regressing the difference between the audited business practice score and the survey score on treatment assignment. The 8
are compared with the actual income growth from panel data. Following the literature using GIC, we change the forecasting direction. That is, we predict second round household per capita incomes for every household interviewed in the first round of data.<sup>29</sup> We find encouraging results for the prediction of income changes from the LASSO-PMM method. The predictions tend to trace out the actual income changes across the distribution. Therefore, in comparison to the parametric synthetic panel prediction method, the LASSO-PMM provides additional information on income growth, that approximates actual changes quite well. With some exceptions, our predictions overlap with the 95% confidence intervals for all subgroups of poverty dynamics and quintiles of the income distribution. In some cases, the LASSOPMM approach is optimistic, predicting higher income growth for the bottom two quintiles in certain countries. Once again, there is suggestive evidence that results are better when the length of the panel is longer.

# _4.4 Validation: sensitivity and robustness tests_

These findings suggest that the LASSO-PMM approach is well-suited to predict aggregate poverty rates, transitions, and welfare changes with few assumptions and simple models that use harmonized variables. While our empirical approach is fairly general, we now proceed to test whether departures from the main assumptions and changes in how the method is applied affect the precision of the resulting welfare predictions. The main objective of these validations is to test the sensitivity and robustness of the LASSO-PMM approach to different scenarios that may be encountered in practice by other researchers that only have access to cross-section data.

> 29 We also test the robustness of predictions to changes in the forecasting direction and find similar results.

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|**Ta**<br>Dependent Variable:|**ble A1: Conflict**|**and SPEI in Afri**<br>DummyIndicat|**ca**<br>or for AnyCo|nflict in a Year||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|SPEI|0.0154***|-0.00524|-0.00860*|-0.00546|0.00672|
||(-0.00555)|(-0.00503)|(-0.00442)|(-0.00464)|(-0.0068)|
|SPEI X G5 Sahel|-0.0580***|-0.0282***|-0.0161*|-0.0200**|-0.0359***|
||(-0.0106)|(-0.01)|(-0.00924)|(-0.00934)|(-0.0131)|
|N|37095|37095|37095|37095|37095|
|R-sq|0.017|0.086|0.189|0.192|0.253|
|Controls|N|Y|Y|Y|Y|
|Country FE|N|N|Y|Y|Y|
|Year FE|N|N|N|Y|Y|
|Country-Year FE|N|N|N|N|Y|
|The dependent variable is a dummy equal to one if an<br>evapotranspiration index. Controls include the following<br>presence of roads, distance to river, presence of a country<br>errors in parentheses clustered at the grid-cell level<br>* p<0.1, ** p<0.05, *** p<0.01|y conflict event occurr<br>time-invariant cell char<br>border, presence of any|ed in a grid-cell in<br>acteristics (also used<br>mineral resources a|a given year. The S<br>by HF in their spe<br>nd a measure of eth|PEI is the standar<br>cification): terrain,<br>no-linguistic fractio|dized precipitation-<br>elevation, cell area,<br>nalization. Standard|

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