rafmacalaba/datause-encoder-data huggingface dataset:
Our theoretical framework is also related to the literature on hysteresis and trade flows, which shows that with sunk costs and uncertainty about market conditions, positive shocks that lead to entry may not produce exit when they are reversed.<sup>6</sup> In these models, only bad market conditions induce exit and hence entry and exit will not be positively correlated. Our departure from these models is that we assume there is uncertainty about the firm’s potential in a market. Specifically, export costs are revealed only if the firm enters, and the firm can exit if the cost is high. This generates a strong positive correlation between entry and exit, a feature confirmed in the data.

In sum, our work builds on previous theoretical and empirical developments in the literature of exports at the firm-level, but instead of focusing on equilibrium effects, we focus on the dynamics of a growing sector. In particular, the patterns of entry and exit of firms in exporting and the discovery of new products and new markets.

The paper is organized as follows. The next section develops the model. Section III examines the predictions from the model using transactions level data from customs. Section IV offers background information on the nontraditional agriculture exports in Peru that supports the findings from the previous section. Finally, Section V concludes.

# **II. Model**

Before developing the model, we use an example to highlight the issues that we wish to address. Consider three entrepreneurs that want to access foreign markets. Sr. Lopez wants to start an export business but he does not know whether the cost of exporting will be prohibitive. He must gather information on regulations and paperwork

> 6 See Baldwin (1988), Baldwin and Krugman (1989), and Dixit (1989).  Roberts and Tybout (1997) also use this framework and find evidence that sunk costs are important in explaining entry into exporting by Colombian firms.

6
mortality using data from two rounds of DHS surveys. They estimate piecewise exponential hazard models with shared frailty including as special covariates five age intervals, a dummy indicating whether the child preceding the index child died, and mother’s race. They show that inequalities in the personal and household resources of South Africa’s four main racial groups substantially account for racial differences in child survival rates. This suggests that the dismantling of the apartheid system cannot eradicate the racial disparity in child mortality unless persisting racial inequities in basic levels of living are radically reduced.

Recently, child mortality studies have also examined the impact of HIV-AIDS. Using two DHS surveys carried out in Kenya in 1993 and 1998, Hill et al. (2001) examine the trends and determinants of child mortality in Kenya in the late 1980s to mid 1990s. They show that rising child mortality in the 1990s cannot be explained by changes in socioeconomic, demographic or health service utilization factors and that the HIV epidemic hence appears to be the most probable cause of the recent increases in child mortality in Kenya. Derose and Kulkarni (2005), who estimate the effect of HIV prevalence and other community characteristics on childhood mortality, come to similar conclusions for Zambia. They find that community HIV rates significantly elevate the risk of child death. Yet, this does not hold for infant death, indicating that own or parental infection matters more than indirect mechanisms like a high dependency ratio.

In sum, the review of infant and child mortality studies with a focus on evidence from Sub-Saharan Africa yields the following main insights: (1) Child and maternal proximate determinants do have the expected important impact on child survival. (2) The evidence with regard to socio-economic factors, in particular with respect to maternal education, is mixed. (3) When included, access to health infrastructure, in particular access to vaccination, turns out to have a strong impact on infant and child mortality outcomes. (4) In Sub-Saharan Africa in particular, infant and child mortality differ substantially between urban and rural areas as well as ethnic/racial groups; this being rooted in major socio-economic differences. (5) In high prevalence countries, HIV-AIDS may be an important, but difficult to observe, factor in explaining changes in child and infant mortality.

Yet, this brief review of empirical studies on child mortality in SSA also reveals some gaps and shortcomings of the existing literature. An important shortcoming of almost all the reviewed studies, and, indeed, the present one, is their reliance on DHS data. While these datasets certainly have their merits and are among the few datasets suitable for child mortality analyses, they also have some deficiencies. The most pertinent example of these deficiencies, which may at least partly explain some of the mixed findings with regard to socio-economic 13
Figure 5: HCSUA and synthetic benefits per recipient, New Hampshire and Wyoming <!-- Start of picture text --> (a) HCSUA (b) Synthetic benefits per recipient<br>600<br>140<br>130<br>500<br>120<br>Farm<br>Bill ARRA 110<br>400<br>100<br>Farm<br>Bill ARRA<br>300 90<br>2005m1 2010m1 2015m1 2005m1 2010m1 2015m1<br>Year-month Year-month<br>NH (FB: 29.20%) WY (FB: -22.90%) NH (FB: 20.05%, ARRA: 17.26%) WY (FB: 7.88%, ARRA: 18.99%)<br>Standard Utility Allowance<br>Synthetic benefits per participant<br><!-- End of picture text --> Notes: This figure plots out the heating and cooling standard utility allowance (HCSUA) and synthetic SNAP benefits per recipient from 2006-2015 for two states, New Hampshire and Wyoming, to illustrate how differential changes in the SUA lead to differential changes in synthetic benefits per recipient and synthetic benefits per population. Inside the parentheses behind each state, we show the percentage change of the HCSUA during the Farm Bill (FB) for Figure 5a, and the percentage change in synthetic benefits per population during the FB and the ARRA for Figure 5b.

Figure 6: HCSUA and synthetic benefits per recipient, Missouri and New York <!-- Start of picture text --> (a) HCSUA (b) Synthetic benefits per recipient<br>700 160<br>600 140<br>500<br>120<br>400<br>100<br>300<br>200 Farm B i ll ARRA 80 FarmBill ARRA<br>2005m1 2010m1 2015m1 2005m1 2010m1 2015m1<br>Year-month Year-month<br>MO (FB: 3.97%) NY (FB: 0%) MO (FB: 15.22%, ARRA: 20.26%) NY (FB: 11.50%, ARRA: 15.31%)<br>Standard Utility Allowance<br>Synthetic benefits per participant<br><!-- End of picture text --> Notes: This figure plots out the HCSUA (heating and cooling standard utility allowance) and synthetic SNAP benefits per recipient from 2006 to 2015 for two states, Missouri and New York, to illustrate how differences in initial levels of synthetic benefits per recipient lead to different percentage changes in synthetic benefits per recipient and synthetic benefits per population. Inside the parentheses behind each state, we show the percentage change of the HCSUA during the Farm Bill (FB) for Figure 6a, and the percentage change in synthetic benefits per population during the FB and the ARRA for Figure 6b.

46
Table 6. Impacts of Extremism on Labor Market Access

||Labor<br>Index<br>(1)|Employed [=1]<br>(2)|Monthly<br>Wage*<br>(3)|Weekly<br>Hours<br>Work*<br>(4)|
|---|---|---|---|---|
|_Panel A. Taliban Exposure_|||||
|_ETi_|-0.161|-0.053|-0.598|-0.229|
||(0.175)|(0.066)|(0.517)|(0.305)|
|FDR q-values|[0.822]|[0.822]|[0.822]|[0.822]|
|R-squared|0.165|0.156|0.169|0.163|
|_Panel B. Contested Territories Exposure_|||||
|_ECi_|-0.083|-0.027|0.077|0.060|
||(0.068)|(0.026)|(0.105)|(0.059)|
|FDR q-values|[0.697]|[0.697]|[0.697]|[0.697]|
|R-squared|0.166|0.156|0.177|0.176|
|_Panel C. U.S.-Allies Exposure_|||||
|_EAi_|0.061|0.020|0.238|0.083|
||(0.052)|(0.020)|(0.154)|(0.091)|
|FDR q-values|[0.568]|[0.568]|[0.568]|[0.568]|
|R-squared|0.166|0.156|0.169|0.163|
|Mean Dependent Variable|0.117|0.219|1.722|1.004|
|Observations|5,592|5,592|5,592|5,592|
|_Controls in All Panels_|||||
|Individual Covariates|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: The Labor Market Index is constructed using the outcome variables of columns (ii) to (iv) using the methodology of Kling, Liebman and Katz (2007). It implies i) standardizing the variables, ii) averaging, and iii) standardizing the final average again. Wages and Hours Worked were transformed using the inverse hyperbolic sine transformation. See Appendix B for details of variable’s constructions. The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

45
# **1. Introduction**

This paper provides a comparative summary of the payout phase in five countries. All five countries (Australia, Chile, Denmark, Sweden, and Switzerland) have large pension systems with mandatory or quasi-mandatory retirement savings schemes that are mostly based on defined contribution (DC) plans. But the five countries also exhibit important differences in the structure and role of different pillars, in the regulation of payout options, in the level of annuitization, in market structure, in capital regulations and in risk management and use of risk sharing arrangements. The different institutional and regulatory arrangements provide a rich set of experiences that may be useful and relevant for other reforming countries.<sup>1</sup> The paper is structured as follows. The next section provides an overview of the pension systems of the five countries, focusing on the relative role of different pillars and highlighting the target replacement rates in each country. The third section examines the menu of available products and discusses the crucial role that product and marketing regulation plays in shaping the total market. This section also summarizes information on the level of annuitization that prevails in each country. The fourth section focuses on the regulation of providers of retirement products, looking in turn at institutional arrangements, the prevalence of centralized or decentralized management, and the design of investment and capital regulations. This section also contains a brief discussion of risk management issues and the role of risk-sharing arrangements. The paper concludes with a summary of policy lessons for other countries.<sup>2</sup>

# **2. Overview of Pension Systems**

## **2.1 Overall Structure of Pension Systems**

All five countries covered in this paper have well-developed pension systems. As shown in Table 1, they all have a multi-pillar structure combining public and private provision, including a mandatory or quasi-mandatory private pillar. All countries have a zero public pillar providing basic benefits, but the level of provision varies considerably. Only Sweden and Switzerland have contributory and earnings-related public schemes (first pillars). Denmark and Sweden have supplementary public schemes, the ATP in Denmark and the PPM in Sweden, that are mandatory and fully funded and operate alongside the private occupational funds. All counties have voluntary third pillars for additional provision and for self-employed workers.

> 1 The paper is part of a broader project on annuities that comprises more in-depth country studies, and draws on these individual studies: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Thorburn (2007); Rocha and Rudolph (2010); and Vittas (2008). 2 A more detailed discussion of policy issues and constraints is contained in Rocha and Vittas (2010).

2
ES i ; l, Table 7, Perforumne of _Hedgedw_ andhltdged Portfoltforn for Pakistan Cotton.

|||_________<br>ApriL|_____________<br> 1991Hed<br>ge||_<br>_<br>_<br>_<br>|
|---|---|---|---|---|---|
|Period|PortfoLio<br>A 88N 91<br>|Hedge<br>Ratio<br>|Return<br>|Risk<br>|jRtu<br>|
|AUuIS-NarP<br>Av91 - Feb 92|ua - ar<br>Urtedged<br>Naive<br>Ex-Ante<br>Hedged<br>Ex-PostHedged<br>.<br>.~~~~~~~~~~~~~|h;C<br>h 1<br>hi .43<br>ha.90<br>~~~~~~~~~~~~~~~~~~~R|-2.05<br>-.47<br>-1.37<br>j-.63|5.3<br>.32<br>1.64<br>.26|-0<br>194<br>60<br>1952|

Note: _We_ were munebe to contruct hedges far other years due to mkissng observatiom.
units are balanced: we cannot detect differences in pre-determined characteristics of individuals and households.

At the end of each household’s enumeration the household survey team gave each household head or primary couple of the household an invitation to a laboratory session on a later day in that village. After the household survey had passed through the village, the second survey team organized these laboratory sessions in the village. 524 of the 548 households participated in these sessions for an attendance rate of 95.6%. We did not sample substitute households for the experimental sessions to stay consistent with the household survey sample. The likelihood of absent households is the same in treated and control villages, and the few missing households are therefore unlikely to have systematic impacts on our main results.

# **_3.3 Empirical specification_**

We will present simple regression-based differences-in-means between treated and control villages. Furthermore, since LEAP targeted households that were officially classified as poor beforehand (IDPoor 1 and IDPoor 2), we also split the sample by poor and non-poor households to see how effective LEAP was in targeting the poor, explore possible spillovers across poverty groups, and gauge access to the program by the non-poor. In these models, all standard errors are clustered at the village level (36 villages) to account for arbitrary correlation of errors. In particular, this is to account for game-session-specific (i.e. village) error correlation and error correlation due to the fact that SHGs were created at the village level. Alternatively, we also clustered at the level of randomization, that is at the (administrative) commune level. Recall that all villages in a commune were treated. Standard errors are very similar when clustered at the commune level (24 communes) and available on request.

Finally, we group all indicators and provide average, standardized effects in the final column of each table following Kling et al. (2004) and Clingingsmith et al. (2009). This method reduces the danger of cherry-picking significant results that arise by chance. It provides us with an overall effect for each group of indicators.

12
**1**

# INTRODUCTION

The link between macroeconomic policies, trade regimes, and<sup>competitive factor markets has</sup> been well documented.y The choice of an inward- or an<sup>outward-fccused trade regime is an</sup> important determinant of relative factor prices and employment.<sup>While considerable attention has</sup> been given to the connection between trade and employment,<sup>much3ossattention has been given to</sup> the effect of this relationship on human resources development.<sup>This paper looks at how a country s</sup> choice of devc-lopment strategies shapes incentives for the efficient<sup>use of resources in education and</sup> training. It examines the importance of export-led developmer4<sup>to competition in factor markets and</sup> incentives for private and public investments in sk lis development.<sup>It is particularly interested in how</sup> competitive factor markets affect the efficient use of public<sup>resources in education and training.</sup> A comparative approach is used to address these issuas.<sup>Six developing countries -- Egypt,</sup> Malaysia, Nigeria, South Korea, Thailand, and Tunisia --<sup>were chosen to achieve a mix of development</sup> strategies for study. Background papers were prepared<sup>describing the macroeconomic policy</sup> environment, development strategies, and the structure and<sup>performance of the vocational education</sup> and training system in each country during the 1980s. Other<sup>country-specific sources of information</sup> were used, including studies by the World Bank. The comparative<sup>approach furnishes evidence of a</sup> link between the macroeconomic environment and human<sup>resources development. Developing</sup> countries with outward-focused trade policies are found to<sup>rely more heavily on private resources in</sup> skills development and encourage greater efficiency in the public<sup>delivery of skills training.</sup> The study begins with an ee^planation of the theory that links macroeconomic policies, trade regimes, and competitive factor markets. It describes how<sup>the choice of a trade regime influences</sup> competition in capital and labor markets, and how this competition affects<sup>the market signals that</sup> guide investments in human resources development. The theory is<sup>used to develop questions for</sup> fuRther study. Next, macroeconomic trends and development<sup>strategies in each of the six countries in</sup> i' See, for example, Krueger (1988); Haggblade, et al.<sup>(1986); and Belassa (1988)</sup>
with Bargain et al. (2020) analyzing the interplay between intra-household decision-making and the migration of the husband, and Mu ̃noz-Blanco (2023) and Champeaux et al. (2023) studying the relationship between migration decisions and the marriage market. We draw out the implications of such changes for assessing migration impacts.

The rest of the paper is structured as follows: Section 2 describes how the decisions concerning migration and remittances, and the analyses of their effects, and shaped by the migrant’s family. Section 3 discusses how the occurrence of a migration episode can, in turn, modify the family of those who move, notably via an influence on marital union and dissolution. Section 4 draws on the American Community Survey to present some stylized facts on the initial marital status of migrants from developing countries, and on the evolution over time of their marital status, accounting for the confounding effect of return migration. Section 5 discusses the influence of the frequent changes in marital status on the analysis of the impact of migration on the family members left behind, while Section 6 draws the main conclusions.

# **2 How the family affects the migration and remittances decision and their impacts**

The vast majority of the literature has taken the family as fixed or predetermined, and then postulated different ways in which it can affect migration and remittance decisions. In the classic individual utility maximization model of Sjaastad (1962), reluctance to leave family is one of the “psychic costs” of migration, and then the optimal decision takes this “taste” for family presence as given. Much of the subsequent literature takes the decision-making unit as the family instead of the individual, with the decision to migrate then depending on the net gain in family income (Mincer, 1978), on how migration of one member enables risk diversification and insurance within households (Stark and Lucas, 1988), and on how it affects the family’s informal insurance arrangements with others in their community (Munshi and Rosenzweig, 2016; Morten, 2019). Internal transfers among family members then enable the total income of the family to be shared amongst the different members in accordance with their bargaining power. If some members migrate and others do not, then these internal family transfers take the form of remittances. These remittances can be state-dependent as in the case of remittances providing insurance, but they could also reflect within-family 5
We construct a high spatial resolution dataset that combines administrative microdata on the universe of households and firms in rural India with remote sensing data and village aggregates describing amenities, infrastructure and demographic information. Because variation induced by program rules is across villages rather than across larger aggregates, and because of the possibility of heterogeneous effects by individual characteristics, villageidentified microdata are essential for studying the impacts of roads. The limitation of this approach is that the administrative data are based on shorter questionnaires than traditional regional sample surveys.

In contrast with the dramatic economic benefits anticipated by policymakers, rural roads do not appear to transform village economies. Roads cause a substantial increase in the availability of transportation services, but we find no evidence for increases in agricultural production, assets or income. Farmers do not own more agricultural equipment, move out of subsistence crops or increase agricultural production. We can rule out a 10% increase in consumption with 95% confidence, with no significant or economically meaningful subgroup heterogeneity in terms of occupation, education or position in the consumption distribution.

We do find that rural roads lead to a large reallocation of workers out of agriculture. A new road causes a 10 percentage point decrease in the share of workers in agriculture and an equivalent increase in wage labor, measured on average four years after road completion. These impacts are most pronounced among the groups with the lowest costs and highest potential gains from participation in labor markets: households with small landholdings and working age men. We find suggestive evidence that reallocating workers are not the primary earners of the household, which may explain the limited effects on consumption.<sup>1</sup> The growth in non-agricultural workers appears to be driven by work outside the village. We find small and insignificant increases in village non-farm employment (4 workers per 1Hicks et al. (2017) suggest an alternative explanation: rural-urban wage gaps are not as large as previously thought, at least for workers able to change occupation.

3