rafmacalaba/data-use-mentions-extended huggingface dataset:
53|0.30|

________________________________________________________________________ Source: calculated from Tabulation on 2000 Population Census of the People’s Republic of China, Table L7-1b and L7-2b.

note: occupation title: 1, manager and administrative personals, 2. professionals and technician, 3. staff and other auxiliary workers, 4. salesperson, 5. agriculture, animal husbandry, and fishery, 6. production workers, 7. unclassified.

# **7. Conclusion**

Our analysis reveals a diversified pattern of town migration in the three provinces. Zhejiang exhibits steady and significant growth in town in-migration over 1995-2000, whereas the other two provinces, Sichuan and Henan, remain quite staggering. Nevertheless, there is one thing in common that majority of town in-migrants were from rural areas.

The diversified pattern of town in-migration in the three provinces probably suggests that migration has been quite responsive to the economic opportunities and living conditions in towns. Many migrants went to towns in Zhejiang province for the sake of seeking business and job opportunities. Nearly half of migrants came from other provinces, especially from the western and central regions. In contrast, a lack of a strong manufacturing sector and good living conditions in Henan and to some extent in Sichuan did not attract outsiders. Most in-migrants came from the same province for non-business reasons.

Our study also suggests that town in-migrants generally possess a higher level of educational attainment than the general population in towns; this is particularly true in less-developed provinces such as Sichuan and Henan. Thus, migration could benefit the development of towns, especially in the western and central regions. Data further suggest that as a town develops, as in the case of the richer province of Zhejiang, better-educated people probably did not leave the countryside to migrate to the cities in other provinces and instead were more likely to shift their jobs from agriculture to the non-agricultural sectors in towns nearby within the province. Thus, the development of towns provides a buffer that could hold more migrants, reducing the migration pressure on large cities.

14
in the form of lower prices, revenue-based measures using industry price deflators will not capture the improvements in productivity.<sup>3</sup> Recent work by Garcia-Marin and Voigtlnder (2019) on Chile suggests that this could be the reason why empirical studies have not been able to find evidence of firms improving productivity after they begin exporting. They compare revenue-based measures of productivity that are typically used with marginal costs and find that while firms do not become more productive, as measured by revenue productivity, they do become more efficient as measured by marginal costs, post entry into export markets.<sup>4</sup> This finding on Chile has highlighted the need to correct for biases that arise from revenue-based measures of productivity (that use industry rather than firm prices) to understand the relationship between exports and productivity. The data set I use in this paper contains information on firm prices and allows me to recover measures of physical productivity that account for variation in prices across firms.<sup>5</sup> In this paper, I ask, do firms become more productive after they enter export markets? And if so, what are the mechanisms through which firms improve productivity? I examine the role of two possible mechanisms - imports of intermediate inputs and investment in R & D.

A large number of firms are two-way traders i.e., they export final goods while also importing intermediate inputs. An Indian manufacturing firm that exports its products is five times more likely than not to also be an importer of intermediate inputs. If firms import intermediate inputs from abroad to improve productivity and reduce marginal costs of production, when imports are not accounted for, differences in measured productivity could possibly be reflecting these differences in the use of imported intermediate inputs across firms. This export-import complementarity, where access to export markets allows firms to pay the fixed costs of importing intermediate inputs and thus improve productivity has largely been ignored in the export-productivity literature. The export-technology complementarity has been emphasized in theoretical models such as
38|0.38|0.00|0.54|0.64|-0.11<sup>***</sup>|
|Early marriage|0.19|0.72|-0.53<sup>***</sup>|0.18|0.60|-0.42<sup>***</sup>|
|_MPI poor households_|||||||
|Years of schooling|0.83|0.86|-0.03|0.78|0.77|0.01|
|School attendance|0.23|0.28|-0.05<sup>**</sup>|0.36|0.40|-0.04|
|Unemployment|0.40|0.28|0.12<sup>***</sup>|0.04|0.04|0.01|
|Legal identification|0.56|0.56|0.01|0.76|0.81|-0.05<sup>*</sup>|
|Early marriage|0.36|0.84|-0.48<sup>***</sup>|0.41|0.62|-0.21<sup>**</sup>|

Source: Authors’ calculations using the Ethiopia SPS (2017). Significance at 1% ***, 5% **, and 10% * levels.

The only significant gender gap in legal identification is in the wider population (an 11percentage points gap), where women are more deprived than men in the host community. Meanwhile, the only significant gender gap in unemployment is in the poor population (a 12-percentage points gap), where men are more deprived than women in the refugee community, although
4|10,017|451|<br>2.1|
|Slovakia|153|386|2,475|29|72|460|1.0|1.8|10.2|12,104|2,249|<br>4.8|
|Slovenia|96|241|1,107|49|123|564|0.6|1.3|5.1|5,817|2,962|<br>2.5|
|**TOTAL or**|5,616|14,588|20,749|55|142|201|2.1|3.9|4.6|164,225|1,594|<br>3.6|
|**average**<br>Memorandum: B|ulgaria||||||||||||
|Percent of the<br>average or total|1.2|4.3|5.8|15.9|56.0|76.4|30.4|133|170|4.3|57.2|<br>108|

Source: various issues of Economic Survey for Europe (UN Economic Commission for Europe), EBRD Transition Reports and World Development Indicators 2003.

Second, one has significant doubts whether foreign trade considerations were behind significant portion of FDI inflows especially in 1998-99. The share of FDI originating in highly developed OECD economies fell to 56 percent and 50 percent of total annual inflows in this 23 See Blonigen (2001) for a literature review.
After qualitative piloting, an extended version of the skill questionnaire was piloted in November 2013 on 120 farmers from an area in Siaya, close to the study area, and on farmers that had been selected in a similar way as those of the actual study population. A small subset of these farmers was also retested in December 2013 with the same survey instrument, in order to obtain retest statistics of the pilot. Based on this quantitative pilot, we eliminated questions with little variation.<sup>11</sup> We also removed questions that showed negative correlations with other variables meant to capture the same latent trait, and fine-tuned phrasing and translation of questions.<sup>12</sup> The final survey instrument took about 2.5 hours to complete.

The vast majority of farmers in the sample (97%) were native Luo speakers (the local language) – the others were more comfortable in Swahili or English (Kenya’s two official languages). The English-language survey therefore was translated in both Luo and Swahili. All versions were homogenized after independent back translation.<sup>13</sup>

# **2.3. Alternative measures of skills**

Prior to the set of questions in the three main modules described above, respondents were asked their self-assessment for the same set of skills using a set of 14 questions, formulated to proxy the different subdomains captured by the questions in the main modules. And after answering all questions from the three main sections, each farmer was asked to assess the skill level of one of the other farmers of his village in the sample using similar proxy questions. This provides an independent (though clearly subjective and possibly mis-measured) assessment. A second proxy measure comes from asking the same questions to another household member (typically the spouse) also involved in farming. And a third independent measure was obtained prior to the survey from the village health worker, who was asked to classify each farmer according to his

> 11 For instance, experience
0018|-0.0019|-0.0147|
|Mother has tertiary education|||||-0.0048|-0.0082|-0.0220|
|2nd wealth quintile||||||0.0145|0.0124|
|3rd wealth quintile||||||0.0316**|0.0311**|
|4th wealth quintile||||||0.0181*|0.0185*|
|5th wealth quintile (richest)||||||0.0301**|0.0316**|
|Rural area|||||||0.0015|
|Central province|||||||0.0010|
|Coast province|||||||-0.0061|
|Eastern province|||||||-0.0128|
|Nyanza province|||||||0.0553**|
|Rift Valley province|||||||-0.0075|
|Western province|||||||0.0149|
|North Eastern province|||||||0.0165|
|Constant|0.0237***|0.0473***|0.0475***|0.0612***|0.0538***|0.0483***|0.0381*|
|Number of observations|4668|4668|4668|4668|4668|4668|4668|
|R-squared|0.0234|0.0249|0.0255|0.0288|0.0316|0.0344|0.0433|
|F-statistics|4.4604|4.0084|3.0946|2.4864|2.8731|2.6283|2.2600|

Source: Authors ̳ calculations using Demographic and Health Surveys microdata.
33 transfers should be larger, though as the previous discussion indicated, the effect should be weak to the extent that fiscal transfers are easily observed even by uninformed voters. In fact, none of the information variables in Table 2 are significant, whether media independence or the availability of trade statistics (the dichotomous variable that takes a zero if information is unavailable in the ZFS to compute the terms of trade variable, and a one if it can). Only the trade statistic variable is of the predicted sign, albeit insignificant.

This lack of significance of the information variables persists under numerous alternative specifications. If checks and checks * M,/GDP are included into each of the four regressions, these new variables retain their significance levels as in Table 1, but the information variables remain highly insignificant. Nor does the omission of GDP/capita increase their significance. Predictions about the importance of voter information are premised on political systems in which there are competitive elections. Although the information coefficients increase in the sub-sample that exhibits greatest electoral competitiveness (EZEC equals seven), as predicted, they remain insignificant. An evaluation of the determinants of forbearance constitutes a more appropriate test of the influence of information but, as the earlier discussion indicated, a less appropriate test of the influence of the number of veto players. Table 3 presents evidence on the impact of veto players and incomplete information on this more subtle aspect of the government response to crisis: did government officials exercise forbearance in the oversight of insolvent banks? The dependent variable is the (1,0) policy decision to forbear or not. The regressions in the table utilize a probit methodology. Regressions 1 and 2 of Table 3 demonstrate that the number of veto players contributes in the predicted way to the probability that governments undertake actions that favor special interests, but the results are less striking than in the case of fiscal transfers. A crisis equal to 16.1, compared to 2.8 and 11.6 in the countries that do report these data.
# **Figure 1: Total Fertility Rates in Azerbaijan, 1996-2001**

<!-- Start of picture text --> 1996 1997 1998 1999 2000 2001<br>year<br>2.1<br>2<br>TFR<br>1.9<br>1.8<br><!-- End of picture text --> Source: Unicef (2003) Transmonee Project, Social Monitor 2003, Table 2.9 page 62 The consumption model was derived using only independent variables that were similar in both wording and distribution in both data sets. In some strata, where the selected variables on the strict test of comparability did not yield a reasonable high R square, the criteria for selection of the regression variables were relaxed<sup>10</sup> . A set of three dummies for the quarter in which the household has been interviewed, was included to control for fluctuations in household consumption due to the different quarter of the year<sup>11</sup> . The final specification included only those variable that were significant at least at 90 per cent level and the quarterly dummy variables.

Following this, a set of census means at the village level were then merged with the HBS data. The location residuals were then regressed on a set of census means at village level. A selection criteria of significance at 95 % was applied, along with a ceiling for the maximum number of census means. Following the inclusion of these additional variables the OLS

> 10 In few cases we selected variable that were within two standard deviation from the mean value in the census.

> 11 When we proceed for the imputation in the census we construct three variables with the value of 0.25.

37
# III. DATA  SOURCE AND DESCRIPTION

Data used in this paper come from the household surveys carried out by the National Institute of Statistics and Census (INDEC) twice a year since the 1970s. The survey, known as the Permanent Household Survey ( _Encuesta Permanente de Hogares_ or EPH), has incorporated new regions and cities over time in order to have better coverage of urban households. It now reaches approximately 70 percent of the urban population. For comparability reasons over the years, we consider only conglomerates available for all years in the EPH.<sup>1</sup> Earnings functions are estimated for men and women jointly and separately. The samples include all workers 14-65 year of age with positive employment earnings. Three type of variables were used in the analysis: educational variables (continuous and dummy variables), as well as quantitative data and price variables (see Annex 1 for definitions). Appendix Table 1 presents the means of these variables for the 1992-2002 samples.

During the last ten years, average years of education have increased by one whole year for the whole sample. Additionally, the proportion of workers with less than secondary education decreased. Conversely, there was a significant increase in the proportion of workers with tertiary-level qualifications (9.5 percent had higher education in 1992, compared with 16 percent in 2002). These figures show an apparent improvement in human capital levels in Argentina. Finally, women’s years of education are higher than men’s. Years of experience 7
|‐0.146***|‐0.152***|‐0.169**|‐0.146**|
|**S.E.**|(0.0428)|(0.0598)|(0.0555)|(0.0551)|(0.0668)|(0.0665)|
|**Controls**|||||||
|Regional|No|Yes|Yes|Yes|Yes|Yes|
|Household|No|No|Yes|Yes|Yes|Yes|
|Dwelling|No|No|No|Yes|Yes|Yes|
|Conflict|No|No|No|No|Yes|Yes|
|Assistance|No|No|No|No|No|Yes|
|**Observations**|1,591|1,591|1,563|1,536|1,536|1,536|
|**R‐squared**|0.032|0.226|0.359|0.487|0.501|0.522|

Source: Authors’ calculations.

Note: ***p<0.01, **p<0.05, *p<0.1. Poverty status results estimated using Probit, Consumption results estimated using OLS. Drought effect expressed in standard deviations of NDVI loss.

Second, the analysis is replicated on households only in regions overlapping between wave 1 and wave 2 of the SHFS. This reduces the geographical scope of the analysis to rural areas in the north‐west of Somalia and to urban areas in the north‐west and Mogadishu. The results from this subsample are in line with the main findings from the full sample of households (Table A.5). In rural areas, more drought‐exposed households are 36 percent more likely to be poor, experiencing a 15 percent reduction
well integrated in the global trading system and could take advantage of the new exporting opportunities across multiple sectors. Our data do not allow us to measure these potential explanations, but we view this as an area for future research.

# **6 Conclusion**

The US-China trade war was seen as a major turning point in the globalization era. Our results do not support this view, at least for the time horizon we analyze: we find that several countries increased global exports in products with higher US-China tariffs, relative to non-taxed products.

We also find cross-country heterogeneity in this export growth in targeted products, and that a country-specific component of the tariff elasticities explains the bulk of the variation. Through the lens of a standard trade model, the export responses suggest negatively-sloped supply curves for several countries as well as differences across countries in terms of whether their exports substitute or complement China. These results contrast with trade models where trade or scale elasticities are typically assumed to vary across sectors rather than across countries.

While product-level global trade data can uncover broad reallocation patterns, firm-level data can unpack the factors driving the country-specific elasticities —whether they consist of increasing returns to scale, trade-war-induced investments in new plants, or participation in trade agreements. We anticipate further research on these mechanisms as the data becomes available.

11
education and 28 years of age), whether the respondent is married, and whether there is at least one child in the respondent’s house<sup>7</sup> . These variables were chosen because they either reflect human capital (lacking direct information on total garment sector experience, age is a close proxy) or increased demand for nonwage benefits among those with care-taking responsibilities. Because there may be important interactions between these variables and gender, we include each variable’s interaction with both 7This measure may miss respondents’ biological children if they are living with relatives in a rural area, which anecdotally is not uncommon among garment workers, but not available in the LFS surveys. Still, however, the measure captures garment workers who are likely to have increased care-taking responsibilities.

16
Policy Research Working Paper 9350

# **Abstract**

Despite income growth, fertility decline, and educational expansion, women’s labor force participation in rural India dropped precipitously over the last decade. This paper uses nationwide, individual-level data allow to explore whether random reservation of village leadership for women affected their access to suitable job opportunities, demand for participation in the labor force, and income as well as intrahousehold bargaining in the short and medium term. Political empowerment through reservation affected women’s but not men’s participation in public works, but also women’s participation in labor markets, income, and participation in key household decisions, with a lag.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at kdeininger@worldbank.org, jins@msu.edu, ssingh12@worldbank.org, and hknagarajan@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
divided into 20 _dzongkhags_ (administrative districts). The 20 _dzongkhags_ are further divided into 205 _gewogs_ (groups of villages).

Bhutan is maybe most famous for introducing and popularizing the concept of Gross National Happiness (GNH), now enshrined as a principle of state policy in Article 9 of the country’s constitution. Exact characterizations of GNH differ but one widely cited publication defines it as a “multi-dimensional development approach that seeks to achieve a harmonious balance between material well-being and the spiritual, emotional and cultural needs of our society” (GNHC, 2012, p. 3). Over the last decades, Bhutan has seen rapid and sustained economic growth. In 2014, its GDP per capita reached US dollar 2,379 according to the World Development Indicators. Over the last

- 4 -
# 6 Conclusions

National advocates and international frameworks like the Sustainable Development Goals have called upon governments to use the policy tools at their disposal to reduce income inequality and gender inequality. However, development stakeholders do not have a compact, low-cost, and transparent tool for estimating how fiscal policies are ameliorating or exacerbating inequalities in opportunities or outcomes between genders.

This paper has sought to develop such a tool. While our approach cannot illuminate all dimensions of gender inequality, it represents a transparent and low-data-cost way to expand on the well-established and globally prevalent CEQ assessment methodology to help stakeholders determine whether fiscal policy targets gender inequalities in economic production and social reproduction. Our proposal involves no extra data but can incorporate additional data when such data are available. We developed a method for applying FIA to household typologies based on underlying survey information that helps put in focus the different roles men and women play, and the different activities they pursue in paid labor outside the house and in unpaid social reproduction activities inside the house. By bringing social reproduction activities – which are critical for the continued strengthening of social, cultural, legal, and economic institutions –together with economic activities, we can begin to estimate whether and how fiscal policy supports household members in their individual and varied roles.

The application of a gendered perspective in fiscal incidence analysis is revealing in a number of ways. It adds greater richness to the standard analysis of fiscal policy’s impact on poverty and inequality by highlighting which male-type or female-type households within a welfare distribution gain or lose from fiscal policy. Because it adds information on the sex, age, and employment status of individual household members, it also reveals the extent to which fiscal policy supports household members in their individual roles and activities.

In Armenia, for instance, the household type defined by the sex of the main wage earner was critical for understanding where and how fiscal policy affects household members differently. The direct tax burdens in households where women accounted for 60 percent (or more) of wage earnings were lower than in households
Figure 1. Change in Recent Migration <!-- Start of picture text --> Recent Migrants (1 or less than 1 year in Turkey) Recent Migrants (2 or less years in Turkey)<br>60.00% 60.00%<br>50.00% 50.00%<br>40.00% 40.00%<br>30.00% 30.00%<br>20.00% 20.00%<br>10.00% 10.00%<br>0.00% 0.00%<br>2010 2011 2012 2013 2010 2011 2012 2013<br>-10.00% -10.00%<br><!-- End of picture text -->

**Recent Migrants (3 or less years in Turkey)**

**Recent Migrants (4 or less years in Turkey)**

<!-- Start of picture text --> 60.00% 60.00%<br>50.00% 50.00%<br>40.00% 40.00%<br>30.00% 30.00%<br>20.00% 20.00%<br>10.00% 10.00%<br>0.00% 0.00%<br>2010 2011 2012 2013 2010 2011 2012 2013<br>-10.00% -10.00%<br>Regions Bordering Syria Rest Regions Bordering Syria Rest<br><!-- End of picture text --> _Source_ : LFS 2009-2014. World Bank author’s calculations.

Figure 1 illustrates the percentage increase of _recent foreign migrants_ when compared to the level of _recent foreign migrants_ present in Turkey in 2009 (baseline year omitted). The dashed line shows the trends in the border regions and the solid line shows trends in the rest of the country.

While there