rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Appendix:</u>**

## **Figure A1: Persistence of Productivity over time**

<!-- Start of picture text --> 1<br>All Countries<br>0.8<br>Advanced Economies<br>0.6 Developing Economies<br>0.4<br>0.2<br>0<br>1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->

## **Figure A2: Cross-Country Productivity Correlations**

<!-- Start of picture text --> 1<br>All Countries<br>0.8 Advanced Economies<br>Developing Economies<br>0.6<br>0.4<br>0.2<br>0<br>1970 1980 1990 2000 2010<br><!-- End of picture text -->

## **Figure A3: Evolution of Intertemporal Smoothing**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>All Countries<br>0.6 Advanced Economies<br>Developing Economies<br>0.5<br>1970 1980 1990 2000 2010<br><!-- End of picture text --> Notes: A1: Median rolling AR(1) coefficient estimates of regressing TFP growth on its lag for each country group. The length of the rolling window is 20 years. Results are robust to various window lengths. 1960-2011.

A2: Productivity correlations refer to the average rolling 9-year bilateral correlations of productivity of each country with that of all the other countries in the sample. The results are robust to various window lengths, and are similar for weighted-averages when export shares and import shares are used as weights. 1960-2011.

A3: Inter-temporal smoothing refers to the estimated coefficients (dots) of 12-year rolling panel regressions of consumption growth on output growth for each country group. Aggregate consumption growth is also used as a control in the panel regressions. The lines show the 9-year moving average trendlines.

20
**Figure 1. Tobacco Price Variation, 2008–13**

Once a measure of the price of cigarettes in Ukraine is obtained, the tobacco price elasticity across population groups is estimated (refer to Appendix I for more details). Table 2 shows the tobacco price elasticity across income deciles and other subgroups in the population: total population, household heads ages 25–40, and household location (rural versus urban).

**Table 2 - Tobacco Price Elasticities, by Income Decile, Age, and Location (%)**

|_Price elasticity_|_Decile 1_|_Decile 2_|_Decile 3_|_Decile 4_|_Decile 5_|_Decile 6_|_Decile 7_|_Decile 8_|_Decile 9_|_Decile 10_|<br>_Average _|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Total population|−59|−51|−52|−46|−44|−43|−42|−41|−36|−33|−45|
|Under age 40|−77|−69|−73|−70|−61|−64|−56|−64|−51|−51|−64|
|Rural|−47|−30|−32|−30|−27|−27|−33|−26|−22|−21|−29|
|Urban|−62|−65|−63|−55|−53|−52|−48|−50|−45|−41|−53|

_Source:_ Estimates based on data of the household budget survey, 2010–13.

Estimates of the average tobacco price elasticity of −0.45 are higher than those calculated by Krasovsky et al. (2002) for Ukraine (-.25). To incorporate different assumptions of price elasticities, lower-bound and upperbound elasticities are simulated. These estimates show differences between −0.2 and +0.2 relative to the previously estimated elasticities. The lower-bound elasticities are similar to Krasovsky et al. (2002) and tend to reflect income groups that typically do not change patterns of consumption, such as rural residents or older population groups. These groups tend to exhibit less change in consumption when prices change.

10
All the models are estimated using the Heckman correction method in Stata. The fullinformation maximum-likelihood approach, which is more efficient than the Heckman two-step procedure, is used for estimation (Leung and Yu, 1996; Puhani, 2000).<sup>18</sup>

# **V. Results**

This section presents the results of the empirical models discussed in Section IV. As a first step, we estimate the returns to education for the urban adult population in the selected low- and middle-income countries.

## **_Effects of schooling_**

Our results show a positive and significant return to schooling (see Table 4). For instance, an additional completed year of schooling is associated with a 5- to 7-percentage-point increase in hourly earnings, controlling for experience, gender, type of employment, and occupational group.<sup>19</sup> Montenegro and Patrinos (2014) report, on average, a 10-percentage-point return to each additional year of education worldwide, controlling for potential experience.<sup>20</sup> The heterogeneity in country estimates indicates differences in how the labor market rewards educational attainment across the countries in our sample.

> 18 The full-information maximum likelihood method relies heavily on normality assumptions and could have difficulties converging in the absence of exclusion restrictions.

> 19 The coefficient in Table 4 represents an increase in log points. We use the following formula to convert to percentage points: ���������� ��������<sup>�</sup> �1 . For example, in Kenya the coefficient is 0.087, which suggests an 8.7 log point increase in earnings, which, using the suggested formula, indicates that there is a 9.1 percentage point increase ( �<sup>�.���</sup> �1� in hourly earnings.

> 20 A major difference between the estimates reported here and those reported in other studies (e.g., Hanushek et al. (2013); Montenegro and Patrinos (2014); and Fasih, Patrinos, and Sakellariou (2013) is that this study uses an urban sample that includes both wage and self-employed workers. Further, previous studies have estimated returns using OLS and a smaller set of control variables, while this study corrects for selection bias and uses a larger set of controls.

20
**Figure 11 Top 10 developing countries according to economic exposure to impacts of EU, US, and China policies on battery exports**

<!-- Start of picture text --> Economic exposure (LHS) Sector exposure (RHS)<br> 1.6 100<br> 1.4<br> 80<br> 1.2<br> 1.0 60<br> 0.8<br> 0.6 40<br> 0.4<br> 20<br> 0.2<br> - -<br>North Malaysia Vietnam Mexico Philippines Tunisia Indonesia Türkiye Colombia Serbia<br>Macedonia<br>Percent Percent<br><!-- End of picture text --> _Source_ : Authors’ calculation using 2022 data from the World Bank’s WITS. _Note_ : Data includes components and end-products. Sector and economic exposure are export of batteries to three economies divided by exports of batteries to world and GDP, respectively.

**Unless leasing rates increase significantly in the US, EV exports to the US from developing nations like Viet Nam are expected to decline.** This is due to rebates requiring final assembly in North America, decreasing US imports from outside that region. However, this decline may be partly or fully offset by a regulation enabling leased EVs to qualify for rebates regardless of origin. This regulation prompted an increase in EV leasing from under 10 percent in December 2022 to 34 percent by March 2023 (Bown 2023). The US government aims for at least 50 percent of all new vehicle sales to be electric by 2030 (White House 2022). Should leasing rates increase significantly, Viet Nam would stand to benefit (Figure 14).

**The EU's zero-emission regulation by 2035 aims to reshape the automotive industry, significantly impacting developing countries that export EVs.** As traditional combustion vehicles are phased out, EU nations will likely rely more on EV imports to meet this new demand, especially if domestic production lags. Developing countries with automotive manufacturing capabilities are poised to attract investments in their EV sectors and boost exports. Morocco, Türkiye, and Viet Nam are expected to benefit as they adapt their industrial policies to meet EU standards, positioning themselves as major players in the sustainable vehicle market. Though their EV sectors’ exposure to the three markets is high, the economic exposure of developing countries to EV policies remains low because their EV sectors are still emerging (Figure 12). For example, while 94 percent of Morocco’s EV exports went to these markets, they only constituted 0.06 percent of its GDP in 2022.

22
cities, 41.90 for other urban areas, and 32.98 for rural areas. For level 2 eligibility, which we use to determine Sisben eligibility, the respective scores are 39.32, 43.63, and 35.26.

The third criterion revolves around income. It establishes that, in the case of one-member households, a beneficiary’s income may not exceed half of a minimum wage. For beneficiaries living with other individuals, the entire household income may not exceed one minimum wage. In the data described below, we are able to observe how all three criteria would function; yet, we consider a potential beneficiary eligible for the program if the age and Sisben score requirements are met. We have decided not to impose the income criterion because it likely suffers from substantial measurement error in our data. The income criterion is also subject to considerable changes over time. We believe, moreover, that, given the difficulty of sorting out this criterion within the Sisben score, it is also the criterion that is most problematic to apply in practice. In filling out the Sisben questionnaire, respondents have perverse incentives to underreport income to qualify for various government assistance initiatives. Adding support to these concerns, our own data show only a limited overlap between the Sisben scores and households that are eligible based on income. The survey data employed and described in more detail below is designed to allow construction of Sisben scores.

In addition to eligibility, the program also employs a prioritization strategy, mostly based on age, plus other criteria, such as disability status, number of dependents, and so on. The strategy represents an attempt to focus the program on the population 65 years of age and older. Every year, each municipality is given a certain number of slots for new beneficiaries. If the number of eligible petitioners exceeds the number of available slots, the prioritization strategy is intended to determine who receives the benefit.

# **4. Program Targeting**

According to the country’s Ministry of Labor, the Colombia Mayor Program is targeted at the protection of persons in conditions of extreme poverty (Colombia, Ministerio de Trabajo 2013). It is of interest to study not only the program’s impact on beneficiaries, but also the extent to which these correspond to the group at which the benefit is targeted. We start by comparing our aggregate data with the data published by the government. According to the Ministry of Labor, the number of beneficiaries rose from 718,376 in December 2012 to 1,259,333 one year later (Colombia, Ministerio de Trabajo 2014). These numbers almost perfectly coincide with those given to us by the Consorcio 8
sharing in each country. A full list of variables, definitions and sources are provided in Appendix I.

# **2.5. Control Variables**

In examining the relationship between competition and systemic stability we control for a number of bank and country level variables. Bank level controls come from Bankscope. For each bank, each year, we calculate bank size (natural logarithm of total assets), leverage (liabilities divided by total assets), market-to-book ratio (market value of assets divided by book value of assets), provisions (loan loss provisions divided by total assets), reliance on deposits for funding (deposits divided by total assets) and profitability (net income divided by total assets). We winsorize all financial variables at the 1<sup>st</sup> and 99<sup>th</sup> percentile level of their distributions to reduce the influence of outliers and potential data errors.

Country level controls are collected from a number of sources. We obtain economic development measures from the World Bank’s World Development Indictor (WDI) database. We use the natural logarithm of GDP per capita to measure the economic development of a country, the variance of GDP growth rate to measure economic stability, the natural logarithm of population to measure country size, and imports plus exports of goods and service divided GDP to measure global integration (Karolyi, Lee, and Van Dijk 2011). In addition, we obtain years in which a country experienced a banking crisis from Laeven and Valencia (2008). We also use stock market capitalization divided by GDP and private credit divided by GDP from the Financial Structure Dataset (Beck, Demirguc-Kunt, and Levine 2010) to control for differences in financial development and structure. As the R-squared measure may be a mechanically linked to the number of cross-sectional observations, we also control for the log of the number of banks in each country.

# **3. Summary Statistics**

In this section we describe our empirical results. Figure 1 shows the evolution of average bank market power, as measured by the average Lerner index, for the years 1996 to 2009. The pattern of time series changes in the Lerner index is consistent with that in Beck, De Jonghe, and Schepens (2011). The Lerner index has been steadily increasing since 1998 but has declined sharply after 2007 after the onset of the global financial crisis. Figure 2 shows the evolution of 11
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 12. Benchmarking Burkina Faso’s power indicators**

||**Unit**|Nonfragile, low-income<br>countries|**Burkina Faso**|Middle-income<br>countries|
|---|---|---|---|---|
|Installed power generation capacity|MW/million people|20.2|**13**|799|
|Power consumption|kWH/capita|107.4|**44**|4,479|
|Firms’ reliance on own generator|% consumption|21.2|**1**|11|
|Firms’ value lost due to power outages|% sales|6.5|**6**|2|
|Access to electricity|% population|15.0|**18**|49.5|
|Urban access to electricity|% population|57.6|**40**|74.4|
|Rural access to electricity|% population|3.9|**3**|26.3|
|Growth access to electricity|% population/year|0.8|**5.4**|12.1|
|Revenue collection|% billings|93.1|**76**|100|
|System losses|% production|23.7|**16**|20|
|Cost recovery|% total cost|84.4|**8974**|85|
|Total hidden costs as % of revenue|% of revenue|68.8|**38**|6|
|||**Burkina Faso**|Mainly thermal|Other developing<br>regions|
|Average effective power tariff|U.S. cents|30.0|17.0|5.0–10.0|

_Source:_ Eberhard and others 2008; World Bank 2007. Derived from the AICD electricity database (www.infrastructureafrica.org/aicd/tools/data). Outages data from the 2006 and 2009 Enterprise Surveys.

# **Challenges**

Burkina Faso’s power supply is insufficient to meet increasing demand. Installed generation capacity is 13 megawatts (MW) per million people, which represents about 2 percent of the average in Africa’s MICs and just about half of the installed generation capacity of other LICs (table 12). Residential power consumption is just one-third of other LICs. Only about 18 percent of the population has access to electricity (about 40 percent in urban areas and 3 percent in rural areas). Per capita consumption is 44 kWh in Burkina Faso, compared with 100 kWh in Cameroon, 200 kWh in Senegal, and 270 kWh in Côte d’Ivoire. The majority of the population (about 90 percent) still relies on wood energy (firewood and charcoal).

Under these circumstances, extending power supply—either by increasing installed capacity or via trade—is at the top of the government agenda. Improving the operational efficiency of SONABEL and revising the prevalent tariff scheme are also necessary before or as part of the expansion process.

In recent years, SONABEL has faced system transmission and distribution losses of over 60 percent, much higher than the internationally accepted norm of 10 percent. In addition, tariffs barely recover the operating costs of the expensive SONABEL power (table 13). The hidden costs of these operational inefficiencies (box 1) are approximately equivalent to 37 percent of SONABEL’s revenues. These hidden costs, though lower than the average in low-income African countries represent a significant burden for the expansion plan of the sector (table 12).

29
being extended.<sup>6</sup> Beck and Martinez-Peria (2010) show that the sharp increase in foreign bank participation in Mexico over the period 1997-2005 is associated with a decline in outreach as measured by deposit and loan accounts. Mian (2006) analyzes 80,000 bank loans in Pakistan over the period 1996-2002 to show that foreign owned banks were less likely to lend to small firms, firms that were not part of business groups or those without other banking relationships. Gormley (2010) also documents cherry picking by foreign banks in India, showing that they financed only a small set of very profitable firms upon entry, and finds that foreign bank entry lowers credit access overall especially for smaller firms. Beck, Ioannidou, and Schafer (2017) study firms borrowing from domestic and foreign banks in the same month in Bolivia over the period 1998 to 2003 to show differences in lending technology used by the banks. While foreign banks rely on collateral, credit ratings and shorter maturity, domestic banks rely more on relationships and soft information as lending technologies in lending to the same borrower. This in turn suggests that foreign banks may not be able to lend to SMEs especially in countries where there are poor credit histories on firms and where collateral rights are not effectively created or enforced.

Overall, the current empirical evidence suggests that opening up markets to foreign bank entry introduces competition, increases efficiency and stability and is likely to increase overall credit access. However, this varies across countries and size of the foreign banks, with the benefits likely to be greater in countries that have

> 6 Cull and Martinez Peria (2008) however show that this relationship disappears once we account for acquisition of distressed banks by foreigners.

20
WPS4033

# Do Intensity Targets Control Uncertainty Better than Quotas?

# Conditions, Calibrations, and Caveats

## Robert Marschinski _*_ and Franck Lecocq _†_

Potsdam Institute for Climate Impact Research, Postdam, GERMANY Laboratory of Forestry Economics, Nancy, FRANCE

#### **Abstract**

Among policy instruments to control future greenhouse gases emissions, well-calibrated general intensity targets are known to lead to lower uncertainty on the amount of abatement than emissions quotas (Jotzo and Pezzey, 2004). This paper tests whether this result holds in a broader framework, and whether it applies to other policy-relevant variables as well. To do so, we provide a general representation of the uncertainty on future GDP, future business-as-usual emissions and future abatement costs, and derive the variances of four variables, namely (effective) emissions, abatement effort, marginal abatement costs and total abatement costs over GDP under a quota, a linear (LIT) and a general intensity target (GIT)—where for the latter the emissions ceiling is a power-law function of GDP. We confirm that GITs can always yield a lower variance than a quota for marginal costs, but find that this is not true for total costs over GDP. Using economic and emissions scenarios and forecast errors of past projections, we estimate ranges of values for our model parameters. We find that quotas dominate LITs over most of this range, that calibrating GITs over this wide range is difficult, and that GITs would yield only modest reductions in uncertainty relative to quotas.

### **JEL Classification:** Q28, Q54, D81

### **Keywords:** climate change, intensity target, post-Kyoto, quota, uncertainty

## World Bank Policy Research Working Paper 4033, October 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._ _*_ Corresponding Author. Address: P.O. Box 60 12 03, 14412 Potsdam, Germany. Email: robert.marschinski@pik-potsdam.de. Phone: +49-331-288-2419. Fax: +49-331-288-2642.

> _†_ Franck Lecocq was with the Development Economics Research Group, World Bank, during the course of this research, and he is also associated researcher with CIRED, France. The authors thank the Carlo Schmid Program of the German Academic Exchange Service for support. They also thank O.Edenhofer, R.Klein, G.Petschel-Held _†_ , and the participants of a seminar at the Postdam Institute for valuable comments on the paper.
and economic demography analyzing the impact of cohort size on the labor market in the United States and Europe. Research on the United States, such as Freeman (1979), Welch (1979), and Berger (1985, 1989), looked at the initial earnings and subsequent earning growth of the large baby boom cohort that entered the labor market in the 1970s. Many European countries also experienced declining fertility in the 1960s that resulted in declines in the size of youth cohorts entering the labor market in the 1980s. Korenman and Neumark (2000) provide a useful review of the research that analyzing the impact of cohort size on labor markets in Europe.

A broad consensus from the research on the labor market impact of relative cohort size in the United States and Europe is that there does appear to be a negative impact of relative youth cohort size on the wages and employment opportunities of young people. Most of these studies use some measure of the relative number of young workers to older workers, similar to the relative cohort size used in Figure 12. Most studies that examine the longer term experience of cohorts find that the negative impact of being in a large youth cohort tends to diminish over time. In some studies, the disadvantage of being in a large cohort almost completely disappears as the cohort ages. Other studies find more persistent effects. Using cross-national data from OECD countries from 1970 to 1994, Korenman and Neumark estimate an elasticity of youth unemployment to relative cohort size of around 0.5. They conclude that the impact of relative cohort size is easily swamped by other macroeconomic and labor market conditions, and argue that cohort size _per se_ is unlikely to have a major impact on unemployment rates in OECD countries.

While it is potentially misleading to apply the results of this research on OECD countries to developing countries, several points are worth noting. First, as shown in the figures above, the fluctuations in relative cohort size in OECD countries are roughly similar in magnitude to the 19
16 3D PRINTING AND TRADE _Table 4: Effects of 3D printing on imports_

|Sector control|(1)<br>HS Ch. 90 and<br>High-tech|(2)<br>HS Ch. 90 and<br>High-tech|(3)<br>HS Ch. 90 and<br>High-tech|(4)<br>HS Ch. 90 and<br>High-tech|(5)<br>HS Ch. 90 and<br>High-tech|
|---|---|---|---|---|---|
|VARIABLES|log(imports)|log(imports)|log(imports)|log(imports)|log(imports)|
|(Hearing Aid 2007-)|0.715***|0.715***||0.493***||
||(0.068)|(0.072)||(0.084)||
|(Hearing Aid 2007-)*(Dummy RCA 1995-2000 > 1)||-0.004||||
|||(0.164)||||
|(Hearing Aid 2007-)*(Dummy Developing)||||0.351***<br>(0.124)||
|(Hearing Aid 2007-)*(Dummy Low RCA 1995-2000 < 1)|||0.989***|||
||||(0.117)|||
|(Hearing Aid 2007-)*(Dummy High RCA 1995-2000 < 1)|||0.545***<br>(0.082)|||
|(Hearing Aid 2007-)*(Dummy Low RCA 1995-2000 > 1)|||0.724***<br>(0.117)|||
|(Hearing Aid 2007-)*(Dummy High RCA 1995-2000 > 1)|||0.694**<br>(0.319)|||
|(Hearing Aid 2007-)*(Dummy Low income)|||||0.891***<br>(0.171)|
|(Hearing Aid 2007-)*(Dummy Lower middle income)|||||0.812***<br>(0.139)|
|(Hearing Aid 2007-)*(Dummy Upper middle income)|||||0.860***<br>(0.150)|
|(Hearing Aid 2007-)*(Dummy High income)|||||0.493***<br>(0.084)|
|Observations|137,376|137,376|137,376|137,376|137,376|
|R-squared|0.907|0.907|0.907|0.907|0.907|
|Sum coefficients|.715***|.711***|2.951***|.843***|3.056***|
|Period|1995-2016|1995-2016|1995-2016|1995-2016|1995-2016|
|Country-Year FE|YES|YES|YES|YES|YES|
|Country-Product FE|YES|YES|YES|YES|YES|
|% Zeroes|.28|.28|.28|.28|.28|

Note: Robust standard errors, clustered at the country-product level, are in parentheses. Countries with population over 5mln. *** p<0.01, ** p<0.05, * p<0.1