rafmacalaba/datause-encoder-data huggingface dataset:
In this paper, we do not exploit the longitudinal nature of the data. In order to maintain the representativeness of the national population and because of the high attrition, the sampling frame of the RLMS was revised in several years. For example, of the 18,302 adults interviewed in 2011, only 1,788 were also interviewed in 1996. In addition, the attrition bias was tested by comparing the estimates coming from a Mincer earnings equation for 2011 using those in the sample that survived the attrition (i.e., were observed since 1996) with those in the full 2011 sample (i.e., whether observed since 1996 or not). Both Wald and likelihood ratio tests indicated the two samples were not comparable. Thus, we analyze three years—1996, 2002, and 2011—as if they were three crosssections.<sup>5</sup> The sample for the analysis includes all wage workers. Self-employed workers are excluded since the information on their wages might not be comparable. In addition, self-employed workers constitute a small percentage of the labor force in Russia: 86 percent of employed men and 88 percent of employed women were wage workers in 2010 (Gamberoni and Posadas, 2013). The analysis is restricted to men and women between 18 and 60 years of age. We chose to use 60 as the upper cutoff for the working age population, as it is the mandatory retirement age for men. Although women can retire at 55, many of them continue working after retirement. On average, women between 60 and 64 years of age worked six years after having retired, while men worked only four (Gamberoni and Posadas, 2013). We repeated the analysis for the age range 18–55 and the main conclusions of the study were not altered.

In this section we describe the variables used for the decomposition of the gender pay gap, and we restrict the summary statistics to the sample used for the regression estimates. We follow previous studies performing decomposition analysis (Blau and Kahn, 1997, 2003) and estimate an augmented Mincer earnings model. The most conservative specification includes measures of experience and schooling, with controls for place of residence. Augmented models also include a set of dummies for occupation and industry, and in some cases union affiliation. An additional contribution of this study to the literature of the gender wage gap is the use of additional variables that determine productivity and thus wages. The richness of the RLMS data allows us to explore the effect of additional firm characteristics such as type of ownership (public, foreign) or size of the firm, degree of responsibility approximated by the number of subordinates, quality of employer–employee match, and changes in occupation. However, this latter group of variables is only available for 2011. Tables A2a and A2b in the appendix show the descriptive statistics for all the variables used in the decomposition analysis.

> 5 Regression results were also estimated for 2012 when they became available, showing similar conclusions.

10
43 at the 5% level. There is no discernible effect on _ETRL_ . The absence of an increase in the capital share is intriguing; we note that Chari, Henry, and Sasson (2012) find large wage effects, suggesting a proportionate (and high) growth of both factor incomes. Foreign inflow of capital, as well as any subsequent increase in capital goods import and aggregate investment, may positively impact _ETRK_ by contributing to the growth of large, complex firms with employees and thereby raising the tax-enforceable share of capital income (Section 7.3). Consistent with a role for tax capacity, we find that the capital liberalization events led to a decrease in the non-corporate sectors (Appendix Figure A15).

Qualitatively, these results are consistent with those from the previous sections, suggesting that the positive impact of globalization on effective capital taxation in low and middle income countries is robust to using capital instead of trade openness. However, given the inherent limitations with the measurement of capital flows, we consider that our results based on trade openness provide more meaningful and robust insights into globalization’s impacts on factor taxation.

# **9 Concluding remarks and perspectives**

In this paper, we combine a new global database with several empirical strategies to provide novel evidence on trends and causal effects of globalization on tax structures worldwide. Our starting point is the systematic harmonization of novel historical national accounts data and dis-aggregated government revenue statistics. This data collection permitted the construction of a new measure of effective tax rates on capital and labor in 156 countries between 1965 and 2018.

Using this database, we make two contributions. First, we establish new facts. Taking a global perspective, the average effective tax rates on labor and capital have converged, due to a increase in labor taxation and a fall in capital taxation. We find differences between developed and developing countries: while the effective tax rate on capital fell in OECD

> 43Consistent with our result, Quinn (1997) finds a positive correlation between de-jure capital account openness and corporate taxation as a share of GDP. Note that the events considered here remove restrictions on capital _inflows_ ; it is possible that increased capital _outflows_ may, conversely, reduce _ETRK_ .

33
Finally, this study shows that nutrition interventions are able to substantially contribute to the reduction of malnutrition. In showing this, the paper fills an important gap as impact evaluations of nutrition interventions are scarce (see Mkenda 2004 for a review for Tanzania). Even the much studied Iringa project is inconclusive about the effectiveness of various strategies such as growth monitoring and promotion, integrated care and nutrition, communications for behavioral change, supplementary feeding for women and young children, school feeding, health related services, micronutrient supplementation and foodbased strategies (Allen and Gillespie 2001). Though our study suggests that the described interventions are effective, their (cost)-effectiveness remains an unknown. A recent review on cost effectiveness suggests that some interventions are more cost effective than others, particularly vitamin A supplementation for children under age five, iron supplementation for pregnant women, and some types of nutrition education and behavioral change (Gillespie and Haddad 2001; Behrman, Alderman and Hoddinott, 2004). In this light it is of importance to take the next step and to assess the cost effectiveness of the programs considered in this paper.

# **References**

- Ainsworth M. and I. Semali, (2001), “The impact of Adult Death on Children’s Health in Northwestern Tanzania”, World Bank working paper, n. 2266.

- Abdulai A. and D. Aubert. (2004). A Cross-Section Analysis of Household Demand for Food and Nutrients in Tanzania. _Agricultural Economics_ .

- Alderman H. (2000), “Anthropometry”, in _Designing Household Survey Questionnaires for Developing Countries_ in Glewwe P. and M. Grosh, vol.1

- Allen, L., and S. Gillespie, (2001), “What Works? A Review of Efficacy and Effectiveness of Nutrition Interventions”. Asian Development Bank, Nutrition and Development Series # 5, Manila.

- Arellano, M. and S. Bond. (1991).  Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. _The Review of Economic Studies_ 58: 277-297.

- Attanasio O. and A.M. Vera-Hernandez (2004), “Medium and Long Run Effects of Nutrition and Child Care: Evaluation of a Community Nursery Programme in Rural Colombia”, unpublished paper

- Baltagi B.H. (2001), “Econometric Analysis of Panel Data”, Chichester ; New York : John Wiley, 2001.

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Table 7: 2SLS Estimation of Labor Supply by Age

||Wage Emp|loyment|Self Empl|oyment|
|---|---|---|---|---|
||(1)<br>Young Adults|(2)<br>Old Adult|(3)<br>Young Adults|(4)<br>Old Adults|
|Panel A: Labor market par|ticipation||||
|Household with migrant|-0.292***|-0.260**|0.252**|0.236***|
||(0.113)|(0.122)|(0.103)|(0.085)|
|Observations|10861|6018|10861|6018|
|Wald _χ_<sup>2</sup>|2028.588|1552.717|15778.586|3651.974|
|_F_-test 1stage|58.207|50.061|58.207|50.061|
|Coecient 1stage|1.836***|1.937***|1.836***|1.937***|
|Panel B: Hours supply|||||
|Household with migrant|-13.546*|-13.516**|-8.632*|-14.457**|
||(7.380)|(5.618)|(5.037)|(5.925)|
|Observations|3913|1932|6901|4617|
|Wald _χ_<sup>2</sup>|2379.303|1482.415|2401.545|545.837|
|_F_-test 1stage|31.766|24.765|52.713|43.511|
|Coecient 1stage|1.645***|2.078***|1.804***|1.957***|
|Controls|||||
|Individual characteristics|Yes|Yes|Yes|Yes|
|Household characteristics|Yes|Yes|Yes|Yes|
|VDC characteristics|Yes|Yes|Yes|Yes|
|Region Fixed Eect|Yes|Yes|Yes|Yes|

Note: Young adults are ages between 18 and 40 while old adults are ages between 41 and 60. Sample in Panel B is conditioned on being employed in that particular sector. Log of hourly wage is added as an extra individual control for column (1) and (2). 2SLS estimates are reported in the table. Instrument used for 2SLS is share of international migrants in a VDC in 2001 (IV1). Sample is working age (18 to 60) adults and standard errors are clustered at VDC level. Standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 1 ,<sup>_∗∗_</sup> _p <_ 0 _._ 05 , _∗∗∗ p <_ 0 _._ 01 . Individual controls are age age<sup>2</sup> , years of education, gender, household head identier, and marital status. Household controls are female HH head, share of male and female adults, share of elderly, share of children, amount of land-owned, house ownership and social caste. Similarly VDC level controls are, unemployment rate, poverty rate, illiteracy rate, inequality (Gini), and urban/rural location. Regions are Kathmandu, other urban areas, Western hills, Eastern hills, Western Terai, and Eastern Terai. Coecient 1stage is the estimated coecient on the exogenous instrument in the rst-stage.

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7. Potential Benefits from Further Integration

# **7. Potential Benefits from Further Integration**

To evaluate the potential benefits from further integration between B&R Economies and China, we estimate a regression model of value-added flows on trade costs (Figure 37). We match our value-added database to the trade costs database of Arvis et al. (2013). The trade costs explain the variation in our database well.

FIGURE 46: TRADE COSTS AND TRADE IN VALUE-ADDED INDICATORS, B&R ECONOMIES, 1995-2010 <!-- Start of picture text -->
-10 -5 0 5 10 -10 -5 0 5 10<br>Foreign Content of Exports, in Logs Domestic VA Re-Exported , in Logs<br>7<br>6<br>5<br>4<br>3<br>2<br>7<br>6<br>5<br>4<br>3<br>2<br><!-- End of picture text -->

**Source:** Author’s calculations based on Trade Costs Database (Arvis et Al. 2013) and GVC indicators extracted using the Wang et Al. (2013) methodology from TiVA.

We find that a reduction of trade costs of 1% is likely to increase bilateral trade between B&R Economies by 1.3 %. Concerning trade in value-added indicators, the order of magnitude stays the same except for the indicator of domestic value-added that returns home (VS1*). For this indicator, we find that trade cost reduction by 1% would yield an increase of more than 1.7 %. The result is especially appealing since this type of value chain is the most beneficial to both economies as it builds not only on their production capabilities but also on their domestic markets.

36
11 accelerates economic growth which helps the poor. We are finding that finance exerts a disproportionately positive influence on the poor. While illustrative, these results are suspect because of the small sample, which makes it difficult to use instrumental variables and panel procedures to control for endogeneity.

Next, consider Figure 2. This also illustrates some of the results from Beck, Demirguc-Kunt, and Levine (2007). Here we focus on the Gini coefficient of income inequality. For the Gini coefficient, we have data running from 1960 – 2005 for 72 countries. This allows us to use a dynamic panel instrumental estimator to control for potential endogeneity bias. We obtain the same results when using the panel estimator or when using a simple cross-country regression with one observation per country. Thus, we just present the partial scatter plot from the multivariate regression that controls for many country characteristics, including real per capita GDP growth, the initial value of income inequality for each country, and others.

There is a strong, negative relationship between the level of financial development and income inequality. Finance exerts an especially positive impact on those at the bottom of the distribution of income. These results are also not definitive. The measure of financial development is not closely tied to theory. The study does not examine policy; rather, it examines a proxy for overall financial development that reflects many factors. Future work that develops better measures of financial development and uses exogenous innovations in particular policy changes will substantively improve our understanding.
Assessments indicate that public capital will be far from sufficient to meet sustainable development goals.<sup>1</sup> Governments and multilateral development banks (MDBs) therefore hope to crowd in private capital via financial structures that incentivize sustainable investments.<sup>2</sup> This paper develops a stylized theoretical framework for understanding how MDB-supported investment, and public investment more generally, can attract additional private investment by raising the marginal product of capital. To mobilize that private capital, however, requires financial structures (contracts and platforms) that augment financial intermediation. Drawing on Merton’s functional approach to financial intermediation (Merton, 1995), the paper then describes how these structures can crowd in private capital for sustainable investment by pooling funds for large-scale investment, mitigating asymmetric information and incentive problems faced by private investors, managing uncertainty and controlling project risk, and facilitating the transfer of economic resources (geographically and across sectors). The paper then discusses five specific types of financial structures that can mobilize private capital for investments – guarantees, public-private partnerships (PPPs), syndicated loans, sustainable financial contracts, and climate and banking policies – highlighting academic literature in those areas, the amount of private capital mobilized to date, and the challenges in scaling up those structures to mobilize more private capital for investment. The paper closes with an overall assessment of how successful efforts to mobilize private capital have been thus far and offers suggestions about the types of investment projects and financial structures that hold the most promise for crowding in private capital going forward in furtherance of sustainable development goals.

> 1 See, e.g., World Bank (2023), Bhattacharya et al. (2022), Le Houerou and Lankes (2023), and Dachicourt (2022). Under a business-as-usual scenario, World Bank (2023) estimates that the financing gap for achieving development and climate goals will be $3.5 trillion annually by 2030. Unmet needs for global infrastructure investment have been estimated at $3.3 trillion per year through 2030 (McKinsey Global Institute, 2016; Arezki et al., 2017).

> 2 MDB Task Force on Private Finance on Private Finance Catalyzation, 2017.

4
had a substantial and statistically significant effect on the duration of the round-trip: the average fell from 94.2 minutes ‘before’ to 62.6 minutes ‘after’: _t_ = _−_ 2 _._ 46 (robust standard error); combined K-S, _D_ = 0 _._ 3807 ( _p_ = 0 _._ 033); K-W, _χ_<sup>2</sup> (1) = 8 _._ 48 ( _p_ = 0 _._ 003). Accumulated over a whole school-year, savings of 30 minutes a day add up to a fair slice of time.

The alternative to comparing the rolling cohorts is to restrict attention to the common sub-group of 40 children. This has the advantage that any unobserved, timeinvariant effects specific to these individuals and their villages can be purged by forming the set of individual differences between ‘before’ and ‘after’. Recall from (3), however, that there may be clustering on the periods, and this turns out to be so here. To eliminate the effects of switches in the location of the schools attended, we discarded 8 children in all. Most of these were promoted from primary to secondary school during the period in question, and so had to commence commuting. Of the remaining 32 children, 11 were attending primary school. This latter group spent no less time making the trip than they had before, which, as remarked above, is no surprise; for only one of them was attending a school outside the village, and then close by. As for the mode of transport, here there were no changes: 10 went by foot, the other cycled. What did change was the average number of days lost due to bad weather, which declined from 8.8 to 2.4, a difference that is statistically highly significant ( _t_ = _−_ 6 _._ 24). The two nonparametric tests confirm this rejection of the null: combined K-S, _D_ = 1 ( _p_ = 0 _._ 000); K-W, _χ_<sup>2</sup> (1) = 15 _._ 77 ( _p_ = 0 _._ 000).

The 21 secondary-school pupils who did not change schools faced, on average, an each-way trip of 4.4 km. With the new road at hand, four of the nine who had gone on foot, cycled to school instead, a switch that is not statistically significant (Fisher’s exact test, _p_ = 0 _._ 337). In stark contrast to the cross-section finding once more, this group of 21 pupils enjoyed, on average, a substantial and statistically highly significant saving of time, namely, 16.9 minutes a day ( _t_ = _−_ 3 _._ 50). To refine this result somewhat, it is desirable to control for the varying length of the trips to school: OLS yields an estimated saving of 4.5 minutes per km. ( _t_ = _−_ 3 _._ 92 _, p_ = 0 _._ 001; robust standard errors), and the estimated constant, _−_ 2 _._ 2 minutes, is not significantly different from zero ( _t_ = _−_ 0 _._ 57). The average number of days lost involuntarily fell from 12.7 to 2.1 ( _t_ = _−_ 6 _._ 89), a difference which is almost twice as large as the corresponding cross-section difference of 6.5 days for the whole sample of 30 villages. It should be added that the distribution of these individual differences betrays non-normality (a cluster at zero, with none positive). The two non-parametric tests overwhelmingly reject the null that the ‘before’ and ‘after’ distributions are equal: combined K-S, 22
beginning of the survey. Thus, each of the specific business environment element questions were removed from the end of each section and repositioned to the beginning of the survey. The purpose of the experiment is to compare the business environment perceptions questions between the two groups so as the ascertain whether the question-order influences the responses.

# **4. Empirical Strategy**

For estimation purposes we convert the business environment element rating variables into binary Yes/No variables. The variable takes a value of 1 if the business environment element is a “major obstacle,” or a “very severe obstacle.” The variable takes a value of zero if business environment element is “no obstacle,” a “minor obstacle,” or a “moderate obstacle.” This is the standard construction of Enterprise Surveys indicators based on the ratings variable.<sup>5</sup> We compute the percentage of firms in each group that finds the business environment obstacle to be a major or severe obstacle to operations, and then compare this across the groups for each business environment element. The mean differences between the groups of the business environment elements are tested for statistical significance.

We first test for business environment elements that are statistically significantly different across both groups, and then we explore if the difference is robust after controlling for other covariates. To achieve this, we estimate the following using a Linear Probability model.<sup>6</sup> B 0 1 E E 2 R 3 4L L 5M 6 7 8 9 10 11E ii ii ii ii ii ii ii = αα + ββ BBE 12 + ββ RREER RREE13+ ββ EES EE + ββ (1)EE + ββ MMRRSSBBR RRM SSRRh + ββ BBEERRE RR ii ii ii jj ss + ββ BBLLBBEERRMM + ββ IILLIIE EEMMEEIIEE + ββ IILLRRLLEEL RRh + ββ LLEEIIR SSEELL + ββ IIRRE ii ii ii + ββ IILLRRE EESSEEIIE + ββ EEMMRRE EESSRRE + εε

> 5 We explore defining the business environment obstacle in alternative ways in section 5 of the study.

> 6 We chose the linear probability model given the number of fixed effects in our estimation. We are concerned about the incidental parameters problem. However, our main results that show an effect of question-order on corruption and business, licensing and permits perceptions stands when using a Probit model.

11
Policy Research Working Paper 9095

# **Abstract**

This paper analyzes the simultaneous impacts and interplay of exports and technology adoption on the demand for different types of skills and aggregate labor market indicators in Indonesia over a period characterized by a commodity boom (2005–10) and a period of declining exports (2011– 15). The results for the 2005–10 sub-period are in line with the evidence available for developed countries, that is, technology is complementary to analytical and soft skills and is labor-saving, while exports are labor increasing. In 2011–15, the relationship between technology and skills, and between technology and labor demand, differs from the - evidence available for the developed world. That is, technol ogy increases the demand for analytical and interpersonal skills in high-exporting industries only, and technology and exports are labor increasing for some population subgroups. The findings for the more recent period confirm that differences in economic structures matter for understanding the impacts of technological advances and globalization.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region, produced as a background paper to the World Bank’s East Asia and Pacific regional study, _“A Resurgent East Asia: Navigating a Changing World”_ . 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 atat mviollaz@cedlas.org, fdarko@worldbank.org, and amason@worldbank.org.

_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