rafmacalaba/data-use-mentions-extended huggingface dataset:
11 Estimation of �� yields an estimate of the average effect of natural disaster k on household consumption. Yet different socio‐economic groups are likely to be affected differently either because they are less/more exposed to the disaster ex ante or because they are better/worse able to cope with it ex post. For example, farm households with irrigated fields are less likely affected by droughts than those without, and the more educated may be better able to cope with natural disasters ex post. Similarly, the reception of disaster relief or food aid may lead one to (erroneously) conclude that there were no welfare losses associated with the disaster, even though they induced substantial budgetary outlays elsewhere (by the local or national government) (Yamano, Alderman and Christiaensen, 2005). To explore such heterogeneity in damage or the existence of indirect/hidden welfare losses, natural disasters are interacted with different household and community characteristics and assets ����������� ).

Much of the empirical literature has so far assumed natural disaster measures to be exogenous. Not only does this depend on how they have been measured (objective versus subjective, as discussed above), even when objective measures are used, they may still yield biased estimates. The occurrence of a natural disaster in a locality is likely correlated with the likelihood of it occurring in the first place, which may in turn affect the level of consumption, e.g. through accumulated asset loss at the household and/or community level. Moreover, households in localities that are frequently plagued by natural disasters have likely adapted to these circumstances (for example by adopting more disaster resistant, but less remunerative portfolios (Dercon, 1996), or at times even by building successful livelihood systems around it—“living with floods”). The use of panel data provides the cleanest way to protect welfare estimates of natural disasters from (time invariant) unobserved heterogeneity across communities. However, sufficiently long panels of communities are usually not available
floor (0/1)|0.580|0.459|0.613|0.688|0.533|0.000|0.619|0.022|0.220|
|Has cultivable land smaller than 1 Ha (0/1)|0.503|0.748|0.613|0.656|0.567|0.000|0.096|0.024|0.477|
|Number of observations|734|460|62|32|30|||||
|P-value ofjoint F-test that all coefficients are 0||||||_0.000_|_0.003_|_0.014_|_0.471_|

Note: This table reports means of characteristics of baseline villager survey respondents (Column 1), baseline household survey respondents (Column 2) and VVC members (Columns 3-5, data collected at baseline). Columns 6-9 show p-values of the equality t-tests. In the villager and eligible surveys, questions were asked about the household head. In order to appropriate compare the data in Columns 1 and 2, Columns 3-5 restrict the data to VVC member who were head of their households (around 70% of respondents). (0/1) indicates dummy variables, equal to one if the response is yes. See Appendix Table 1 for definition of variables.
# Understanding the Trends in Learning Outcomes in Argentina, 2000 to 2012<sup></sup>

Rafael de Hoyos Peter A. Holland Sara Troiano

## **I. Introduction**

Human capital is one of the most important determinants of productivity, the engine of long-term economic growth. As shown in the literature review by Hanushek and Wößmann (2007), years of schooling in itself is not conducive to human capital formation, productivity, and growth. Rather, what triggers a positive relationship between education and economic growth are the skills and abilities acquired in school. According to the authors, these abilities are well measured by standardized tests.

In addition to economic development, quality of education is increasingly acknowledged as crucial to increasing health outcomes, reducing poverty, and fostering civic participation. For instance, Cutler and Lleras-Muney (2008) estimate that the health returns to education may be even higher than the monetary returns. World Bank (2011) shows that more and better education leads to a citizenry that is more responsible, more able to cope with shocks, better at parenting, more able to sustain a livelihood, and better at adopting new technologies. All of these outcomes help drive progress for society at large.

In Argentina, as in other Latin American countries, learning outcomes are well below levels that their per capita GDP would predict, jeopardizing the country’s long-term development potential. While other countries in the region have improved learning outcomes since 2000, measured by the OECD’s PISA test, Argentina’s scores show no progress (at best), or even a marginal decline between 2000 and 2012. Little is known about what is behind these disappointing levels and trends in test scores and, in particular, whether the increased resources that Argentina allocated to education during the period of analysis improved learning outcomes.

In 2002 Argentina suffered a deep economic crisis that contracted GDP by 20 percent, coinciding with a significant reduction in learning outcomes between 2000 and 2006. Output per person increased steadily between 2003
Figure 3: Joint Distribution of WTP for Self and Other <!-- Start of picture text --> WTP For Self<br>0 10,000 20,000 30,000 40,000<br>WTP For Other<br>40,000<br>30,000<br>20,000<br>10,000<br>0<br><!-- End of picture text --> _Notes:_ The figures depicts a scatterplot of the joint distribution of the willingness-to-pay for self against willingness-to-pay for another farmer to receive the training, for each of the 21 possible training prices.

total value to the participant of the externalities produced by the other farmer. In line with this assumption, the willingness-to-pay data suggest that participants do not internalize the social benefit of training. If they did, we would expect to observe two patterns in the data. First, valuations would be uniform within villages - both within and across participants. The previous paragraph disproves that valuations are uniform within participant, as it shows that willingness-to-pay for self is substantially higher than willingness-to-pay for others; in addition, the average coefficient of variation of willingness-to-pay for others at the participant level is 35%. Having uniform valuations across participants means that different participants have a similar willingness to pay for the same farmer; in the data, the average coefficient of variation of willingness-to-pay for the same farmer is equal to 74%, pointing to high dispersion. Second, if participants internalized the social benefit of the training, their willingness-to-pay would increase with village size or with the number of maize farmers in a village. I find that willingness-to-pay for others is not positively correlated with village size and number of maize farmers in a village (the correlation is negative, -11% and -7% respectively), and willingness to pay for self is not meaningfully correlated with either (0 and 2% respectively). Figure A.7 plots willingness to pay (for self or others) against
The model is simulated for the Colombian economy, a country that in the 1990s and 2000s implemented substantial market-oriented reforms whose main goal was to deregulate labor and markets.

There are some previous empirical studies focusing on the effects of Colombian structural reforms on labor markets: Cardenas and Gutierrez (1996); Cardenas, Kugler and Bernal (1998); Kugler (1999); Eslava, Haltiwanger, Kugler and Kugler (2010); Camacho, Conover and Hoyos (2013). Most of these studies employ reduced form estimation and none of them build a search model as an analytical framework to understand incentives behind flows between the formal and informal sectors. I provide a new lens to study the impact of the reforms by building and estimating

> 1 The informal sector is particularly large in Latin American economies (from 30 to 70 percent of GDP) according to some studies (Maloney, 2004). Other studies (Schneider, 2005) suggest also that the size of the informal sector in other economies is not negligible. They estimate that, for the period 1999-2000, the average size of the informal economy (as percent of GDP) is 41.2 percent in African countries, 26.3 percent in Asian countries, 37.9 percent in transitional economies and 16.7 percent in OECD countries.

> 2For a survey of the impact of labor market regulations in Latin America see Heckman and Pages (2004), on the impact of trade liberalization and job turnover, see Haltiwanger et al (2004). For a survey of the impact of trade on wage inequality and informality in Latin America and other developing economies see Attanazio, Goldberg and Pavcnik (2004).

2
br>20 20<br>Urban share - pop. (%) - DB C-WorldPop 0 Urban share - pop. (%) - DoU C-WorldPop 0<br><!-- End of picture text -->

**Note:** In each graph, the x-axis shows the share of a country’s population that lived in officially defined urban areas in 2015 based on data from the United Nations World Urbanization Prospects: 2018 Revision database (https://population.un.org/wup/). The y-axis in part (a) shows a country’s urbanization rate based on the dartboard approach, while the y-axis in part (b) shows a country’s urbanization rate based on the degree of urbanization. Results exclude São Tomé Príncipe.

As Figure 2 shows, as with the dartboard approach, estimates of urbanization rates – i.e., of the share of a country’s population that is urban – derived from the degree of urbanization tend to be higher than urbanization rates based on official national definitions of urban areas. Therefore, according to both

> 36 Consistent with this, the shares of population and area accounted for by all centers within a city are, on average across countries, not much higher than the shares accounted for by a city’s largest center (results available on request). 37 A related difference between the two approaches is that our application of the dartboard approach takes account of the presence of significant desert areas, which the degree of urbanization does not. Hence, the dartboard approach identifies urban grid cells based on their density _relative to the density of all cells within a country that are potentially habitable_ , where the potentially habitable areas exclude deserts.

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Relying on the quasi-natural experiment of a state split raises the understandable concern about the validity of our research design. The difference in employment patterns might have existed across the border before the state split and thus before the introduction of the New Industrial Policy. Alternatively, there might have been a difference in employment growth trends across the border before the state split. Both would bias our results upward.

We address these concerns sequentially. First, we investigate employment patterns in modern sectors right before the state split. We geo-reference data from the 1998 Economic Census to match the towns and villages presented in the 2013 Economic Census and replicate the analysis of Equ.(1) for employment outcomes in 1998. Table 6 presents the results. _Target_ does not appear to affect total employment or total number of firms. The results hold across all distance buffers. Therefore, in 1998, there were no systematic differences in employment size and firm numbers for town and villages close to but on different sides of the state border that is defined in 2000.

Second, we address the issue of pre-split trends by examining the growth of employment and the change in total firms at the town and village level prior to the state split. To measure changes, we further georeference the data from the 1990 Economic Census. We then compute the changes in employment outcomes between 1990 and 1998 as the outcome variables and apply Equ.(1). As shown in Table 7, prior to the split, for towns and villages close to the border, neither employment nor the number of firms was growing faster in Uttarakhand than in Uttar Pradesh. If anything, among these places, employment in modern sectors was growing slower in Uttarakhand than in Uttar Pradesh.

Admittedly, the matching between towns and villages over the three rounds of Economic Censuses is not perfect. Depending on the distance buffers, we capture 54 to 60 percent of the places when looking at the patterns in 1998, and 29 to 31 percent of places when analyzing the pre-split trends between 1990 and 1998.
few individuals, figure 8 suggests that less than 10 percent of individuals who lose their job with access to IMSS in an ineligible industry get hired in an eligible industry in subsequent quarters and that this small percentage does not increase in quarters after the wage subsidy program.<sup>12</sup> The patterns in the ENOE data thus do not suggest that the wage subsidy program had displacement effects on ineligible industries.

# **4.7 Through which channel did the wage subsidies increase employment?**

As a final exercise, I use ENOE data to shed light on the channel through which the wage subsidies increased employment. The intention of the program was to allow firms to keep workers with job‐ specific skills, thus eliminating search and training costs during the recovery. However, the estimated effect on employment, while positive, is not statistically significant during the program’s duration. Larger and statistically significant effects only emerge after the program ended. One possible explanation is that although the program did not limit layoffs during the crisis, it affected which types of workers firms kept vs. let go. The program may have allowed firms to keep the workers with the most specialized and relevant skills in greater proportion than firms in ineligible industries.

> 12 On average, four quarters following the quarter when they report having lost their job with access to IMSS in an ineligible industry, most individuals work in an industry outside durable goods manufacturing (47 percent), remain unemployed (29 percent), or work in an ineligible industry again (16 percent). Only 8 percent work in an eligible industry.

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defunct Trans-Pacific Partnership (TPP) except the United States. Indonesia has recently signaled its intention to eventually join the CPTPP.<sup>3</sup> The FTAAP is an agreement among 21 Asia-Pacific Economic Cooperation (APEC) economies, which aims to link Pacific Rim economies from China to Chile including also the United States.

**Figure 1 Membership of the TPP-11 (CPTPP),  TPP-12 (CPTPP + USA), and RCEP**

Source: Authors’ elaboration The analysis employs the LINKAGE model, which is a dynamic global CGE model particularly suited to capture the feedback mechanisms of trade shocks across countries. On the basis of trade and relative prices channels, the model evaluates the impact of a reduction of tariffs and NTMs brought about by the prospective agreements. The CGE model is then combined with the Global Income Distribution Dynamics (GIDD) microsimulation tool to study the impact of these potential PTAs on poverty and shared prosperity. In particular, GIDD distributes the macro-economic effects captured by the CGE analysis across households using Indonesia’s National Socio-Economic Household Survey for the year 2014.

The results of the analysis suggest that out of the PTAs under investigation, the EU-CEPA would yield the largest macro-economic gains for Indonesia in terms of income, output and exports. Relative to the business as usual baseline scenario, Indonesian GDP is expected to increase by 2.13% as a result of the EU-IDN CEPA, compared to 0.18% and 0.89% in case of RCEP and FTAAP respectively. The large impact of the EU-IDN CEPA is mainly driven by the highest expected reductions in trade barriers and the high share of international trade between the partners. The signing of the CPTPP in its current 11-member version would have negligible impact on Indonesia due to the very low trade diversion compensated by the trade creation related to the agreement. On the other hand, joining the CPTPP along with other major Southeast Asian economies would generate a positive economic impact for Indonesia with an expected 0.58% increase in GDP.

These macro
using the Commitment to Equity for Children (CEQ4C) methodology. The analysis is based on 13,820 children (0-17 years old) from the 2018/19 Ethiopia Socioeconomic Survey. Individual and household level information collected from the survey is combined with budget figures and administrative data on programs and subsidies. The study then examines the burdens of taxation and the benefits from government transfers and spending in rural and urban settings, boys and girls, as well as poorer and richer children. It also analyzes the effect of these taxes and transfers on poverty and inequality.

The incidence analyses show that the fiscal system on average is progressive and mainly driven by direct taxes and indirect in-kind transfers. However, important differences in the distribution of some of the elements of taxes and transfers exist. For example, indirect taxes are regressive while public spending on primary education is by far the largest in-kind transfer and is generally progressive across levels of child deprivation. Secondary education spending is regressive, while public spending on health care is progressive across all children. However, in rural areas spending on primary education and health is neutral, in sharp contrast to strong progressivity in urban areas. Regarding impacts on poverty and inequality, the fiscal system reduces poverty by 21% from market 23
in Columns 2 and 4 include vessel by season fixed effects, day-of-sample fixed effects, and two-degree grid cell by season fixed effects. Standard errors clustered at level of two-week-of-sample by two-degree grid cell.

to occur near potential closures catch 11% more tons on average (Column 2). But when I instead define the near indicator relative to actual closures declared by the regulator, I find that sets near actual closures do not catch more tons of anchoveta (Column 4). This null result is consistent with Proposition 2a in Appendix B: in the Bayes-Nash equilibrium, vessels fish near closures until the profit from doing so equals the profit from fishing elsewhere.

These results illustrate one benefit of my potential closures identification strategy. Since closures are publicly announced, the _ex post_ value of this information can be competed away due to congestion; there are diminishing marginal returns when more vessels fish from the same local, exhaustible anchoveta population (Smith, 1969; Huang and Smith, 2014). I create potential closures from the same electronic logbook data the regulator uses to determine actual closures. If they were announced, potential closures would communicate similar information to vessels as actual closures: fishing occurred recently in the area, so anchoveta are likely abundant nearby. But because potential closures and the electronic logbook data 38
aims to fill this gap by assessing the impact of geographic proximity between formal and informal firms on ICT adoption by informal businesses.

Using a unique set of geocoded data from the 2019 World Bank Enterprise Surveys in Zambia allows to examine these ICT spillovers from formal to informal businesses.<sup>4</sup> Geographic proximity is measured as the distance of an informal business to its closest formal firm. Informal businesses operating closer to formal firms have greater exposure to the formal firms’ business activities, their customer base and network, and potential value chain linkages. This geographic proximity reflects the benefits of these complementary forces.

The results suggest that geographic proximity to formal firms is associated with a significantly higher likelihood of digital adoption by informal businesses. The finding holds for various types of digital technologies, ranging from computers, tablets, and cell phones to mobile money transactions, and is robust to different assumptions of informality and geographic distance. These effects vary by the firm owner’s level of education, and firm age. Informal businesses run by more educated owners or younger in age—benefit more from potential technology spillovers of neighboring formal firms. In addition, we find that the role of geographical proximity in generating spillovers in ICT use can be explained by greater competition in the local market and by learning from formal businesses.

This paper contributes to the literature on the crucial role of geographic proximity for technology adoption and productivity advancement. For instance, Carboni (2013) applies a spatial autoregressive model to manufacturing firms in Italy and finds that geographical and industry proximity have positive effects on firms’ ICT investments. Hjort and Poulsen (2019) exploit the gradual arrival of submarine internet cables on the coast to show the positive impact of internet access on employment, firm entry, and productivity in Africa. Using a similar approach, Houngbonon et al. (2022) show a positive effect of access to high-speed internet on innovation at the firm level. Combining information on the GPS location of firms with the location of the internet backbone in
measure of _persistence_ , so in our simulations we assign the weight 0.51 to the zero-mobility case and the complement (0.49) to the perfect mobility case. For robustness, we also (i) consider the correlation coefficient as a measure of persistence rather than the regression coefficient, and (ii) alternatively use intergenerational mobility parameters estimated with Latinobarometro and national household surveys microdata, both provided in the original study by Neidhöfer _et al_ . (2018). Our results are robust to all different specifications.

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when overall growth rates for most industries began declining.

# **2.2 Entry, Exit, and Job Growth in Fast-Growing Industries**

The figures presented in the previous section showed that the Korean manufacturing industries have undergone a striking structural transformation over the past 40 years. This shift in flagship industries was accompanied by the reallocation of resources between industries. We believe that entry and exit (i.e., the opening of a new plant and closings of plants in declining industries) played a crucial role, gradually shifting resources from one industry to another.<sup>7</sup> Given that some of these industries did not exist in the past, differences in growth rates across existing plants alone do not fully explain the transformation process. Therefore, it is important to examine the behavior of new firms and plants, which deliver new products and technologies to the economy and alter value creation in various industries.

Figure 4 plots the entry and exit rates of the four industries we focus on.<sup>8</sup> Electronics and transportation have relatively high entry and exit rates. About 30% of plants in the late 1980s and early 1990s were new. Entry rates for textiles were also high in the late 1980s, when the industry maintained a relatively high growth rate. However, as the growth rate went down in the early 1990s, 7 The Mining and Manufacturing Survey collected information for plants with more than five employees until 2006. From 2007 and on, the survey criterion changes to 10 employees and more. We construct a sample with 10 and more employees to keep consistency. Thus, plants that reduce size below 10 would be considered as an exit. In a similar way, if an existing plant with fewer than 10 employees increases the number of workers and enters the survey sample, it will be considered as an entry. Figures for entry and exit rates from the earlier survey sample (that includes plants with five and more employees) are available upon request.

8 The