rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Despite the large theoretical and empirical literature documenting that access to international trade can be beneficial for economic development (Edwards 1993; Goldberg and Pavcnik 2016; Wacziarg and Welch 2008), there has been a rise in protectionism in discourse and measures around the world recently (Donnan 2016). This trend is also reflected in the perceptions of individuals on trade. Pavcnik (2017) shows that the share of people reporting that trade is beneficial to the economy has declined since 2002.

Consistent with these trends, recent empirical evidence points to the negative impact import competition from developing countries had on workers in developed economies (for example, see Autor, Dorn, and Hanson 2013; Balsvik, Jensen, and Salvanes 2015; Hakobyan and McLaren 2016). Meanwhile, standard trade models predict that a rise in exports from developing to developed nations would benefit workers in countries with abundant labor. In fact, recent studies show a substantial impact of exports on poverty reduction and improved labor market outcomes at the local level in China and Vietnam (Erten and Leight 2019; McCaig 2011, McCaig and Pavcnik 2018) and on poverty and inequality among states in Brazil (Castilho, Menéndez, and Sztulman 2012). Yet other empirical studies find contrasting results, including that trade liberalization does not necessarily benefit all households in developing countries and may lead to widening inequality and declining labor market outcomes (De Loecker et al. 2016; Dix-Carneiro and Kovak 2017; Erten, Leight and Tregenna 2019; Goldberg and Pavcnik 2007; Nicita 2009; Pavcnik 2017; Revenga 1997; Topalova 2010). The underlying channels through which international trade may or may not benefit individuals and regions in developing countries are not fully understood.

This paper investigates the effects of a rise in exports on poverty and inequality using a rich municipality-level data set from a developing country. Mexico is an interesting case given its lackluster performance in economic growth and poverty reduction, despite a significant trade expansion (Hanson 2010). Mexico joined the forerunner of the
and the exchange rate of a country vis-a-vis the dollar is the correlation between the price of that good and the value of the dollar in international markets vis-a-vis other major currencies.

## 2.4 Stationary versus transition equations

The empirical specications in equations (7) to (8) describe the long run equilibrium of a country's trade pattern, conditional on other characteristics. Since the force that leads sectors with natural hedge to become more prevalent in more volatile environments probably operate over long periods, this is an accurate representation of the long run steady state composition of exports. However, since the available trade data covers at most 17 years, during which many countries engaged in structural reforms that might have aected the steady state composition of exports, such as trade liberalization, from an empirical perspective it may be also appropriate to estimate the transitional version of the equation that relates natural hedge to the growth of the export share of a commodity over the sample period, given by, where the notation is as above, except that _T_<sup>_−_1</sup> ∆log � _Xi,W_<sup>_h_</sup> � _t,t_ + _T_<sup>istheaveragegrowthrateof</sup> the global export share during the sample period of length _T_ , and log � _Xi,W_<sup>_h_</sup> � _t_<sup>isthe(log)initial</sup> value of this share for each country-good pair. The long run, stationary equilibrium of this equation corresponds to equation (7), where the long run coecients correspond to the short run ones divided by _−η_ . The long run coecient associated with the interaction would, therefore, be ( _−β/η_ ) _._ A similar transition equation applies to the bilateral export shares model of equation (8).

In addition to allow for non-stationarity, this specication is also less likely to be aected by the reverse causality
, small developing economies including islands states may not move containers inland but rather unstuff them and store the goods in bonded facilities. The current system does not detect these old-fashioned logistics practices, which are progressively disappearing.

- Representativeness of transport corridor data for landlocked countries: For rich landlocked countries in Europe, moving containers inland is not representative of their international supply chain. In Europe, goods are cleared mostly at entry into the EU. Thus lead time data from container tracking is not representative of the cross-border processes of these countries and may exaggerate the actual delays. For instance, trucking of imports to landlocked countries in Europe is not directly measured given direct trucking and clearance at the port of entry. In developing landlocked countries, trucks (or rail) are going to inland ICDs,<sup>16</sup> thus the data is representative.

> 16 Except for some landlocked developing economies, such as the West Bank and Gaza, or SACU countries, where goods are cleared at the port of entry.

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groups, including married women and mothers, such as those by Castañeda (1981), Magnac (1992), Arango and Posada (2007), Amador, Bernal, and Peña (2013), González and Daza (2015), and Santamaría and Rojas (2001), have contributed unique perspectives. They have highlighted the positive influence of education on labor participation and the challenges posed by childcare responsibilities and young children.

This paper contributes to further our understanding of the drivers behind the behavior of female labor participation in Colombia for almost seventy years. To our knowledge, this is the first paper that uses historical aggregate data for an emerging economy, considering that the relationship between female labor participation and its determinants has changed over time. Moreover, Colombia could serve as an example for promoting the rapid economic empowerment of women through progressive reforms and advancements. The country could provide a reference point for countries currently struggling with low female labor force participation.

This study underscores the significance of allowing variables to evolve and recognizing the importance of historical context in shaping women's empowerment. It is important to acknowledge that history cannot be condensed into a single average, as it is through historical analysis that we gain insights into the dynamics and transformations of women's empowerment. The analysis reveals the significance of the demographic transition, educational opportunities, economic conditions, social norms, and key events such as the peace process on female labor participation over time. The findings emphasize the need for policies and interventions that address barriers, promote gender equality, and create inclusive labor markets. By understanding and addressing these drivers, society can foster greater gender equity and unlock the full potential of women as contributors to economic growth and development.

The remainder of the paper is organized as follows. Section II describes the data and the empirical strategy used in our analysis. Section III presents the principal component analysis results and the individual variables' effect on the female labor force participation rate in Colombia. Section IV concludes.

# **II. Empirical strategy
excludes all other costs borne by the firm such as inputs or raw materials, utilities, and salaries paid to employees. The use of self-reported profitability as the main variable of interest follows the critique of revenue-based productivity measures by Katayama et al (2009) and the arguments of De Mel et al (2008) for using profits measured through a direct question.<sup>8</sup> In particular, De Mel et al (2008) find that direct reports of profits tend to be less noisy and at least as reliable as asking firms for revenue and expenditure details; moreover, even if the true level of profits is likely to be understated, the ranking across firms is likely to be reasonable.

**Figure 7: Self-reported profits vs. calculated profits and labor productivity**

<!-- Start of picture text --> 30,000 12<br>10<br>20,000<br>8<br>10,000<br>6<br>0<br>4<br>-10000<br>2<br>-10000 0 10,000 20,000 30,000 4 6 8 10 12 14<br>Monthly revenues less expenses, US$ Revenue per worker, log US$<br>Self-reported profits, log US$<br>Self-reported monthly profits, US$<br><!-- End of picture text --> _Source_ : Authors’ calculations with EMES data.

Following the suggestions of De Mel et al (2008), the EMES did not contain very detailed questions on the structure of firms’ costs. However, we did ask firms about their revenues as well as their expenditures on salaries, inputs or raw materials, and electricity.<sup>9</sup> The correlation between reported profits and the difference between revenues and expenditure on all of the above categories is very high: 87 percent for monthly data and 97 percent for annual data. Moreover,
internationally.<sup>3</sup> Labor productivity in services has been between 3.4 and 3.7 times as high as in agriculture (in constant prices) (this ratio has been unchanged since 1993 at around 3.7).<sup>4</sup>

# **3. IDENTIFYING CHINA’S PATTERN OF GROWTH AND POLICY FACTORS**

Given the large surplus labor and large productivity gap between agriculture and the rest of the economy, why has so little reallocation of labor from agriculture to the higher productivity sectors taken place? Why has China’s economic growth led to such modest urban employment growth? The answer is directly tied to China’s growth pattern and the associated policies pursued.

China’s pattern of growth especially since the early 1990s can be characterized as follows:

- The bulk of GDP growth since the early 1990s has come from explosive growth of industrial production. Industrial value added increased on average over 11 percent per year during 1993 and 2004, and the share of industry in GDP rose from 38 percent in 1990 to 53 percent in 2004 (Figure 4), the highest among countries for which the World Bank’s World Development Indicator database has data. In 2002-04, industry contributed around 2/3 of GDP growth.

<!-- Start of picture text --> Figure 4. Shares in value added<br> (in percent)<br>60<br>Industry<br>50<br>40<br>Services<br>30<br>20<br>Agriculture<br>10<br>0<br>1978 1986 1994 2002<br><!-- End of picture text --> _Source: NBS, and authors' estimates._ Over 90 percent of the growth in industry in 1993-2004 was in the form of labor productivity growth rather than employment growth, mainly led by a large-scale investment effort and increased capital-labor ratio. As a result, absorption
entrepreneurial than those deciding to stay. It follows that areas that are already poor see an outflow of important resources that could have contributed to the local development (Awumbila, 2007). True is that via remittances poor areas might eventually get some compensation for this brain drain but the effects can manifest with a certain lag and sometimes the benefits do not offset the loss.

Second, and linked to the previous aspect, is that those who stay might become even poorer after the migration of the youngest and fittest. Awumbila and Ardayfio-Schandorf, (2008) argue that, poverty may also be as a result of migration. In families and communities where husbands and the active productive youth populations have migrated, poverty among children, wives, elderly people and other dependents may be worsened. This line of argument clearly interrogates the poverty migration nexus.

Third, there is increasing evidence that those migrating are not only adults with potentially better economic opportunities, but also kids who might end up being street children. Kwankye et al. (2009), find that migration of children from Northern to Southern Ghana has become a very common phenomenon as, in their survey, almost every household is reported to have a child migrant in Southern Ghana. While not all child migrants become street children, estimates are not encouraging. Using a nationwide survey of 2,314 street children, Kwankye et al. (2009) calculate that over a third of the street children were from the Northern region (38.1 percent) followed by the Upper East (12.1 percent).

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rates of inflation have been expressed in the course of 1987, in the wake of more expansionary fiscal policies. So far, however, these fears have not materialized, despite falling levels of unemployment. The same seems to be true of Japan, where the 1987 fiscal package appears, so far, to have had its main impact on quantities rather than on prices.

A very impressionistic conclusion to this discussion would probably be that neither physical constraints in labour markets nor too high a level of real wages are at present very serious obstacles to the growth of the OECD area. Most of the unemployment in Europe would seem to be involuntary, many of the rigidities that are claimed to exist have either diminished because of recession (e.g. trade union strength), or may be expected to diminish in intensity if demand recovered (e.g. low labour mobility), while the level of real wages, even if too high in the later 1970s, no longer appears too high at present. This is indirectly supported by the results of an end-1985 EEC survey on labour market flexibility which showed tha the most important reason preventing an increase in industrial employment in Europe was lack of demand (with excessive wage levels coming only sixth, and insufficient capacity last, among the ten factors surveyed) [Commission of the European Communities, 19863.

The upward drift in NAIRUs, however, suggests that caution may have to be used if demand were to be stimulated. Though the NAIRUs themselves could be expected to decline in the wake of faster growth, initial problems on the wage front cannot be excluded. To avoid or minimize such problems, it may be necessary to target demand expansion to particular high-unemployment categories of workers [Layard, 19861 and/or to introduce forms of incomes policies.
Figure 1: Distribution of coefficients when dropping one country at a time <!-- Start of picture text --> (a) Education SP aid (b) Education TC<br>.8 1 1.2 1.4 1.6 −.04 −.02 0 .02 .04<br>Education SP aid Education TC<br>(c) Health SP aid (d) Health TC<br>.6 .7 .8 .9 1 1.1 −.3 −.2 −.1 0 .1<br>Health SP aid Health TC<br>80<br>80<br>60<br>60<br>40<br>Frequency 40 Frequency<br>20 20<br>0 0<br>80 80<br>60 60<br>40 40<br>Frequency Frequency<br>20 20<br>0 0<br><!-- End of picture text --> Source: Author’s analysis based on data described in the text.

This assumption would be violated if, for instance, the allocation of education (health) SP aid and TC were partially determined on the basis of past or current values of public education (health) expenditure. In fact, Table 6 contains some evidence indicating that strict exogeneity for SP aid is unlikely to hold. If a firstdifferenced version of equation (11) is estimated with OLS (columns 2 and 4, labeled FD), then the effect of SP aid differs markedly from its FE estimate and even becomes negative. This stark difference between the FE and FD estimates of the SP aid coefficients suggests a violation of the strict exogeneity assumption because such a violation causes both FE and FD to be inconsistent and to have different probability limits (Wooldridge, 2002, pp. 284-285). However, the effect of TC is similar in the first-differenced model. There is some evidence of a negative effect of
years after) yields similar patterns (4.35 vs. 2.33 total projects, and 0.86 vs. 0.28 HD, and both differences are statistically significant).

Total lending per year also declines by 60.4 percent in the 5 years after graduation compared to the 5 years before graduation. Analysis of 10-year periods shows that the decline is similar at 59.7 percent, thus signaling that the declines are not only transitory. The decline in HD lending is even larger and statistically significant. Comparing the 5-year periods before and after graduation, average HD GP lending per country per year declines from $87.91m to $17.9m, representing a 79.6 percent change. The pattern for 10-year periods is similar – average HD lending declines from $84.7m to $12.5m, or 72 percent.

In contrast, results for the countries graduating in the pre-1988 period are quite the opposite (Table 3B). These countries increased borrowing in the years after IDA graduation although significantly less so for the HD areas (107 percent overall vs. 77 percent for HD before and after 5-year periods). Similarly, while the overall number of projects increased, the number of HD projects did not change. These patterns are consistent with the notion that countries borrow proportionately less for the HD when they graduate from IDA.

These differences, however, do not account for other explanatory variables, including country size, and could be driven by the behavior of a few countries. For instance, if big countries like India and China stop borrowing for HD, the difference in average borrowing could be large even if the average country did not change borrowing behavior after graduation. To check for this, we look at per capita borrowing before and after graduation. Looking at the 5-year before and after periods, we see that per capita total lending and per capita HD lending also decreased, although the results are not statistically significant. These results suggest that country size is an important variable to
**Table D.6:** Health Service Delivery

||(1)<br>|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Control<br>Mean|Pooled|CM|NFA|N|
|**Health service delivery index**(SUR)||0.021<br>|0.037<br>|0.015<br>|2877|
|||(0.029)|(0.035)|(0.031)||
|||[0.355]|[0.254]|[0.463]||
|(1) Absenteeism index|0.000|0.075|_−_0.017|0.109|2874|
||(1.000)|(0.057)|(0.070)|(0.063)<sup>_∗_</sup>||
|||[0.887]|[1.000]|[0.710]||
|(2) Paid for treatment|0.416|_−_0.002|_−_0.036|0.011|2872|
||(0.493)|(0.023)|(0.033)|(0.024)||
|||[1.000]|[1.000]|[0.786]||
|(3) Amount paid|8520.158|553.416|_−_1472.904|1321.751|2843|
||(31895.827)|(1409.161)|(1725.193)|(1496.821)||
|||[0.
being a direct victim is considerably low, the fear that these events arouse in the general population enables them to result in important impacts (Becker and Rubinstein 2011). Therefore, even in the context of sustained armed conflict this is a better measure of a local shock.

In addition to the variation in the occurrence of violent events at the municipal level, there is exogenous variation in the individual violence exposure resulting from the gestation stage of pregnancies within municipalities when such an event occurs. To obtain this high-frequency variability in the data, I link historical records of violent attacks by municipality of residence of the mother and month of occurrence of the events with the month of gestation coming from household survey data. Violence data are compiled by the _Centro Nacional de Memoria Histórica_ (CNMH),<sup>2</sup> and household survey microdata with information about pregnancies and birth outcomes come from three rounds of the Demographic and Health Surveys (DHS).<sup>3</sup> The impact of violence is assessed on weight at birth and neonatal mortality, measures that reflect the cumulative pregnancy conditions. Birthweight influences a wide range of outcomes over the life cycle, from adult disease prevalence to educational attainment, labor market outcomes, earnings and social status, highlighting its relevance (Black, Devereux, and Salvanes 2007; Case, Fertig, and Paxson 2005; Currie and Hyson 1999; Johnson and Schoeni 2011).

Overall, I found a negative effect of exposure to violence on the health of newborn babies, but with distinctive gender patterns. For boys, there was a scarring effect of exposure to a violence shock during early-pregnancy, reflected in a birthweight reduction of about 40 grams (gr), on average, and an increase in the probability of being born with low birthweight. The effects were much stronger for the boys of uneducated mothers, diminishing by 29 gr for each additional year of education of the mother. Despite these findings, I did not find strong evidence of an overall or predominantly male increase
economic impacts of the insurance, we use national household income and expenditure surveys (ENIGH), which are carried out every other year with the latest one occurring in 2014. These household expenditure and income surveys are repeat crosssections of households with a rotating sample of municipalities.<sup>4</sup> Households are surveyed between the months of August and November and the income, expenditure, and remittance variables refer to the previous three months. Lastly, the analysis sample when using ENIGH data is limited to households living in rural localities. The Mexican Statistical Institute (INEGI) defines rural localities as those with less than 2,500 inhabitants.

> 4The rotating sample means that in any given year some insured municipalities will not be included in the analysis sample because they do not have income or expenditure data, which substantially decreases the sample of municipalities for analyzing economic outcomes.

3
21 relationship has become monotonically decreasing, and that the fertility of the middle educational group Jls reduced by a much larger proportion of the differential between the lowest and the highest groups. Finally, for those countries well into the transition (especially in Latin America and some in Asia), we find a monotonically decreasing relationship again, but with a much smaller gradient, because for these countries fertility reduction has permeated even to the groups of women with only partial primary or no education (Ashurst, Balkaran and Casterline (1984)).

The influence of education on marital fertility is age-specific, identifiable as a change in the tempo of childbearing at different durations from the start of marriage (or union). Generally, women with a higher level of education have the same fertility, or higher, than women with lJttle or no education - early on in their unions. It is only after 5 10 years of union duration that the fertility of more educated women begins to fall off (Singh and Casterline (1985)). To understand the forces at work here we must turn to analyses of the proximate determinants of fertility for all these subgroups. These are available from the WFS data, and tend to confirm the results of earlier surveys (Cochrane (1978)). Analysis of the proximate determinants of fertility attempts to allocate the total difference between actual fertility and the biologically possible maximum for any given population to factors such as marriage, contraception, post-partum infecundability due to lactational amenorrhoea, and
.**

**_<u>Percent</u>_**

||**Gross wage**<br>**differential**|**Productivity differential**<br>**(without occupation controls)**<br>|**Productivity differential**<br>**(with occupation**<br>**controls)**|
|---|---|---|---|
|Estonia, 2000|24|3|5|
|Latvia, 2000|19|7|10|
|Latvia, 2002|31|8|12|
|Lithuania, 2000|14|-1|0|

_Notes:_<sup>a</sup> These differentials, according to conventional methodology of decomposition, are calculated as _exp(d)_ – 1, where _d_ is the difference between mean log net monthly wages of males and females; b Differential in (geometric) mean predicted wages of males and females, using earnings function estimated over pooled sample without gender dummy; known also as explained gender pay gap.<sup>c</sup> Sector of economic activity is controlled for. Males’ and females’ earnings functions used to derive estimates in columns 2 and 3 are partly presented in Appendix Tables 24 and 25. _Sources:_ Calculations based on LFS data.

**<u>Table 9.  Patterns of Industrial Segregation by Ethnicity in the Baltic States, 2000</u>**

||<br>**Estonia**|<br>**Latvia**|<br>**Lithuania**|
|---|---|---|---|
|||**Public administration**||
|Sectors where<br>**minorities**||Education<br>Agriculture||
|are**under-represented