rafmacalaba/data-use-mentions-extended huggingface dataset:
ZIMBABWE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Corridor serves as a link to other important corridors that branch off from it, such as the Trans-Kalahari, Beira, Lobito, Dar es Salaam, and Nacala corridors (Curtis 2009). Along the corridor, the core road artery runs 7,500 km from Dar es Salaam in Tanzania to Durban in South Africa, via Lusaka, Zambia, through the Chirundu border crossing and on to Harare, Zimbabwe, and then into South Africa at Beitbridge. But as a result of recent political unrest in Zimbabwe, an alternative route has opened up, bypassing Zimbabwe and going through Botswana instead.

The road sector went through important institutional reforms in 2000–01. Before the reforms most of the financing for roads came from government appropriations out of the national budget. But in the early 2000s dedicated road-user charges were created and assigned to the road fund, to be managed by the Zimbabwe National Road Administration (ZINARA). But maintenance and rehabilitation is constrained by insufficient funding, aggravating the extent of undermaintenance.

# **Challenges**

While Zimbabwe boasts one of the most extensive road networks in the region, its condition has deteriorated rapidly over the past two decades. As of 2008 Zimbabwe has one of the lowest percentages of roads in good condition the region (figure 11). The roads in poor condition include key regional arteries, such as the corridor leading to the port of Beira in northern Mozambique, hindering the competitiveness of the port even though it is significantly closer to Zimbabwe than the port of Durban. The 2010 Zimbabwe Road Condition Survey paints an even more dismal picture of the current quality of classified roads (table 10). Only 34 percent of total classified roads are in fair or good condition and only 14 percent in good condition. The quality of paved/sealed roads is somewhat better than that of gravel and earth roads; nevertheless, a daunting 50 percent of classified paved roads are in poor condition and require costly rehabilitation. Based on the results of this survey
# Why Quality Matters: Rebuilding Trustworthy Local Government in Post-Conflict Sierra Leone<sup>_∗_</sup>

Audrey Sacks<sup>_†_</sup> and Marco Larizza<sup>_‡_</sup> Keywords: Sierra Leone, Trust, Legitimacy, Decentralization JEL: H1,H7, D73, D74 Sector Board: PSM

> _∗_ This paper is part of a larger research project on “Political Decentralization, Electoral Accountability and Government Legitimacy” managed by the Public Sector Governance Group, Poverty Reduction and Economic Management Network and the Public Sector Reform and Capacity Building unit, Africa region. We wish to thank the Institutional Reform and Capacity Building Project (IRCBP) staff and the Abdul Latif Jameel Poverty Action Lab at the Massachusetts Institute of Technology for granting access to the survey data used for the analysis. The authors are also greatly indebted to Brendan Glynn, Margaux Janin Hall, Stuti Khemani, Jens Kromann Kristensen, Vivek Srivastava, Erik Wibbels and participants of Duke’s Political Economy Workshop for their valuable feedback and suggestions.

> _†_ `asacks@worldbank.org` . Leadership and Governance Practice, World Bank Institute

> _‡_ `mlarizza@worldbank.org` Public Sector Reform and Capacity Building, Africa region, the World Bank. Visiting Fellow, Institute for Democracy and Conflict Resolution University of Essex, UK

1
<u>μy μy μy</u> = C −5 <u>m m m</u> PPMM2.5 PPVVKK,tt PPVVKK,tt<sup>3</sup> PPVVKK,tt<sup>3</sup> PPVVKK,tt<sup>3</sup> where CCC , SSll ∗5.35XX and are coal, oil and gas consumed in Pakistan in year + GGSSss ∗2.96XX<sup>−5</sup> + PPiill ∗3.53XX<sup>−5</sup> , measured in kkSSCCpp kkSSCCpp kkSSCCpp ktoe. Notice that the total PM 2.5 pollution concentration in the country can be higher than tracked in the PPVVKK,tt PPVVKK,tt PPVVKK,tt SSll GGSSss PPiill SS model due to additional emissions from natural sources, industrial processes other than combustion and pollution from foreign sources. Further, pollution concentration varies considerably within the country. The country-wide aggregation adopted from CPAT is a simplification, but a necessary one for a country model.

# 3.8 Damage module

Potential GDP is the supply potential of the economy and anchors the real side of the model. It determines how much output can be produced when all of the resource in the economy are fully employed (given existing distortions, technology and preferences). In PAKMod, the standard CobbDouglas specification for potential GDP ∗) is modified along five dimensions to accommodate the climate focus of the model: tt (YY

1. Energy is included as a factor of production (see Hassler, Krussel and Olovsson, 2012);

and the production function is modified to account for damages from climate change, including: 2. Reductions in Aggregate TFP due to lower agricultural productivity 3. Reduction in labor productivity and supply due to higher temperatures 4. The impact of pollution on the labor force and 5. The impact of flooding on capital stock.

Before accounting for damages the standard MFMod production function is modified to include energy as a factor of
Kleven, H., Knudsen, M., Kreiner, C., Pedersen, S., & Saez, E. (2011). Unwilling or unable to cheat? evidence from a tax audit experiment in denmark [cited By 474]. _Econometrica_ , _79_ (3), 651–692. https://doi.org/10.3982/ECTA9113

- Künzel, S. R., Sekhon, J. S., Bickel, P. J., & Yu, B. (2019). Metalearners for estimating heterogeneous treatment effects using machine learning. _Proceedings of the National Academy of Sciences_ , _116_ (10), 4156–4165. https://doi.org/10.1073/pnas.1804597116

- Lu, M., Sadiq, S., Feaster, D. J., & Ishwaran, H. (2018). Estimating individual treatment effect in observational data using random forest methods. _Journal of Computational and Graphical Statistics_ , _27_ (1), 209–219. https://doi.org/10.1080/10618600.2017.1356325

- Luttmer, E. F., & Singhal, M. (2014). Tax morale. _Journal of economic perspectives_ , _28_ (4), 149–168. Nie, X., & Wager, S. (2020). Quasi-oracle estimation of heterogeneous treatment effects. _Biometrika_ , _108_ (2), 299–319. https://doi.org/10.1093/biomet/asaa076

- Parikh, H., Volfovsky, A., & Rudin, C. (2022). Malts: Matching after learning to stretch. _Journal of Machine Learning Research_ , _23_ (240).

- Powers, S., Qian, J., Jung, K., Schuler, A., Shah, N. H., Hastie, T., & Tibshirani, R. (2018). Some methods for heterogeneous
>countries are in 100s. Source: USPTO grants in PATSTAT.<br><!-- End of picture text --> Figure 2 suggests that a significant capacity for frontier green innovation exists in a small group of more technologically-sophisticated developing countries. Hence, appropriate green innovation policy is likely to differ between these and other developing countries (though given substantial differences among the technologically more sophisticated countries, appropriate policy would differ significantly between them as well). A group of nine emerging economies (Argentina, Brazil, China, Hungary, India, Malaysia, Mexico, the Russian Federation and South Africa) account for nearly 80 percent of all US green patent grants to developing countries between 2006 and 2010.<sup>6</sup> Moreover, unlike the technologically less sophisticated countries, these ̳emerging‘ economies display a sharp upward trend in green patenting, with their green patent grants more than doubling between 2000-2005 and 2006-2010.

> 5 We have omitted regions where total green patenting is so low that this percentage is a highly volatile, uninformative indicator. Moreover, we have taken 3-year moving averages to smooth out annual fluctuations. 6 We considered indicators of technological sophistication (R&D personnel per capita) as well as the scale of the R&D sector (total R&D personnel) in making this distinction.

5
are constructed based on accumulating and depreciating past investments using the perpetual inventory method.

-  Trade Data. Data on international trade ows come from the United Nations Statistical Division (UNSD) Commodity Trade Statistics Database (COMTRADE). We complement the international trade ows data with data on domestic trade ows from Anderson et al. (2020), which we use both for the estimation and for the counterfactual analysis. Anderson et al. (2020) construct domestic trade ows at the aggregate level in two steps. First, they use the ratio between aggregate manufacturing in gross values and total exports of manufacturing goods to construct a multiplier at the country-time level. (Data on gross manufacturing production, which came from the United Nations' IndStat database.) Then, they use this multiplier along with data on aggregate exports to project the values for domestic sales. Availability of data on domestic trade ows predetermined the time coverage of our estimating sample.

-  Other Data. Finally, in the estimation analysis we employ the following additional covariates as control variables. We control for the presence of bilateral investment treaties with an indicator variable _BITij,t_ , which comes from the UNCTAD 's data on international investment agreements (IIAs), which can be found at http://investmentpolicyhub.unctad.org/IIA. Data on sanctions come from the Global Sanctions Database (GSDB), cf. Felbermayr et al. (2020) and Kirilakha et al. (2021) (http://www.rug.nl/research/ggdc/data/pwt/). We use the GSDB to include and indicator variable ( _SANCTij,t_ ) for the presence of sanctions in our estimations. Finally, data on WTO membership, captured by an indicator variable _WTOij,t_ in our analysis, come from the Dynamic Gravity Dataset (DGD) of the U.S. International Trade Commission, cf. Gurevich and Herman (2018) (http://www.rug.nl/research/ggdc/
<!-- Start of picture text --> $ billions deals<br>1,800 3,000<br>Value (Emerging)<br>1,600 Value (Advanced)<br>2,500<br>Deals (Emerging)<br>1,400<br>1,200 2,000<br>1,000<br>1,500<br>800<br>600 1,000<br>400<br>500<br>200<br>0 0<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Source: Authors' calculations, based on Thomson-Reuters SDC Platinum<br><!-- End of picture text --> Figure 1: Total cross-border M&A deals by firms from advanced economies and emergingmarket economies, 1997–2010. The upward trend held by emerging market firms is evident in both absolute deal number and relative deal values.

# **2 Recent Trends in Emerging Economy M&A**

Emerging market multinationals have become far more assertive in their M&A activities on the global stage over the past decade. Save a dip during the global crisis in 2008 and 2009, the overall trend in cross-border M&A has been upward, especially in the post-dot-com period since 2001. This is evident both in terms of the total number of deals (increasing from 661 deals in 2001 to 2,447 in 2010), as well as—perhaps more dramatically—in the value of M&A deals concluded by emerging economy firms (the rise from $30 to $254 billion over this period represented an increase in shares from 6 to 29 percent) (see Figure 1).

Although not the focus of this paper, it is illuminative to consider, by way of comparison, whether patterns in emerging market M&A are also replicated in the other component of FDI, greenfield activity.<sup
# **3 Data Description**

This section describes the data used in the analysis and the methodology adopted to construct the variables of interest.

## **3.1 RoO Index at the Intermediate Level**

We start the analysis by discussing the construction of the main variable of interest, which summarizes the difficulty in sourcing an intermediate product according to the RoOs faced by the related final good. We follow Conconi et al. (2018) in constructing a data set that allows us to map input-output linkages to the EU-RoO (which are defined according to the six digits of the 1992 Harmonized System). These input-output linkages are based on the US IO1997 table and are converted into the six digits of the 1992 Harmonized System of classification, HS6 (see Conconi et al., 2018). Despite focusing on the EU context, we use the US IO tables for two reasons. Firstly, the match allows us to keep the same level of disaggregation of the EU-RoO Index, that is HS6. Secondly, it enables us to follow the rationale in Rajan and Zingales (1998), who construct a measure of the extent of an industry’s dependence on external funds in the United States, and apply this measure to all other countries. As pointed out by the authors, the assumption is that “there is a technological reason why some industries depend more on external finance than others” and these technological reasons are the same in the United States as in other countries. Additionally, the authors use US data on capital markets since, among other things, these markets are among “the most advanced in the world, and large publicly traded firms typically face the least frictions in accessing finance. Thus the amount of external finance used by large firms in the United States is likely to be a relatively pure measure of their demand for external finance.” In the same spirit, we exploit the US IO tables to better identify a production process that is less subject to distortions (such as product market
literature: self-selection into trade; and learning-by-exporting. This will also depend on the direction of the causal relationship.

The idea of a self-selection process argues that only the most productive firms are able to cover the sunk costs of exporting and engage in trade (Bernard et al, 2012; Wagner, 2007; Melitz, 2003). Trade participation requires significant and continuous investment in innovation, which may also influence the degree of internationalization (Teruel et al, 2021). For example, the use of new technologies can enable new marketing and sales channels or reduce costs related to entry into foreign markets – factors that hamper smaller firms with limited resources. Lowering the cost of entry into trade can make the selection process work more efficiently. More firms will be able to compete with international counterparts, while the least productive firms, faced with expanded competition from home and abroad, will exit the market.

The idea behind the learning-by-exporting mechanism is that exporters gain knowledge from exposure to foreign markets and practices, allowing them to grow and increase their efficiency. The presence of factors that affect entry costs into trade or preferential access to foreign markets – for example, specific regulatory barriers, the time to clear customs, and direct informal or formal payments – will make it more difficult for firms to learn from global markets, adopt new technologies and become more innovative. This is particularly true for firms that are part of GVCs and may gain

> 9 On average, firms that invest in innovation and engage in trade are better managed. Enterprise Surveys include detailed questions on core management practices related to addressing problems arising in operations, monitoring of performance indicators, production targets and incentives rewarding staff performance. This information can be summarized in a normalized management index, where a higher score reflects better practices.

10
.

survey ENIGH, which measures incomes and expenditures, to identify, self-declared, scholarship holders and divide them between recipients of “Oportunidades” and “other government program”. Using ENIGH 2010<sup>12</sup> we identify households with students enrolled in EMS and, among this subsample, those that declared having an “Oportunidades” scholarship or a scholarship from another government program. Figure 5 shows the share of scholarship beneficiaries by decile of the distribution of per capita household income. Overall, “Oportunidades” scholarships seem to be well-targeted, benefiting the poorest EMS students. However, beneficiaries of “other government programs”, a category basically capturing PROBEMS, tend to be located in deciles 5 to 9 of the income distribution. The lack of targeting combined with the marginally positive effects among the most marginalized suggest that PROBEMS could increase the likelihood of finishing high school among the poorest students, but the program’s targeting mechanism was not very effective in reaching the poorest, or they were already covered by the conditional cash transfer program (Oportunidades).

> 12ENIGH is collected every two years and there was no survey in 2009, the year when PROBEMS applicants filled the online format with the socio-economic information.

18
br>0.45|0.36|0.36|0.35|<br>0.36|<br>0.52|0.52|0.51|0.52|

Outcome is dummy for student age above 20. Sample is students taking grade 12 exit exam in Regions 2-6, 1998-2012. Interaction with male based on individual student. Distant is dummy for above national median distance from school to main road. All other interactions are district characteristics from 1998 household survey. Low enrollment = below national median secondary-age (13-18) enrollment rate. Rural is above national median proportion of 1998 district population living in rural area. Wealthy = at or above national median household assets, by first principal component. “Treatment effect with interaction” is sum of program coefficient and coefficient on interaction term. All regressions include school and year fixed effects. Regression with both male and female students in sample also includes female dummy. Sample sizes differ across regressions because not all districts surveyed in 1998. Standard errors in parentheses, clustered by region. p-values in brackets, calculated from 200 iterations of wild-t cluster bootstrap of Cameron et al (2008) with Webb (2013) 6-point distribution of weights. Starred significance levels are based on these p-values. * significant at 10%; ** significant at 5%; *** significant at 1%.
Second, each individual in the JLMPS restricted sample is matched to the 2010 Jordanian school census. The matching process determines for each individual the number of sexappropriate public basic and secondary schools per 1,000 individuals available in the individual’s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level).<sup>7</sup> A school is considered sex-appropriate for a female if it is a girls’ or a mixed school and for a male if it is a boys’ or mixed school. The empirical analysis is also performed by entering boys’, girls’, and mixed schools separately.<sup>8</sup> Measuring the local supply of public schools at the subdistrict of birth of the individual (i.e., the child) mitigates potential endogeneity originating from parents who had a higher taste for schooling moving to subdistricts where public schooling was more abundant when their child was of school age, although it is not possible to rule out that parents might have moved across subdistricts _prior_ to the birth of their child.

The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father’s schooling, mother’s schooling, and local supply of schools in subdistrict of birth.<sup>9</sup> These exclusions resulted in a sample of 4,139 males and 4,131 females, which are referred to as the male and female full samples, respectively.

> 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error
tween gndi and total consumption, tc. Total consumption, tc, is the sum of household ...nal consumption expenditure, hc, and general government ...nal consumption expenditure, te.<sup>21</sup> All the variables are measured in local currency at current prices. The source used to obtain annual time series of each of the variables was The World Development Indicators.<sup>22</sup> The main determinant of our interest is the pension saving rate, psr. It is de...ned as the ratio between pension saving, ps, and gross national disposable income, gndi. Pension saving is de...ned as the change in the value of ...nancial assets of pension funds, pa. Annual time series showing the evolution of pension assets were basically obtained from national sources and from The Institutional Investors Statistical Yearbook.<sup>23</sup> Following Impavido, Musalem and Tressel (2003) we divide the countries between two groups. The ...rst group comprises the countries in which our data on pension assets are predominantly (i.e., more than 50 percent) the result of mandatory funded pension programs. The second group includes the countries in which our data on pension assets are predominantly the result of voluntary funded pension programs. In the ...rst group we have: Argentina, Bolivia, Bulgaria, Chile, Colombia, Costa Rica, Egypt, El Salvador, Hungary, India, Jordan, Kazkhstan, Malaysia, Mexico, Morocco, Nepal, Panama, Peru, Philippines, Poland, Singapore and Uruguay. In the second group we have:

> 21Total consumption includes the statistical discrepancy that results from estimating gdp using the expenditure and output approach.

22All the series were downloaded electronically through the World Bank’s internal database system, SIMA.

> 23A separate appendix describes in detail the length of each of the annual time series obtained and the corresponding source.

27
G., Steketee, R. W., Black, R. E., Bhutta, Z. A., Morris, S. S., & the Bellagio Child Survival Study Group. (2003). How Many Child Deaths Can We Prevent this Year? _The Lancet_ , _362_ (9377), 65-71.

- Krugman, P. (1988). Financing versus Forgiving a Debt Overhang. _Journal of Development Economics_ , _29_ (3), 253-268.

- Kudamatsu, M. (2012). Has Democracy Reduced Infant Mortality in Sub-Saharan Africa? Evidence from Micro Data. _Journal of the European Economic Association_ , _10_ (6), 1294-1317.

- Maluccio, J. A., Hoddinott, J., Behrman, J. R., Martorell, R., Quisumbing, A. R., & Stein, A. D. (2009). The Impact of Improving Nutrition During Early Childhood on Education Among Guatemalan Adults. _The Economic Journal_ , _119_ (537), 734-763.

- Marcelino, S. R., & Hakobyan, I. (2014). _Does Lower Debt Buy Higher Growth? The Impact of Debt Relief Initiatives on Growth_ (Working Paper No. WP/14/230). Washington, D.C.: International Monetary Fund.

- Marshall, M., Jaggers, K., & Gurr, T. R. (2011). _Polity IV Project: Political Regimes Characteristics and Transitions, 1800-2012._ Vienna, VA: Center for Systemic Peace.

- Powell, R. (2003). _Debt Relief, Additionality, and Aid Allocation in Low-Income Countries_ (Working Paper No. WP/03/175). Washington, D.C.: International Monetary Fund.

19
employ Output-side real GDP at current PPPs ( _CGDP_<sup>_o_</sup> ), which compares relative productive capacity across countries at a single point in time, as the initial level in our counterfactual experiments, and we use Real GDP using national-accounts growth rates ( _CGDP_<sup>_na_</sup> ) for our income-based cross-country growth regressions. We measure employment in eective units by multiplying the Number of persons engaged in the labor force with the Human Capital Index, which is based on average years of schooling. Finally, capital stocks in the Penn World Tables 8.0

> estimation analysis in this paper, we also utilize the time variation in the FDI data. In the counterfactual experiments, we rely on the methods of Anderson et al. (2019) to calibrate some parameters and vectors. See Section 4.2 for further details.

> 18Specically, we used the DTA 2. Database: Information by Trade agreements. Bilateral observations.

18