rafmacalaba/data-use-mentions-extended huggingface dataset:
productivity growth than would be predicted by global growth convergence trends. This is not broadly the case for African countries in any time period or sector, with within-sector productivity growth across sectors showing no pattern of countries either consistently outperforming or underperforming predicted levels of convergence.

The next section investigates trends in changes in economic structure for African countries in recent decades to understand whether their growth patterns have benefitted from convergent pressures in manufacturing in past decades, or in services in more recent decades, or neither. Appendix B contains further comparisons of African sectoral convergence to other countries for which the data are available.

# **Structural Change in African Economies**

Using the same GGDC data, we adopt the framework of McMillan and Rodrik (2012) to show the pace, intensity, and consistency of structural change in African countries over four decades—dating back to the 1970s. These analyses plot, for a given time period, the sector-level change in aggregate labor share on the horizontal axis with the within-country, relative productivity level of the sector on the vertical axis. These graphics show visually the pattern of structural change for each decade for the countries for which data are available. We are thus able to assess and characterize historical structural change on three dimensions: pace (given by the relative length of the fitted line), intensity (given by the slope of the fitted line), and consistency (comparing pace and intensity across decades).

These analyses identify two broad growth patterns among African countries for which there are data:

# **<u>Pattern 1</u>**

# **<u>Pattern 2</u>**

- Early transition of labor out of agriculture

- Pace of structural change slows in the 1990s/2000s

- Wholesale & retail trade is not the main labor absorbing sector in the 2000s

<u>Countries: Botswana, Nigeria, South Africa,</u> Mauritius, the Arab Republic of Egypt

- Late transition of labor out of agriculture, if at
of spillover benefits generated, may be justified (Collier and Venables 2012) provides the _raison d’etre_ for agricultural investment promotion agencies all over the globe.

In light of the policy relevance of this issue, the marked differences between general FDI and large-scale agricultural investment (Arezki _et al._ 2015), and the fact that in many African countries the large majority of land-related investment originates with domestic rather than foreign investors, empirical evidence to explore the presence and magnitude of such effects would be highly desirable. Yet, partly due to limited data availability, often justified by the sensitive and potentially controversial nature of such investment, such evidence is currently not available. This limits not only governments’ and investors’ ability to make rational decisions and acquire experience, but may also constrain the availability of resources to the sector, as financial intermediaries have no basis to assess and try to insure the risk associated with such ventures.

To show how often widely available survey data can help assess the presence and magnitude of spillovers, we combine data from the smallholder agricultural production survey annually conducted by Ethiopia’s Central Statistical Agency (CSA) in 2003/4-2013/14 with evidence on the evolution of the universe of currently operational large farms over this period from CSA’s large farm survey. GPS coordinates for large farms and smallholder villages allow us to construct, for every village and year, the distance to the next 2
of financial constraints for the same set of firms over time. More importantly, we use the reported severity of financial constraints on a firm‘s operations as our dependent variable, whereas Brown et al. (2011) use measures of access to and use of credit. The reported constraints variables are likely to be forward-looking about the potential effects of the crisis, whereas the access/use variables better summarize the financial situation of firms before the crisis.

Other papers have used firm, bank, and country-level data to study past crises within the ECA region. Using data from large multinational banks from 1991 to 2004, de Haas and van Lelyveld (2010) find that their lending did not slow during systemic crises in a host country, which they attribute to the support received by subsidiaries from their parent banks. Similarly, domestic banks contracted their credit base during crises from 1993 to 2000, whereas greenfield foreign banks did not (de Haas and van Lelyveld, 2006). Furthermore, a study of banks and firms in 13 countries in Eastern Europe from 2000 to 2005 shows that lending relationships for foreign banks tended to be more stable than those of domestic banks in that foreign banks were less likely to drop their clients, even in the aftermath of an acquisition, and that the presence of foreign banks was associated with a lower probability of crisis in the ECA region (Giannetti and Ongena, 2009 a,b). Of course, the financial crisis episodes studied in those papers grew within ECA countries themselves, and thus the recent global crisis, which emanated from the banking sectors of advanced economies, offers an opportunity to test whether foreign affiliates behave differently when their parent banks face difficulties in their home markets.

Another strand of recent literature focuses on capital flows, particularly the cross-border component of bank flows, measured at the country level to assess the effects of the crisis (Cetorelli and Goldberg, 2009, 2011; Milesi-Ferretti and Tille, 2011; Hermann and Mihaljek, 2011). A number of those studies suggest that
,006|

Source: Own calculations based on PARE's database.

* Students in school that received all PARE components simultaneously.

This result deserves more analysis. An intriguing possibility is that high-achieving students in native communities move to rural schools where they are immersed in a Spanish-speaking environment. On the other hand, a multivariate analysis (controlling for "<sup>4</sup> supply" and "demand" variables) of the probability that the student was in school in the 6t grade (given that she had been at school in the 4t grade) indicates that the program had a positive impact on both rural _and_ native schools, see Table 8a. The percentage change 13
WPS3829

## **FORECASTING INVESTMENT NEEDS IN SOUTH AFRICA’S ELECTRICITY AND TELECOMMUNICATIONS SECTORS**

# by

# Željko Bogetić and Johannes W. Fedderke

Abstract: The paper uses a panel-data set for the period 1980-2002 to estimate demand for electricity and telecommunications services and project investment needs in South Africa through 2010 for two growth scenarios. Projections of average annual investment needs in electricity and telecommunications for the current growth scenario (3.6% per annum) are of the order of 0.2% and 0.75% of GDP, respectively. An alternative, accelerated growth scenario (6% per annum) implies approximate doubling of investment needs in these sectors.

# JEL classification: H54, H, R, O, O57

## World Bank Policy Research Working Paper 3829, February 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._ Authors: Bogetić is lead economist at the World Bank, Africa Region, Washington D.C. (E-mail: <u>zbogetic@worldbank.org) and Fedderke is professor of economics at the</u> University of Cape Town, School of Economics (E-mail: jfedderk@commerce.uct.ac.za). Research assistance by Andrew Hill is gratefully acknowledged. The paper builds in large part on the earlier work by Bogetić and Fedderke (2005 a,b).
poverty gap in line with the richer states of the S-SE. This result, however, should be viewed with caution, since that state has a very small share of total population and the result could be due to sampling bias. The PNAD survey does not cover the rural areas of the Northern states, where poverty is usually concentrated.

More information about the labor structure of the economy can be seen in Table 4 and Table 5. In these tables sectoral wage bills are split into the model's 10 occupational groups. The occupational groups are defined in terms of a unit wage ranking. More skilled workers, then, would be those in the highest income classes, and vice-versa. As can be seen in Table 4, Agriculture is the activity that uses more unskilled labor (40.5 percent of that sector’s labor bill), while Petroleum and Gas Extraction and Petroleum Refinery are the most intensive users of skilled labor (10<sup>th</sup> labor class) using activities, with Financial Institutions coming next. If labor inputs were measured in hours (rather than in values) the concentration of low-skill labor in Agriculture would be even more pronounced.

Agriculture is also the sector that hires most unskilled labor in Brazil, around 41 percent of total workers in income class 1 (Table 5). The Trade sector is the second largest employer of this type of labor. As for the higher income classes, we see that the Financial Institutions and Public Administration sectors hire the largest numbers of well-paid workers.

Table 6 shows the distribution of occupation wages (OCC) classes among the household income classes (POF classes). In this table, the rows show household income classes, while the columns show the wage earnings by occupation. It is evident from this table that the wage earnings of the higher wage occupations (OCC10, for example) are concentrated in the higher income households, and _vice-versa_ . Most of the wages earned by workers in the first wage class (OCC1) accrue
# **4.4 Empirical Specifications**

## **4.4.1 Analysis of Survey Data**

In the survey analysis, each observation is an individual. Respondents in districts where ads were shown are considered “treated” and those in districts where ads were not shown are considered the “control” group. Because survey respondents in treatment districts were not necessarily exposed to the MNM campaign, our results estimate intent-to-treat (ITT) effects. Our workhorse model, estimated via Ordinary Least Squares, takes the following form: where _i_ is an individual residing in district _d_ of state _z_ . _T_ is the treatment indicator, which is randomized across districts. _β_ is our coefficient of interest, which captures the effect of being assigned to the treatment on the outcome _Y_ . Note that when the outcome is binary, the equation describes a Linear Probability Model. **x** and **w** represent, respectively, vectors of individual-level and districtlevel control covariates measured at baseline and potentially correlated with our outcomes. Since the treatment was randomly assigned, incorporating these control variables should not influence our estimates of _β_ . However, their inclusion enhances the efficiency of the estimator. Lastly, _θ_ denotes state fixed-effects, which account for unobserved state-level factors that could impact our outcomes of interest, given the potential influence of such factors in the Indian context.<sup>28</sup> We cluster the standard errors at the district level to account for intra-cluster correlations in the error term _ε_ . We also investigate the robustness of our results to different model specifications (e.g., adding controls and interaction terms) and estimators. For the latter, we estimate a logistic regression for all binary outcomes.

## **4.4.2 Analysis of Administrative Data**

Administrative data were gathered at the subdistrict level, covering a total of 395 subdistricts across the 79 districts included in the analysis. Focusing on smaller areas rather than the entire district reduces measurement error and increases
br>24.6<br>13.5<br>-8.8<br>-17.7 -19.7<br>-34.0<br>Servicified Goods exporter Servicified exporter Servicified exporter Goods Servicified<br>Source: EIS database; author’s calculations<br>50 50<br>25 25<br>0 0<br>Percentage difference Percentage difference<br>-25 -25<br>-50 -50<br><!-- End of picture text --> The results are demonstrated in the two panels of Figure B3.1, while the extended output regresion tables can be found in Table A1a and b in Annex 1. The left‐hand panel shows the results where goods firms (i.e. non‐exporters) are omitted. It shows that servicified domestic firms in Turkey are _18 percent_ less productive than non‐servicified goods firms. In line with the trade literature, exporters are more productive than non‐exporters. The right‐hand panel shows the results when goods exporters (with no services affiliate) are omitted, and therefore these results should be interpreted against this category. The panel shows that servicified exporters are _9 percent_ less productive than non‐servicified exporters. In short, firms with a services affiliate are less productive, whether they are exporters or not.

**Turkish firms with service affiliates have more sluggish TFP performance over time.** Although manufacturing firms in Turkey have seen some improved firm productivity performance, particularly from 2009 to 2014 (after the global financial crisis), the productivity trend for servicified firms is less optimistic

16
completed in 2016] focusing on welfare, poverty and vulnerability. The study: (i) compares the socioeconomic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and nonmonetary vulnerability.

- In **Lebanon, Jordan and Iraq** , the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.

- A recent Bank paper, “ **Turkey** ’s Response to the Syrian Refugee Crisis and the Road Ahead” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households. Impacts on the labor market are unclear; initial research suggests that there has been a supply shock to informal labor markets. This has had a large-scale impact on the employment of natives in the informal sector. At the same time, research suggests there has been a boost to formal employment for the Turks, but this has been uneven: the low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.

- The “ **Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities** ” [ongoing], to be undertaken
still predominant in commercial activities that can be viable for the private sector<sup>51</sup> such as fiber and textile manufacturing (e.g., the Arab Republic of Egypt), manufacture of food and cardboard (e.g., Bolivia), meat production (e.g., Botswana), groundnuts production (e.g., The Gambia), and even financial intermediation firms and digital payments (e.g., Russia, Vietnam) that could be served by the private sector. State participation in these sectors could potentially crowd-out private sector participation and thus private sector competitiveness, and as such, warrants further attention from policy makers.<sup>52</sup> Nonetheless, it is important to note that the BOS database collects only the primary activity of the SOEs, even though some companies could be multi-product. Since the BOS database captures the different legal entities even if under a conglomerate group, the risk of misclassification of the main activity is limited, but for the policy agenda and assessment of options of reforms this can be complemented with other tools (e.g., MCPAT).

The Global BOS database shows that state ownership and relationships among firms with state participation can be very complex. For the first time, the Global BOS database provides an in-depth view of the ownership relationships between the government and the corporate sector. It allows to identify countries where there are large conglomerate groups (e.g., Egypt and Vietnam) with strong upstream and downstream relationships. It also provides new tools to assess the degree of vertical integration of some firms across markets, which can also be relevant to understanding potential implications in terms of competition and firm dynamics.

Last, the Global BOS database has the potential to inform key reform agendas in areas such as corporate governance, anticorruption, debt management, among others.<sup>53</sup> The rich information contained in the database has been used to inform country-level analysis around SOE reform
they include more restrictions that the Core NTBs and agricultural domestic support we estimated because they include many other policies (e.g., exchange rate controls). Also differences in tastes and quality across countries can partly explain differences in prices. For these reasons one should expect them to be a bit higher (and they generally are). Our estimates compare to Bradford (2003, Table 2) as follows: 8 percent for Australia (compared to 15 percent), 5 percent for Canada (compared to 8 percent), 10 percent for Japan (compared with 58 percent), 8 percent for the United States (compared with 9 percent), 13 percent in Belgium (compared to 32 percent), 10 percent in Germany (compared to 18 percent), 10 percent in Italy (compared to 12 percent), 11 percent in the Netherlands compared to 31 percent, and 10 percent in the United Kingdom (compared to 38 percent). There are obvious reasons why these numbers may differ, but the order of magnitudes seem more or less on line, except perhaps for Belgium, Netherlands, United Kingdom and Japan, where Bradford’s estimates are much larger.<sup>17</sup> Andriamananjara et al. (2004) also provide estimates of AVEs of NTBs for 12 groups of product (that correspond to some aggregate of the GTAP product classi fi cation). They use price data from the Economist Intelligence Unit for 18 regions/countries and estimate the impact NTBs on retail prices controlling for several variables capturing distribution costs (GDP per capita, distance, wages in the non-traded sector, etc..). The most complete exercise is undertaken for Apparel. Andriamananjara et al. (2004) estimate a simple average AVEs of NTBs in apparel across countries of 73 percent (it varies between 16 and 190 percent). Our simple average for apparel is 20 percent (it varies between 0 and 95 percent). Thus, the order of magnitude seems a bit higher in Andriamananjara
_Table 1: Average index of gender/disability equality by opinion about the justification of violence toward children_

||Parents<br>beating<br>children|Teachers<br>beating<br>children|
|---|---|---|
|Never be justified|0.16|0.21|
|Something in|||
|between|-0.06|-0.23|
|Always bejustified|-0.44|-0.45|

Source: Computations from the CDG–World Bank 2022 microdata. All of these figures are statistically different from zero at the 5 percent threshold. The index standard deviation over the whole population is set at 1 by definition. The difference in the equity score between the group whose members never find violence to be justified and the one where people always find violence to be justified is about 0.6 to 0.7 of a standard deviation. This gives a Cohen’s d of 0.6 to 0.7, which corresponds to a medium-tolarge effect size.

- 4.6 Officials believe that violence toward children is justified much less often in EAP, and perceive less of it

Officials are more likely to state that violence from parents towards children is justified than violence from teachers toward children. This is true in all surveyed countries (Figure 10). Agreement with statements supporting acceptance of violence towards children is much lower in the EAP region than in other surveyed countries: about 60 percent of respondents in EAP reported that violence toward children is never justified, which is almost twice as high as the average of non-EAP countries surveyed (34 percent) (Figure 11). Mongolia is the only EAP country where some officials justified violence from both parents and teachers. These opinions correlate with estimates of the prevalence of sexual violence. In the EAP region, the guessed prevalence of sexual violence toward children is 16 percent, versus 32 percent for respondents outside of the region.

Domestic violence against children
However, there seemed to be differences in the impact of reform policies between developing and developed countries.

We then provided a more formal assessment of these apparent correlations through an econometric analysis to refine the conclusions emerging from the basic data analysis. More specifically, we could confirm that countries with private capital have more subscribers, lower local call price, lower faults, and higher labor productivity. However, in terms of fixed costs, econometric results differ from the basic data analysis. We found that private capital is actually associated with an increase in residential and business subscription fees, which is consistent with the tariff rebalancing that typically takes place when ending direct subsidies as part of most reforms. With respect to the independent regulator, we could confirm that countries with an IRA have lower local call price and higher labor productivity, but we found again some discrepancies with the basic

> 15 Tariff rebalancing usually takes place as part of the reforms after the government eliminates historical cross-subsidies that favor local calls.

> 16 In the absence of a good measure of the probability of detection we cannot but remain agnostic about the true effect of reform policies on the quality of service. For instance, if we further control for log subscribers in the quality equation the coefficient on PRIV*DV grows, suggesting that congestion externalities due to the enlargement of the sector following privatization do not explain the results.