rafmacalaba/data-use-mentions-extended huggingface dataset:
Delhi, have the highest electrification rate (almost 100 percent in 2012). Among the regions, South India has the highest electrification rate—about 87 percent in 2005 and 96 percent in 2012. East India, which includes the states of Bihar, Jharkhand, Orissa, and West Bengal, has the lowest rate among the regions. But electrification increased quite a bit in these states during the seven years from 2005 to 2012, rising from a rate of 37 percent to almost 65 percent. Overall in rural India, the electrification rate increased from 62 percent in 2005 to 77 percent in 2012, implying annual growth of about 2 percentage points.

Data on the duration of power outages (in hours per day) for the two survey years show that outages worsened during the sample period: While 9 percent of connected households reported no outages in 2005, that share dropped to 7 percent in 2012 (table 3). And while about 35 percent of the households reported power outages of at least 13 hours a day in 2005, almost 45 percent experienced outages of similar duration in 2012. The average duration of outages was 9.3 hours a day in 2005 and 10.7 hours a day in 2012. The hours of electricity availability per day vary substantially across states (figure 2). While connected households in union territories such as Daman and Diu have more than 20 hours of electricity per day on average, those in such states as Arunachal Pradesh and Assam have less than 6 hours per day on average.

Summary statistics for outcome variables show that grid‐connected households consume less kerosene and spend less time collecting fuel than off‐grid households do (table 4). They also have higher incomes and expenditures and a lower poverty rate. In households with an electricity connection both boys and girls spend more time studying and complete higher grades. Labor force participation is lower among adults in grid‐connected households than among those in off‐grid households—for both men and women. But when employment is measured
# **3 Quantitative Analysis**

The framework in section 2 establishes a relationship between the sectoral distribution of the workforce and the five variables outlined before. In this section, I will use the data to perform a set of counterfactuals to highlight issues related to the industrial share of employment in the Latin American economies.

## **3.1 Cross-country variations in employment shares**

The first exercise pertains to the stark pattern of premature deindustrialization observed in Latin America. The idea is to stack the Latin American countries against the economies that have attained a high industrial share of employment and gauge the relative importance of the aforementioned variables in explaining the gap across the two. Given that the sectoral share of employment varies systematically with development, it is desirable if the comparator countries have similar levels of economic development. My selection of comparator countries follows these two principles subject to the availability of data.

The input-output data from the OECD database (OECD, 2017) are available for the period 1995–2010/11 which defines the universe against which to compare the Latin American economies. Of more than 40 countries that feature in the GGDC 10-Sector database (Timmer _et al._ , 2015), the peak industrial share of employment exceeded 30 percent in 8 countries during this period. Mauritius is the only country among these 8 for which the input-output data are not available. I also include China as a comparator country as its industrial share of employment was just under 30 percent in 2011. China also merits inclusion as it is often argued that the development process in China has been different from many countries. It will be useful to see if China serves as an outlier benchmark relative to others in the present context.

Table 1: Comparator Countries

|Country|CHN|ESP|ITA|JPN|KOR|MYS|SGP|TWN|
|---|---|---|---|---|---|---|---|-
. Tables A1, A2 and A3 and Figure A1 in Annex A provide an overview of the cumulative firm distribution of the four innovation variables and gives summary statistics by country and sector.

Note that our dependent variables are formulated for which responses are only allowed in a binary way. The Enterprise Survey database reports these answers with a simple Yes or No. We have transformed the variables in a dummy so that effectively it becomes a non‐linear estimation in the sense that INNO���� ∈�0,1� . We are therefore compelled to perform a Probit model. However, we first perform an LPM model with fixed effects before moving into a Probit regression, as the former provides us additional information about the direction in which the Probit results are most probably going to when regressing.<sup>8</sup> Moreover, for our three country cases only LPM regressions can be performed and thus for reasons of consistency we report both types of result. We estimate our Probit model with a conditional (fixed‐effects) logistic regression, because of the inclusion of our various dimensions of fixed effects.

As described above, our DL variable is defined at country‐sector‐year level following equation (1) and therefore varies over all three dimensions. Although we have data for our data policy index up until 2019, we can only include up to 2018 as the Enterprise Survey data do not go any further.

provided above and because it effectively tests for two different kinds of variations. Moreover, the country‐ specific data source reports different types of innovation variables which are used as our dependent variable. 8 There are however problems with the LPM. One of the main issues is that the LPM does not estimate the structural parameters of a non‐linear model (Horace and Oaxaca, 2006). If the Conditional Expectation Function (CEF) is linear (which means that conditional mean of a random variable is its expected value), then even an LPM regression gives the CEF. Instead
(9) also demonstrate that there was little change in school attendance following a limited ceasefire signed in late September that led to significantly less violence in the governorate between October and January, which further corroborate estimates in columns (4)-(6).

Although all results in table 3 are consistent with the summary statistics of school attendance presented in figure 4 and consistent with the lack of a relationship between the severity of violence and school attendance presented in table 2, the lack of a change in school attendance following any of these sharp conflict events is surprising. Both the secessionist capture and the capture of Al Jawf led to significant increases in violence and a change in governance, each of which could significantly impact attendance. Furthermore, news and humanitarian sources emphasized the negative impact each event had on the population (e.g., Al Jazeera 2020; OCHA 2020; etc.). Alternatively, the partial ceasefire led to decline in violence was large enough that the total amount of violence in the country followed a nearly identical pattern, and some of the most violent regions in the country were much calmer for the first half of the 2019-2020 school year (figure 2). However, despite the significant changes in violence when the partial ceasefire went into effect and when it broke down, there were no changes in school attendance.

Additionally, these results demonstrate that the impact of sudden changes in violence is

> 34In results not shown, the lack of a change is identical in all four governorates affected by the ceasefire. 35The sample for estimating the impact of the capture of Al Jawf includes all surveys completed during the school year between September 2019 and February 2020.

16
Log(Energy Intensity)|0.023|-0.003|-0.023|0.036|0.015|-0.005|0.165<sup>_∗∗_</sup>|0.161<sup>_∗∗_</sup>|0.126<sup>_∗∗_</sup>|
||(0.053)|(0.034)|(0.054)|(0.049)|(0.034)|(0.053)|(0.058)|(0.044)|(0.058)|

Notes: Each row represents a different regression of a given firm performance variable on the model specified in equation (1). Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. We evaluate the estimated model at average firm size and GDP per capita within each group of firms listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs and LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.<sup>_∗_</sup> (p<0.10),<sup>_∗∗_</sup> (p<0.05)
The regressor sets are nearly identical to those in specification (2), though they exclude a few of that specification‘s independent variables (R&D intensity, foreign JV, new licensing agreement).<sup>21</sup> Finally, the fourth equation is aimed at explaining variations among the rates of the enterprises‘ employment growth, with OLS estimation: In this equation is comprised of the vector of the innovation variables studied in equations (1), (2) and (3) above (ln[TFP], introduced new product, introduced new process), and characterizes the composition of the firm‘s workforce along the dimensions of skills (e.g. percentage of employees who are low-skilled) and gender (share of workforce comprised of females). Other vectors of independent variables are similarly defined as in equation (3).

We estimate these specifications using establishment-level data from all available countries, as well as separately using the subset of establishments located in non-OECD developing countries ( ̳enhanced engagement‘ and other developing countries), for all firms and for various subsamples of firms sorted by their age, size and innovating status.

# **4. Empirical findings**

## **_4.1 Innovation is an important driver of enterprise employment growth_**

Enterprise innovations, which are here reflected by the level of TFP and by self-reports of process and product innovation, are very strong positive correlates of employment growth among firms across our entire sample. The first column of table 2 displays the results of estimating the

> industry regression is the natural logarithm of its TFP – higher values imply lower average and marginal costs of producing value.

> 21 We exclude these regressors to keep the TFP specification parsimonious, although including them in the specification does not change our results.

16
expected years of schooling while test scores remain constant or vice versa, so it is useful to break down these overall elasticities to illuminate which component is most responsible for gains in LAYS due to increased spending. The elasticity of spending on expected years of schooling was higher (0.04) than the elasticity of increasing resources on improvements in harmonized learning outcomes (0.02). This shows that countries have found it somewhat easier to transform increases in resources into higher educational attainment rates than into better learning outcomes.

**Figure 12: Outcomes Increased by a Greater Amount than Spending in Only 18 Percent of Cases** _a. Country episodes with spending decreases b. Country episodes with spending increases_

<!-- Start of picture text --> 36<br>17<br>7<br>2 5<br>0<br>LAYS improved LAYS LAYS<br>deteriorated less deteriorated LAYS LAYS LAYS<br>than spending more than deteriorated improved less improved more<br>spending than spending than spending<br><!-- End of picture text --> _Source:_ World Bank calculations based on HCI, UIS, and IMF data.

# **_How Do a Country’s Initial Conditions Affect Its Ability to Transform Resources into Outcomes?_**

While intuition suggests that a country’s initial conditions determine the most likely paths for education outcomes, their relationship is not straightforward. First, it depends on which outcome is in question. Transforming resources into wider access has proven to be easier to achieve than improving learning outcomes. Second, starting from a low spending point may result in larger or smaller changes in outcomes depending on how access and learning are determined. If inputs have decreasing returns (that is, the provision of additional inputs has a larger effect on outcomes when existing inputs are low), we would expect that low-spending countries would have larger expected returns. If, on the other hand, it takes a combination of various good-quality inputs
# **Table A.2. Description and source of geographical controls**

|**Geovariables**|**Description or source**|
|---|---|
|Annual mean temperature (°C * 10)|Average annual temperature calculated from<br>monthly climatology, multiplied by 10 (°C)<br>(University of California, Berkeley,<br>WorldClim Bioclimatic Variables)|
|Annual precipitation (mm)|Total annual precipitation, from monthly<br>climatology (mm) (University of California,<br>Berkeley,<br>WorldClim Bioclimatic Variables)|
|Slope (percent)|Derived from unprojected 90m SRTM using<br>DEM Surface Tools|
|Elevation (m)|Elevation (m), aggregated to 1 km block|
|Potential wetness index|Downloaded from AfSIS website. Derived<br>from modified 90 m SRTM. Local upslope<br>contributing area and slope are combined to<br>determine the potential wetness index: WI =<br>ln (A s / tan(b) ) where_A s_is flow<br>accumulation or effective drainage area and<br>_b_is slope gradient.|
|Terrain roughness|Derived from 90 m SRTM using 15<br>Meybeck relief classes and 5x5 pixel<br>neighborhood (LSMS-ISA)|
|Average 12-month total rainfall (mm) for<br>Jan.–Dec.|Average 12-month total rainfall (mm) for<br>Jan.–Dec. (National Oceanic and<br>AtmosphericAdministration)|

_Note:_ Summary statistics of these variables are reported in table A.1.

iv
five years worth-especially if monthly data is used.<sup>4</sup> Since many of the projects that will be considered for privatization in less developed countries will be provided either by unquoted companies or international firms, it will not be possible to establish a specific beta value for the project from the available information. The next section is concerned with estimating this value when sufficient information is not available.5

# 2.2 MEASURES OF MARKET **RISK**

Although the CAPM is based around the use of the equity beta as described above, when determining the appropriate allowed rate of return it is important to start from the basic building block of an asset beta since this allows:

- an evaluation of the impact of different gearing levels; and

- a comparison of underlying risk across companies.

While the theory underlying beta calculations is now increasingly well understood among regulators, the practical needs of the estimation of a beta value are not yet that well appreciated among regulatory practitioners. The broad steps are:

1. establish an equity beta value;

2. determine the level of gearing for the company; and 3. establish an asset beta value.

- 4 There is a large academic literature on the subject of how the beta value should be measured. This is surveyed in an annex to Regulatory Structure and Risk and Infrastructure Firms: An International Comparison by Alexander, Mayer and Weeds, Policy Research Working Paper 1698,1996, World Bank.

- 5 For a more general discussion of the techniques described here see Alexander, OXERA, 1995 or Industry Commission (Australia), 1996.
P a g e <u>|</u> **34**

capacity to pay. We suggest that each city establish and constantly monitor the distribution of households’ income in order to ensure its housing policy is responsive to changes in demand.

With the data available we have estimated an income distribution profile for Mumbai and Ahmedabad<sup>9</sup> (Figure 4a and b). Incomes are shown at INR 2,500 intervals from 0 to INR 100,000 per month. Each income profile includes 2 graphs, an histogram showing the number of households in each income interval (on the primary vertical axis on the left) and a cumulative curve showing the percentage of total households below a specific income (expressed on the secondary vertical axis on the right).

The data shown on Figure 4 shows how useful accurate information on city-wide income distribution can be for setting housing policy. The profiles of household income in Mumbai and Ahmedabad are markedly different. In Mumbai is a more prosperous city (median income is around INR 20,000 per month and average income is 40,000 as compared to a median of 15,000 and a mean of 28,000 in Ahmedabad) but Mumbai has a far more substantial slum population.

The shape of the distribution curve may change with time. Some households will shift to the right when their incomes increase because of higher productivity, on the other hand, an influx of migrants from rural areas may increase the number of households in the left part of the graphs. These changes will put pressure on the housing stock and will test the elasticity of housing supply as households’ income changes. This phenomenon can be observed in Mumbai where higher income groups represent a more substantial share of the population.

Once income distributions are available, identifying households that would be eligible for assistance in any likely supply conditions, i.e. those facing a binding demand side constraint is the first step. These households will need direct assistance. We can define
specifically, we test for the significance of adding the industry variable to the 1-digit occupation variable in a two-way multivariate analysis of variance (MANOVA) of averaged importance scores. The joint occupation-industry variable is highly significant indicating that it is informative to account for the industry of work beside a worker’s occupational group in characterizing the work activities carried out.<sup>5</sup> _Data set on exports._ In order to determine what activities in an economy are exported, information on the value added of a countries’ exports is needed. The industry composition of value added in exports is determined following the method outlined in Hummels et al. (2001) and Koopman et al. (2012). This is based not only on the value added in the exporting industry, but also on value added further up the production chain in other domestic industries delivering intermediates. Computation requires the use of national input-output tables that contain information on inter-industry deliveries, as outlined in Appendix A1. National input-output tables are taken from the Multiregional Input–Output table (MRIO) Database of the Asian Development Bank available for the year 2000 and annually for the period from 2007 to 2018. The industry classifications used in these tables were harmonized and mapped to a common set of 35 industries akin to the set of industries defined in the activities data.<sup>6</sup> Value added consists of compensation for workers (labor income) and a gross operating surplus (capital income) that accrues as income to the owners of capital assets. Capital assets cannot be straightforwardly allocated to activities, in contrast to workers. For example, a computer can be utilized in many activities and there is no information on its particular use. Data used for the baseline regressions only includes labor incomes. In in the task content of a particular occupation, suggesting that the task content of occupations is not necessarily constant across countries.

5 O*Net provides a list of 41 work activities with importance indicator scores on a scale of 0-100 for
6 coordinating tax reforms: can lost tariffs be recouped?

The main explanatory variable of interest is trade tax revenue relative to GDP (TTit). If its coefficient β1 is significantly negative, it can be concluded that a fall in trade taxes has been associated with a rise in non-trade tax revenue. In the long −β1 term, the relevant coefficient is (1−βo)<sup>. Time and country-fixed effects are captured</sup> by μt and αi. The control variables ( **X** it) are those that affect either the costliness of raising revenue from non-trade sources or the valuation of public expenditure. If the marginal value of public expenditures foregone with lost trade taxes is high, the urgency to seek alternative sources is greater. The control variabes are:

- GDP per capita: demand for government expenditures increases as average incomes of citizens grow (Wagner’s Law). GDP per capita also proxies for administrative and institutional capacity in the country to collect and manage taxes. (Institutional capacity is proxied better by measures of the quality of governance like the World Governance Indicators (WGI), but their crossnational time-series does not go as far back as the 1980s.)

- Imports: it is the share of total imports relative to GDP. It captures “openness” of the economy as well as the fact that imports are a substantial part of the domestic tax base in poor countries. Baunsgaard & Keen (2010) use for openness a slightly broader measure: the share of exports and imports in GDP, citing Rodrik (1998) who finds this measure of openness to be closely associated with the size of government.

- Natural resources per capita: two measures are introduced as important controls to capture the fact that states that derive a large share of revenues from natural resources do not need to tax their citizens highly (Ross 2001).

- Foreign aid as a share of national income: this could have a perverse effect on the urgency of finding an alternative source of
Note: Poverty is defined as the percent of the population living with less than $2 a day (World Development Indicators database). The annual number of refugees in each country is given by the Center for Systematic Peace (http://www.systemicpeace.org/).

# **4.2. Lessons from Case Studies in Kenya, Tanzania, and Uganda**

Given the limits of cross‐country comparisons, we present below three short case studies on the impact of protracted refugee situations on hosting communities. These case studies were not chosen based on a systematic review but they are sufficiently close to each other to allow for comparative learning. These case studies are also those emerging from a growing literature on the quantitative assessment of the impact of refugees on hosting communities (Mabiso et al. 2014).

# **Case Study #1: The protracted refugee situations in Tanzania**

Tanzania has been known as a refugee‐hosting country for long due to its peaceful history and its location surrounded by conflict‐affected countries (Burundi, Rwanda, Uganda, Mozambique). The first president of Tanzania, Julius Nyerere, welcomed most of refugees as a sign of pan‐African solidarity in the post‐independence periods from many African nations. For example, in 1972, refugees fled from Burundi and were directed towards, what is called now, the “old settlements”. The Tanzanian policy with respect to refugees radically changed in the 1990s when more than one million refugees from Burundi (1993), Rwanda (1994) and the Democratic Republic of Congo (1997‐1998) moved to Western Tanzania, i.e. in the regions of Kagera and Kigoma. As documented by Rutinwa (2002), regulation moved from an “open‐door” policy promoting local integration to a “no more refugees” policy promoting repatriation in the country of origin. In addition to the lack of mobility outside of refugee camps, that shift in policy translated into more restrictions on the integration