rafmacalaba/datause-encoder-data huggingface dataset:
Appendix Tables 1a-1c report three portions of our basic framework to show differences and similarities, and we highlight the key lessons here. First, unlike the organized sector, our difference-in-difference estimations find that non-nodal districts close to the GQ system behave similarly to those located farther away with respect to aggregate levels of unorganized manufacturing. This comparability of districts 0-10 km from the GQ network with those that are 10-50 km apart holds irrespective of the whether the highway development close to the district was an upgrade or a new construction (although there are traces of a stronger response for new construction). Likewise, unreported estimations do not find differences in unorganized activity depending upon the completion date of the GQ upgrade (e.g., considering areas completed prior to 2002). There is some evidence that GQ upgrades most impact the unorganized sector activity in industries that have low labor intensity, but these sorting patterns are again quite diminished compared to what is observed for the organized sector. Thus, it appears that the highway improvements had limited aggregate effects for the unorganized sector, especially in comparison to the organized sector.<sup>5</sup> These results are reasonable given the structure of unorganized manufacturing in India. By definition, unorganized enterprises are small establishments. Most employees in these enterprises are not full time, and many of these establishments are household based. Given the informality of this sector, it is less likely that such establishments would depend on materials and products and connectivity to the market through national highways. The small-scale nature of these establishments makes it more likely that they target local product and labor markets. Thus, it is likely that they are more dependent on state or district roads vis-à-vis national highways.<sup>6</sup> 5 We observe more consistent evidence for increased entry of young plants in the unorganized sector in nodal cities. As described in the prior section, these results are hard to interpret given the degree to which the GQ upgrades were chosen to support these nodal cities.

6 NSS data identify the gender of establishment owners, which is not possible in ASI surveys. We use this information to investigate whether GQ upgrades had any differential effect on economic outcomes of female- versus male-owned plants. We assess economic outcomes separately by the gender of owners and through ratios of economic outcomes in female- versus male-owned plants. Both approaches find no change in the gender balance of establishment owners due to proximity to highways in the unorganized sector.

18
outsourcing. For this reason, publicly owned utilities—which outsource at lower levels than their private peers—were chosen as more appropriate comparators for utilities in SSA.

- Automation of network operations (such as smart meters and prepayment meters) and works in general is much less advanced in SSA than Latin America, with many processes still manually carried out, requiring larger complements of staff.

- Data limitations

- Plant-level data are not always available within utility annual reports, the primary source of data used for estimating capex in the main section of this paper. For the purposes of the overstaffing analysis, an industry database was used for plant-level data which may introduce inconsistencies between installed capacity data used in this paper.

- Actual capacity factors of generation plants are unknown. The approach for efficient levels of staff in generation assumes conservatively that all plants are running at full capacity.

- Staff numbers reported by utilities may not be consistent. Reported staff numbers are assumed to be core full-time equivalent staff. Where a breakdown of staff is not available, there could be part-time employees included or non-core utility staff such as secondees. Most utilities also report a breakdown of temporary staff and casual workers. Where these are not reported separately, they may not be included in the reported total, thereby underreporting full-time equivalent staff.

- Customer data reported by utilities may not be accurate, particularly for utilities with poor customer management systems.

# Generation assumptions used in the overstaffing analysis

Efficient levels of staffing for generation were estimated based on the type and size of each generation plant. The assumptions applied are outlined in Table A1.6 together with additional assumptions as follows:

- **Outsourcing maintenance:** Maintenance is outsourced, except in South Africa where it is known that maintenance is not outsourced. The study reduces maintenance staff assumed in Table A1.6 by 70 percent.

- **Small plants:** There are 5 staff members for O&M of any plants smaller than 2 MW.

- **Limit on operational staff** : The number of operational staff members is limited to 30 per plant for any plant with more than four units, except coal-fired generation which has no limit.

- **Technology specific assumptions:**

- **Gas turbines versus motors:** If no turbine data are available, assume gas plants are using motorized units unless there is a turbine listed by a known manufacturer of motors, such as Wärtsilä, MAN, Hyundai, Sulzer, Catepillar, Jenbach, and Cummins.

- **Wind and solar** : Assume a flat rate of five O&M staff members for plants smaller than 2 MW and 20 O&M staff members for plants larger than 2MW.

- **Nuclear** : Assume a flat rate of 600 staff members for South Africa’s nuclear plant.

78
_The Creative Wealth of Nations_

# **Appendix A || Cultural Industries: Some Definitions**

## **United Nations Education Science and Culture Organization (UNESCO)**

The cultural and creative industries are those that combine the creation, production and commercialization of creative contents that are intangible and of a cultural nature. These contents are usually protected by Copyright and can take the form of a good or a service. Besides all artistic and cultural production, they include architecture and advertising.

## **United Nations Conference on Trade and Development (UNCTAD)**

The creative industries are at the core of the creative economy, and are defined as cycles of production of goods and services that use creativity and intellectual capital as their main input. They are classified by their role as heritage, art, media and functional creations.

## **World Intellectual Property Organization (WIPO)**

The Copyright-based industries are those that are dedicated, interdependent, or that are directly or indirectly related with the creation, production, representation, exhibition, communication, distribution or retail of Copyright <u>protected material.</u>

## **Department of Culture, Media and Sports of the United Kingdom (DCMS)**

The creative industries are those activities based on creativity, individual talent and skill, and that have the potential to create jobs and wealth through the generation and exploitation of intellectual property.

## **Economic Commission for Latin America and the Caribbean (ECLAC)**

The content industries are: publishing, film, TV, radio, phonographic, mobile contents, independent audiovisual production, web contents, electronic games, and content produced for digital convergence <u>(cross-media).</u> Source: The Orange Economy | Inter-American Development Bank

<mark>68</mark>
> <sup>30</sup>

constituencies. If it were the case that social fragmentation increased political incentives to utilize MPLADS and at the same time reduced the likelihood that a constituency was a party stronghold, its omission would bias the political stronghold coefficient in a negative direction, potentially accounting for the results we report. However, we test the effects of social fragmentation directly by including a variable received from Banerjee and Somanathan (2008), which measures caste and religion-based fragmentation using population data from the Census of 1991. Column (2) of Table 5 reports these results. As they predict, social fragmentation has a negative effect on MPLADS spending, though it is not precisely estimated. More importantly, the coefficient on _PartyStronghold_ remains significant and large, though with slightly reduced magnitudes.

# **Electoral volatility**

Where voter behavior is subject to greater shocks or for some other reason is expected to have a larger random component, any given effort by politicians to satisfy constituent interests has a lower payoff. Political incentives to satisfy constituent interests, for example through full utilization of MPLADS, are therefore reduced. Greater volatility is also likely to be negatively correlated with being a party stronghold, so it may be that the estimated negative correlation between party strongholds and MPLADS spending is driven by the exclusion of electoral volatility from our previous specifications. To address this possibility, the specification in column 3 of Table 5 includes a variable measuring volatility of elections in a constituency. The measure comes from Chhibber and Nooruddin (2008): 1 _n Volatility d_    _i_ , _t_   _i_ , _t_  1 where volatility in constituency _d_ between two consecutive 2 _i_  1 elections at times _t_ and _t-1_ is measured as the sum of change in vote shares of _n_ political
# **Introduction**

Economists explain the closure of firms during recessions with Schumpeter’s (1934) creative destruction theory, where during downturns small and less efficient firms are the ones to exit the market. In times of extreme economic distress, however, firms in every country are reeling from the inability to do business as usual. To make things worse, many sectors see collapsed demand and economic uncertainty stretching months, if not years. In the current pandemic, governments rightly focus on dealing with the health aspects first, and only then on the recovery of the economy once the immediate danger of the pandemic is over.

In the meantime, businesses are rapidly running out of cash. In the United States, half of small firms – those with fewer than 500 employees – have cash reserves for less than a month, and another quarter of businesses may run out of cash in two months. For service industries, the period to illiquidity is even shorter. Restaurants, for example, have less than a month of cash in hand (Didier et al., 2020).

This breathing period is extended with government programs already in place to support worker retention through subsidizing jobs, freezing interest payments on loans, and extending new bank credit. This extension differs across industries – it helps labor-intensive sectors more, and firms with established lines of credit benefit more as well. Still, other payments – like rent and cost of materials – are weighing on businesses. Exporters are unable to ship goods due to disrupted transport links. Even when transport is possible, new trade restrictions may apply or demand has simply collapsed.

New analysis on 12 high-income and middle-income economies across Africa, Central Asia, Europe, Latin America, and the Middle East reflects similar patterns. The analysis uses a sample of 6,345 firms from the World Bank’s Enterprises Surveys in Colombia, Greece, Italy, Jordan, Kazakhstan, Kenya, Morocco, Peru, Portugal, the Russian Federation, Turkey, and Ukraine. All surveys were conducted within the last three years and have wide scope, covering a representative sample of at least 600 firms in each country.<sup>2</sup> The share of firms responding to the financial questions, however, varies.

> 2 We use this sample from an overall sample of 75 economies with recent data, as it is broadly representative of Southern Europe and upper middle-income emerging markets. We intentionally shy away from lower middle-income

2
Figure 1. Change in democracy and income inequality between 1988-89 and 1997 <!-- Start of picture text --> Turkmen*<br>-20-<br>O ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ - 200 .. ~~ --------- --------- ------- -------------------<br>0 ~~~~~~~~~ zkh<br>-------- -bek-----------10-0 Croa4a<br>Belarus<br>I -2 2 4 6 * 10 1 2<br>Democracy change<br><!-- End of picture text --> Note. Transition countries only. Change in democracy is calculated as the difference between the Freedom House (inverted) index of civil and political freedom in 1997 and 1989. The maximum value of the index is 14 (7 for civil freedom; 7 for political freedom); the minimum value is 2 (I for erach). The maximum gain would be 12. Change in the Gini index is calculated as the difference between the Ginis in 1996-98 (when available, otherwise 1993-95) and Gini in 1988-89.

Sources: for democracy, see Freedom House; for Gini, Milanovic (1998) and DEC data base.

However, Figure 1 shows also an interesting feature-namely that increases in inequality were less in countries that became more democratic. The regression line is negatively sloped if all countries are included,"<sup>1</sup> and even more so if several outliers (three Central Asian republics, Belarus, and Croatia) are left out. In other words, while democracy and inequality increased practically in all transition economies, higher increases in democracy were associated with lower increases in inequality. Moreover, if we simply plot _levels_ of cumulative democracy achieved between 1989 and 1997 against post-transition (1995-97) _levels_ of inequality, the relationship is, as the 23
Table 6 : Gravity regressions - All countries <u>(dep</u> = log of bilateral migration stocks)

||(1)|(2)|
|---|---|---|
|Constant|14.123|15.399|
||(71.06)***|(78.39)***|
|Log(distance)|-1.067<br>(70.57)***|-1.070<br>(71.46)***|
|Colonial Link|2.061<br>(14.00)***|2.086<br>(14.16)***|
|Common colonizer|0.328<br>(9.49)***|0.373<br>(10.98)***|
|Common o¢ cial language|0.499<br>(17.15)***||
|Linguistic Proximity||0.512<br>(16.75)***|
|Origin dummies|Yes|Yes|
|Destination dummies|Yes|Yes|
|Observations|39800|39800|
|R-squared|0.78|0.78|

Robust t statistics in parentheses * signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% In the second step, we use ln(nb<sup>d</sup> ) instead of ln(n<sup>~~d~~</sup> ) to reestimate model (19). We start from the parcimonious speci...cations. Columns (1) and (2) of Table 7 report the results for the whole sample of countries while columns (3) and (4) concern developing countries only. Columns (1) and (3) report OLS restimates while columns (2) and (4) use instrumental variable estimation accounting for the possible endogeneity of the global emigration rate. It should therefore be obvious that those IV estimates account simultaneously for two sourves of reverse causality : one due to the endogeneity of bilateral migration rates used to build the fertility variable at destination and one due to the endogeneity of the global emigration rate of country i with respect to its fertility rate. Results of the OLS estimations in Table 7 are fairly similar to results of Tables 2 and 4. The elasticity of the norm amounts to about 0.35 and 0.43, well in line with the one estimated before. The IV results exhibit a decrease in the signi...cance of the ln(nb<sup>d</sup> ) variable. This decrease is due to an increase in the standard error rather than the size of the estimated coe¢ cient and is related to the instrumenting procedure of the global migration rate. A similar results had also been observed before (see Tables 3 and 5). To sum up, we ...nd that the impact of fertilty norms brought by migrants still survives concerns about the endogeneity of migration weights.

29
**Table 6: Comparing short and long questionnaire. Mlogit**

|Dependent variable: Type of discrepancy between ans|wers provided in|the two questio|nnaires||||||
|---|---|---|---|---|---|---|---|---|
||(1a)|(1b)|(2a)|(2b)|(3a)|(3b)|(4a)|(4b)|
||yes_no|no_yes|yes_no|no_yes|yes_no<br>‐|no_yes|yes_no|no_yes|
|Firm is potential innovator Y:1 N:0|‐‐1.045***|0.615***|‐0.956***|0.702***|0.993***|0.685*|‐1.034***|0.260|
||(0.219)|(0.203)|(0.235)|(0.212)|(0.361)|(0.398)|(0.380)|(0.432)|
|Manager's years of experience|‐0.007|0.010|||‐0.026*|0.016|||
||(0.009)|(0.009)|||(0.015)|(0.019)|||
|Respondent experience with firm|||‐0.000|0.036***|||‐0.020|0.047*|
||||(0.013)|(0.013)|||(0.021)|(0.025)|
|Respondent is woman Y:1 N:0|0.237|0.459|0.264|0.541*|0.715**|0.181|0.517|0.115|
||(0.237)|(0.310)|(0.254)|(0.325)|(0.319)|(0.454)|(0.347)|(0.462)|
|Use of show cards Y:1 N:0|||||0.053|‐0.103|‐0.098|‐0.257|
||||||(0.239)|(0.372)|(0.260)|(0.398)|
|Time interval btw svy (days)|0.003***|0.003***|0.004***|0.002**|0.004***|0.003*|0.005***|0.002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.002)|(0.001)|(0.002)|
|Same respondent Y:1 N:0|‐‐0.598***|‐0.003|‐0.599***|0.122|‐0.342|0.725*|‐0.467|0.660|
||(0.169)|(0.204)|(0.181)|(0.216)|(0.307)|(0.410)|(0.319)|(0.462)|
|Nr of interviews per supervisor|0.032***|0.019|0.031***|0.015|0.021|‐0.039|0.017|‐0.024|
||(0.008)|(0.012)|(0.009)|(0.013)|(0.025)|(0.041)|(0.022)|(0.042)|
|Enumerator's perception of truthful interview Y:1|||||||||
|N:0|0.084|‐0.240|0.048|‐0.258|0.150|‐0.080|0.272|0.164|
||(0.159)|(0.200)|(0.167)|(0.226)|(0.251)|(0.403)|(0.260)|(0.464)|
|Length ES interview (minutes)|‐0.002|‐0.001|‐0.004**|‐0.001|‐0.003|‐0.004|‐0.006*|‐0.006|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.003)|(0.004)|(0.003)|(0.004)|
|Average number of meetings with tax officials|0.022|0.013|0.021|0.023|0.110**|0.026|0.091|0.061|
||(0.019)|(0.022)|(0.018)|(0.021)|(0.056)|(0.089)|(0.060)|(0.085)|
|Firm expected to pay informal payment Y:1 N:0|‐0.117|‐0.543**|‐0.138|‐0.480**|‐0.421|‐0.786*|‐0.503|‐0.763*|
||(0.181)|(0.223)|(0.196)|(0.239)|(0.301)|(0.440)|(0.310)|(0.456)|
|Employment growth (%)|‐0.001|0.010*|‐0.001|0.011*|‐0.004|‐0.005|0.001|‐0.003|
||(0.004)|(0.005)|(0.004)|(0.006)|(0.006)|(0.008)|(0.006)|(0.009)|
|Size (log)|‐0.072|‐0.476***|‐0.028|‐0.529***|0.002|‐0.552**|‐0.034|‐0.450*|
||(0.090)|(0.085)|(0.097)|(0.095)|(0.118)|(0.218)|(0.123)|(0.235)|
|Age (log)|0.079|‐0.003|‐0.020|‐0.153|0.110|0.179|0.084|‐0.030|
||(0.130)|(0.162)|(0.149)|(0.172)|(0.208)|(0.300)|(0.230)|(0.354)|

30
WPS3982

# **Quality of Schooling and Quality of Schools for Indigenous Students in Guatemala, Mexico and Peru**<sup>∗</sup>

Martha Hernandez-Zavala<sup>1</sup> Harry Anthony Patrinos<sup>1</sup> Chris Sakellariou<sup>2</sup> Joseph Shapiro<sup>3</sup>

## **Abstract**

A substantial gap in test scores exists between indigenous and non-indigenous students in Latin America. Using test score data for 3<sup>rd</sup> and 4<sup>th</sup> year primary school pupils in Guatemala and Peru and 5<sup>th</sup> grade pupils in Mexico, we assess the magnitude of the indigenous/non-indigenous test score gap and identify the main family and school inputs contributing to the gap. A decomposition of the gap into its constituent components suggests that the proportion that is explained by family and school characteristics is between 41 and 75 percent of the overall testscore gap. Furthermore, family variables contribute more than school variables to the overall explained component.

JEL codes: J15, I20 Keywords: Indigenous, education, discrimination, test score

## World Bank Policy Research Working Paper 3982, August 2006

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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

> ∗ Address all correspondence to: Harry Patrinos, World Bank, 1818 H Street NW, Washington DC 20433, email: <u>hpatrinos@worldbank.org.</u>

> 1 World Bank; 2 Nanyang Technological University; 3 Oxford University
-52-

TABLE 3.14 Purchasing Power Parities by Major Branch **of Manufacturin** Brazil/USA (Cruzeiros to the USS) and Mexico/USA (Pesos to the USS). 1975

||PPP:|Cruzeiro|s/US<br>$|PPP:|Pesos/US|$|
|---|---|---|---|---|---|---|
||US<br>Quantity<br>Weights|Brazil<br>Quantity<br>Weights|Geometric<br>Average<br>|<br>US<br>Quantity<br>Weights|Mexico<br>Quantity<br>Weights|Geometric<br>Average<br>|
|Food Products|5.69|2.69<br>|3.91|9.65|5.29|7.14|
|Beverage<br>Products|8.10<br>|7.73<br>|7.92<br>|17.66<br>|17.66<br>|17.66<br>|
|Tobacco Products|4.94|4.38|4.65|9.35|7.64|8.45|
|Textiles<br>and Wearing Apparel|13.33|9.81<br>|11.43|18.69|17.45|18.06|
|Footwear<br>and Leather<br>Products|5.62|4.77|5.18|12.85|11.32|12.06|
|Wood and Paper Products|10.53|8.24|9.32<br>|22.77|19.83|21.25|
|Chemical<br>Products|11.92|9.92|10.87|11.14|10.0|9<br>10.60|
|Rubber and Plastic<br>Products|13.17|11.64|12.38|32.68|29.28|30.94|
|Stone, Clay and Glass Product|s7.85<br>|4.91|6.21|11.35|12.38|11.85|
|Metal products|8.95|7.27|8.07|14.09|11.25|12.59|
|Electrical<br>Machinery|7.72|7.64|7.68|8.92|11.39|10.07|
|Machinery and Transport|||||||
|<br>Equipment|6.49|<br>6.32|6.40|13.74|13.55|13.64|
|Other|8.79|6.26|7.42|14.92|10.94|12.77|
|Total|8.79|6.26|7.42|14.92|10.94|12.77|

# Source and note:

PPPs from table 3.13. The PPP for food products is the weighted average for grain mill and sugar and sugar products. The PPP for chemical products is a weighted average for soap and detergents, paints, agricultural fertilizers, petroleum refining and products. The PPP for stone, clay and glass products is a weighted average for cement and bricks. In all cases value added figures (US census concept) were used as weights. The cruzeiro/US$ PPPs and peso/US$ PPPs for "Other Manufacturing" and "Total Manufacturing" are derived from the sum of the values for all other branches from tables 3.15 and 3.16 respectively.
# _3.1 Experimental design_

In order to evaluate the impacts of Jeevika, the rollout of the project was randomized across 180 panchayats, randomly selected from within 16 blocks in seven districts where Jeevika was planning to scale up. In each of the study panchayats, one or two villages were then randomly selected for data collection.

Since the intervention was targeted to poorer households, within each of the study villages, hamlets in which the majority of the population belonged to a scheduled caste or scheduled tribe were identified. Households were then randomly selected from these hamlets to be interviewed. A baseline survey was administered during July to October of 2011 to 8,988 households across 333 villages in 179 panchayats.<sup>6</sup> Following the baseline survey, panchayats were randomly assigned to an early rollout group or a late rollout group, after stratifying the sample on administrative block and the panchayat-level mean of outstanding high cost (monthly interest rate of 4% or higher) debt held by households at baseline. The project began in the early rollout panchayats between January and April 2012, and the follow-up survey was completed between July and September 2014. Implementation in late rollout areas began after the 2014 round of data collection. Baseline and follow-up surveys included detailed questions on debt, asset holdings, consumption expenditures, livelihood activities, and women’s mobility, role in household decisions, and aspirations. In addition, in each village, a focus group discussion was conducted, through which data were collected on village-level attributes such as local sources of credit, interest rates from each source, local wage rates, and the presence of or distance to markets and other institutions and amenities.

# _3.2 Analysis_

The SHG intervention is expected to lead to a series of potential impacts, which we classify as direct, indirect and downstream. One or more direct impacts (increased SHG membership; increased utilization of credit though SHGs) is a necessary but insufficient condition for the intervention to lead to indirect effects on informal credit markets. Changes in downstream outcomes (wealth, consumption level, women’s empowerment) may follow from either direct or indirect impacts.

6 One of the selected Panchayats could not be surveyed due to political instability.