rafmacalaba/data-use-mentions-extended huggingface dataset:
It is possible to omit some destinations, international or local, to overcome data collection issues or other empirical problems. Overall, the ability to ease a pervasive model-selection problem and circumvent potentially prohibitive data constraints help to achieve a more complete inference about the welfare impact of wars.

To show these points formally, we first set up a general discrete choice framework with random utility, where agents choose among a finite number of locations. This decision is guided by (i) location-specific fixed utility common to all agents in a given location, (ii) an individual-specific utility for each location, and (iii) bilateral mobility costs associated with migration. For generality, the framework remains agnostic about the components of location-specific fixed utility, which can include wages, local amenities and, potentially, expected future utility, among others. We do not impose any strong assumptions on whether the people are myopic or forward-looking, how much they discount the future outcomes, or how they form their expectations about the future after conflict. Then, we derive sufficient statistics equations and mark the lower-bound estimates for the welfare impact of a war.

To estimate the main parameters of the model, we consider a case study: the conflict in Eastern Ukraine (i.e., the Donbas Region, covering Donetsk and Luhansk oblasts) between 2014 and 2019<sup>1</sup> . Specifically, we estimate the inter-region migration elasticity parameter using flows among Ukrainian regions prior to the conflict in 2014. This estimate is then used to back out the welfare impact of the war in Donbas as revealed by the flows of about 1.4 million internally displaced people (IDPs) after 2014. Our data includes yearly regional statistics and bilateral gross migration flows from the State Statistic Services (UkrStat) for 2008-2012 and IDP numbers from the Ukrainian Ministry of Social Policy for 2014-2019.

Our empirical analysis yields a migration elasticity parameter between 0.458 and 0.682 depending on the agents’ risk aversion and time preferences. Using these estimates, and following
scheme. Our results thus indicate that employment subsidies are also effective in increasing formal employment among smaller firms, which are also presumably more prone to hire workers informally in the absence of employment subsidies compared to larger firms.

# 6.3. Distinguishing between job creation and formalization effects

A question of interest for policy purposes is whether the observed increase in formal employment originates from new job creation in treated firms or, instead, from the formalization of already existing jobs in the same firm. Our social security dataset, by design, does not capture workers who are employed informally, i.e. not registered with social security institutions. To provide some indicative evidence on whether the increase in employment was driven by formalization or new job creation, we use data from the nationally representative Household Labor Force Survey in Turkey, which asks each working individual in the household whether they are registered with social security institutions. To provide indicative evidence of whether the rise in formal employment was mostly driven by the formalization of existing informal jobs, we run similar regression as our firm-level estimation, but at the worker level. We run two separate regressions: one in which the dependent variable is a dummy variable for being wage employed, and a second specification where the dependent variable is a dummy variable for being a formal wage employee. Tables 9 and 10 show that while the likelihood of being a wage employee did not increase after the policy change, there was a statistically significant increase in the likelihood of being formally employed after the policy change. This indicates that the increase in formal employment originated mainly from the conversion of existing jobs into formal jobs, rather than from new job creation by firms.

In Figure 5, we follow Betcherman et al. (2010) by looking at changes in firm electricity consumption before and after the policy change in treated and control firms, as a proxy for economic activity. The rationale behind this corroborative analysis is that if firms actually hired new workers in treated provinces thanks to the subsidy, one should observe an increase
consensus about, and scant empirical evidence of, a “clash of liberalizations”. We provide evidence of such a clash during the last multilateral trade round by applying product-level protection data to estimate a model of the interaction of preferential and multilateral liberalization.

Most of the early theory analyzing the effect of PTAs on MTL assumed that MTL implied free trade. So the research focused on the effect of PTAs on a binary choice between free trade and no MTL and it effectively asked whether PTAs made a multilateral trade round more or less likely.<sup>3</sup> Assuming that a round leads to free trade and focusing on the probability of a round simpli fi es the

> 1 The latest round was launched in 2001 and, according to a recent article in the leader section of the Economist, a key factor that may lead to its collapse is that “Poor countries with preferential access to rich world markets want to make sure that freer trade will not reduce these preferences”. (“Talking the Talk”, July 17th 2004, p.14.) The possibility that these preferences would reduce MTL is not new: it was a concern raised by opponents of the generalized system of preferences when it was originally proposed (Johnson [1967] p. 166).

> 2 Romalis (2004) provides evidence of trade diversion for the North American Free Trade Area and its predecessor between the US and Canada. Chang and Winters (2002) fi nd that Mercosur caused lower export prices for non-members.

> 3 Krishna (1998) argues that PTAs reduce the likelihood of a multilateral round because the export rents generated by PTAs disappear when countries liberalize multilaterally and so the producers that bene fi t from those rents will oppose MTL. Levy (1997) shows that the median voter may reject multilateral free trade after voting for a PTA even though he would have accepted it if no PTA had been available.

1
mortality statistics draws on a second solid source, reports of the numbers of death certificates issued by the health system. The present study uses the homicide rate per 100,000 inhabitants as the main measurement of crime and violence in Mexico. This indicator is collected monthly by the Mexican Statistical Office (INEGI).

As a robustness test, the incidence of car theft is used as a complementary measure of crime. According to the most recent victimization survey, about 83 percent of cars stolen are reported, making it a generally reliable measurement of crime (ENVIPE 2014).<sup>10</sup> However, while this is a good proxy for crime in general, it is a limited one for violence because not all car thefts involve violence. The information on car robbery is gathered by the National Security System (SNSP) based on preliminary inquiries ( _averiguaciones previas_ ) by the judiciary system.

The estimates for the number of ninis come from the _Encuesta Nacional de Empleo_ (ENE), an annual employment survey conducted between 1995 and 2004, and from the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), a quarterly survey conducted in Mexico since 2005. Both ENE and ENOE are statistically representative at the state level. Since ENE is collected during the second quarter of each year, we include information from ENOE only for the second quarter of each year; this has the double advantage of making both employment series compatible while avoiding variations due to seasonality. We define as ninis those individuals in the 15-24 age range who are neither enrolled in formal schooling (whether public or private) nor working at the time of being surveyed. Working youth are defined—following the conventional definition—as individuals who have worked at least one hour in the past week.

All but one of the controls were constructed from the employment surveys (ENE and ENOE). The income measure used is the log of the hourly labor remunerations; inequality is captured by the Gini coefficient of the per capita labor remunerations; the identification of informal
of productivity growth, such as institutional quality, urbanization, and educational attainment pose further headwinds to both new and continuing transitions to high productivity levels.

This chapter examines convergence in labor productivity, defined as output per worker (at 2010 prices and exchange rates, Annex B). Labor productivity data are available for 103 countries since 1970, consisting of 29 advanced economies and 74 EMDEs. Labor productivity is more readily measured than total factor productivity (TFP), which can only be estimated on the basis of special assumptions. Labor productivity is also conceptually closer to per capita income, the variable of primary interest in discussions of global average living standards and the global income distribution. The dataset is constructed from national accounts, the World Bank’s World Development Indicators, The Conference Board, and the Penn World Table 9.1 (Annex B).

Section 2 discusses the evolution of convergence over time. Section 3 estimates the speed of convergence, both regardless of country characteristics and conditional on country characteristics. Section 4 provides evidence for the presence of club convergence assesses the characteristics of EMDEs who have demonstrated faster degrees of convergence. Section 5 concludes and discusses policy implications.

3
TABLE 4 CREDIT MONTHS, MOSHAVIM'S DEBT TO MISHORIM RELATIVE TO SALES

|1970|12.1|1974|5.9|1978|6.0<br>:|
|---|---|---|---|---|---|
|1971|9.9|1975|5.3|1979|9.0|
|1972|7.5|1976|4.6|1980-|10.2|
|1973|5.9|1977|5.7|1981|10.7|

Source: Kislev, Marvid (1988).

Note: Credit months = (average annual debt of moshavim to Mishorim)/(annual moshavim's sales through Mishorim)x12.

blunted=. As a result, heavy financial inter-reliance of first and second order cooperatives lead to moral hazard behavior on part of many of the borrowing associations and was one of the major factors that exacerbated the severity of the current crisis. The alarm signals were building up for a long time and are worth examining further. Evidence comes from the third regional organization we are studying.

Data for Mishorim, a-supply cooperative with young moshavim similar to those of the Mountain Region, shovfthat not only across moshavim of different vintage, but over time for the same moshavim too--financial exposure is affected strongly by economic performance of the members of the regional organization. Mishorim's credit months ratio (Table 4) improved significantly in the mid 1970s with the expansion of export crops, in which the region specializes, but evidently the moshavim found it convenient not to cut back on their credit in the regional organization in 1979-1981 when agricultural terms of trade deteriorated with the weakening of the European currencies against the dollar and the region's production of exportables and products for the domestic market was reduced.

Differences are also found between moshavim in the same region. In a sample of 13 moshavim, members of Mishorim, one
around the trend in earnings inequality in Latin America during the last two decades. To a lesser degree, it also seeks to contrast these trends with those of other middle- and high-income countries in the world. To analyze the trend in labor income inequality, we aim to document changes in the inequality of the earnings structure (relative returns of different skills/attributes). We also analyze the context of these changes in terms of real earnings growth because different movements in real earnings could lead to the same change in relative returns to different attributes, but not to the same conclusions about the underlying causes. Finally, our paper presents a set of stylized facts on the variance in earnings across workers of observable different characteristics (the earnings structure) and the variance across workers with observable equal characteristics, which is defined in the literature as residual earnings inequality and corresponds to the variance that cannot be explained by the differences among workers in a set of observed characteristics. Unlike Azevedo et al. (2013), we are not imposing any assumption about the dynamics of the residual distribution.

By conducting our analysis at the regional level, we enhance the external validity of our results, while obtaining broad estimates of a common underlying trend in the distribution of labor market earnings in the region. In particular, we use data from the SEDLAC database on 17 countries in Latin America, which cover around 90 percent of the population. This allows us to identify underlying factors that are beyond specific country contexts (political, institutional, or economic). In addition, analyzing data since the early 1990s allows us to define whether the factors considered important in the 2000s were also present during the previous decade, when labor market inequality showed a different trend. In other words, by taking a long-term perspective, we can test whether the reverse of previous trends in overall inequality implied a reverse in previous trends in other factors, or a surge in new forces that were previously absent.

Our main finding is that, after a period of modest expansion
sense of belonging to the community (Rao and Walton, 2004). Additionally, distance _per se_ may not be the main barrier for educational enrollment decisions but rather related safety considerations (Lloyd et al., 2005). This issue can be particularly salient for young children in this context given that the civil strife that took place in Guatemala between 1960 and 1996 affected predominantly rural areas (Chamarbagwala and Morn, 2011).

On the other hand, the mentioned 0.5 kilometers minimum-distance restriction applies to the linear distance to _any_ community. In rural Guatemala the population typically lives in mountainous, rugged areas with poor access to infrastructure or public transportation. Therefore, the linear distance would dramatically underestimate a simple calculation regarding how long it would take to a child to walk to neighboring community to attend a pre-primary. Also, many communities in rural Guatemala are very small and they can be better interpreted as groups of households. For example, the community in the 25th percentile in the population distribution of the 2002 Census contained only 40 individuals. Finally, we note that the possibility of spill-over effects may be less of an issue for girls compared to boys, as existing empirical evidence suggests that the distance to an educational establishment may be a stronger barrier for girls (Burde and Linden, 2012). Hence, if spill-over effects are at play they will operate predominantly for boys, and, consequently, we should observe differences in effects across genders. However, Table 4 shows that effects are similar across genders suggesting a limited role for spill-over effects.

Notwithstanding these considerations, we empirically explored the robustness of the main findings to the minimum-distance restriction. In particular, we estimated heterogeneous treatment effects across communities that vary in their distance to its closest neighboring community. If spill-over effects play a role in the estimated effects, we should expect differential treatment effects across distance to neighboring communities. In practice, we divided the sample of rural communities under analysis based on whether the distance to the neighboring community was above or below the median distance (
|Part of a larger group?|||||2.165**|
|---|---|---|---|---|---|
||||||(0.989)|
|Own security system?||||0.673***||
|||||(0.157)||
|Experience a criminal attempt last||||-0.303||
|year?||||(0.235)||
|Externally audited?||||0.895***||
|||||(0.231)||
|N|1,081|216|127|419|56|
|R-squared|0.124|0.000|0.531|0.244|0.514|
|*** p<0.01, ** p<0.05, * p<0.1. Rob<br>whicha sufficientlylarge sample of|ust standard<br>innovativefi|errors in paren<br>rmsis available|theses. Showin<br>.|g results only for|countries for|

# 4. Conclusions

We study the link between innovation and productivity in major developing East Asian countries (China and ASEAN 5), highlighting the role of the complementary business environment and firm characteristics.

We investigate the determinants of R&D investments, the returns of R&D investment on innovation outcome, and the association between innovation and productivity. Our main findings are threefold. First, elements that contribute to R&D investments are using technology licensed by a foreign company, having higher human capital at work, and being associated with a large group, for example, being a subsidiary of a multinational with easier access to and absorption of new and sophisticated knowledge. Second, R&D investment can significantly boost innovation,
do not see any evidence of a positive impact when we evaluate the last community group. Partial exposure, net of time trends, remains negative and insignificant.

# **7. Discussion**

In order to put our results in context, we compare the estimates to those found in previous studies. (See Appendix 1 for a more detailed explanation of the previous literature.) As mentioned before, the Oportunidades rural evaluation sample covers localities incorporated in 1998. Therefore, we can compare our results for the first group of rural communities to the existing evidence in the rural areas. Experimental evidence suggests that children 12 to 36 months at baseline were about 1 cm taller after one year in the program. Analyses that evaluate one additional year of exposure after both treatment and control groups were in the program, only find positive effects on children living in more disadvantaged households (1 to 1.5 cm). We choose to convert our results for a 2 year-old boy, as it lies in between the 12 to 36 month range of previous estimates. Our baseline estimated effect of 0.88 standard deviations translates into 2.68 cm. Our second specification shows estimates in the range of 0.18 to 0.28 standard deviations. A middle point, 0.23 sd., translates into 0.7 cm. Given that these estimates represent an average additional exposure of two years, under the assumption of linear effects we get an estimated impact between 0.35 cm and 1.34 cm for an additional year. As we see, this range is consistent with previous evidence.

Even though our results cover a wide range of values, our preferred specification for this locality group is the one that uses all children and all locality groups in the estimation. By using all locality groups we can control for nation-wide time trends, and by including all children we are maximizing the sample of children in control communities without going too far away from the target population in community-group 1, as participation rates are very high
<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>1-in-20 1-in-100 1-in-500<br>Data: JAXA EORC, 2018 (land use maps), Braese et al. 2020 (costal flood maps), Fathom Braese et al. 2020 (costal flood maps), Fathom 1 (riverine flood maps).<br>Note: Shown percentages are percentages of flooded crop area for a 1-in-500-year flood. A flood threshold of 25cm has been<br>applied,<br>Figure 4.2: Rice production in the Red River and Mekong Deltas are particularly exposed to flood risks, especially for coastal<br>flooding.<br>a) Rice production area exposed to coastal flood risk<br>200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Return period: 1-in-25 1-in-100 1-in-500<br>49%<br>27%<br>42%<br>98%<br>95%<br>49%<br>36% 32% 25% 33%<br>68% 24% 25%<br>70% 39% 27% 42% 32% 26%<br>(hectares, thousands)<br>Exposed agriculture land 24% 25% 32% 29% 44%<br>39% 32% 9% 13%<br>Quang Ninh Hai Phong City Thai Binh Nam Dinh Ninh Binh Thanh Hoa Nghe An Ha Tinh Quang Binh Quang Tri Thua Thien-Hue Da Nang City Quang Nam Quang Ngai Binh Dinh Phu Yen Khanh Hoa Ninh Thuan Binh Thuan Ba Ria-Vung Tau Ho Chi Minh City Tien Giang Ben Tre Tra Vinh Soc Trang Bac Lieu Ca Mau Kien Giang<br>44
>23.52<br>20.12<br>14.33<br>Dep. Var. is revenue x med inc<br>48.02<br>20.76<br>26.49<br>Dep. Var. is revenue x gini<br>18.73<br>9.51<br>16.38<br>Kleibergen-Papp rk LM Stat<br>30.80<br>16.59<br>14.43<br>(underidentifcation)<br>_Notes:_ An observation is a municipality - year. Large municipalities <br>of participatory budgeting institutions at some point during 1995-200<br>simulated revenue and its interaction with the same covariates. Robust<br>All specifcations include municipality and year fxed efects, as well as <br>revenue, per capita transfer revenue, the fraction of public primary schoo<br>and pre-primary enrollment squared. ** indicates p_<_.01; * indicates p_<_<br>average per capita income is 0.52, the mean of 2000 median per capita i<br>in 100,000s.<br>_Sources:_ Author’s calculations based on data from IBGE, INEP, Tesour|

49
for large landholders.

Why is there a large discrepancy between self-reported tax payments and imputed tax liabilities? There are several possibilities. First, the Revenue Bureau or the chairman of the _kebele_ (village) peasant association, delegated by the Revenue Bureau, may not have the most current landholder data and agricultural land area for tax assessment. Some of the land registries may not have tracked changes in ownership (World Bank 2012), and the land registry from the first stage registration is paper-based, making it difficult to update information on land title and farm area (Bezu and Holden 2014a). Four regions (Amhara, Oromia, Tigray, and SNNP) are conducting second-stage registration to update the land registry but each is moving at a different pace (Bezu and Holden 2014a). Yet the average land size has been declining for the last 10 years because of subdivision of farms (Holden and Tilahun 2020), which suggests that tax liabilities calculated with outdated information from the land registry may overestimate farmers’ tax liabilities.

Second, different methods used to measure land area could be another source of the discrepancy. The firststage land certification process obtained the area of farms using low-cost methods and local technologies, such as ropes and measuring tapes (Bezu and Holden 2014a; Deininger et al. 2008). Farmers are expected to provide updated information to the Revenue Bureau, but it is not clear whether this happens, and if it does, whether farmers provide land area data using self-reports, tapes and ropes, or GPS measurements.<sup>18</sup> Measurement discrepancies would result in different tax assessments for an area-based tax.<sup>19</sup>

> 17 As a comparison, about 60 to 70 percent of property owners in Lima, Peru, paid property taxes, with the compliance rate (percentage of owners paying taxes) ranging from 25 to 90 percent depending on the district (Del Carpio 2014). In Mexico City, the compliance rate is about 60 percent <mark>(Brockmeyer
Figure 1: Digital goods imports in AfCFTA and rest of the world, 2012-2019 <!-- Start of picture text --> 22<br>20<br>18<br>16<br>14<br>12<br>Africa Rest of the world<br>2012 2013 2014 2015 2016 2017 2018 2019<br>Digital bundle share in total imports (%)<br><!-- End of picture text --> Notes: Figure depicts the share of the bundle of digital goods imports in total imports in AfCFTA and in the rest of the world over the period 2012-2019. Authors’ calculations based on COMTRADE.

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