rafmacalaba/datause-encoder-data huggingface dataset:
because they had substantial financial resources at their disposal. It has been disappointing, however, to see that local governments have not been able to use fully their allocated budgets, especially regarding much needed capital investment projects. This paper contributes to understand the factors that determine a municipality’s ability to use the resources at its disposal or, in simpler terms, to execute their budget. For this purpose, the paper links detailed information on the fiscal accounts of most municipalities in Peru with data on key economic, social, and political characteristics of their jurisdictions and their elected governments.

We study the execution rates of aggregate fiscal expenditure, as well as of its current and capital components, focusing on the latter because of its remarkably low execution rates. For analytical convenience, we separate the determinants of the budget execution rate in four broad categories, related to budget size and allocation process, local capacity, local needs, and political economy considerations. We find that all these groups contain independently relevant explanatory variables, at least in terms of statistical significance. Moreover, using quantile regression analysis, we find that the negative effects tend to be larger in magnitude for municipalities with weaker budget execution rates. Finally, focusing on the variables’ quantitative impact, we must highlight the relevance of the budget size itself, its allocation rules, and the local capacity to absorb it.

It stands to reason that increasing municipalities’ budgets without technical support and sustained capacity-building efforts is not wise policy. In Peru, and possibly in many other countries as well, effective fiscal decentralization can be achieved if budget allocations are matched not only with local needs, but also with local capacity. However, in the medium term local capacity itself can be the target of purposeful economic policy. Changing the incentives for local leaders to hire better public managers, facilitating coordination between small municipalities for large common projects, and clarifying the different mandates of different levels of government are but a few elements of much needed, second-generation fiscal decentralization reforms.

26
U.S.-owned subsidiaries that produce car inputs, such as plastic, seat-belts, glass, and so on, in a foreign country.<sup>22</sup> It is important to note here the difference with the measure of FDI in Alfaro and Charlton (2009). In fact, as a measure of FDI, they use the value of sales aggregated at the sector of the subsidiaries. While their approach measures the value of FDI done in an industry, our way of aggregating firms' revenues allows us to evaluate the amount of FDI done by an industry. Following the previous example, they look at the total value of sales of all the firms in the plastic, seat-belts, or glass sector. On the contrary, since we are interested in the reasons why firms in a particular sector do more FDI, we aggregate revenues at the sector of the lead firm. In other words, instead of looking at the total amount of FDI done by firms in the car industry and wine sector in the production of glass, we focus on the amount of FDI done by firms in the cars (or wine) industry in all sectors that produce the inputs needed to produce cars (or wine).

# **4. Empirical findings**

In this section we empirically investigate the relationship between deep agreements and internationalization of production. First, we analyze whether deeper agreements have a positive impact on vertical FDI. Then, we go a step further and evaluate whether particular provisions included in a trade agreement are related to firms decisions on whether to vertically integrate or not. In particular, we test whether �- type provisions are positively related to increases in vertical FDI.

## **4.1. PTA Depth and Vertical FDI**

We first look at whether and how deeper agreements attract more FDI. In order to do this, we estimate the following equation:

> 22 Despite the fact that there is no availability of intra-firm trade data in the ORBIS database, total revenues of vertically integrated subsidiaries are a good proxy for it. In fact, the correlation between our data on vertical FDI and related party trade from the Bureau of Economic Analysis is 0.69.

18
**Figure 15: Top 15 Countries with highest EXPRELY of overall GVC Exports, 2014**

<!-- Start of picture text --> 2<br>1.5<br>1<br>0.5<br>0<br>EXPRELY of GVC Exports HHI of GVC Exports Normalized year 2001 GDP (GVC exports‐weighted average)<br><!-- End of picture text --> <u>Notes: Values in ratios from original variables expressed in thousand current US$ and calculated on the total number of 181</u> countries/geographies with available data on the variables necessary for the construction of both indexes and non‐missing normalized GDP, in at least some years of the period 1988‐2014, even though some have missing data in some years over the GVC mirror data series over the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits). Normalized GDPs are calculated for each country and year as a ratio relative to the USA GDP in the fixed year 2001, with both countries’ GDPs measured in constant 2005 USD (normalized GDP for the USA equals 1). All trade data is drawn from Comtrade on WITS, and countries’ GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database (http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).</u> World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators

**Figure 16: Bottom 15 Countries with lowest EXPRELY of overall GVC Exports, 2014**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>EXPRELY of GVC Exports HHI of GVC Exports Normalized year 2001 GDP (GVC exports‐weighted average)<br><!-- End of picture text --> <u>Notes: Values in ratios from original variables expressed in thousand current US$ and calculated on the total number of 181</u> countries/geographies with available data on the variables necessary for the construction of both indexes and non‐missing normalized GDP, in at least some years of the period 1988‐2014, even though some have missing data in some years over the GVC mirror data series over the period 1988‐2014 (HS 1988/92 nomenclature, six‐digits). Normalized GDPs are calculated for each country and year as a ratio relative to the USA GDP in the fixed year 2001, with both countries’ GDPs measured in constant 2005 USD (normalized GDP for the USA equals 1). All trade data is drawn from Comtrade on WITS, and countries’ GDP data comes from WB WDI.

<u>Sources: Trade: World Bank MC‐GVC database</u> <u>(http://wits.worldbank.org/WITS/WITS/AdvanceQuery/GVC/GVCQueryDefination.aspx?Page=GVCIndicator).</u> World GDP: World Bank WDI (http://databank.worldbank.org/data/reports.aspx?source=world‐development‐indicators 31
lend empirical support to the hypothesis that policy restrictiveness is a significant factor in determining bilateral M&A flows in services sectors. As we would expect, we find a baseline negative effect of policy restrictions on foreign investment. At the same time, though, we find intriguing evidence of policy effectiveness to be state-dependent. Specifically, relatively high shares of manufacturing and services in value added seem to allow countries to maintain a more restrictive regime without deterring M&A in service sectors.

This effect might be sizeable. Take for example the cases of Vietnam and Botswana, two countries which share a roughly similar STRI score. The industrial structure of the two countries, however, is quite different. Vietnam has a manufacturing share of 20% of GDP and a services share of about 38%. In Botswana, the service share is 43%, while the manufacturing share is only 3.8%. Suppose both countries would engage in policy reforms that lowered their STRI scores in an identical manner. Using as reference point the results displayed in column (7) of Tables 6 and 7, that same liberalization policy would in Botswana have an impact about five times as large as in Vietnam in terms of both the probability of observing an M&A flows and of number of M&A deals.<sup>30</sup>

# **5.3 Individual Policy Measures (Second Stage)**

While the STRI is an composite index, the Services Trade Restriction Database also makes available a range of individual policy measures.<sup>31</sup> While there are econometric problems related to multicollinearity and degrees of freedom, one would ideally want to identify the individual policy measures that most affect M&A decisions. We divide the policy measures into different categories, namely restrictions affecting market entry, licensing and operations. We present results by pooling the different sectors, conditioning on GDP and including sector Table 10 reports the results.<sup>32</sup> In the first four columns, we report the results obtained

> 30Alternatively, the same policy _restriction_ would have much larger _negative_ effects if imposed in Botswana than in Vietnam.

> 31These include measures that underpin the STRI score plus additional variables and contextual information.

> 32We pool different sectors in order to increase the degrees of freedom in our regressions. However, we

24
Policy Research Working Paper 7387

# **Abstract**

This paper studies gender education gaps among indigenous and nonindigenous groups in Bolivia. Using the National Census of Population and Housing 2012 and an estimation method analogous to difference-in-differences, the paper finds that the intersection of gender and indigenous identity confers cumulative disadvantage for indigenous women in literacy, years of schooling, and primary and secondary school completion. Although gender education gaps have become narrower across generations, there remain significant differences among indigenous groups. The Aymara have the largest gender gap in all outcomes, despite having high overall attainment rates and mostly residing in urban centers, with greater physical access to schools. The Quechua have relatively smaller gender gaps, but these are accompanied by lower attainment levels. The paper discusses the possible sources of these differentials and highlights the importance of taking gender dynamics within each indigenous group into greater consideration.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mreimao@ucdavis.edu or etas@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
# **References**

- Arulampalam, W., A. L. Booth and M.L. Bryan (2007). “Is There a Glass Ceiling over Europe? Exploring the Gender Pay Gap across the Wage Distribution,” _Industrial and Labor Relations Review_ , 60(2): 163-186.

- Bertrand M, Goldin C, Katz LF (2010) “Dynamics of the gender gap for young professionals in the corporate and financial sectors,” _American Economic Journal: Applied Economics_ 2(28): 228–255.

- Del Bono, E. and D. Vuri (2011) “Job mobility and the gender wage gap in Italy,” _Labour Economics_ , 18(1): 130-142.

- Fairlie, Robert W. (2005). “An extension of the Blinder-Oaxaca decomposition technique to logit and probit models,” Journal of Economic and Social Measurement 30: 305-316.

- Juhn, C., Murphy, K.M. and B. Pierce (1993). “Wage Inequality and the Rise in Returns to Skill,” _The Journal of Political Economy_ , 101(3): 410-442.

Kunze, A. 2003. “Gender Differences in Entry Wages and Early Career Wages,” _Annales d’économie et de statistique_ , 71/72: 245-265.

Kunze, A. 2005. “The Evolution of the Gender Wage Gap,” in _Labour Economics_ , 12(1): 73-97.

Machado J, Mata J (2005), “Counterfactual decompositions of changes in wage distributions using quantile regression,” _Journal of Applied Economics_ , 20(4): 445–465

- Manning A, Swaffield J (2008) “The Gender Gap in early-career wage growth,” _Economic Journal_ , 118(530): 983–1024.

Munich, D., Svejnar, J. and Terrell, K. (2005). “Is women’s human capital valued more by markets than by planners?” _Journal of Comparative Economics_ , 33(2): 278-299.

- Pastore F (2010a) “The Gender Gap in Early Career in Mongolia,” _International Journal of Manpower_ 31(2): 188–207.

- Pastore F (2010b) “Returns to Education of Young People in Mongolia,” _Post-Communist Economies_ , 22(2): 247-265.

- Pastore F, Sattar S and Tiongson E (2013) “Gender Differences in Earnings and Labor Supply in Early Career: evidence from Kosovo’s School-to-Work Transition Survey,” _IZA Journal of Labor & Development_ 2:5.

Pastore, Francesco and Alina Verashchagina (2011) “When Does Transition Increase the Gender Wage Gap? An Application to Belarus” _The Economics of Transition_ , 19(2): 333-369.

23
It is useful to understand what labor markets and in particular employment trends looked like in the set of countries that we study pre-pandemic. Columns 12 –14 of Table 1 show the International Labour Organization’s pre-pandemic 1st quarter of 2020 and 2nd quarter 2020 data for the countries that have both quarters available and the change between the two quarters. For the subset of countries of the HFPS that have ILO employment data available for the two quarters, the range shows very small increases in employment (0.73) to large declines (-23.98).

# **3 Data and Methods**

## **3.1 High-Frequency Phone Survey Data**

We use harmonized data from the World Bank’s High Frequency Phone Surveys (HFPS). The surveys have been harmonized both ex ante and ex post in the data production stage, but differences across countries remain in terms of questionnaire and sampling design. The data used from the surveys cover 12 countries in the Sub-Saharan Africa (SSA) region, 12 countries in the Latin America & the Caribbean (LAC) region, 7 countries in the East Asia & Pacific (EAP) region, 6 countries in the Europe & Central Asia (ECA) region, and 2 countries in the Middle East & North Africa (MENA) region. Currently no countries in the South Asia (SA) region are included. Eight countries are low income, 17 are lower middle income, 10 are upper middle income and 4 are high income. Going forward, more countries will be added to the HFPS data, including several in South Asia. We analyze the first wave of data for each country, for waves collected between April and July 2020. Table 3 shows information on the set of countries included, the month data were collected, the number of survey respondents, the survey sampling, and the availability of some key variables. The final sample size for countries ranges from 692 (Paraguay) to 5,346 (Vietnam). The mean number of respondents is 1,666, while the median is 1,288.

12
Husbands No Children <!-- Start of picture text --> Children<br><!-- End of picture text --> <!-- Start of picture text --> 100% - ; :<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>NP S56 UE 19 PT 10 Fras NP 43 UE 24 PT 12 FiE22<br><!-- End of picture text --> Wives No Children Children <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>NP 82 UE 10 PT 3 FES NP 83 UE 10 PT2 FT4<br>m Home production TM Leisure m@ Work<br><!-- End of picture text -->
10 machinery investment, inward orientation). Lau, Jamison and Louat (1991) estimated different effects of education by level (primary versus secondary) and five regions and found that primary education had an estimated negative effect in Africa and Middle East and North Africa, insignificant effects in South Asia and Latin America and only positive and significant in East Asia. Jovanovic, Lach and Lavy (1992) use annual data on a different set of capital stocks and the N-S-D education data and find similarly negative coefficients for their non-OECD sample's. Using even cruder proxies than educational capital, such as changes in adult literacy, gives similar results as Behrman (1987) and Dasgupta and Weale (1992) find that changes in adult literacy are not significantly correlated with changes in output.

Finding that a variable is not statistically significant in a regression is typically not very interesting, for three reasons. First, since pure measurement error attenuates estimated coefficients (that is, creates a bias towards zero) using a sufficiently badly measured proxy for the true variable of interest will always suffice to produce an estimated coefficient that is insignificantly different from zero no matter how large the true imnpact of the correctly measured variable. Second, we frequently do not have a good prior reason for knowing what the coefficient 'ought' to be so it is difficult to know how far any given point estimate is from the 'expected" value. Finally, the failure to reject may simply be low statistical power, so while a zero effect cannot be rejected, it is possible a wide range of other plausible values are also not rejected. The next subsections address these problems in turn.

18 One estimate using stocks that do not find a negative coefficient are those reported in the East Asia Miracle study (World Bank, 1993a), which uses annual data in a CRS constrained production function and find a coefficient of .154. First, the use of annual data in this regression will add enormous noise and potential bias given that by far the major determinants of anmual GDP growth rates are cyclical, not long rim growth, effects while education growth is very smooth (Easterly, et al, 1993). Second, the i-statistic on human capital using 2,093 observations is just 1.5, which again suggests tremendous noise. Third, their estimate of the physical capital stock estimate, .178, is just not on, as it is completely implausible and at odds with nearly all other empirical results and with very solid non-regression evidence.
The remainder of the paper is organized as follows. Section 2 discusses the data. Sections 3 and 4 present the results. We conclude in Section 5.

# **2. Data**

To document the time-series patterns of _q_ and its components as firms internationalize and compare these patterns to firms that remain domestic, we collect substantially more data than previous studies. First, previous studies examine cross-sectional data, but theory provides predictions about the time-series patterns of _q_ and its components. Thus, we collect accounting, balance sheet, and stock market data on both international and domestic firms over a twelve year period for firms from many countries. Second, most papers examine only the American Depositary Receipt (ADR) market, but theoretical predictions apply to internationalization beyond ADRs and firms internationalize into other countries and access the U.S market through vehicles other than ADRs. Furthermore, some theories stress that the effects of internationalization depend on the comparative effectiveness of corporate governance in a firm’s home country relative to the market into which it internationalizes. Thus, it is important to examine internationalization into financial centers other than the U.S. Moreover, many models argue that the impact of internationalization is a function of the legal characteristics and regulatory requirements associated with the particular financial vehicle used to internationalize. Thus, we gain analytical power by considering internationalization through non-ADR instruments. Besides the ADR market, we include firms that internationalize (i) by issuing depositary receipts in other international financial markets, (ii) by cross-listing in the U.S. and other financial centers, and (iii) by raising equity capital through private or public placements in the U.S. or other international equity markets. We use these different subsamples of 7
|Table**5.**United States Border**P**|**rotection**||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Corruliodity|Post**U**<br>TariffsEquiv|**R**<br>alent|Final<br>Rate|**FisI'm**<br>Rale|<br>Final<br>Rate|<br> <br>Tar|Pre**UR**<br>ffeqitivua|]ii|Dirty<br>tai tf6casoll|Chiiige inp|rolecdio||
||Base|Bound|2001)|20001|2000||||x6-88|FInal|FiIal -|FNitalI|
||rate<br>(1986-88prie|rale<br>es)|Point|Lower<br>Bound|Upter<br>Bound|82-92|R6-88|89-93|Base-<br>PTe-UR|R9-92|S2-92 ||Base|
|AGGREGATION OF COMMODI|TIES||||||||||||
|I WHEAT|6%,,|<br>4%|4re|4|4%|<br>_13%_|<br>20-,|201|%<br>_-14_|<br>2|14|<br>-36%|
|2 RICE|_5%_|3%l.|3|3%|3|It|I'%|<br>2'|4|i|-I|-36%|
|3COARSEGRAINS|8%q|<br>2%<br>,|2%|2|2'.|2%|42|2',|5|<br>-65|-124|-74%|
|4 SUGAR|197%|_16RT,_|91%G|<br>57%a|164%,|<br>126%|1<br>131_%_|671|67|XI|166|<br>-15%,|
|_5_NIEAT|31%|26%|26%r|<br>26%|26%,|<br>2%v|<br>3%,|<br>2"'i|<br>27|<br>25|24|-15%'|
|6 OTHERMEffi<br>AT|4%|3,|3%b|o<br>3%|3%,|<br>2%S|<br>6%|<br>1%|e<br>0|<br>3|5|-36%|
|7 COFFEE|0%|0%|0||(0%0|0%o|<br>3%,|<br>_0%,_||||0%|
|9 COCOA|0%|0%|1%|<br>_(0%_|<br>0D|<br>(%|<br>0%,|<br>1||||-60%|
|9TEA|3%|s<br>_%_|2%|'%|2'|2%|<br>2r%|<br>2T||||36|
|10OILSEEDS|0%|01%|<br>0%|0%|0%.|'<br>0%|0%|0%'|<br>t|-46|-92|0%f|
|ItMILK|144%|122q%|<br>03%|8 3%|107%|L<br>92%,|<br>132%|1 46|1[2|<br>43|122|<br>_-15_|
|12 FRJITS&VIVGETABLI|91%|77%.|77%|77%|77%<br>11|6%'y|||||||
|13WOOL|7 %|4%|4S%|4,|4%|Y.|7a||0n|<br>4|4|-42%Y,|
|1COTTON|31%|26%-|26%,|_26%_|2'%,|||||||-15%,,|
|15OTHER NON FOOD|_19%_|I**%**|80%|<br>_8%,_|8%|||||||-56%'|
|Average**(Unwetghted)**|36%|30%'|23%|_20%_|28%,|<br>_2I%_|25%,|<br>13-|||||
|Stacldard**devlatloii**<br>Coiiversioni factors:|_60%_|5_2%_|35a,|29%7|,<br>44%|1<br>42%.|<br>50%.|<br>22%|||||

**Boneless** = 1.36 carcass weight: Carcass weight = 1 85 Line **aririnal I nS** Of Fresh table grapes = 1172866 NIT

OECD referencepricestisea forBeefanid Veal, Fignieat, Sieepnieat, Sigar and Wool Imnport onit values u sed in all other coiuiocbdlies Soirce: USDA (89-93). 86-RR: adtisted using rate of growth in UN trade dala Pre UR border **ieasutres:** hiporl tari ffs; Barley, Poultry, Rice, Wheat; Frice support/qootas, Milk, **SII** gar, Tarilfn and Beef PFrchases; Beef **aid** Veal (a) except Mainufactured uilk. Barley, oats, rye: USDA (b) Beef and Veal. Manufactured **rtilk** aud Sugar represeut CIF prices Souirce: Auithor's calculations; basic data are frotii Uruguiay Rosind couintry schedilles.

In Canada, production of milk, dairy products, poultry meat, and eggs received high protection during the pre-UR period through support from stabilization programs, transportation subsidies and quantitative import restrictions. In Australia. the progressive reduction in the tariff imposed on sugar has made the sector more exposed to international markets, but the remaining tariff still provides a significant amount of protection. Prior to the late 1980s, there was an administered home consumption price on sugar which, on average, resulted in negative rates of protection.

33