rafmacalaba/datause-encoder-data huggingface dataset:
a�ect current economic outcomes<sup>3</sup> . Areas with bad colonial activities should thus have lower levels of economic development than areas with good colonial activities, which included many other economic activities that did not rely on coerced labor. Similarly, areas with high pre-colonial population density should have lower levels of economic development today.

This line of argument is not entirely new and is largely based on Engerman and Sokolo�(1997 and 2002) and Acemoglu et al. (2001, 2002, and 2005). The contribution of this paper is mainly two-fold:

1. We extend the previous arguments to explain within-country variation in levels of economic development. We present both anecdotal and empirical evidence supporting a within-country correlation between colonial activities and development today. In addition, we provide indirect evidence suggesting that institutions are the mechanism through which history a�ects current levels of economic development.

2. We argue, in contrast to Engerman and Sokolo�, that having good colonial activities did not always lead to a good development path. Instead, the technologies used in di�erent areas with good activities were endogenous to the availability of a local labor force. Areas suitable for good activities that had low pre-colonial population density followed the predictions of Engerman and Sokolo�in terms of creating a big middle class based on a disperse property structure. However, areas suitable for good activities that had high pre-colonial population density tended to feature exploitation of labor and have a high concentration of income. Some areas that had good activities thus also had ugly activities.

We collect data on economic activities performed in di�erent regions during the colonial period for 16 countries in the Americas.<sup>4</sup> Each region is assigned three dummy variables summarizing whether it had predominantly good, bad or no colonial activities. We also collect data on pre-colonial population density (mainly from Denevan, 1992 and the references therein). The paper then correlates these historical variables with two current measures of economics development for states or regions in the eight countries (PPP GDP per capita and poverty rates). The results show that, in 2000, pre-colonial population density is negatively and correlated with current GDP per capita. Going from the 25th percentile in pre-colonial population density (-0.97) to the 75th percentile

> 3For the purposes of this paper, we refer to institutions as bundle of variables that may a�ect long run development, including the availability of human capital.

> 4The sample includes data for Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Panama, Paraguay, Peru, United States, Uruguay, and Venezuela.

2
While investigating whether the Bolivian gas boom of the 1990s has increased the income of poor households and thereby reduces income inequality, Lay et al. (2016) also account for the informal sector in their CGE model-based analysis. The study classifies both capital and labor between the formal and informal categories. Capital mobility is allowed only within the category, meaning that formal capital can move within the formal sector and so does the informal capital. There is no direct substitution between formal and informal capital. On the labor front, labor market is differentiated between the skilled and unskilled; the latter is further differentiated between the rural and urban categories. Skilled labor is fully mobile across production sectors, that is not the case for unskilled labor. There is a huge difference in wage rate within the unskilled labor between the formal and the informal sectors. For example, unskilled workers receive about half the average wage in the informal construction sector, whereas they receive about the average wage in the formal sector.

Robilliard et al. (2011) also incorporate the informal sector in their CGE model developed for assessing the impacts of the financial crisis in Indonesia. They first divide the labor into eight categories: urban male unskilled, urban male skilled, urban female unskilled, urban female skilled, rural male unskilled, rural male skilled, rural female unskilled, and rural female skilled. The labor markets are then segmented between formal and informal sectors. The formal sector, with an increasing wage-employment curve, allows imperfect competition resulting in informal sector labor equivalent to self-employment. The informal sector absorbs any labor not employed in the formal sectors. Wages adjust to clear all labor markets in the informal sectors, whereas employment adjusts in the formal sectors. The informal sectors are also assumed not to require any imported intermediate inputs Atuesta and Hewings (2013) developed a CGE model for Colombia to analyze the effects of the legalization of drugs (narcotics) on the Colombian economy. Drug or narcotics production and trade is the informal sector. The model also introduced unemployment. Households are divided into 20 categories based on income and location. Changes in wages and migration are estimated using two separate microeconomic models: a labor participation model and a Nearly Ideal Demand System (NIDS), which are then linked to the CGE model. Microeconomic models use data from a household survey that collects information about the demographics, income, expenses, and labor characteristics of 64,119 different households in rural and urban areas. The labor participation micro model first estimates the labor supplies fed into the CGE model to determine the wage 6
capital, to affect growth. A World Bank ( 1991) study found that economic growth **rates** in a sample of 60 developing countries during 1965-87 (Updated to 1994 later) were especially high where there was a combination of a high level of education and macroeconomic stability and openness. The impact of trade openness on long-term growth thus depends on how well people are able to absorb and use the information and technology made available through trade and foreign investment.

Thomas and Wang (1997) looked at the interaction of openness and education and the impact on the performance of the World Bank's lending projects.<sup>3</sup> On an average, countries with a more educated labor force and a more open economy had a rate of return on projects 3 percentage points higher than those that had only one or the other (figure 1).

**Figure 1. Education and openness interact and increase investment returns**

<!-- Start of picture text --> Education, Openness and Economic Rates of Return<br>of 1,265 World Bank Projects<br>18.0<br>18.0 15.2<br>17.0<br>0 16.0 14.8__<br>E 14.0 ,,, Nbre open<br>0 ~~13.0<br>12 0 Less open<br>Low<br>education High<br>education<br><!-- End of picture text --> Note: Economic rates of return are from the evaluation database of the Operations Evaluation Department. Education is measured by the average level of schooling of the labor force. and openness by the logarithm of the foreign exchange parallel market premium. Source: Author's calculations.

We have examined case studies of the importance to economic growth of the distribution of education and of the interaction of policy reform and education. We turn next to theoretical models and empirical investigations of the distribution of education and the interaction of policy relForms and education.

> 3 The cross-country project-level data set includes variables on education, per capita income, openness, government expenditure, and project performance. The project data cover 3,590 lending projects in 109 countries evaluated by the Operations Evaluation Department (OED) in 1974-94, with the OED rating of overall performance (satisfactory/not) and economic rates of return.

9
alternative definitions of FDI<sup>inflows.Still, our simple and restricted correlationbetween</sup> the IPA budget and FDI must<sup>be interpretedwith caution.The positive and significant</sup> correlation does not necessarily imply<sup>that promotionalwayspositivelyaffectsFDI,but it</sup> makes it more difficult to argue<sup>that promotionis infact bad for attracting FDI. The</sup> positive correlation, in other words,<sup>restrictsthe range of possibilities.</sup>

**Table 1: Estimated Elasticity Coefficients**<sup>**a/,b/,c/**</sup> (t-statistics in Parenthesis)

||FD|I|FDI(ADJ<br>FORM<br>|USTED<br>&A)|
|---|---|---|---|---|
|IPABudget|.25||.31<br>||
||(2.41)||(3.33)||
|IPAStaff||.26<br>||.20<br>|
|||(1.56)||(1.14)|
|investmentclimate-Heritage|-1.62|-1.64|-1.78<br>|-1.90<br>|
||(4.09)|(-3.80)|(4.15)|(4.12)|
|GDPpercapita|.57|.73|.42<br>|.49<br>|
||(2.48)|(3.10)|(1.67)|(2.11)|
|Observations<br>|58<br>|49<br>|51<br>|43<br>|
|AdjR<sup>2</sup>|.682|.683|.696|.681|

# Notes:

a/ All variables are in logs except<sup>for the Heritage Index.for whichan improvementcaptured by</sup> a decline in the indicator.

b/ We omitted to report the constant<sup>term.</sup> c/ Numerous other external variables<sup>were tested inthe regressionsincluding GDP,regional</sup> dummies, other investment climate indicators.<sup>In general,the results wererobust independently</sup> of the inclusion or exclusion of these variables.

This positive association<sup>is found whenthe promotioneffort is measuredby theIPA</sup> budget. When effort is measured<sup>by the levelof humanresources,therelationshipisalso</sup> positive, but not statistically significant.<sup>It is possible that theclose relationshipof FDI</sup> to expenditures means that promotion<sup>activitiesrequire lesslabor thanmoney.Anecdotal</sup> evidence collected at the country<sup>levelreveals thatfixed labor costsusuallyaccountfor</sup> less than one-third of an agency<sup>total budget--the bulk of expensesis associatedwith</sup> buildings, promotion materials,<sup>advertisement,andpromotionaltrips.</sup> 13
Kim, Sangmook and Wouter Vendenabeele (2010), “A Strategy for Building Public Sector Motivation Work Internationally,” _Public Administration Review_ 70.5: 701-709.

Kuipers, Ben, Malcolm Higgs, Walter Kickert, Lars Tummers, Jolien Grandia and Joris Van Der Voet (2014), “The Management of Change in Public Organisations: a Literature Review,” _Public Administration_ 92.1: 1-20.

Leisink, Peter and Bram Steijn (2009), “Public Service Motivation and Job Performance of Public Sector Employees in the Netherlands,” _International Review of Administrative Sciences_ 75.1: 35-52.

MacIntyre, Alistair (2000), “Funny Money: Fiscal Policy, Rent Seeking and Economic Performance in Southeast Asia,” in K.S. Jomo and Mushtaq Khan eds, _Rent-Seeking in South-East Asia_ (Cambridge: Cambridge UP), 248273.

McLoughlin, Clare (2015), “When Does Service-Delivery Improve the Legitimacy of a Fragile or ConflictAffected State?” _Governance_ 28.3: 341-356.

McNulty, Terry and Ewan Ferlie (2004), “Process Transformation: Limitations to Radical Organizational Change within Public Service Organizations,” _Organization Studies_ 25.8: 1389-1412.

Neves, Pedro, and Antonio Caetano (2006), “Social Exchange Processes in Organizational Change: The Roles of Trust and Control,” _Journal of Change Management_ 6.4: 351-364.

Pak Kimchoeun and David Craig (2008), “Accountability and Public Expenditure Management in Decentralized Cambodia,” Working Paper no. 38 (Phnom Penh: Cambodia Development Resource Institute), available online at <u>http://www.cdri.org.kh/workpap.htm, accessed 13 June 2016.</u> Parks, Thomas and William Cole (2010), Political Settlements: Implications for International Development Policy and Practice (Bangkok: Asia Foundation).

Pearce, Craig and Henry Sims Jr (2002), “Vertical Versus Shared Leadership as Predictors of the Effectiveness of Change Management Teams: An Examination of Aversive, Directive, Transactional, Transformational, and Empowering Leader Behaviors,” _Group Dynamics: Theory, Research and Practice_ 6.2: 172-197.

Pech Sotheary (2017), “Pensions Rise in the Pipeline,” _Khmer Times_ 13 March, online at <u>http://www.khmertimeskh.com/news/36381/pensions-rise-in-the-pipeline/, accessed 26 April 2017.</u> Robertson-Snape, Fiona (1999), “Corruption, Collusion and Nepotism in Indonesia,” _Third World Quarterly_ 20.3: 589-602.

Rodan, Garry and Caroline Hughes (2014), _The Politics of Accountability in Southeast Asia_ (Oxford: Oxford University Press).

37
With respect to out-of-pocket spending on services, among villages included in the population surveys with a voucher client who delivered most recently at a private facility, the proportion of respondents who paid for the services declined by 44 percentage points between 2008 and 2010–11 (table 5). In villages with no voucher client at follow-up, the proportion of women who delivered their most recent babies at a private facility and paid for the services declined by only 11 percentage points between 2008 and 2010–11. The decline in the proportion of women paying for delivery in private facilities was therefore greater—by 33 percentage points—in villages with a voucher client than in villages with no voucher client at follow-up. The difference-in-differences estimate from the multilevel logit model shows that the decline was significantly greater in villages with a voucher client, compared with villages with no voucher client.

Table 5 Percentage of Women Who Paid for the Delivery of the Most Recent Birth and Difference-in-Differences Estimates in Changes over Time between Villages with and Villages without a Voucher Client Resident in 2010–11

|Services|Vouch<br>resident<br>at fol<br>(|er client<br>in village<br>low-up<br>%)|No vou<br>resident<br>at fol<br>(|cher client<br>in village<br>low-up<br>%)|Differen<br>differe|ce-in-<br>nces|
|---|---|---|---|---|---|---|
||2008|2010–11|2008|2010–11|Percentage<br>points<sup>a</sup>|Odds<br>ratios<sup>b</sup>|
|Paid-for delivery<br>at|||||||
|Private facility|98|54|97|86|33|0.1*|
||(_N_=|(_N_=|(_N_=|(_N_= 21)||(0.0–|
||206)|133)|112)|||0.9)|
|Public facility|30|25|24|13|−6|2.2|
||(_N_=|(_N_=|(_N_=|(_N_= 60)||(0.7–|
||327)|149)|180)|||6.9)|

27
- 17 -

## **Table 1.** **<u>Summary statistics</u>**

|Variable|Obs|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|Firm inventory stock (days of operations)|1754|25.5|41.1|0.0|730.0|
|Road density around each city (km/km2)|1754|0.7|0.5|0.2|1.5|
|Road distance to the nearest port (km)|1754|796.5|501.3|6.0|1469.6|
|Transport cost to the port (US$/ton)|1754|142.4|36.5|78.3|199.1|
|City population (million)|1754|1.8|1.4|0.1|4.4|
|Annual sales before three year (US$ million)|1754|2.3|26.5|0.0002|1010.0|
|Dummy for foreign-owned firms<sup>1</sup>|1754|0.14|0.35|0|1|
|Interest rate (%)|554|16.1|6.9|0|100|
|Firm age (years)|1754|15.2|12.5|4.0|98.0|
|Dummy for importing firms<sup>2</sup>|1754|0.20|0.40|0|1|
|Dummy for exporting firms<sup>3</sup>|1754|0.15|0.35|0|1|
|Industry dummy variable: Textile|1754|0.10|0.30|0|1|
|Agrobusiness|1754|0.26|0.44|0|1|
|Metals & chemical|1754|0.11|0.32|0|1|
|Service|1754|0.39|0.49|0|1|
|Other manufacturing|1754|0.10|0.30|0|1|

1/ Foreign-owned firms are defined by companies of which majority stakes are owned by foreign investors.

2/ Importing firms are referred to as those who claimed to import any material inputs or supplies directly from abroad.

3/ Exporting firms are defined by those who exported at least 10 percent of sales to abroad.

# **IV. EMPIRICAL ISSUES**

A potential empirical challenge to estimate Equation (2’) is that transport connectivity might be endogenous, because infrastructure placement is normally targeted. Therefore, there is potentially an endogeneity issue: While productive firms prefer to be located where transport access is good,<sup>12</sup> the road authorities may also invest more in the places where productive firms exist or may be committed to invest in infrastructure to invite new firms. The reasons for investing in particular infrastructure are complex and hard to be specified. True abilities of firms are also unobservable for econometricians. Thus, the ordinary estimation is likely to be upward biased, if this self-selection matters.

> 12 Evidence is supportive of this. Good road infrastructure can attract more firm investment and enable exportoriented growth (Cieślik and Ryan, 2004; Boudier-Bensebaa, 2005; Qureshi, 2008). Exporting firms also prefer to be located close to motorways with better accessibility to the interregional market (Holl, 2004).
- 26 -

The paper will take advantage of an alternative approach using national accounts data. Some countries have data on fixed capital formulation for the livestock sector; however, such breakdown is not always available in developing countries. The investment rate, which is gross fixed capital formulation (GFCF) divided by GDP, is used as a proxy for infrastructure investment. Additional attention will be paid to the public GFCF ratio, since agricultural infrastructure is mostly invested by the government. These are weakly associated with the road infrastructure variables on a correlation basis. Although the sectoral composition of investment is never uncovered by this approach, there could be general policy implications for public investment in the livestock industry.
Educational and Health Impacts of Two School Feeding Schemes: Evidence from a Randomized Trial in Rural Burkina Faso<sup>∗</sup> Harounan Kazianga Damien de Walque Harold Alderman Oklahoma State University The World Bank The World Bank <u>harounan.kazianga@okstate.edu ddewalque@worldbank.org halderman@worldbank.org</u>

> ∗ We thank the World Food Program and the World Bank Research Committee for their financial support.  We thank the Ministry of Education of Burkina Faso, and its regional and provincial offices in the Sahel region for supporting and facilitating the roll out of the experiment. We thank Pierre Kamano, Tshiya Subayi and Timothy Johnston from the World Bank and Annalisa Conte, Olga Keita, Kerren Hedlund, Ute Maier, Ali Ouattara and Bernadette Tapsoba from the World Food Program for their support and advice. We thank Jean-Pierre Sawadogo and Yiriyibin Bambio from the University of Ouagadougou and Laeticia Ouedraogo from the Institut de Recherche en Sciences de la Santé for coordinating the field work.  The findings, interpretations and conclusions expressed are entirely those of the authors.  They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent, nor do they reflect the views of the World Food Program.
selected, including 4 beneficiary and 4 non‐beneficiary households.<sup>16</sup> The follow‐up survey sample included 1,296 households, of which 94% were successfully interviewed. The follow‐up survey was collected between October 2016 and January 2017.

Program administrative data complement the survey data. These include costs, household‐level information on registration and payment status, as well as monitoring data from the CBT implementation process.

_Figure 1: Study timeline_

|**2015**|||||**2016**|||||
|---|---|---|---|---|---|---|---|---|---|
|Jun<br>Jul<br>Aug<br>Sep<br>Oct<br>Nov|Dec|Jan|Feb<br>Mar<br>Apr|May|Jun<br>Jul<br>Aug|Sep<br>Oct|Nov|Dec|Jan|
|LEAN<br>HARVEST|||||LEAN|HARVEST||||
|REGISTRY SURVEY|PMT,C|BT & FCS|CVALID.|||||||
|BASELINE|||REGIST.||PAYMENT||FOLLO|W‐UP||

The study uses different measures of welfare, including consumption per capita, food consumption score, assets and livestock indices, income per capita, the food stock coverage, stunting, wasting, and underweight. Consumption includes annual expenditure on food, non‐food (including health and schooling) and durables. The asset and livestock indices are constructed using Principal Component Analysis (PCA) based on different asset and livestock variables. Annex II contains detailed definitions of each of the welfare measures.

# 3.4 Key sample characteristics and balance

Table 1 presents general household‐level descriptive statistics of the baseline sample, as well as tests to document balance in characteristics across villages. This is a poor, homogeneous, and uneducated population. Of the sample households, 85 percent are found to be below the national poverty line, compared to a national poverty rate of 48.2 percent for Niger as a whole (INS 2016). The level of inequality between households in project villages is low, with a Gini coefficient of 0.30.<sup>17</sup> Only 1 in 5 households has at least one member who has any formal education. Beneficiary households represent 43 percent of the sample.

Although food insecurity was measured during the harvest season, when it is usually at its lowest levels, it still affected about 13 percent of households at baseline. Households reported that their own agricultural production covered food needs for an average 3.4 months during the year prior to the survey. Households are large with a mean size of 7. Twenty‐two percent of all households are polygamous. Nine percent of households are headed by a woman.

> 16 In some villages, due to sampling variation, the total number of beneficiary or non‐beneficiary households sampled at baseline was lower than 4. In these cases, additional non‐baseline households were added to the sample. Of the 1,296 households included in the sample, 86% were also sampled at baseline.

> 17 If project areas represented a country, it would be among the most equal in the world.

11
- Lall, S.V. and T. Mengistae. (2005). “The impact of business environment and economic geography on plant level productivity: an analysis of Indian industry.” Policy Research Working Paper 3664. Washington, DC: World Bank.

- Lall, S.V. and H. G. Wang (2007). “Improving the Development Impact of Infrastructure – proposal for a research program” mimeo.

- Mani, M., S. Pargal and M. Huq. 1997. “Is there an environmental ‘race to the bottom’? Evidence on the role of environmental regulation in plant location decisions in India.” Poverty, Environment and Growth Working Paper No. 15, Washington, DC: World Bank.

- Ministry of Finance, Planning and Economic Development: Republic of Uganda. Draft Estimates of Revenue and Expenditure (Recurrent and Development). 2005-2006.

- Mirachy, W. (1995). Cities and the Product Cycle.: M.I.T. Ph. D. Dissertation.

- Puga, D. (2002). “European regional policies in light of recent location theories.” Journal of Economic Geography, 2(4), 373-406.

Rivera-Batiz, F. (1988). Increasing returns, monopolistic competition, and agglomeration

- economies in consumption and production. _Regional Science and Urban Economics_ , 18, 125-154.

- Thomas, T. S.  (2007). _Terrain Roughness, 10 km Aggregate, for Africa_ .  Washington: The World Bank, FEU, SLD.

- Thomas, T. S.  (2007). _Travel Time Friction Grid for Africa at 500-meter Resolution_ . Washington: The World Bank, FEU, SLD.

Uganda Bureau of Statistics. 2002. Population and Housing Census: 2002

- Uganda Bureau of Statistics. 2006. Uganda National Household Survey 2005 / 2006. Uganda Bureau of Statistics. 2002. Uganda Business Register 2001/2002. Uganda Bureau of Statistics. 2002. Uganda Business Inquiry 2000/2001

USGS Global GIS Database: Africa CD, VMAP level 0. 2001.

Vernon, R. (1966). International investment and international trade in the product cycle.

_Quarterly Journal of Economics_ , 80, 180-207.

- World Bank (2004). Project in support of the third phase of the road development program. Project Appraisal Document 27585-UG.

- World Bank (2008). Reshaping Economic Geography. World Development Report 2009. Oxford University Press.

- You, L., S. Wood, U. Wood-Sichra, J. Chamberlin. (2007). Generating plausible crop distribution maps for Sub-Sahara Africa using a spatial allocation model. _Information Development,_ Vol.23, No.2/3, p.151-159

- You, L., S. Wood, U. Wood-Sichra. (2007). Generating plausible crop distribution and performance maps for Sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. _IFPRI Discussion Paper 720_ , International Food Policy Research Institute, Washington, DC, USA

25 -