rafmacalaba/data-use-mentions-extended huggingface dataset:
# 1) INTRODUCTION

International trade is viewed as one of the key factors underlying the success of the fastest growing economies yet many countries remain isolated and have failed to achieve this integration. To what extent are the SADC countries afflicted? To answer this question, this paper looks at recent trends in intra-regional trade flows and in the region‟s trade with the rest of the world. Its analytic objective is to identify the extent to which SADC economies have integrated into the global trading system as well as the regional market. In addition, the paper addresses some of the main institutional and policy barriers to trade in the region.

The extent of market integration is evaluated using trade flow data. SADC‟s aggregate trade performance since 1990 is benchmarked against international counterparts. One aim of this analysis is to identify whether SADC is being marginalized in world trade. The results in section 2 show that SADC has continued to experience a decline in its share of world trade over the past decade and a half. However, the decline also reflects relatively poor economic growth and not necessarily structural impediments to trade. Benchmarked against GDP, SADC has experienced an increase in openness over the past decade that is comparable to other developing countries. Similarly, regressions indicate increased exports by SADC members over time even controlling for GDP growth. This change is equal in magnitude for SADC exporters as other countries. SADC has therefore become more integrated with the world economy over the past decade and a half and the extent of this increase in integration is equivalent to other comparable countries.

In section 3, the paper then looks at intra-SADC trade. Intraregional trade rose as a share of total trade during the 1990s, but progress in this regard has slowed recently. The level of and trends in intra-regional trade, however, are not even across countries. South Africa has become an important source of imports for SADC countries with the ending of sanctions in the early 1990s, yet imports by South Africa from the SADC region remain small. Regression results suggest that, _ceteris
entering primary school is 7 years of age, in many cases parents enroll their children of both genders at age 6, particularly if their child went to pre-primary or attended a play group. Parents also said they often enroll girls earlier in primary school because they are ready for school sooner than boys.<sup>19</sup> Children who are enrolled before age 7 are categorized as early entrants (EE) in primary school. This group usually continues to be early entrants when they reach junior and senior secondary schools. Susenas data from 2011-15 show that the average EE rate is between 25.78 to 27.13 percent. These figures indicate that more than one-fourth of children are not accounted for in the NER calculation, which excludes children whose age does not match their expected grade. The more recent data in Susenas 2019 show that the EE rate for girls (22.03 percent) is more than 2 percentage points higher than for boys (19.78 percent). This

> 18 Susenas, 2018. 19 In one school visited for this study, children were tested to determine if they are ready or mature enough to start school.

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- _The relevance of search costs_ : In October 2009, three months after the wage subsidy offer was made, we asked those eligible for the wage subsidies a series of questions related to search. At the time of the survey, 29 percent of those eligible had hired an employee, indicating they were able to find someone fairly quickly. Another 43 percent indicated that they had no intention of hiring an employee.<sup>12</sup> Among the remaining 29 percent of owners who indicated they were still searching and expected to hire an employee, only 38 percent (representing 11 percent of the sample) indicated that a reason for not hiring was that they had not found the right employee. The majority indicated instead that at present the additional employee would not be profitable given the subsidy level. These data suggest that search costs are not a main constraint for the majority of owners in our sample. This conclusion is supported by data from a survey of owners of small and medium sized businesses conducted around the same time.<sup>13</sup> In April 2009, among 160 owners with five or fewer employees, the median owner said it would take seven days to locate an employee if s/he wanted to hire one; the mean search period was 14 days. Moreover, these owners said they could hire a worker for a median of 400 LKR per day, or 2,000 LKR per week, just above the average wage the employees hired under our subsidy program report being paid.  That search costs are important for only a small percentage of owners of small firms is consistent with long-held views that labor markets work relatively well among informal firms. This is perhaps not surprising given that most workers are hired to do relatively routine, physical tasks. Our owners say that the sex of the worker and physical strength of the worker are the two most important characteristics of workers they consider hiring, with education the least important of the characteristics listed. Search tends to be local, with a
higher infrastructure endowment also had better economic performance (measured in terms of provincial GDP). The study also concludes that transportation infrastructure is the main factor in explaining provincial economic inequality. Cohen and Paul (2004) investigate cross-state variations of public infrastructure in the united states using state-level manufacturing data for the 1982-1996 period. They find that the productivity gains caused by the public infrastructure investment during the period vary significantly across the states. It finds the largest effects in the western states, whereas the effects are smallest in the eastern and southern states.

> 6 Infrastructure development was represented through indices for transport, water and sanitation, telecommunications and energy sectors constructed through principal component analysis.

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IT Call Kurtosis, IT Recharges Day Skew, # Contacts WE Day<br>Call, IT Recharges Day Kurtosis, IPC WD Night Call Kurtosis|

_Notes_ : Machine learning results for predicting: (A) Below-poverty-line status, using consumption data and based on Afghanistan’s national poverty line; (B) Total consumption (log-scale); (C) Asset index; and (D) Community Wealth Ranking. Performance is evaluated on the sample of 535 matched households. Binary metrics (A) are evaluated using the mean AUC score over 10-fold cross validation; Continuous metrics (B-D) are evaluated using the mean _R_<sup>2</sup> score over 10-fold cross validation. See Appendix B for details of features.

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more children are being enrolled since 2000 but learning becomes a serious concern. It is estimated that more than 617 million children and adolescents are not achieving minimum proficiency levels in reading and mathematics and most of these individuals are living in developing countries (UIS, 2017). The World Bank’s Human Capital Index (HCI) shows that the next generation of workers in low‐ and middle‐income countries would only have 51 % of the productivity that they could have if they had complete education and full health.<sup>2</sup> A decomposition of the average HCI shows that the education component is associated with the lowest achievement and the average of the learning‐adjusted years of schooling (LAYS) seems very low in most developing countries and far below the benchmarks.<sup>3</sup> A simple visualization of the relationship between the total public education expenditure (government expenditure + donors’ contribution) per school‐age individual and the LAYS shows that there could be some correlation between public expenditure on education and access to quality education (Figure 2). This relationship has been reported in previous studies (Jung & Thorbecke, 2003; Dissou, Didic, & Yakautsava, 2016; Al‐Samarrai, Cerdan‐Infantes, & Lehe, 2019). More interestingly, Figure 2 shows that countries with similar levels of average public expenditure on education over the past two decades register different achievements in terms of access to quality education. This indicates that countries may face issues related to their ability to translate resources into quality education.

**Figure 2 Average public expenditure per school‐age individual and the LAYS**

<!-- Start of picture text --> lays = 5.2593 + .00175 expeduc R 2 = 43.0%<br>RUS<br>KAZ<br>SRB<br>HRV<br>VNM BGR UKR BGR<br>MUS MNG MUS<br>MYS<br>TJKLKAKENPHLARGKGZARMGEOLKAIDNMEXALBPERMDAAZE JORCOLIRNECU THAIRNTURMEXROUBRA CRIARG<br>
3 area thus tends to be anecdotal, rather than empirical. At the same time, household budget surveys, which represent the best possible source of information about how remittances are spent, are often poorly designed. Oftentimes, these household surveys do not even include questions about remittances. Moreover, the limited number of surveys that do ask questions about remittances typically ask “naïve” questions about these monies were spent or used. Since remittances are fungible like any other source of income, simply asking respondents about how remittances were spent is not enough. Remittances that are not being spent directly on investment may well have freed other resources for expenditures on investment. Third, the small handful of empirically-based studies that do exist on remittances and economic development are often based on small, unrepresentative household samples. For instance, Adams (1991) study of how international remittances are used in rural Egypt is based on only 150 households, while Alderman’s (1996) and Adams’ (1998) studies in rural Pakistan are based on 500 households.<sup>ii</sup> Clearly, there is a need to extend the scope of these studies to examine the impact of remittances on economic development by using larger, nationally representative samples.

The purpose of this paper is to extend the debate concerning how remittances are spent or used and their impact on economic development by using the results of a recent large, nationally-representative household budget survey in Guatemala. The results of this survey are used to compare the marginal spending behavior of three groups of households: those receiving no remittances, those receiving internal remittances (from Guatemala) and those receiving international remittances (from the United States). Since all survey households are separated into one of these three groups, it becomes possible to
# 1. Introduction

The COVID-19 pandemic has put a lot of pressure on already strained health care systems across the Latin America and the Caribbean (LAC) region (World Bank, 2020). Among others, the explosion of costs, such as prices paid for pharmaceuticals (Chernew & May, 2011), has contributed to failings in equitable and quality health care provision in the region. COVID-19 emergency states and the accompanying pressures to deliver critical goods and services fast have further contributed to price pressures in already fragmented markets.

Responding to these policy challenges, this study aims to

- document hitherto understudied variations in prices paid for pharmaceuticals, equipment and medical supplies within countries and markets; and

- identify effective procurement strategies for lowering prices within existing regulatory frameworks with the help of purchase-level explanatory models.

These ambitions are made possible by the unique data set compiled from public sources enabling price analysis at the very detailed, purchase level (i.e. within countries and markets over time). This represents a major departure from most prior studies which had to rely on market-level average prices across countries (e.g. Danzon & Chao, 2000). We make use of public procurement data as gathered by public authorities in 9 countries and territories across the LAC region through their electronic procurement systems. This data is uniquely detailed and complete, encompassing the minute detail of purchasing decisions and processes made across all regulated public entities in the study countries and territories.

Our sample of 7 countries (Ecuador, Brazil (federal), Paraguay, Panama, Uruguay, Peru, and Costa Rica) and 2 territories (Amazonas (Brazil) and Santa Catarina (Brazil)) is exceptional in its breadth; still, it misses out on important countries in the region and hence may represent a skewed sample. Nevertheless, the wide country coverage of our sample allows for making cross-country claims in the region, by exploiting the variance in terms of institutional and regulatory set-up. We make use of traditional regression analysis as well as machine learning (random
women relative to men reduces violence and its associated costs. Note that this study is based on administrative data on female hospitalization for assault. Therefore, its conclusions are unaffected by the important degree of nonrandom underreporting that affects other studies based on self-reported measures of domestic violence.

Microfinance, both credit and savings, has also been proposed as a potential tool for empowering poor women in developing countries. However, these programs have a varied effect on men’s violence against women (Goetz and Gupta 1996; Kabeer 2000; 13
- USDA (2013), 2013 agricultural resource management survey, Technical report, National Agricultural Statistics Service and Economic Research Service.

- Vossler, C. A., Doyon, M. & Rondeau, D. (2012), ‘Truth in consequentiality: theory and field evidence on discrete choice experiments’, American Economic Journal: Microeconomics 4(4), 145–71.

- Vossler, C. A. & Holladay, J. S. (2018), ‘Alternative value elicitation formats in contingent valuation: Mechanism design and convergent validity’, Journal of Public Economics 165, 133 – 145.

- Whittington, D., Adamowicz, W. & Lloyd-Smith, P. (2017), ‘Asking willingness-to-accept questions in stated preference surveys: A review and research agenda’, Annual Review of Resource Economics pp. 317–336.

- Whittington, D. & Pagiola, S. (2012), ‘Using contingent valuation in the design of payments for environmental services mechanisms: a review and assessment’, The World Bank Research Observer 27(2), 261–287.

- W ̈unscher, T. & Engel, S. (2012), ‘International payments for biodiversity services: Review and evaluation of conservation targeting approaches’, Biological Conservation 152, 222–230.

- Yu, J. & Belcher, K. (2011), ‘An economic analysis of landowners’ willingness to adopt wetland and riparian conservation management’, Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie 59(2), 207–222.

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in the north.

# **Firm-Level Analysis**

The district-level evidence suggests that employment in heavy manufacturing has grown in mining districts between 2003 and 2013. An increase in the number of heavy manufacturing firms could generate this result, without an increase in average employment in heavy manufacturing firms in mining districts. We test whether average log employment in different sectors (heavy manufacturing, in particular) is higher in mining districts in the south, based on the following model: ′ log _Empi_ = α +φ _Southi_ +γ _MDisti_ + δ _DDSouthi_ × _MDisti_ +θ _Xi_ + ε _i_ . (3) We estimate the double-difference parameter ( δ _DD_ )in equation 3 using 2014 IBES data, controlling for various firm characteristics (<sup>_X_</sup> _i_ ) including informality status, type of ownership, and legal organization.

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, from a fairly concentrated 0.62 to 0.14, which is more consistent with moderate levels of diversification. This diversification trend is also broadly supported by the decomposed Theil (between) statistics, which illustrate a declining trend after 2003. By way of contrast, other economies in the region possess Herfindahls that range from well-diversified (Morocco, 0.03 and Lebanon, 0.03), to moderate diversification (Egypt, 0.14), to concentrated (Iran, 0.69 and Yemen, 0.72).<sup>10</sup> Seen another way, Syria has, over a seven-year period, moved from an export diversification structure consistent with oil-exporters to one more akin to non-oil exporters.<sup>11</sup> These changes in the extent of

> 9Notably, Table 4 leaves out one other (relatively) common measure of export diversification, the Gini index. We have chosen to do so for two main reasons. First, most of the dynamics of changes in export concentration are well captured by the other reported measures. Second, the main advantage to using a Gini index—its immutability under different sample sizes—is of less consequence in our case, where we are considering only one country with very limited changes in the availability of data.

> 10Since Herfindahls for the other economies are more stable over the period, these values are calculated as averages for the period 2001–2007, inclusive.

> 11Relative to other economies at a similar stage of development, however, Syria remains less diversified, in part due to its historical relationship with oil. For example, Honduras and Indonesia possess

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disaggregation of export products according to the Standard Industrial Classification review 3 (SIC-3) of 4 digits for the period 1993-2009.<sup>14</sup> Starting with the simpler measurements of diversification, the more than 1,228 categories of export products are first ranked independently for each year covered in the sample. Once ranked, the value of exports of the major categories are added up and calculated as a percentage of total exports in the year. Figure 7 shows the evolution of the proportion of the value of total exports of the main 5, 10, 15 and 20 products, using the data by customs tariff sections.<sup>15</sup> The different versions of the indicator yield a similar trend, i.e. a slight trend to diversification (downward in the Figure) between 1993 and 1998 (except for the case of the principal 5 products in which the trend seems less evident) followed by a phase of secular growth of the concentration (interrupted by slight declines in some years of the sample). The 5 principal categories account for nearly 47% of exports in 1993, 45% in 1999 (at its nadir), and from then onwards a gradual upward leads to 59% in 2009. In the other versions of this indicator shown in Figure 6, the results are similar, with a significant increase of around 10% in the share of the leading products between 1999 and 2009.

**Figure 7: Proportion of Total Export Value Principal Exports by Customs Tariff Sections, 1993-2009**

<!-- Start of picture text --> 85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>55%<br>50%<br>45%<br>40%<br>1993 1995 1997 1999 2001 2003 2005 2007 2009<br>First 5 First 10 First 15 First 20<br><!-- End
across regions in fact has the opposite effect, increasing the disparity in regional intersectoral productivity gaps.

The rest of the paper is organized as follows. Section II provides an overview of national and regional patterns and trends in incomes, structural transformation, and informal nonfarm employment. Section III describes the enterprise data we use in our analysis and provides estimates of regional intersectoral productivity gaps. Section IV presents the model for deriving regional productivity losses from intersectoral misallocation of labor and compares these regional losses to regional intersectoral productivity gaps. Section V discusses the simulation results of the effects of hypothetical adjustments to the disparity in regional intersectoral productivity gaps on the level of national labor productivity and on the contribution of structural transformation to national labor productivity. It also discusses the robustness of the 5
# _5.3._ **_El-Nino or El-Peso?_**

We also looked into relative contributions of the three shocks to the total impact on mean consumption and poverty. This is also shown in Table 4. The labor market shock alone accounted for about a quarter of the total impact on mean consumption, the El-Nifio shock alone contributed 38% and remaining 36% was attributable to the joint labor market-El Nifio shock. We are unable to decompose the last component for the joint shock any further, and thus the shares for the individual shocks can be interpreted as lower bounds for the labor market and El Nifio shocks. Hence, by these estimates, the contribution of the labor market shock was somewhere between 26-62% while that of El Ninio was somewhere between 38-74%.

The contribution of the El-Nino shock to the total impact is higher for the poverty measures. For instance, this shock alone contributed about 46% to the increase in the headcount index. For the poverty gap and the squared poverty gap measures, more than half the impact is on account of the El-Nifio shock alone, while the labor market shock alone contributed only about 10-11%. About one-third of the total impact is attributable to the joint El Nifio-labor market shock. If one were to split this joint shock in proportion with the shares of the individual shocks, the contribution of the El-Nifto shock to the depth and severity of poverty would be upwards of 80%.

# **_5.4. Income vs. consumption impact_**

The analysis of crisis impact presented thus far has been in terms of household consumption. The APIS also collected data on household incomes which offers us an opportunity to compare income and consumption impacts. In particular, a smaller consumption shock (in absolute terms) would be consistent with the idea of consumption smoothing, i.e., households at least partially protecting their consumption against income shocks through such 21
# **2.3 Publication bias**

As documented by Ioannidis et al. (2017) and Andrews and Kasy (2019), null effects tend to disappear from the literature because of the tendency of researchers and journal editors to prefer studies with statistically significant results. To limit such issues, as mentioned above, we did not restrict our literature search to published articles. However, a remaining concern is that researchers may not have written up papers for studies with null findings because they anticipate a harder publication process or because of their own preferences to not pursue the publication of null results. We take advantage of the AEA randomized controlled trial registry to check whether there are studies that meet our inclusion criteria but whose results have not been reported in the literature.<sup>15</sup> In February 2022, we conducted a simple search on the registry using the keyword “index insurance”. After reviewing the description of each project, we found a total of 15 studies that met our eligibility criteria, seven of which had been identified through our search process. Among the remaining eight studies, three have results publicly released in academic papers but not available when we registered our study (Boucher et al., 2021; Mishra et al., 2021),<sup>16</sup> one analyzes an intervention that was completed only recently (Cilliers et al., 2020), and four explore interventions that are still ongoing (Kazianga and Wahhaj, 2021; Kramer et al., 2021; Vieider et al., 2021a,b). If anything, this provides some reassurance that concerns over publication bias are not too severe in this literature.

# **2.4 Data requests**

Before the preparation and the registration of our pre-analysis plan, in which we specified the different variables we would use, we contacted the authors of each paper to present our project and to ask whether they would accept to share the underlying data.<sup>17</