rafmacalaba/datause-encoder-data huggingface dataset:
dependency ratio have a positive impact on life insurance demand, while social security expenditures have a negative impact.

More recent empirical studies include Browne and Kim (1993), Outreville (1996), Beck and Webb (2003), and Li et al (2007). These studies rely on larger cross-country datasets, especially the latter two. Browne and Kim (1993) use data from 1987 for a cross-section of 45 developed and developing countries, while Outreville (1996) bases his analysis on a crosssection of 48 developing countries for the year 1986. Beck and Webb (2003) use a large dataset of 68 developed and developing countries over the 1961-2000 period, while Li et al (1997) rely on a panel of 30 OECD countries over the 1993-2000 period. These studies use life premiums as the dependent variable (usually expressed as ratios to GDP or the population). Browne and Kim (2003) and Li et al (2007) define the estimated equation as the demand for life insurance. Outreville (1996) specifies briefly the demand and supply for life insurance and defines the estimated equation as a reduced form. Beck and Webb (2003) also indicate that the life premium reflects both demand and supply factors, while stressing the difficulties of distinguishing between the demand and supply sides.

Table 1 summarizes the main variables that should drive the development of the life insurance sector. Most of these variables have been explored in empirical research. The variables are clustered into four major groups: (1) economic variables (income level, income inequality, inflation, and interest rates); (2) demographic variables (population, population density, age dependency, and life expectancy); (3) social/cultural variables (education, religion); and (4) institutional/market structure variables (size/scope of the social security system, the level of financial development, the share of state and foreign insurers, market concentration., and the quality of the legal/regulatory framework). The second column indicates the expected impact of the variable on the life insurance sector, and the four following columns show the signs of the estimated coefficients in recent studies: positive (+), negative (-), or not significant (N/S). Shaded cells indicate that the variable was not included in the particular study.

Income is a central variable in insurance demand models. Higher income should increase the demand for life insurance for several reasons, including the greater affordability of life 4
They also note that if their results were in fact driven by endogeneity of their IV then they would find (spurious) effects on variables that should not be affected, such as child birth weight. However, they do not find such effects, supporting the choice of IV.

The geography of program placement sometimes involves natural, topographic or agroclimatic, features that can aid identification. An example is provided by the Duflo and Pande (2005) study of the district-level poverty impacts of dam construction in India. To address the likely endogeneity of dam placement they exploit the fact that the distribution of land by gradient affects the suitability of a district for dams (with more positive gradients making a dam less costly).<sup>57</sup> Their key identifying assumption is that gradient does not have an independent effect on outcomes. To assess whether that is a plausible assumption one needs to know more about other possible implications of differences in land gradient; for example, for most crops land gradient matters to productivity (positively for some negatively for others), which would invalidate the IV. The authors provide little defense for their exclusion restriction. They find that a dam increases poverty in its vicinity but helps the poor downstream; on balance their results suggest that dams are poverty increasing.

<u>Political characteristics</u> of geographic areas have been another source of instruments. Understanding the political economy of program placement can aid in identifying impacts. For example, Besley and Case (2000) use the presence of women in state parliaments (in the US) as the IV for workers’ compensation insurance when estimating the impacts of compensation on wages and employment. The authors assume that female law makers favor workers’ compensation but that this does not have an independent effect on the labor market. The latter condition would fail to hold if a higher incidence of women in parliament in a given state reflected latent social factors that lead to higher female labor force participation generally, with implications for aggregate labor market outcomes of both men and women.

To give another example, Paxson and Schady (2002) used the extent to which recent elections had seen a switch against the government as the IV for the geographic allocation of social fund spending in Peru, when modeling schooling outcomes. Their idea was that the geographic allocation of spending would be used in part to “buy back” voters that had switched against the government in the last election. (Their first stage regression was consistent with this

> 57 Note that what the authors refer to as “river gradient” is based on the distribution of land gradients in an area that includes a river.

47
<!-- Start of picture text --> Baldwin, Richard E., and James Harrigan, 2007, "Zeros, Quality, and Space: Trade Theory and<br>Trade Evidence", Working Paper No. 13214, NBER.<br>Baller, Silja, 2007, "Trade Effects of Regional Standards Liberalization: A Heterogeneous<br>Firms Approach", Policy Research Working Paper No. 4124, The World Bank.<br>Bernard, Andrew B., Stephen J. Redding, and Peter K. Schott, 2007, "Comparative Advantage<br>and Heterogeneous Firms", Review ofEconomic Studies, 74, 31-66.<br>Bown, Chad P., 2007, "Global Antidumping Database Version 3.0", Mimeo, http://people.<br>brandeis.edu/~cbown/global_ad/bown-global-ad-v3.0.pdf.<br>Broda, Christian, and David E. Weinstein, 2006, "Globalization and the Gains from Variety",<br>The Quarterly Journal ofEconomics, 121(2), 541-585.<br>Cadot, Olivier, and Jaime de Melo, 2007, "Why OECD Countries Should Reform Rules of<br>Origin", Discussion Paper No. 6172, CEPR.<br>Chen, Maggie Xiaoyang, and Aaditya Mattoo, 2004, "Regionalism in Standards: Good or Bad<br>for Trade?", Policy Research Working Paper No. 3458, The World Bank.<br>Collier, Paul, and Anthony J. Venables, 2007, "Rethinking Trade Preferences: How Africa Can<br>Diversify its Exports", Discussion Paper No. 6262, CEPR.<br>Czubala, Witold, Ben Shepherd, and John S. Wilson, 2007, "Help or Hindrance? The Impact<br>of Harmonized Standards on African Exports", Mimeo, http://siteresources.worldbank.org/ INT-<br>TRADECOSTANDFACILITATION/Resources/EU_Standards and TC_Trade.pdf.<br>Disdier, Anne-Célia, Lionel Fontagné, and Mondher Mimouni, 2007, "The Impact of Regu-<br>lations on Agricultural Trade: Evidence from SPS and TBT Agreements", Working Paper No.<br>2007-04, CEPII.<br>Essaji, Azim, 2005, "The Political Economy of Technical Regulations", Mimeo, www.wlu.ca/<br>documents/6388/pol_econ_tr.pdf.<br>European Commission, 2000, "Guide to the Implementation of Directives Based on the New<br>Approach and the Global Approach", http://europa.eu.int/comm/enterprise/newapproach/ newap-<br>proach.htm.<br>26<br><!-- End of picture text -->
# **How Does Port Efficiency Affect Maritime Transport Costs and Trade?**

# **Evidence from Indian and Western Pacific Ocean Countries**<sup>**†**</sup>

_Matías Herrera Dappe_<sup>_§_</sup> _Charl Jooste Ancor Suárez-Alemán World Bank World Bank Inter-American Development Bank_

**JEL Classification** : F1, N60, N75, N77, R40 **Keywords** : Port efficiency, transport costs, trade, data envelopment analysis

> † This paper is a product of the Transport and ICT Global Practice. The project received financial support from Australian Aid through the Partnership for South Asia Trust Fund (World Bank).

> § Corresponding author (mdappe@worldbank.org).
to either have no dropouts at all, or have a lot of dropouts, relative to the status quo. These results suggest that, in the presence of a feedback mechanism, farmers’ decision to stay or leave the service was more coordinated across members. Demand effect spillovers.

We next test whether providing users with feedback tools had any impact on non-users in the vicinity. The underlying hypothesis is that the lack of opportunity to influence the delivery of OAF’s agricultural services presents a significant barrier to farmers’ take up.

Again, we are interested in capturing both the extensive and intensive margins of expanded membership across treatment and control. Using the administrative data, we regress both a dummy for having new members join the group and the number of number of new members on our treatment assignment variables, as described in equation (1). We find evidence of large, positive spillover effects to non-users in the vicinity of treated groups (cols 6 and 7, Table 4). Groups that were offered any channel of feedback are 26-28 percentage points more likely to attract new members (col. 6, Table 4); these are very large effects relative to an 8 percent chance in the control group. Looking at the intensive margin, we find logbooks have a larger, more precise effect than scorecards. Logbooks add on average 1.12 new members to a group (significant at the 1% level), or 1.35 of a standard deviation, relative to 0.61 for scorecards (not significant). However, we cannot statistically tell these coefficients apart in this single equation.<sup>23</sup> Taken together, these results show that providing a simple feedback tool such as a logbook positively affected users’ sustained demand for the service and had large, positive demand spillovers for non-users. This indicates that our intervention addressed a significant barrier to adoption of this service—a lack of accountability. Neighboring client farmers

> 23 A Wald test of joint significance indicates that most of these results are robust to multiple hypothesis testing.

18
## Figure: E.10.4

# GUATEMALA LABOR INTENSIVE EXPORTS

<!-- Start of picture text --> LABOR INTENSIVE TOTAL EXPORTS<br><!-- End of picture text -->

**LABOR INTENSIVE EXPORTS BY PARTNER** 1970

<!-- Start of picture text --> 80000<br>060000D<br>Eo 40000<br>1X20000<br>0 1<br>1 970 l1980 1 990l<br><!-- End of picture text -->

**LABOR INTENSIVE EXPORTS BY PARTNER 1980**

<!-- Start of picture text --> ROW (5.97%) NA (7.57%)<br>MEX (1. 1 1%)<br>CA CA (85.36%)I 3 _<br><!-- End of picture text --> <!-- Start of picture text --> NA (1.67%)<br>ROW (0.34% MX (0 03%)<br>CA (97.97%)<br>LABOR INTENSIVE EXPORTS BY PARTNER<br>1990<br>ROW (560<br>RW(.4<br>NA(37 17%)<br>CA (54.93%)<br>_ MEX (2.26%)<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1990<br><!-- End of picture text -->
- Instead, the brunt of the impact appears to be concentrated within sectors. Thus, studies using plant- or firm-level data conclude that major impacts of trade reforms are natural selection among firms and reductions in X-inefficiency: less efficient firms in a sector are forced to downsize, improve efficiency or exit, with more productive (efficient) firms expanding their market shares. Overall total factor productivity increases more in industries that liberalized more.

- Correspondingly, the direct effects of trade reform on aggregate employment are muted. Different models imply different predictions for the long run with the neoclassical ‘no change’ being the frequently held view. The evidence is varied: it does not suggest longrun adverse effects and in some cases suggests long-run employment gains as accessing international markets with their high elasticities of demand permits expansion and accumulation in successful sectors without encountering large declines in prices. In the short run Keynesian employment responses and/or adjustment strains can be adverse, but are not generally very large relative to total employment.<sup>15</sup>

Turning to future research priorities, we would note the following questions:

- **Who/what is protected?** Some of the micro-econometric research to date suggests that the most heavily protected sectors in many developing countries are sectors that employ a high proportion of unskilled workers who earn low wages. A corollary is that trade liberalization, especially when also accompanied by investment liberalization and inward FDI, has a negative impact on unskilled workers in the short- and medium-run—be it in the form of lower wages and/or unemployment. A puzzle stressed by Harrison and Hanson (1999) is why these countries find it optimal to protect low-skill intensive sectors when this is their abundant factor.<sup>16</sup> However, this finding may also be a function of the set of countries that have been analyzed, which in turn has been limited by the

- availability of firm-level datasets. In fact, there is also evidence that countries tend to

> 15 They are large, of course, to those who lose their jobs.

> 16 Explanations could include political economy (along Anderson, 1992, lines) or the fact that it ignores the fact that countries such as China are even more unskilled-labor abundant than the developing countries on which research has centered (e.g., such as Morocco, Mexico, Chile, Colombia) – see Wood (1997).

25
_17_ We conduct an extended school survey twice, once at baseline and again 8 months after treatment assignment in May 2013 (Round 1 in Appendix Figure C2), collecting information on school characteristics, practices and management, as well as household information on school owners. In addition, there are 4 shorter followup rounds every 3-4 months that focus primarily on enrollment, fees and revenues. Finally, children are tested at baseline and once more, 14 months after treatment (Round 3). During the baseline, we did not have sufficient funds to test every school and therefore administered tests to a randomly selected half of the sample schools. We also never test children in public schools. At baseline, this decision was driven by budgetary constraints and in later rounds we decided not to test children in public schools because our follow-up surveys showed enrollment increases of at most 30 children in treatment villages. Even if we were to assume that these children came exclusively from public schools, this suggests that public school enrollment across all grades declined at most 2-3% on average. This e↵ect seemed too small to generate substantial impacts on public school quality.<sup>23</sup> _C._ **_Regression Specification_** We estimate intent-to-treat (ITT) e↵ects using the following school-level spec-<sup>24</sup> _Yijt_ is an outcome of interest for a school _i_ in village _j_ at time _t_ , which is measured in at least one of five follow-up rounds after treatment. _Hijt_ , _L_<sup>_t_</sup> _ijt_<sup>,and</sup><sup>_Lu_</sup> _ijt_ are dummy variables for schools assigned to high-saturation villages, and treated and untreated schools in low-saturation villages respectively. We use strata fixed e↵ects, _↵s_ , since randomization was stratified by village size and revenues, and _δt_ are follow-up round dummies, which are included as necessary. _Yij_ 0 is the baseline value of the dependent variable, and is used whenever available to increase precision and control for any potential baseline mean imbalance between the treated and control groups (see discussion in section III.D). All regressions cluster standard errors at the village level and are weighted to account for the di↵erential probability of treatment selection in the _L_ arm as unweighted regressions would assign disproportionate weight to treated (untreated) schools in smaller (larger) _L_ villages relative to schools in the control or _H_ arms (see Appendix B). Our coefficients of interest are _β_ 1, _β_ 2, and _β_ 3, all of which identify the average ITT e↵ect for their respective group.

> 23Another option would have been to test those students at baseline whom we expected to be marginal movers due to the treatment and see their gains from the switch. Detecting marginal movers ex-ante however is difficult especially given that churn is not uncommon in this setting.

> 24We focus on ITT e↵ects and do not present other treatment e↵ect estimates since take-up is near universal at 94 percent.
status since we are not able to observe in the data transitions across informality status using our definition (the questions on registration with SAT and IMSS were not available in previous waves of the census).<sup>16</sup> Still, in our quantitative exercises, we do allow for establishments to change their informality status during their life cycle to assess the potential effect on their growth rates.

We sort formal and informal establishments in every period into age bins (0-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, and 35-39). Next, we restrict the data to those establishments that were ages 0-4 in any cross-section between 1998 and 2013. That is, we restrict the analysis to establishments in the 2018 cross-section (when we observe their formality status) that started operations between 1998 and 2013 (3,006,254 unique establishments). For each establishment, we follow Eslava et al. (2022) and divide their size in every period by their size at age 0-4. We then regress these cumulative growth rates on the (double) interaction between age (in bins) and a binary indicator for whether the plant is formal or informal, 2-digit sector and cohort fixed effects, and another indicator for whether the year is before or after 2008, to control for whether the identifier that links establishments across waves is synthetic (as generated by Busso et al. (2018)) or developed by INEGI.

The exercise just described only allows us to obtain average growth rates through ages 20-24 since we only observe an establishment for 5 periods at most. We therefore repeat these steps for different starting ages. For example, to obtain the average growth rate through ages 30-34, we restrict the data to establishments in the 2018 cross-section that were 10-14 at any point between 1998 and 2013. Our life cycles, which we show in Figure 2, are the average of the different estimates for the cumulative growth rates from the separate regressions using different starting ages. More details on our methodology, together with the full set of results from least squares, are offered in the appendix.

Our estimates show that formal establishments more than double their size during their life cycle. In Figure 2, the size of formal establishments at ages 35-39 is on average 2.4 times their size at birth. In contrast, informal plants increase their size only by 77%. These estimated growth rates do not differ significantly when we focus only on

> 16One potential proxy is the transitions across status of informality on the intensive margin. Busso et al. (2019) estimate that almost 90% of informal establishments in 1998 were still informal in 2013. Formality on the extensive margin, however, is more likely than informality on the intensive margin. While 34% of establishments is registered with SAT and/or IMSS, less than 10% of the 2018 cross-section employ salaried employees and fully contribute to their social security (the definition of formality on the intensive margin first coined by Busso et al. (2012)). Moreover, only 27% of formal establishments according to our definition are also formal on the intensive margin (Table B.4 in the appendix).

8
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