rafmacalaba/datause-encoder-data huggingface dataset:
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- Martin, P., T. Mayer, and M. Thoenig (2008). Civil wars and international trade. _Journal of the European Economic Association 6_ (2-3), 541–550.

- Neri-Lain ́e, M. (2025). Firms under fire! How insecurity affects formal firms’ existence. Technical report.

- OECD/SWAC (2020). _The Geography of Conflict in North and West Africa_ . OECD Publishing, Paris.

- Oster, E. (2019). Unobservable selection and coefficient stability: Theory and evidence. _Journal of Business & Economic Statistics 37_ (2), 187–204.

- Piemontese, L. (2023). Uncovering illegal and underground economies: The case of mafia extortion racketeering. _Journal of Public Economics 227_ , 104997.

- Pinotti, P. (2015). The economic costs of organised crime: Evidence from Southern Italy. _Economic Journal 125_ (586), F203–F232.

- Raleigh, C., R. Linke, H. Hegre, and J. Karlsen (2010). Introducing ACLED: An armed conflict location and event dataset. _Journal of Peace Research 47_ (5), 651–660.

- Rauch, J. E. (1999). Networks versus markets in international trade. _Journal of International Economics 48_ (1), 7–35.

- Rauch, J. E. and V. Trindade (2002). Ethnic Chinese networks in international trade. _Review of Economics and Statistics 84_ (1), 116–130.

- Rohner, D. (2023). Conflict, civil wars, and human development. In K. Zimmermann (Ed.), _Handbook of Labor, Human Resources and Population Economics_ . Springer.

- Rozo, S. V. (2018). Is murder bad for business? Evidence from Colombia. _Review of Economics and Statistics 100_ (5), 769–782.

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- Schrodt, P. A. and O. Yilmaz (2012). Conflict and mediation event observations codebook. _Center for International Political Analysis Institute for Policy and Social Research—University of Kansas_ .

28
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# **7.  Road Upgrading and Ecological Risk**

We calculate expected biodiversity loss for a cell as the product of its previous-clearingadjusted biodiversity indicator value (Figure 8) and the change in forest clearing induced by road upgrading (Figure 5). Figure 9 displays the geographic distribution of predicted biodiversity indicator values after road upgrading, while Figure 10 displays the change in indicator value that is attributable to upgrading. The map in Figure 10 has been created by subtracting indicator values in Figure 8 from those in Figure 9, and re-normalizing the result to the range [0,100]. Once again, the results indicate a broad geographic distribution of the largest biodiversity impacts. Significant impact clusters are visible in southern Phôngsali, northwestern Louang Namtha, western Oudômxai, east-central Louangphrabang, northern and southeastern Houaphan, northeastern Xaisômboun, southwest Vientiane, northwest Khammouan, southeast Savannakhét, northeast Champasak and southern Xékong. Clusters of lower but substantial impact are also visible in widely-scattered locations.

# **8.  Summary and Conclusions**

In this paper, we have developed, estimated and applied a spatially-explicit model that links road upgrading to forest clearing and potential biodiversity loss in Lao PDR. Our approach combines high-resolution forest-clearing information from Hansen et al. (2013); information on biodiversity and protected areas from IUCN and WWF International; digital road maps from Delorme, Inc.; digital elevation maps from CGIAR; and information on agricultural opportunity values from Deveny et al. (2009). Using the 30 m Hansen data, we estimate cumulative forest clearing rates for all 500 m cells in Lao PDR. For each 500 m cell, we measure the distance to the closest point on the nearest road; the quality of the road link; legal protection status; travel time to the nearest urban market center; the mean agricultural opportunity value of the land; and elevation.

30
Table 5: Heterogeneity

||Marketiza|tion Index|Corruptio|n(ETC)|Prior Mayo|r/PS Exp.|1(Growth<br>�|_>_Target)|
|---|---|---|---|---|---|---|---|---|
||(1) High|(2) Low|(3) High|(4) Low|(5) Yes|(6) No|(7) Yes|(8) No|
|PS YearsInOffice|0.031|0.189<sup>∗∗∗</sup>|0.147<sup>∗∗∗</sup>|0.064<sup>∗</sup>|0.142<sup>∗∗∗</sup>|0.068|0.013|0.325<sup>∗∗∗</sup>|
||(0.033)|(0.044)|(0.046)|(0.035)|(0.036)|(0.082)|(0.031)|(0.085)|
|PS YearsInOffice<sup>2</sup>|−0.004|−0.021<sup>∗∗∗</sup>|−0.017<sup>∗∗∗</sup>|−0.005|−0.016<sup>∗∗∗</sup>|−0.001|0.001|−0.045<sup>∗∗∗</sup>|
||(0.005)|(0.007)|(0.006)|(0.006)|(0.005)|(0.008)|(0.004)|(0.013)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|1726|1916|1683|1959|2697|851|1920|859|
|# of Cities|133|148|130|151|272|165|273|242|
|Adj_R_<sup>2</sup>|0.57|0.62|0.64|0.54|0.60|0.68|0.70|0.68|

_Notes_ : This table presents the heterogeneity with respect to the baseline results on the political cycle of GDP manipulation along different dimensions. In columns (1)–(2), the “High” and “Low” groups are divided based on the median of the provincial marketization index in 2000 while in columns (3)–(4), the two groups are divided based on the median of the provincial share of entertainment and travel costs over sales following Cai, Fang, and Xu (2011). City controls include city-level time-varying economic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights), and leader controls are the same as the ones displayed in the table of baseline results. Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

43
# **1. Introduction**

The Russian Federation sustained significant economic growth over the past decade, accompanied by high rates of income mobility across the population. Between 2000 and 2013, increases in gross domestic product (GDP) averaged 5.15 percent a year, above the regional mean for Europe and Central Asia (ECA) (4.53 percent). Over the whole decade, the positive trend observed was only interrupted by the 2008-09 crisis (when GDP declined by around 7.8 percent), after which growth quickly resumed (Figure 1). Indeed, GDP per capita nearly doubled between 2000 and 2012 (from $8,613 to $15,177, in PPP 2005 dollars). The trend, however, has been decelerating and high growth is not expected to resume in the coming years.

The positive outcomes in economic growth during the first decade of the 2000s have been accompanied by economic mobility for most households, reflected in substantial poverty reduction. The share of people living in poverty declined from around 30 percent in 2000, to about 11 percent in 2014, based on the national poverty line.<sup>2</sup> The overall positive trend, however, masks the fact that the declining trend stagnated in 2013-14 (after reaching a record low of 10.7 percent in 2012, the poverty rate remained at 10.8 percent in 2013 and increased to 11.2 percent in 2014). On the other hand, extreme poverty is nearly nonexistent in Russia. Considering the national extreme poverty line, roughly equivalent to US$2.50 a day, extreme poverty was well below 1 percent (0.77 percent) in 2012.

Income inequality has also decreased in the Russian Federation during the 2000s, though it still remains high for international standards (Figure 2). Along the transition to a market economy, income inequality escalated throughout the 1990s, with the Gini index increasing from 0.22 to 0.52 between 1989 and 1996 (Milanovic, 1999). In fact, according to Clarke (1997), the Gini index experienced a dramatic rise in the early 1990s, from 0.29 in 1992 to 0.50 in 1993. Then, after peaking during the 1998 crisis, income inequality per capita fell by 20 percent between 2002 and 2012 (see Figure 3).

> 2 Poverty rates are even lower when considering international poverty lines. Based on the US$5 a day poverty line (in real 2005 PPP values), poverty was 7.3 percent in 2012. According to the international line of US$1.25 a day, the extreme poverty rate is virtually zero (0.03 percent in 2012).

2
Table A6: Sample balancing after matching for school that adhered to _Mais Educação_ in 2010—Average school characteristics in 2009 by treatment status

||Panel A: Fifth|-grade schools||Panel B: Ninth-|grade school|s|
|---|---|---|---|---|---|---|
||Schools in_Mais_<br>_Educação_(2010–2011)|Control group|<br>_P_value|Schools in_Mais_<br>_Educação_(2010–2011)|Control<br>group|_P_value|
||(1)|(2)|(3)|(4)|(5)|(6)|
|∆ Portuguese grade|11.03|11.15|0.79|11.47|10.95|0.25|
|∆ Mathematics grade|13.25|13.05|0.69|3.74|3.36|0.34|
|∆ Dropout rate|-0.66|-0.55|0.28|-1.00|-1.08|0.64|
|Portuguese grade|179|179|0.29|238|238|0.77|
|Mathematics grade|197|197|0.83|239|239|0.68|
|Dropout rate|2.43|2.52|0.44|5.67|5.48|0.34|
|IDEB Index|4.30|4.32|0.50|3.50|3.51|0.57|
|∆ IDEB|0.49|0.49|0.95|0.25|0.25|0.99|
|Municipal population|960,000|930,000|0.68|1,100,000|1,200,000|0.43|
|Students' enrollments|701|683|0.18|989|1000|0.51|
|IDHM|0.74|0.74|0.22|0.74|0.75|0.15|
|Urban|0.98|0.98|0.84|0.98|0.98|0.71|
|Basic sanitation|1.00|1.00|0.37|1.00|1.00|0.41|
|Library|0.76|0.75|0.67|0.89|0.88|0.62|
|Sports court|0.56|0.56|0.95|0.76|0.77|0.50|
|Number of classrooms|11.76|11.37|0.03**|13.88|13.94|0.75|
|Number of computers|0.95|0.96|0.653|0.99|0.99|0.74|
|Science lab|0.14|0.12|0.02**|0.34|0.35|0.39|
|Computer lab|0.58|0.59|0.573|0.82|0.82|0.71|
|Teachers room|0.86|0.86|0.823|0.97|0.96|0.17|
|DVD|0.97|0.97|0.302|0.98|0.98|0.72|
|Overhead projector|0.67|0.67|0.896|0.86|0.87|0.71|
|Number of employees|53.80|52.08|0.06*|70.86|71.26|0.71|
|School meals|0.99|1.00|0.16|0.99|0.99|0.68|
|Number of classrooms used|11.66|11.19|0.0***|13.48|13.45|0.87|
|Morning lessons|0.60|0.59|0.36|0.57|0.57|0.74|
|Child care|0.03|0.03|0.79|0.01|0.01|0.89|
|Kindergarten|0.38|0.36|0.26|0.15|0.16|0.22|
|State schools|0.26|0.28|0.25|0.60|0.62|0.28|
|High school|0.12|0.13|0.37|0.47|0.49|0.16|
|Share classes in school with full-time day (7 or more hours)|0.02|0.02|0.72|0.01|0.02|0.28|
|Total duration of school day (hours)|4.36|4.35|0.48|4.46|4.48|0.39|
|Number of observations|2,098|1,670||1,976|1,467||

_Sources:_ MEC/FNDE, INEP, _Prova Brasil,_ and School Census _Note:_ Table reports the average characteristics for the schools that joined _Mais Educação_ in 2010 and remained until 2011 (columns (1) and (4)) and for the control group selected by propensity score matching (columns (2) and (5)). Columns (3) and (6) report the _p_ value of the _t_ test performed to check mean differences of the variables between the two groups. *significant at 10%; **significant at 5%; ***significant at 1%. In control group, the means are weighted using propensity score matching weights. Panel A reports these statistics for the schools from the fifth-grade sample. Panel B reports these statistics for schools from the ninth-grade sample. Our sample include only schools that have information for all variables over all the analyzed period.This table compares treated to control schools in 2009, after propensity score matching.
**Political Connections and Tariff Evasion**

# **Evidence from Tunisia**

Bob Rijkers<sup>1</sup> * Leila Baghdadi2 Gael Raballand<sup>1</sup> JEL codes: F13, H26, D73 Key words: Tariff Evasion, Trade, Corruption, State-Business Relationships, Tunisia, Taxation 1 The World Bank, 2Tunis Business School University of Tunis Tunisia * Corresponding author: <u>brijkers@worldbank.org.</u> Acknowledgements: The paper benefited from KCP funding for the project “Job Creation, Structural Change, and Economic Development in MENA with Lessons from East Asia” (TF 014655, project code P127907). Research for this paper has also been supported in part by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development, and by the UK Department for International Development (DFID) through the Strategic Research Partnership on Economic Development. This work was also sponsored by the Economic Research Forum (ERF) and has benefitted from both financial and intellectual support from the ERF. The contents and recommendations do not necessarily reflect ERF’s views. We would like to thank Caroline Duclos for exceptional research assistance and Rebekka Grun and Melise Jaud help obtaining access to the data. We also benefitted from useful comments from Lotfi Ayadi, Varanya Chaubey, Caroline Freund, Bernard Hoekman, Phil Keefer, Antonio Nucifora, Beata Javorcik, Chahir Zaki, and seminar participants at the World Bank, the Economic Research Forum’s Annual Conference, and the Centre for the Study of African Economies, Oxford University. We are very grateful to the Tunisian Institut National de la Statistique for generously hosting us for several months, helping assemble the database and providing incisive feedback.
# **Promoting Handwashing Behavior in Peru: The Effect of Large-Scale Mass-Media and Community Level Interventions†**

## Sebastian Galiani<sup>1</sup>

## Paul Gertler<sup>2</sup>

## Alexandra Orsola-Vidal<sup>3</sup>

### **JEL codes:** I

**Key words** : hygiene, handwashing, behavior change, randomized evaluation

1 University of Maryland.

2 University of California, Berkeley.

3 World Bank, WSP.

- This trial is registered, number NCT01465204.

Sebastian Galiani is Professor of Economics at the University of Maryland and Alexandra OrsolaVidal is Senior Impact Evaluation Specialist at the World Bank. They served as country investigators for the Peru Global Scaling Up Handwashing Project impact evaluation. This evaluation is part of a cross-country study led by Bertha Briceño (in its early stages led by Jack Molyneaux) together with Alexandra Orsola-Vidal and Claire Chase. Professor Paul Gertler has provided guidance and advice throughout the project. Global advisors also include Sebastian Galiani, Jack Colford, Ben Arnold, Pavani Ram, Lia Fernald, and Patricia Kariger. The authors are grateful to Rocio Florez, the project task team leader in Peru, Glenn Pearce-Oroz, Regional Team Leader for WSP-Latin America and Caribbean region, and to Eduardo Perez, overall task team leader. We also thank Nicolas Ajzenman, Andres Drenik, Laura Jaitman and María Lucía Yanguas for their excellent research assistance. Generous financial support was provided by the Bill and Melinda Gates Foundation.
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two decades – literally hundreds of papers have been devoted to assess the effects of infrastructure on growth, productivity, poverty, and other development outcomes, using a variety of data and empirical methodologies. Calderón and Servén (2008) offer a partial account of the literature on the growth and inequality effects of infrastructure; more comprehensive surveys include Estache (2006), Romp and de Haan (2007) and Straub (2007).

The bulk of the empirical literature on the effects of infrastructure has focused on its long-run contribution to the level or growth rate of aggregate income or productivity. The starting point was Aschauer’s (1989) finding that the stock of public infrastructure capital is a significant determinant of aggregate TFP in the U.S. However, his estimate (based on time series data) of the marginal product of infrastructure capital -- as much as 100% per year -- was implausibly high.

The massive ensuing literature on the output impact of infrastructure has employed a variety of data, empirical methods and infrastructure measures. The most popular approaches include the estimation of an aggregate production function (or its dual, the cost function) and empirical growth regressions. Infrastructure is variously measured in terms of physical stocks, spending flows, or capital stocks constructed accumulating the latter.

A majority of this literature finds a positive long-run effect of infrastructure on output, productivity, or their growth rate. More specifically, this is the case with almost all of the studies using physical indicators of infrastructure stocks, but results are more mixed among the growth studies using measures of public capital stocks or infrastructure spending flows (Straub 2007).

Another strand of recent literature has examined the effects of infrastructure on income inequality. The rationale is that infrastructure provision may have a disproportionate effect on the income and welfare of the poor by raising the value of the assets they hold (such as land or human capital), or by lowering the transaction costs (e.g., transport and logistic costs) they incur to access the markets for their inputs and outputs. These effects may occur through a variety of mechanisms documented in the empirical literature; see for example Estache, Foster and Wodon, (2002), Estache (2003), and Calderón and Servén (2008). Of course, for infrastructure development to reduce 5
3 policy payoffs. This is particularly true in the current context of an increasingly interdependent world economy where the ascendancy of new players could bring new benefits to low-income countries, especially those in Africa.

The economic success of large emerging countries such as China or Brazil reflects their evolving endowment structures and changing comparative advantage. It also ignites new dynamics in the distribution of responsibilities in the global production system: these newcomers are now well positioned to increasingly produce many of the high-value-added goods that used to be the exclusive prerogative of advanced economies. In order to remain successful, they must continue climbing the industrial and technological ladder and get more involved in capital-intensive industries. At the same time, they will have to free up much of their current manufacturing space to low-income countries that could be more competitive in labor-intensive industries. The necessary relocation of large parts of their supply chains in lower-production-cost countries will affect the price of goods, job patterns, and wages everywhere (Spence 2011).

Labor arbitrage, which consists of exploiting lower-wage opportunities in various places around the world—mainly in low-income countries—has never been the only motivation for firms to locate their activities abroad. Other transaction costs are also important determinants for such decisions. But in a world where governments are increasingly offering geographic enclaves such as industrial parks with excellent infrastructure, good business conditions and good governance, labor costs become once again important. As emerging economies are booming, wages there are rising fast,<sup>1</sup> forcing firms to look for labor arbitrage opportunities elsewhere—including in Africa.

The remainder of the paper is organized as follows: Section 2 surveys of some of the main strands of the theoretical literature on unemployment and employment, and stresses the fact that their interesting frameworks may not be transferable to low-income countries whose endowment and production structures are profoundly different from that of high-income economies. Section 3 tries to tackle the knowledge deficit on employment creation in Africa by shedding light on the

> 1 It is estimated, for instance, that pay factory for workers in China soared by nearly 70 percent in just five years (2005-2010). Source: _The Economist_ (2011, p. 79).
provided for free, and thus the difficulty to introduce a culture a payment for these services. Finally, in some cases, it can also be because the services are not affordable. Unfortunately, the LSMS and HBS data provide no information as to the reason for non-payment.

**Table 4.3. Household expenditures housing and communal services**

(percent of total household expenditures)

||**1991**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|
|---|---|---|---|---|---|---|---|---|
|**_Former Soviet Union_**|||||||||
|Armenia|1.7|3.3|5.9|8.5|8.6|12.2|10||
|Azerbaijan|1.4|0.9|0.8|1.4|||||
|Belarus|1.4|5.6|5.6|4.8|3.6|2.0|3.0|4.8|
|Georgia|2.1|||7.5|7.3|7.0|8.2||
|<br>Kazakhstan|1.8|5.9|7.2|10.6|9.8|13.5|11.9||
|Kyrgyzstan|1.3|4.2|4.4|4.7|4.4|4.3|4.9|5.7|
|Moldova|1.7|7.3|6.9||||||
|Russia|1.6|4.2|5.7|5.1|5.4|4.8|4.7|5.2|
|Tajikistan|1.3|1.9|||2.0|1.7|1.8|2.5|
|Turkmenistán|1.4|1.2|0.6|2.5|3||||
|Ukraine|2.2|8.0|11.9|12.5|11.3|8.2|6.5|9.3|
|Uzbekistán|1.7|0.8|||||||
|**_The Baltics_**|||||||||
|Latvia||13.9|14.3|15.0|16.6|17.3|16.3|14.3|
|Lithuania|1.7|14.8|11.8|12.3|12.3|12.9|13.5||
|Estonia|||17.8|18.8|18.1|17.7|15.3|14.9|
|**_Eastern Europe_**|||||||||
|Hungary||||||21.6|20.2|20.2|
|Poland||||||18.4|17.9|18.8|
|Bulgaria||||||15.9|16.3|15.7|
|<br>Slovenia||||||10.4|11.6|11.7|
|Romania||||||17.6|19.2|17.6|
|Croatia|||||10.8|13.3|13.3||
|Macedonia||12.4||||||11.3|
|Czech Republic|9.7|13.7|13.7|15.2|17.7|18.8|19.8|19.0|
|Slovakia||||||14.6|16.4|15.7|
|Serbia/Montenegro|12.4|9.8|12.4|13.6|13.4|14.8|11.1|9.6|
|**_OECD_**|||||||||
|Finland|21.1|25.3|25.3|25.8|25.5|25.6|25.5||
|Italy|17.5|19.4|19.9|19.6|19.4|19.5|19.9||
|<br>UK|18.3|19.1|18.7|18.5|18.2|18.2|18.3|18.7|
|Canada|23.8|24.7|24.5|23.7|23.4|23.0|23.3|23.3|
|Germany|20.1|23.4|24.2|24.5|24.3|24.3|||
|USA|27|28|28|28|28|28|29||

Source: For CIS (except Georgia for 1990-91) - CIS Statistical Handbook of Social and Economic Indicators, 2002; For Baltics - Statistical Yearbooks for each country; For Eastern Europe - Czech Statistical Office http://www.czso.cz/eng/redakce.nsf/i/home, except for Serbia and Montenegro (Yugoslavia Stat. Yearbook), Croatia (Stat Yearbook of the Rep. Of Croatia), and Macedonia (Stat. Yearbook of the Republic of Macedonia); Czech Republic: Czech Statistical Office. Indicators of Economic and Social Development 1990-2003. <u>http://www.czso.cz/eng/edicniplan.nsf/p/1404-03. Accessed on Nov. 22, 2003; USA: Consumer Expenditure</u> Survey, Bureau of Labor Statistics, U.S. Department of Labor; Canada: Statistics Canada (http://www.statcan.ca/); EU Countries: Eurostat Yearbook 2000; data for 2001 from Housing Statistics in the EU, 2002 Notes: Serbia and Montenegro data include Kosovo until 1999.

33