rafmacalaba/datause-encoder-data huggingface dataset:
sector for the following countries: Albania; Azerbaijan; Bulgaria; Belarus; Czech Republic; Estonia; Croatia; Hungary; Kazakhstan; Kyrgyzstan; Lithuania; Latvia; Poland; Romania; Russian Federation; Slovakia; Slovenia; and Ukraine.

For each cell in the matrix with intermediate coefficients in the Armenian data we apply the GTAP coefficients to the corresponding variable. The lower (upper) bound is set equal to the minimum (maximum) of the values calculated for the countries listed above. That is, for each input-output coefficient, we constrain the rebalancing procedure such that each coefficient in the balanced Armenian table we create lies within the observed bounds of the coefficients of the 19 Transition countries we consider.

# **Results**

Table 1 reports calculations based on the supply-use table provided by the National Statistical Office of the Republic of Armenia. In each sector, as an accounting identity, we must have that domestic production plus imports (total supply) is equal to domestic consumption (for intermediate and final use) plus exports (total demand). The excess demand columns show the extent that total demand exceeds or is less than total supply (both in Armenian DRAM and as a percentage of total supply). Excess demand in the original data varies from +75% in the case of electricity to -48% in the case of mining. These percentages show very large inaccuracies in the data at the sector level, and must be corrected to have a correct input-output table or a table that can be used in our model.

“Official” estimates of total GDP are available on the website of the National Statistical Office of the Republic of Armenia. We compare the official estimate with the supply-use table and our balanced table in Table 2.For the supply-use table, the imbalance is below and beyond official GDP depending on the method used. GDP in our balanced table is less than 1% off official GDP, and consistent across methods.

The adjustments in the components that make up GDP are slightly larger (see Tables 3 and 4). The largest change is reported for household expenditure. The main reason is that the official data for the large manufacturing sector report 50 percent more use than is available from production and imports. To attain consistency, a large decrease in consumption of manufactured goods is required; this accounts for about half of total consumption. Similarly, our optimized balancing routine results in an increase in imports of manufacturing goods as a percentage of total imports; this is required in order to balance the officially reported excess demand for manufactured goods (see Table 5).

# **Disaggregation of the input-output table**

Two sectors in the input-output table, “Transport, storage and communications” and “Financial intermediation”, account for most business services in Armenia. Given our focus on these services in the model, these sectors are disaggregated into 9 sectors using two data sources.

For “Transport, storage and communications” we obtained unpublished national accounts data for the year 2007 from the National Statistical Office of the Republic of Armenia. Table 7 shows the data.

107
|bles<br>**Source**<br>Authors’ construction using World Bank WDI<br>World Bank diversifcation database<br>World Bank diversifcation database<br>World Bank diversifcation database<br>Authors’ construction<br>Authors’ construction with UN COMTRADE<br>Authors’ construction with UN COMTRADE<br>Authors’ construction with UN COMTRADE<br>Authors’ construction<br>Chang _et al._ (2009)<br>Chinn & Ito (2008)<br>Calder ́on _et al._ (2005)<br>Chang _et al._ (2009)<br>Chang _et al._ (2009)<br>Authors’ construction based on IMF IFS<br>Chang _et al._ (2009)<br>Chang _et al._ (2009)<br>World Bank WDI<br>World Bank WDI<br>World Bank WDI<br>Chang _et al._ (2009)<br>Chang _et al._ (2009)<br>Chang _et al._ (2009)<br>Freedom House (2009)<br>MarshallJaggers:2008<br>Sarkees & Schafer (2000)<br>Cross-National Time-Series Data Archive|
|---|
|Table A.3: Defnitions and sources of varia<br>**Variable**<br>**Defnition and construction**<br>GDP per capita growth volatility<br>Standard deviation of log diference of GDP per capita<br>Product Herfndahl<br>Herfndahl Index HS-4 Digit<br>5 product<br>Share of 5 largest products in overall exports<br>10 product<br>Share of 10 largest products in overall exports<br>PC product<br>First principal component of 3 product diversifcation measures<br>Market Herfndahl<br>Herfndahl Index for destination markets<br>5 market<br>Share of 5 largest destination markets in overall exports<br>10 market<br>Share of 10 largest destination markets in overall exports<br>PC market<br>First principal component of 3 market diversifcation measures<br>Trade openness<br>Total trade (exports + imports) divided by GDP<br>Financial openness<br>Restrictions on cross-border fnancial transactions<br>Capital fow volatility<br>Capital fows to the region as a percentage of GDP<br>Foreign growth volatility<br>Standard deviation of growth rate of main trading partners<br>TOT volatility<br>Standard deviation of terms of trade (ToT)<br>Exchange rate volatility<br>Standard deviation of real efective exchange rate (REER)<br>Infation volatility<br>Standard deviation of annual log diferences of CPI<br>Banking crisis<br>Number of years a country underwent a banking crisis<br>Initial GDP per capita<br>Log of the value of GDP per capita in the initial period<br>GDP per capita growth<br>Log diference of GDP per capita in constant USD<br>Educational level<br>Initial value of ratio of secondary gross enrollment<br>Government exp volatility<br>Standard deviation of government expenditures<br>Natural disaster<br>Portion of years with natural disasters<br>Government quality<br>Index constructed from ICRG indicators<br>Institutional quality<br>Index of civil liberties and political rights<br>Political volatility<br>Number of years a country experienced political iterregna<br>Confict<br>Number of years a country experienced confict<br>Assassinations<br>Number of attempted assassinations of government ofcials|

34
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-17-

The results reveal that:

- existing policy costs Brazil between $660m and $2500m a year;

- Brazil is producing:

- too little sugarcane;

- too much ethanol for the domestic market;

- - not enough sugar and possibly ethanol for export

- exports of sugar could expand dramatically from around 3 million tons a year to over 20 million tons, making Brazil by far ih dominant exporter;

- Brazil's exports could markedly lower the world price for sugar;

- world price would fall toward the existing Brazi pnrce;

- under some scenarios world price falls by nearly 50 percent;

- e nonetheless, price remains higher han the exisdng Brazilian price by a sufficient margin to induce a marked supply response under most scenarios.

The large impact Brazil's expanded exports have on the world price raises the issue of whether export taxes or other controls over exports could be applied to raise the world price and Brazil's export revenue. In practice to operate such a policy requires having good information about export demand. There are serious difficulties in accurately estimating the nature of demand. Policy markers therefore cannot intervene successfully to ensure that the benefits of their intrvention will exceed the costs (Helpman 1990). What is more, the intervention makes the industry vulnerable to political intervention.

Within the range of estimated welfare effects, the higher ones occur when a reasonably optimistic but not unrealistic scenario is assumed about world price were Brazil to leave its policy unchanged. This scenario is roughly consistent with the World Bank's latest long term projection of the world sugar price at around 13c/lb. It also assumes that Brazil's marina impact on the world market price diminishes with bigger and bigger increases. in the levels of Brazil's exports (a constant export elasticity of demand is assumed). The lower numbers in the range occur assuming the long run outlook the world prce is for it to stay similar to its present hitDrical low of 9cIlb. Another crucial assumption affecting the lower range figure is that - each additional unit of exports lowers the world price by a constant amount a point elasticity is applied to a large change.

What the numbers do not include are the environmental benefits of ethanol. Were the European Commuity to ith pls imm atot -
**Does Child Labor Always Decrease with Income?** **_An evaluation in the context of a development program in Nicaragua_**

Ximena V. Del Carpio<sup>*</sup> The World Bank JEL Codes: D13, I30, J16, J24, O12, O54 Keywords: Child labor, poverty, human capital, impact evaluation, Nicaragua

> * I am very thankful to Naotaka Sugawara for excellent research assistance.  For excellent ideas and comments I am thankful to Kathleen Beegle, Norman Loayza, Karen Macours, Jeffrey Nugent, Duhshyanth Raju, Maria‐Laura Sanchez Puerta, Mark Sundberg, Renos Vakis, Milan Vodopivec and all seminar participants at RAND in Santa Monica, Economic Research Services (USDA), and Human Development Social Protection, labor markets.   I owe special thanks to the program team at the Ministry of the Family in Nicaragua (Carol Herrera and Teresa Suazo), the team from CIERUNIC (Veronica Aguilera, Enoe Moncada, Carlos Obregon) for their excellent data collection and great ideas, and the World Bank Nicaragua Country Office for overall support for this work. Finally, I also thank my current colleagues at the Independent Evaluation Group for their comments and overall support.  The opinions, errors and omissions are my own. Contact: xdelcarpio@worldbank.org
which 79 firms<sup>10</sup> received for a median of 35 hours, and operational productivity, which 71 firms chose and received for a median of 80 hours. Most firms in the program therefore received these two areas, and then were divided in their choice of a third area, with 34 receiving labor productivity, 32 quality, and 19 energy consulting.

# **5.2 Impacts on business and export practices**

The most immediate impacts of management consulting interventions are typically seen on management practices, as firms implement the changes recommended by consultants. We hired Innovations for Poverty Action to conduct a follow-up survey of firms between November 2019 and May 2020. This was able to collect data on 172 of the 200 firms (89 treatment and 83 control), and additionally confirm that 3 other firms were closed (2 control, 1 treatment). We measure impacts on 15 different export-oriented management practices, such as whether the firm participates in trade fairs, has received a quality certification for an export market, does direct marketing to international customers, or had received information about distributors abroad. In addition, we measure impacts on each of 40 different general management practices that cover the five areas of consulting, divided into operations practices (10), labor productivity practices (9), quality practices (6), energy practices (4), and commercial practices (11). Appendix C provides definitions of all measures, which were developed with feedback from the different consulting entities to ensure they covered the main areas of advice given. Our main measures of export practices and management practices are then defined as the proportion of these practices that are used by firms.

Figure 2 shows the estimated intention-to-treat effects of being assigned to consulting on these two indices of management and export practices, as well as on the five sub-components of the overall management practices index. We did not elicit priors on these outcomes, so only show frequentist point estimates and confidence intervals from estimating equation 4.1.<sup>11</sup> The program had no significant impact on our measure of export-specific management practices. On average, control group firms were using 59 percent of the export practices, and the estimated change of -4 percentage points is small and not statistically significant. Appendix Figure C1 also shows no significant impact on any of the 15 individual practices that make up this overall index.

In contrast, there was a significant improvement in general management practices. The control group were using 64 percent of these practices, and there is a significant

> 10Although this was a compulsory area, because it started several months after operational productivity and labor productivity, 4 firms started activities but dropped out before they began their commercial strategy.

> 11We control for a baseline measure of export practices or general management practices in these regressions

20
It is noted that, through differencing, the individual specific characteristics _ηi_ and the other timeinvariant explanatory variables (e.g., gender, ethnicity) are eliminated. Since education levels do not change much for adults and everybody is two years older in 2004, human capital variables such as education and potential experience are not included.<sup>37</sup> **_Location_** _i_ dummies are also discarded since the households who moved out between 2002 and 2004 were not usually interviewed in 2004. If the individual did not change the ownership category of his or her employment, the ownership term also drops out. Thus, the coefficients **_α_** are identified only from those who “switched” between different ownership categories.

# **4.2. Data and Summary Statistics**

Out of the individuals whose ages range 15-59 in the VHLSS 2004, 10,526 persons are matched as having appeared in the VHLSS in both 2002 and 2004.<sup>38</sup> Table 7 demonstrates how these individuals changed their employment/ownership status, showing the numbers of individuals for each cell by the status in 2002 (by row) and 2004 (by column). Of 10,526 panel individuals, about three quarters stayed in the same employment/ownership status, and the remaining quarter either changed employment status (unemployed<sup>39</sup> /employed) or the ownership category in which they work as the first job. Out of 109 individuals who reported working for foreign firms in 2004 (column 1 in Table 7), 36 individuals also worked for the foreign sector in 2002. Among 73 individuals who newly entered into foreign sector employment in 2004, 17 persons were students

> 37 The differential impacts of personal characteristics on wage growth depending on ownership may be estimated through the interaction terms between ownership and personal characteristics (Lemieux _et al_ ., 2009). However, since the coefficients of interaction terms between ownership and education turn out to be individually and jointly insignificant, these terms are not included in the regressions either.

> 38 Since there exist some errors in the panel identifiers in the original dataset, I used the revised identifier codes provided by McCaig (2009) to match the individuals between 2002 and 2004. Then, I eliminated some observations whose information on gender, age and education are inconsistent. In order to mitigate further the measurement error, I dropped one percent of individuals who experienced the largest wage changes. However, the regression results are not sensitive to these adjustments.

> 39 “Unemployed” includes those who responded in the VHLSS 2004 as “do housework”, “don’t work since being too old/retired”, “disabled”, “unable to find a job” and “others”.

21
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actual property rights protection has improved very little.<sup>6</sup> Consistent with this ranking, we provide evidence that how laws are _enforced_ plays a more important role than _de jure_ laws. Our paper is thus related to the literature of the role of _de facto_ property rights protection on firms’ investment, R&D, and corporate risk management (Johnson, McMillan and Woodruff 2002, Cull and Xu 2005, Lin, Lin and Song 2010, Lin, Lin and Zou 2012). We differ by focusing on the effect of _de jure_ land property rights and showing how the effects of _de jure_ property rights depend on a firm’s political connection, and how the property rights effects accumulate over time, very much like “democracy capital” (Persson and Tabellini 2009).

Our paper also contributes to the microeconomic literature on the effects of property rights. This literature has focused on the effects of _rural_ land titling on investment and access to credit in rural areas (Besley 1995; Alston et al. 1996; Banerjee et al. 2002; Jacoby et al. 2002; Field 2005; Deininger and Jin 2006; Hornbeck 2010; Yi et al. 2014), or on transaction costs of land (Libecap and Lueck 2011). The much more limited literature on _urban_ land titling focuses on the effects on household outcomes such as asset value (Jimenez 1984, Friedman et al. 1988; Lanjouw and Levy 2002), responses in labor supply (Field 2007), changes in beliefs toward being pro-market and pro-individual-effort (De Tella et al. 2007), and increases in physical and human investment (Galiani and Schargrodsky 2010). We differ in offering evidence on the effects on urban _firms_ . Indeed, industrial and commercial lands are of great importance as it provides greater value added to the land as reflected in the value of industrial/commercial land compared with agricultural land. The minimum price of industrial land per mu of 0.5 million RMB (i.e., 1.07 million USD per hectare) is 25-fold of the price of agricultural land of 20,000 RMB (i.e., 42,000 USD per hectare). Moreover, since non-agriculture accounts for an everincreasing share of the economy--the average scale of output from the non-agricultural sector of OECD countries is around 98% of their total GDP—it becomes increasingly important to

> 6 See the data from http://internationalpropertyrightsindex.org/country?c=CHINA

9
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