rafmacalaba/data-use-mentions-extended huggingface dataset:
not necessarily the most able and have attended less-selective colleges in small and medium cities. Although they are more able than their peers who do not move out of their college city, they are not positively selected into their big work cities. Taken together, the two types of after-college move help explain our finding of a much higher elasticity of earnings with respect to college than work city size.

This finding highlights the importance of our pre-labor market locational data. Administrative data sets usually follow an individual from the beginning of her work life and use repeated observations per individual to handle the inherent self-selection into the work city, with identification of (work city) agglomeration effects being driven by migrants (Combes, Duranton, and Gobillon, 2008, D’Costa and Overman, 2014, De la Roca and Puga, 2017). In contrast, our pre-labor market locational data enable us to examine the spatial trajectory leading individuals to their work city, thereby underscoring at what point of this trajectory a large city pays off the most.

Our study furthermore highlights the role of luck. Individuals born in big cities or who grew up there do not need to move in order to attend college, in contrast with those who grew up in small cities with limited college access. Given the spatial disparities in college access and quality in Colombia, policies aimed to address poverty and inequality should consider lowering the costs of college mobility for talented high school graduates from small towns and cities.

The rest of this paper is organized as follows. Section 2 describes the data and institutional setting. Section 3 discusses our results, and Section 4 concludes.

> 3See Greenwood (1997), for a survey on evidence of migrants being positively selected relative to non-movers. Borjas, Bronars, and Trejo (1992) show that more educated and productive workers in the us are more likely to migrate regardless of their state of origin. Hunt (2004) finds that migrants are more skilled that non-movers across federal states of West Germany. De la Roca (2017)
05, *** p<0.01. The dependent variable is poverty rate (in percent), interpolated linearly to replace missing values between two data points. Conflict debt is the discounted history of conflict years where a conflict year is defined as by more than 0.08 fatalities per 1,000 population. We use average population to avoid time variation in the denominator of the indicator. Average population is computed from available years in the World Bank data. Continent-time fixed effects are a dummy for every year on every continent. Continent here are 7 world regions. Regressions are on unbalanced cross-country sample during 1989-2018.

An additional, immediate concern in Table 1 is reverse causality where poverty leads to conflict and not the other way around. And indeed, the robust relationship between conflict debt and poverty rates shown in Table 1 is still present if we use a simple dummy for contemporaneous conflict – as shown in Table 2. However, in Appendix Table A2 we show that the association between conflict debt and the poverty rate is robust to controlling for contemporaneous conflict whereas the reverse is not true. Countries with large conflict debts suffer from poverty but, controlling for this debt, there is no association between poverty and armed conflict. Also, in column (4) of Table 2, we see that contemporaneous conflict loses its size and significance when controlling for continent time fixed effects. This means that poverty rates are stronger related to past conflict than to present conflict which implies that reverse causality cannot be the main driver of the correlations shown in Table 1.

||_Table 2: Pov_<br>|_erty Rate and Con_<br>|_flict_<br>||
|---|---|---|---|---|
||(1)<br>Povertyrate|(2)<br>Povertyrate|(3)<br>Povertyrate|(4)<br>Povertyrate|
|Conflict year|17.16**
under the “disability” category reflects a growing recognition of sickness and disability as a major labormarket issue (Immervoll, 2011) **.** ECA countries spend significantly less than the OECD average, but spending is particularly low in Georgia, FYR Macedonia, Armenia, Moldova, Serbia and Albania (See Figure 15). In ECA, ALMPs remain very small in most countries, so it is difficult to expect them to have a large impact if not expanded significantly (as exemplified by Nordic countries). There is need for more rigorous evaluations of what works and under which conditions; how to design activation policies for different population groups; what should be priority groups. The comprehensive labor market reforms in 2003 and 2005 in Germany including changes to ALMPs can present important lessons for policy makers (See Box 4).

# **Figure 15 – ALMP Spending in Many ECA Countries Remains Low**

ALMP spending per unemployed, 2007 <!-- Start of picture text --> 2,000<br>1,800<br>1,600<br>1,400<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>USD Spent per Unemployed<br><!-- End of picture text --> Source: Authors’ calculation based on IZA and UN population data

# **<mark>Box 4 - Comprehensive Active Labor Market Policy Reform in Germany</mark>**

<mark>Having faced high unemployment rates for more than a decade, Germany introduced far-reaching reforms of its unemployment and social assistance benefits between 2003 and 2005 (the so-called “Hartz” reforms), adhering to a three-part reform strategy: (1) improving employment services and active labor market programs (ALMPs); (2) activating the unemployed and social assistance beneficiaries; and (3) fostering employment demand by deregulating the labor market. To
supply of agricultural household in a developing country. _American Journal of Agricultural Economics 91_ (3), 697–710.

- Jagnani, M., C. B. Barrett, Y. Liu, and L. You (2021). Within-season producer response to warmer temperatures: Defensive investments by kenyan farmers. _Economics Journal 131_ (633), 392–419.

- Janzen, S. A. and J. D. Michler (2021). Ulysses’ pact or Ulysses’ raft: Using pre-analysis plans in experimental and nonexperimental research. _Applied Economic Perspectives and Policy_ , forthcoming.

- Jayachandran, S. (2006). Selling labor low: Wage responses to productivity shocks in developing countries. _Journal of Political Economy 114_ (3), 538–75.

- Kalkuhl, M. and L. Wenz (2020). The impact of climate conditions on economic production. evidence from a global panel of regions. _Journal of Environmental Economics and Management 103_ , 102360.

- Kaur, S. (2019). Nominal wage rigidity in village labor markets. _American Economic Review 109_ (10), 3585–616.

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52
was also reported around the same time among citizens of the EU (Eurobarometer 2009), Canada, and Australia (Puglise and Lyons 2010). <u>Franzen and Volg (2013)</u> analyze trends in 33 OECD and non-OECD countries, where they find a moderate decline in environmental concerns over the two decades before 2010.

More recent research, conducted from the late 2010s to today, indicates heterogeneous but mainly elevated climate change concerns. <u>Marlon et al. (2022)</u> find increases in the importance of climate change issues and perceived harm in every state in the US between 2008 and 2020. They show that policy support grew more in liberal states but remained unchanged elsewhere. According to <u>Lachapelle and Borick (2022), between 2011 and 2021, public opinions in Canada and the US</u> shifted toward acceptance of the anthropogenic nature of climate change. Both countries registered only moderate increases in support for policy interventions. However, the degree of politicization and polarization in the debate on nature and optimal policies toward climate change was much greater in the US. Combining data from Eurobarometer and Twitter, Crispino and Loberto (2024) show that in Italy in 2022, attention to climate change had increased but sentiments toward climate change deteriorated. Kenny (2024) reveals that prioritization of environmental issues in the UK rose throughout the 1980s, falling dramatically in the early 1990s and recovering by 2001 before falling in subsequent years. This decline intensified following the financial crisis of 2008. Support for environmental issues recovered during the last decade and increased between the Spring of 2018 and the Winter of 2019.

# **3. Data**

We use data from the 2016 and 2023 rounds of the Life in Transition Survey (LiTS) conducted by the European Bank for Reconstruction and Development (EBRD) and the World Bank (EBRD <u>2023). The survey covers the transition economies of Europe and Central Asia and several</u> comparator countries
11

# **IV Stylized Facts on Fertilizer Imports, Agricultural Production and Exports**

In this section, we present three stylized facts related to the fertilizer import ban’s adverse effects on fertilizer imports, crop yields, and exports of agricultural goods that utilize fertilizer.

## **IV.A Fact 1: Fertilizer Imports Declined After the Ban**

We start by examining whether the import ban was effective in restricting imports. To do this, we estimate dynamic DID regressions using import data at the product-month level, where a product is defined as a unique Harmonized System 8-digit (HS8) code. The treated group consists of fertilizer products whose imports were banned after April 2021 and the control group consists of products that were not banned (both non-banned fertilizers and other types of non-banned products). We compare fertilizer imports in the treatment group with their control group counterparts following the DID methods proposed by De Chaisemartin and d’Haultfoeuille (2022), which allow units to switch in and out of treatment. This is key for our study as the import ban was lifted less than one year after it was first introduced.<sup>26</sup> Our dynamic DID estimator recovers the evolving effects of the ban over time on two outcome variables: a dummy indicating non-zero imports (i.e., extensive margin) and the log of import value (i.e., intensive margin) at the product-month level between January 2019 and September 2022. Estimation details are provided in Appendix B.A.

Figure I plots the DID results, showing a clear decline in imports right after the ban was imposed, with the average probability of nonzero imports across banned products dropping by 31 percentage points in the second month of the ban (Figure Ia). Along the intensive margin, negative effects peaked four months after the ban was introduced, with imports falling by 99.998% (Figure Ib) and remaining low for the next several months.<sup>27</sup>
# **<mark>Box 2 - Balancing Flexibility and Protection: Denmark’s Flexicurity</mark>**

<mark>Every year, one out of five Danes loses his or her job. But they don’t lose their incomes. Unemployment benefits replace close to two thirds of their earnings, and the government helps them find work. This combination of flexibility for employers and income security for workers is called “flexicurity”. The arrangement has been in place since at least the 1970s. But it has evolved over time, principally in that the active labor programs component has been strengthened. It seems to work well. Between 1995 and 2008, Danish unemployment rates averaged 4.9 percent, while the rest of the EU15 suffered rates close to 8.5 percent. Danish employment laws have evolved from a system in which unemployment benefits were paid by labor and trade unions, not the government. In the 1970s and 1980s, unemployment rates remained high, while those without jobs got good incomes.</mark> <mark>The arrangements were reformed in the 1990s. The OECD Employment Protection index for Denmark has fallen from 2.4 in 1983 to 1.5 in 2009. Unemployment insurance financed from contributions and taxes covers around 80 percent of the labor force. Benefits last up to four years, replacement rates cannot exceed 90 percent of wages. After four years, recipients have to switch to social assistance which means a reduction of between 20 and 40 percent of benefits. Active labor market programs, like job search assistance and training nudge the unemployed back to work. The unemployment rate dropped from 10 percent in 1993 to 3.3 in 2008. The incidence of long-term unemployment—those without work for more than a year—fell from a third of total unemployment in 1994 to one tenth in 2009.</mark> <mark>Despite liberal firing and hiring practices, employment has not fluctuated too much in response to output variability. But the spending on active labor
in Infrastructure Database; and World Bank staff calculations.<br>Note: Regional grouping follow s the World Bank classification. High income countries are not included. Total value of<br>investment and number of projects are in accordance to year 2016, or latest year available before that.<br><!-- End of picture text -->

Existing PPPs in MENA are not large both in relation to what is needed (see Figure 2.3) and in comparison to other regions<sup>4</sup> (see Figure 2.4). PPP investment in the region amounted to a few billion U.S. dollars as of 2016. These PPPs are mainly in a few countries—Jordan, Iraq, and Morocco. They are concentrated in telecom, electricity and seaports (see Figure 2.5).

> 4 Due to data availability of the source, only low- and middle-income countries are covered in the figure.

6
cross-sectional surveys, the results show that the percentage of middle class households has increased significantly from 28.3 to 52.8 percent in Chile, from 24.9 to 42 percent in Mexico, and from 23 to 39.4 percent in Peru over the analyzed period (see figure 5). It is important to notice that the growth of the middle class has been sustained in Chile over the complete time span, while in Mexico and Peru this occurred only after the economic downturns of 1994/95 in Mexico, and the downturns of 1998/99 and 2001 in Peru.

**Figure 5: Size of the middle class in Chile, Mexico and Peru**

_Percentage of households_ <!-- Start of picture text --> 52.8<br><!-- End of picture text --> <!-- Start of picture text --> 39.4<br><!-- End of picture text --> _Source_ : Author‘s calculations based on data from CASEN, ENIGH and ENAHO cross-sectional databases.

- 14 -
request.

> 15 The initial procedure resulted in a low matching rate for the PRC (59%), which is probably due to the country’s regulations on collecting and using geographic information, and the unique coordinate system used for geographic data in the PRC. To address the issue, prefecture information available in the WBES data for the PRC is used to assign each unmatched firm to the largest natural city in its prefecture.  This results in a 100% matching rate.

8
technology sophistication in general business functions ( _MOSTj_ ) is compared between all sample and sample excluding significantly different enumerators. Standard errors in parenthesis.

compare variables in RAIS with variables we collected in FAT for the same firms.

First, we analyze the correlation between sales per worker and our technology measures (GBF) and (SBF) from FAT and average wages from RAIS. Table A.23 reports the point estimates of regressing firm-level FAT variables on the log or average wages per worker from RAIS and a set of firm-level controls. The FAT variables are log of sales per worker (column 1), and average technology sophistication (GBF, column 2, and SSBF, column 3). In all three cases we find strong positive associations between the FAT and the RAIS variables.

Second, we compare the differences between labor-related indicators from a matched employer-employee administrative data for firms in FAT versus the universe of firms. To perform this comparisons we obtained the weighted average for firms in FAT, using the weights we constructed as described in section A3 and compare it with the average for all 110
export proceeds surrender requirements, the presence of multiple exchange rates, and the five-year rolling average of the IMF binary capital account restrictions. When each indicator is crudely measured (as is the case with the binary indicators of the AREAER), the first principal component provides an intuitively appealing composite measure (Quinn, Schindler and Toyoda, 2011). This measure is available for 182 countries for the period 1970-2011.

Our second de jure measure, initially put together by Quinn (1997), relies on indicators of capital account regulations based on a coding of the AREAER texts. This is also a composite index assigning equal weights to six categories: payments for imports; receipts from exports; payments for invisibles; receipts from invisibles; capital flows by residents; and capital flows by nonresidents. The Quinn index covers 90 countries over the period 1960-2011. We use these two measures in our regressions since they offer higher degrees of intensity and finer gradation for a large number of countries over an extended time period. We normalize the measures so each ranges between zero and one, where zero suggests restrictions on the capital account and 1 indicates that the capital account is fully liberalized.

We present the temporal evolutions of these measures of financial integration in Figure 1. Irrespective of the measure, the degree of financial integration has been increasing over time for the full sample. The increase has been fueled by a series of trade and financial liberalization initiatives around the world since the mid-1980s. Only about 15 percent of the countries in our sample had fully open capital accounts in 1984 compared to more than 50 percent in late 2000s.

This trend is largely driven by the group of developing economies as they have increasingly become more integrated to the global economy during the last two decades. Almost all of the advanced countries now have open capital accounts as implied by the de jure measures whereas a number of developing countries still maintain relatively closed capital accounts. In the case of the Chinn-Ito indicators, the average degree of financial integration for developing countries peaked at 0.5
- (2) An aggregated food consumption module that corresponds to the “collapsed list” treatment arm in the SHWALITA study, and

- (3) A series of short, aggregated non-food consumption expenditure modules that were informed by the variants from the SHWALITA study but were refined in some instances to better align with the COICOP categories (United Nations, 2018), related, for instance, to education, health, and utilities expenditures.

The TA 3 collapsed food consumption module is slotted immediately after the core modules, covering all 12 broad food categories (including alcoholic beverages), and only asking the respondent to state the monetary value that the consumed quantity of total food in that category would have cost, had it been purchased.<sup>6</sup> TA3 non-food consumption expenditure modules are then slotted immediately after the TA3 collapsed food consumption module, and together, these sets of modules permit the estimation of Models 3 and 4 as presented in Dang _et al._ (forthcoming). The TA 3 sample consists of 698 households.

These data are in turn complemented with the data from the nationally representative TZNPS 2020/21 and 2019/20 rounds, which are used as base surveys to estimate the imputation models that are in turn applied to each treatment arm. The main results are based on the 2020/21 round, while Appendix A includes consistent findings based on the 2019/20 round, as discussed below. The TZNPS is a multi-topic, nationally representative longitudinal household survey that has been implemented by the NBS since 2008, with financial and technical support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) project. The questions for the poverty predictors required for the estimation of Models 1, 2, 8 and 9 are

> 6 TA 3 covers: cereals and cereal products; starches; sugar and sweets; pulses, dry; nuts and seeds; vegetables; fruits; meat, meat products, fish; milk and milk products; oil and fats; spices
.000|1.000|
|Observations|199|19|9|
|Adjusted R<sup>2</sup>|0.996|0.9|96|

_Source_ : Author non-parametric estimates of equation (3). _Note_ : Standard errors in parenthesis.<sup>*</sup> p _<_ 0.1;<sup>**</sup> p _<_ 0.05;<sup>***</sup> p _<_ 0.01.

Lederman, Olarreaga, and Payton 2010 (in the case of the export equation). However, when the study lags one, two, or three years, the coefficients on export promotion budgets are all statistically insignificant.

The third robustness check the study performed involved excluding the third wave of the EPA survey for the period 2010–2014. It only concerned 14 European countries. The study therefore tested the robustness of these results to the exclusion of this third wave from the sample and found that the same variables that are statistically significant in the full sample are also statistically significant in 2005–2010 with the exception of the strategic focus on a few sectors and destinations in the export equation. Also, the share of budget spent on established exporters becomes statistically significant in the smaller sample in both the export and GDP per capita equations. The control function is also statistically significant in the 2005–2010 sample, whereas in the full sample it was not. The reason is simple. In the reduced sample the study loses almost 30 percent of all years corresponding to 31 observations. The within variation is then dramatically reduced so that even population size becomes insignificant when explaining the size of the export-promotion budget, and only the share of fees for services and the ranking of EPA responsabilities remain significant predictors (with p-values of about 4 percent, but only the latter exhibiting some prediction power). This means, that in the reduced sample, _ξ_<sup>ˆ<
2015); both in the pooled sample and the gender-disaggregated independent samples. Urban residence increases the odds of employment in all samples (Contreras et al., 2011; Naudé & Serumaga-Zake, 2010) although opposite effects have been observed in the literature (Fadayomi et al., 2014). Similarly, residence in the Ashanti, rather than the northern region, is associated with higher odds of employment for the male sample. A closer look at the genderdisaggregated results indicates that men who have at least high school education, and reside in male-headed households are more likely to be employed, which is not the case in the female sample.

With respect to the second stage decision of choosing between self- vs. wageemployment, the probability of the latter are higher among men, compared to women (pooled sample column of Table 7). Other factors that are associated with higher probability of wage employment among both men and women include education and urban residence, while being Muslim (H’madoun, 2010) and residing in a large household lowers the probability in both groups. Residing in a male-headed household increases the likelihood of wage employment among men, while having a spouse present in the household has the reverse effect.

# **5.2. MNL regression results of labor market outcomes and traditionalism, personality and other controls**

The _pooled sample_ results of the effects of gender role attitudes and personality traits on labor market participation decisions are presented in Table 8. The naïve regression of traditionalism on employment indicate a negative correlation (specification 1) that is retained even after controlling for other individual, household and geographic characteristics (specification 2). Results from the interactions between traditionalism and personality traits indicate that among less traditional individuals, traits such as work centrality (5) and high achievement (9) are positively associated with being employed, while high internal locus of control (8) has a negative significant effect on employment. The additional effect of being more traditional, however, masks the significant effects of work centrality