rafmacalaba/datause-encoder-data huggingface dataset:
neglected by privately owned financial institutions. A mandate that places the focus on “gapfilling” provides a rationale for DBs’ operations in a countercyclical fashion.

In 2009, the Business Development Bank of Canada (BDC) undertook an international benchmarking study. It looked at some 373 development institutions including DBs, Export Credit Agencies, Development Finance institutions, and government programs from 92 countries and 7 economic regions around the world. The study highlighted all the products and services offered and analyzed the data to draw certain conclusions. The study found that the six most common target markets for DBs are: (i) micro-enterprises/start-ups; (ii) small and medium sized enterprises (SMEs); (iii) international trade/globalization; (iv) housing, (v) infrastructure and (vi) rural/agricultural sector.

By far, the most common target market for DBs around the world is the SME market as about 60 percent of the DBs studied target SMEs. By definition, an SME usually means less than 250 employees and typically more than 5 (less than 5 employees would be classified as a micro enterprise). Worldwide, Micro Small and Medium Enterprises represent 95 percent of all firms and are therefore an important source of employment and a key driver of economic growth in a country.<sup>7</sup> However, the market fails to address this all important economic driver adequately. In particular, SME banking requirements are too large for micro-finance solutions and too small to be serviced by corporate banking models because they are considered too risky or too costly to service. This lack of available financing for SMEs has been called the “missing middle.”

> 7 The SME Banking Knowledge Guide, _IFC_ , 2009

13
# Trade and Local Labor Market Outcomes in Mexico:

Disentangling the Channels and the Role of Geography, Sectors, and Trade Types

## **Emmanuel Vazquez**<sup>**1**</sup> **and Deborah Winkler**<sup>**2**</sup>

**Keywords:** International trade, exports, imports, global value chains, local impacts, labor market, employment, income, migration, informality, flexibility, education spending, connectivity, Mexico.

**JEL Codes:** F14, F16, D3, I3, J61

> 1 Senior Researcher, Center for Distributive, Labor, and Social Studies, Instituto de Investigaciones Económicas, Facultad de Ciencias Económicas, Universidad Nacional de La Plata. E-mail: evazquez@cedlas.org.

> 2 Senior Economist, Global Trade and Regional Integration, World Bank Group. Email: dwinkler2@worldbank.org. The authors would like to thank Carlos Rodríguez-Castelán and Hernan Winkler for their permission to build on and expand their research in Rodríguez-Castelán et al. (2020). They also thank Luis Alejandro Aguilar Luna for his excellent research assistance in computing trade and global value chain measures drawing on Economic Census data. The authors are also grateful to Claudia Berg, Raymundo M. Campos-Vasquez, Sébastien Dessus, Christoph Lakner, Gladys C. Lopez-Acevedo, Maryla Maliszewska, Ambar Narayan, Israel Osorio-Rodarte, Raymond Robertson and Ben Shepherd for their helpful comments and suggestions, as well as to the participants of the World Bank authors’ workshops “Leveraging trade for more and better job opportunities in developing countries” in May and November 2022. They thank Natalia Volkow and Liliana Martinez of the Microdata Center of the National Institute of Statistics and Geography, Mexico, for their support for data use in compliance with the confidentiality requirements set by Mexican law. The work was carried out under the overall supervision of Sébastien Dessus and Antonio Nucifora and has been cleared by Mark Thomas (Country Director of Mexico). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank Group and its affiliated organizations, its Executive Directors or the governments they represent.
and RITMs) most frequently supplied the technical assistance, while the Korea Productivity Center and the Korea Standards Association provided the most training. Nevertheless, the services of these agencies were ranked as only moderately useful. It seems that the car assemblers' need for increasingly sophisticated parts and components has motivated SMEs in this sector to aggressively seek product innovations. Although they turned frequently to collective support systems in this quest, the more generally-oriented collective support systems could not meet all their needs. The new industry-specific trade association, KATECH, is therefore expected to improve collective support systems in this sector, as did KOTITI in the textile-weaving sector. In seeking product innovation, a few auto parts SMEs also actively engaged in joint and contractual research with government technology support agencies.

Electronic Parts and Components. Our survey indicates that SMEs producing electronic pars seldom produce new or highly original products. Consequently, assemblers rely heavily on Japanese suppliers for their more technology-intensive parts and components (Table E13). Critical components of Korean-made personal computers, for instance, are largely made in Japan.

Again, ordtr-placing firms were the most firequent source of ideas for product and process miprovements, although their technical help was not viewed as very useful. This may be because there are far fewer parts and components in the final electronic products than in the automobile industry, and the performance of parts mounted on printed circuit boards tends to be more stable than that of the mostly mechanical parts and components in an automobile. SMEs in this sector are also less active than those producing auto parts in seeking technical assistance from collective support agencies, and find the usefulness of collective support low. The low use of collective technical support systems and high use of international exhibitions suggest that electronics parts SMEs are followers rather than leaders in the international marketplace.

Factory Automation. Technology plays a crucial role in the factory automation sector, and our survey found that SMEs in this sector make the greatest use of the variety of technical support systems available to them. Table **m.3** shows the high degree of both newness and originality in Korea's factory automation products.

Buyers (both local and foreign) and international exhibitions are the most important source of product ideas in this sector. Buyers often order custom-designed automation systems to meet 19
Representation of the 8 demographic groups in each industry is shown for both rural and urban areas in Figure 3. Educated workers, obviously, predominate in the professions, whereas wage‐jobs in rural construction are largely held by unskilled males, even more so than in agriculture and quite substantially more so than in services. Both rural and urban areas have seen a gradual up‐skilling of the workforce over the past two decades, across all industrial sectors.

# **B. Changes in real wages**

Information on weekly wage earnings and days worked per week is available for regular and casual workers. In the case of those who perform multiple jobs in the week, we calculate average daily wages by dividing weekly wage income from all sources by total number of days worked. To compute real wages, we use the state‐level Consumer Price Index for Agriculture (CPI‐AL) and Industrial Workers (CPI‐IW). Originally, the CPI‐AL was available with base‐year 1986‐87 and CPI‐IW with base‐year 1982. We converted these indices to have a uniform base‐year 2004‐05. CPI‐AL is used to deflate wages in rural areas and CPI‐ IW in urban India. Because these deflators are not available for some of the small states, we used available information for larger states either adjacent to them or from which they had been split (see Appendix Table B.2 for details on the CPI calculation for the smaller states).

<!-- Start of picture text --> 0.120<br>0.080<br>0.040<br>0.000<br>Rural Urban<br>1993‐04 2004‐12<br>Uneducated young male Educated young male Uneducated young female Educated young female Uneducated old male Educated old male Uneducated old female Educated old female Rurall average Uneducated young male Educated young male Uneducated young female Educated young female Uneducated old male Educated old male Uneducated old female Educated old female Urban average<br><!-- End of picture text -->

**<u>Figure 4: Annual average real wage changes by demographic group</u>**

6
**Table I** - **Increases** In **VAT Collection: Comparison between the Austral and Convertibility Plans**

|Variation in**a/:**|Au<br>P<br>(May 1985|stral<br>lan<br>- Sep 1986)|Conv<br>P<br>(Mar 1991|ertibility<br>lan<br>- Dec 1992)|
|---|---|---|---|---|
||Total|Monthly<br>Average|Total|Monthly<br>Average|
|VAT Collection (T)|52 73|3 10|108.04|4.91|
|Tax Effort (E)|31.85|1.87|74.04|3.37|
|Real GDP (Y)|4.70|0.28|15.32|0.70|
|Olivera-Tanzi Effect b/|16 18|0.95|6.90|0.31|
|Tax Rate (t)|0.00|0.00|11.78|0.54|

a/ Calculated as variations of natural logarithms, in percent. b/ Measured as the log variation of gross corrected GDP growth and gross corrected inflation.
Table 7. Propensity Score Matching, control variables are same to Column 1-3 of Table 5

|Dep. Variable|Sample|Treated<br>(Female)|Controls<br>(Male)|Difference|S.E.|T (Z)|
|---|---|---|---|---|---|---|
|TFP|Unmatched|-0.175|-0.063|-0.112***|0.034|-3.26|
||ATT (Bootstrap)|-0.175|-0.160|-0.015|0.051|-0.30|
|Log LP|Unmatched|9.410|9.680|-0.269***|0.022|-12.07|
||ATT (Bootstrap)|9.410|9.588|-0.178***|0.030|-5.95|
|LP Growth|Unmatched|-0.022|-0.033|0.011**|0.005|2.34|
||ATT (Bootstrap)|-0.022|0.006|-0.029***|0.008|-3.39|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Bootstrap replicated 100 times.

37
the majority of candidates in contested elections tend to be male, the DPEP should lead to an overall increase in the average competence of candidates. Fourth, the effect of the DPEP should be more pronounced in districts where women were elected in previous elections. In these districts, barriers to women’s political entry may be lower and voters may be more responsive to the benefits of competent program delivery as opposed to politician gender. Moreover, the DPEP-induced increase in women’s probability of winning may have a “demonstration effect” and increase women’s candidacy. Fifth, the impact of the DPEP on women’s likelihood of being elected should be stronger in districts where the program was most effectively implemented.

To test these predictions, we employ a fuzzy regression discontinuity design that exploits the 1991 Census female literacy rate as a cut-off for district eligibility for the DPEP. We use data on state elections that took place after the DPEP began, with 2018 being the latest election year in our sample. Consistent with our model, we estimate that voters are 22 percentage points (p.p.) or four times more likely to elect female MLAs in treated districts compared to untreated districts. We also find a significant drop-out of marginal male candidates as reflected in the number of independent male candidates who contest without political party affiliation; such candidates have a historically low probability of winning of less than 2 percent. As predicted by the model, we document a 10 percent increase in the number of female contestants due to the DPEP (though this effect is not statistically significant). Notably, the probability that the incumbent party and other parties field a female candidate increases, respectively, by five and nine times in DPEP districts relative to nonDPEP districts if the winner in the previous election was female. While data limitations prevent us from an in-depth analysis of whether the DPEP altered candidate competence, we find that candidates in DPEP districts have a higher rate of secondary school completion, potentially indicative of higher equilibrium competence. These results are broadly consistent with the predictions of our theoretical model.

We provide several pieces of evidence to show that the key mechanism underlying these results is that effective DPEP implementation made leader competence more observable in treated districts, prompting voters to increase the weight that they assigned to candidates’ competence relative to their gender. First, we show that the DPEP-driven increase in the probability that women win elections is larger in treated districts where the DPEP led to greater improvements in education infrastructure and performance and in the local economy (as measured by satellite nightlights data). Second, we show that in elections that took place after the DPEP began, voters in treated districts are more likely to re-elect candidates from the incumbent party and candidates that are “unaligned” with the leading party in the state. This pattern of results reflects a marked deviation from historical trends as, in the absence 3
relationship between the labor force participation rates of men and women. These coefficients imply that women's participation rates increased at a faster rate than men's participation rates. During this period, the participation of women increased sharply, reaching a rate of 39%, while the participation rates of men also recovered from a slowdown the decade before. This trend was especially pronounced among married women, as Iregui-Bohórquez, et al. (2021) noted. In contrast to other countries, an increase in _f_lfp_r_ in Colombia did not reduce _m_flp_r,_ as male participation had also increased.<sup>16</sup> During the 1990s and 2000s, the coefficients of 0.93pp and 0.90pp, respectively, indicate a weaker positive relationship between men's and women's labor force participation rates. Women's participation rates slowly increased during the 1990s but stagnated towards the 2000s, while men maintained high participation rates.

**Figure 15: Male and female labor force participation rates 1960-2018 (Average seven cities)**

<!-- Start of picture text --> 90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>mlfp flfp<br>Percentage (%)<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br><!-- End of picture text --> Source: Own calculations based on Iregui-Bohórquez et al. (2021).

The coefficient of 1.51pp for 2010-2018 indicates a strong positive relationship between men's and women's labor participation rates in recent years. During this period, women's participation reached its highest point of 61%, resulting in a decrease in the gap (15pp), the lowest ever experienced. This positive association between labor force participation for men and women is not only due to having more workers but also because women bring new skills to the workplace. These skills reflect social norms, upbringing, social interactions, risk preferences, and response to incentives. Therefore, 16 See Tenjo and Ribero (1998).

25
children under five and 59% of pregnant women used LLINs.<sup>[2]</sup> By 2021, 63% of households had at least one LLIN and of these households, only 39% of children under five and 38% of pregnant women used LLINs.<sup>[17]</sup> The National Malaria Control Program should be strengthened.

All of these interventions should be carried out within the framework of the implementation of UHC. In 2014, the Ministry of Health developed a "Strategy for Health Financing Towards Universal Coverage in Guinea;" however, this strategy is not operational.<sup>[5]</sup> The overall objective of this strategy was to "ensure that the entire population, especially the poorest, have access to geographically and financially accessible and culturally acceptable quality health services." This strategy mainly presented the axes of the strategy and the roles of the different actors. In addition, it recommended the development of (1) a roadmap or operational plan and (2) a monitoring and evaluation framework to accompany the next National Health Development Plan, as well as studies to inform these two documents. However, to date, these two key documents and the underlying studies have not yet been developed. There is therefore an urgent need to establish an operational, evidence-based financing strategy for achieving UHC in Guinea.

# **Limitations**

The study has four main potential limitations. First, there may be a social desirability bias that would have caused respondents in the LSMS to over-report their consumption levels for fear of the stigma associated with being poor. However, this is unlikely since one of the objectives of the LSMS was to generate evidence to inform the development of interventions to increase Guinea's economic level and reduce poverty levels. This objective was communicated to respondents. In addition, the interviewers reassured respondents that the survey was confidential.

Second, because of how the LSMS data were collected, we estimated annual hospitalization expenditures by multiplying expenditures for the last health condition that resulted in hospitalization by the number of hospitalizations. However, respondents could report more than one hospitalization for different reasons, and each reason may be associated with a specific expenditure. Therefore, our estimates of hospitalization expenditures, THE, the share of hospitalization expenditures in THE, and the prevalences of CHE and IHE cases may be inaccurate. Nevertheless, 99% of respondents who were hospitalized reported one to three hospitalizations; 86% reported one hospitalization, 10% reported two hospitalizations, and 3% reported three hospitalizations. In addition, only 5% of respondents reported 19
Table 5: Sibling and Intergenerational Correlations: Urban Sample

||All ch|ildren|Brothers|(Men)|Sisters (|Women)|
|---|---|---|---|---|---|---|
||1993|2006|1993|2006|1993|2006|
||||PANE<br>|L A<br>|||
|Sibling Correlation (SC)|0.664***|0.625***|0.652***|0.648***|0.743***|0.634***|
||(67.333)|(68.340)|(46.995)|(51.552)|(43.956)|(30.248)|
|Intergenerational Correlation<br>(IGC)|0.572|0.526|0.553|0.534|0.593|0.508|
||(54.47)***|(65.49)***|(44.41)***|(54.60)***|(40.06)***|(45.63)***|
|IGC squared|0.327|0.277|0.306|0.285|0.352|0.258|
|Proportion of SC explained by<br>IGC|0.493|0.443|0.469|0.440|0.474|0.407|
|Controling for Caste and religion|||PANE|L B|||
|Sibling Correlation (SC)|0.649***|0.588***|0.638***|0.614***|0.730***|0.600***|
||(62.871)|(59.054)|(44.492)|(45.188)|(41.263)|(25.967)|
|Intergenerational Correlation<br>(IGC)|0.554|0.482|0.537|0.493|0.573|0.461|
||(51.19)***|(57.40)***|(41.89)***|(47.84)***|(37.64)***|(39.79)***|
|IGC squared|0.307|0.232|0.288|0.243|0.328|0.213|
|Proportion of SC explained by<br>IGC|0.473|0.395|0.452|0.396|0.450|0.354|
|Controling for Neighborhood FE|||PANE|L C|||
|Sibling Correlation (SC)|0.440***|0.451***|0.409***|0.470***|0.474***|0.409***|
||(29.546)|(36.931)|(19.127)|(26.581)|(14.451)|(13.173)|
|Intergenerational Correlation<br>(IGC)|0.488|0.441|0.483|0.461|0.531|0.431|
||(59.91)***|(68.37)***|(46.24)***|(55.09)***|(43.78)***|(44.44)***|
|IGC squared|0.238|0.194|0.233|0.213|0.282|0.186|
|Proportion of SC explained by<br>IGC|0.541|0.431|0.570|0.452|0.595|0.454|
|No. of observations|12,277|19,371|7,385|11,378|4,892|7,993|

Robust t statistics in parentheses. Standard errors corrected for clustering at family level * significant at 10%; ** significant at 5%; *** significant at 1% 40
28 pillar strategy: First, adopting a dual-track approach to reforms—giving transitory protections to old comparative-advantage-defying, capital-intensive sectors and liberalizing entry to comparative-advantage-conforming, labor-intensive sectors, and thereby simultaneously achieving both stability and dynamic transformation. Second, as a latecomer, choosing an economic development strategy that taps the potential of the advantage of backwardness along the lines of the flying-geese pattern.<sup>20</sup> Looking forward, China can still rely on the advantage of backwardness and has the potential to maintain dynamic growth for another 20 years or more.<sup>21</sup> Behind China‘s growth over the past three decades has been a dramatic structural transformation—in particular, rapid urbanization and industrialization. At the start of economic reforms in the 1980s China was primarily an agrarian economy. Even in 1990, 73.6 percent of its population lived in rural areas, and primary products accounted for 27.1 percent of GDP. In 2009 these shares had declined to 27.1 percent and 11.3 percent. A similar change occurred in the composition of China‘s exports. In 1990 primary products made up an important share of merchandise exports. Today almost all of China‘s exports are manufactures (figure 16).

**Figure 16. The Structural Transformation of China’s Exports**

<!-- Start of picture text --> Composition of Chinese Exports, Composition of Chinese Exports,<br>1990 2009<br>Primary<br>products and<br>chemicals:<br>Primary<br>products<br>and<br>chemicals:<br>Manufactured Manufactured<br>Goods, (including Goods, (including<br>machinery and machinery and<br>transport transport<br>equipment):<br><!-- End of picture text --> Source: World Bank, WITS database.

China is unquestionably the dragon in the global marketplace for low-tech products today. In the four product categories in which it has the highest global market concentration, China‘s share of global exports exceeds 35 percent—and in travel goods and handbags is close to 50 percent (table 4). Most impressive is the rate at which its share has grown. For travel goods and handbags, for example, its share in 1976 was only 1.6 percent.

> 20 For further discussion of these two points, see Lin (forthcoming b) _._

21 The estimation by Angus Maddison (2010) shows that China‘s per capita income in 2008 was 21 percent of U.S. per capita income (measured in purchasing power parity). This income gap indicates that there is still a large technological gap between China and the industrialized countries. China can therefore continue to enjoy the advantage of backwardness before closing up the gap. China‘s current status relative to the United States is similar to that of Japan in 1951, Korea in 1977, and Taiwan, China, in 1975. Annual GDP growth was 9.2 percent in Japan in 1951–71, 7.6 percent in Korea in 1977–97, and 8.3 percent in Taiwan, China, in 1975–95. China‘s development strategy after the reform in 1979 is similar to that of these three economies (Lin forthcoming a).