rafmacalaba/datause-encoder-data huggingface dataset:
WPS4207 <mark>P</mark> OST <mark>-C</mark> ONFLICT <mark>T</mark> RANSITIONS <mark>W</mark> ORKING <mark>P</mark> APER <mark>N</mark> O <mark>. 11</mark> Short-Term and Long-Term Effects of United Nations Peace Operations Nicholas Sambanis* Yale University, Political Science P.O. Box 208301, New Haven, CT 06520-8301, <u>Nicholas.Sambanis@yale.edu</u>

# Abstract

Earlier studies have shown that United Nations peace operations make a positive contribution to peacebuilding efforts after civil wars. But do these effects carry over to the period after the peacekeepers leave? And how do the effects of UN peace operations interact with other determinants of peacebuilding in the long run? We address these questions using a revised version of the Doyle and Sambanis dataset and by applying different estimation methods to estimate the short-term and long-term effects of UN peace missions. We find that UN missions have robust, positive effects on peacebuilding in the short term. UN missions can help parties implement peace agreements, but the UN cannot fight wars and UN operations contribute more to the quality of the peace, where peace is based on participation, than to the longevity of the peace, where peace is simply the absence of war. The effects of UN missions are also felt in the long run, but they dissipate over time. What is missing in UN peacebuilding is a strategy to foster the self-sustaining economic growth that could connect increased participation with sustainable peace.

# World Bank Policy Research Working Paper 4207, April 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Authors’ Note:  This paper is part of joint research on peacebuilding with Michael Doyle, whom I thank for many discussions and comments.  I also thank Don Green, Alan Gerber, Alexandros Michaelides, Kenneth Scheve, Alan Krueger, and Jim Fearon for extremely useful discussions on methodological issues. The arguments in this paper have benefited from exchanges on UN peacekeeping with George Downs, Stephen Stedman, Michael Gilligan, and Page Fortna.  I thank Katie Glassmyer, Jonah Schulhofer-Wohl, Steve Shewfelt, Douglas Woodwell, Ana Maria Arjona, and Annalisa Zinn for help with data collection and gratefully acknowledge financial support from the World Bank.
_Low-Income Countries’ Access to Private Debt Markets_

# **Figure 14 Standard deviation of real GDP growth and GNI per capita, 1990-2007**

<!-- Start of picture text --> Standard deviation of real GDP growth rate<br>Low-income Middle-income High-income<br>15<br>10 R 2 = 0.10<br>5<br>0<br>4 5 6 7 8 9 10 11<br>Log(GNI per capita)<br><!-- End of picture text --> _Source:_ World Bank World Development Indicators.

Low-income countries have sustained growth rates comparable to middle-income countries over the past five years with similar inflation rates. However, large external imbalances are more common in low-income countries. Current account deficits exceeded 5 percent of GNI in 30 of 40 low-income countries in 2007, and exceeded 10 percent in 14 cases. Given the accounting relationship between the current and capital accounts, one might expect that a negative relationship between current account deficits and private capital flows. We examined this by adding countries’ current account balance as a ratio to GDP (CA / GDP) to the regression equation (1): Eq. (2) log(capital flow / GDP) = α + β1 log(GDP) + β2 log(GNI per capita) + β3 100*(CA / GDP) + ε Estimates of the parameter β3 reported in Table 4 indicate that countries with larger current account deficits have higher FDI inflows, but not private debt flows. One interpretation of this result is that countries with heavy external financing needs are perceived as being more prone to default and hence are less able to access private debt markets. It is important to recognize, however, that causality between current account balances and capital flows runs in both directions, implying that the estimates of β3 are distorted by simultaneity bias.

18
> 3 Empirical Analysis

# **3.1** The Data

We use an original data set, developed by the World Bank, which describes the characteristics of nearly 1,000 infrastructure projects awarded in Latin American and Caribbean countries from 1989 to 2000, in the sectors of telecommunications, energy, transport and water. We restrict ourselves to the sectors of transport and water, both because renegotiations only occur in these sectors and because of their characteristics: first, transport and water projects are concessions stricto sensu, as opposed to telecommunications and energy projects which in some cases are closer to privatizations with transfer of assets; second, they both imply in general significant transfers from the state to the private operators<sup>2 5</sup> Considering only concessions for which we know whether they were renegotiated or not as of 2000, and at what date this renegotiation took place, and restricting to the 5 countries (Argentina, Brazil, Chile, Colombia and Mexico) where concessions were granted on a regular basis through the 1990s in these two sectors, we get a sample of 307 concessions. Table 2 shows the distribution by countries and sectors.

Table 2: Concessions by country and sector

||**_Transport_**|**_Water_**|**_Total_**|
|---|---|---|---|
|Argentina|38|5|43|
|Brazil|**36**|24|**60**|
|Chile|**23**|2|25|
|Colombia|41|7|48|
|Mexico|80|51|131|
|Total|218|89|307|

The database contains detailed information about the characteristics of these concessions, including general details about the projects (sector, activity, year of award), the award criteria, size and duration of the concession, information with respect to the institutional context and degrees of freedom of the regulator, the type of regulatory framework put in place (price cap, rate of return, no regulation), and other details of the concession contract like arbitration clauses, nationality of operators, among others. Appendix 1 presents the full list and definitions of variables used in the analysis below. Table 3 summarizes the frequency of the concessions' key characteristics in our sample which are represented through dummy variables.

> 25See Guasch (2001) for a detailed description of the data.

22
Seymour, Greg. 2018. Discussant comments, Workshop on Improving Data Collection on Women and Youth Employment, World Bank and IFAD, November 2018, Rome.

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Slavchevska, Vanya, Susan Kaaria, and Sanna- Taivalmaa, Sanna-Liisa. 2016. “Feminization of Agriculture in the Context of Rural Transformations: What is the Evidence?” World Bank, Washington, DC.

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Van den Broeck, Goedele, and Talip Kilic. 2018. “Dynamics of Off-Farm Employment in Sub-Saharan Africa: A Gender Perspective.” World Bank Policy Research Working Paper 8540.

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28
The **DCC(p)** curve allows a combined presentation of both the efficiency gain from the reform and its distribution among income groups. If the curve<sup>is increasingat p,</sup> then individuals located at that percentile gain from the policy. If the<sup>curveis decreasing</sup> then they are losing from the reform. **DCC(1)=(1- ak)** provides an indication of<sup>whether</sup> the cumulative gain for the whole society is positive, zero or negative.<sup>Theoutcome</sup> hinges on how **ask** compares to 1. When this ratio is equal to one, the policy<sup>reformis</sup> neutral with respect to efficiency (no efficiency gain, nor loss). However, if<sup>the</sup><sup>**DCC(p)**</sup> curve lies above the horizontal axis for all other values of p, we conclude<sup>that the policy</sup> is welfare improving (in the sense of Dalton) on account of distribution.<sup>When</sup><sup>**DCC(1)<**</sup> 0 (implying that ask>l) condition (4.21) cannot hold for all h, since it<sup>failstoholdfor</sup> h=n.

It is useful to note that the same methodology can help analyze the<sup>case wherea</sup> _subsidy is financed by a proportional income tax._ In this case, we have to<sup>comparethe</sup> concentration curve for commodity s to the Lorenz curve of the income distribution.

Figure 4.1. Difference in Concentration Curves for Cereals and Beverages<sup>inGuinea1994</sup> <!-- Start of picture text --> .30-<br>.2 5-<br>.20-<br>.1 5 -<br>.00<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br><!-- End of picture text --> To illustrate the point, we compare the distributional characteristics<sup>oftwo</sup> commodities, cereals and beverages, using data from the 1994 integrated<sup>household</sup> survey from Guinea. Figure 2.2 shows the concentration curves of the two<sup>commodities</sup> along with the Lorenz distribution of total household expenditure. It is<sup>evidentfrom that</sup> figure that the consumption of cereals in Guinea is more equally distributed<sup>thantotal</sup> consumption and the consumption of beverages. Not having estimates for relative efficiency costs, we proceed as if **ask** were equal to one (the case of neutrality<sup>vis-a-vis</sup> efficiency). Under this assumption, we would expect welfare dominance<sup>totheextent</sup> that the concentration curve for cereals lies totally above the concentration<sup>curvefor</sup> 36
methodology can be used to answer these questions. Given the richness of the data, the paper also presents results using different definitions of SME as well as results for large firms which can be used as a benchmark for SMEs.

This paper provides an innovative way of measuring credit-constrained firms based both on their usage of and ability to obtain new credit. This is an important contribution to the literature since most papers analyzing SME’s credit either look only at usage of credit, as opposed to access, or focus on self-reported obstacles based on perceptions instead of objective data based on the experience of the firm (e.g., whether access to finance is an obstacle for the firm or whether the firm has a bank loan or a line of credit).

We find that SMEs are more likely to be credit constrained than large firms. In fact, the probability of being credit constrained decreases with firm size. Firm age does not relate to the credit constrained status. Once we control for size, age of the firm has no explanatory power with regards to the probability of being credit constrained. When we use a measure based on the perception of access to credit as an obstacle, we find that both size and age are negatively related with the increasing degree of obstacle access to credit represents. Our measure of being credit constrained based on hard data has a very high explanatory power over the perception measure. That is, firms that are credit constrained in reality are more likely to report access to finance as an increasing obstacle. This is an important check since several of the papers written on access to finance using Enterprise Surveys data focus on the perception measure.

Regarding the sources of finance, the data show that SMEs rely more on trade credit and informal sources and less on equity and formal debt than large firms. This finding applies both to financing of investment and working capital although equity is not one of the explicitly provided options for financing working capital in the survey.

3
**Table 5: Main results Pooling sample (three countries)**

||OLS: Ag|ree to Vaccina|te = 1|Logit(odds|ratio): Agree to|Vaccinate = 1|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Any Treat|-0.187***||-0.189***|0.400***||0.394***|
||(0.019)||(0.019)|(0.035)||(0.035)|
|T-Arm1||-0.201***|||0.371***||
|||(0.022)|||(0.042)||
|T-Arm2||-0.190***|||0.393***||
|||(0.022)|||(0.044)||
|T-Arm3||-0.191***|||0.392***||
|||(0.022)|||(0.044)||
|T-Arm4||-0.167***|||0.446***||
|||(0.023)|||(0.050)||
|Inc. Per Capita|||-0.000|||1.000|
||||(0.000)|||(0.000)|
|Rural Location|||0.077***|||1.526***|
||||(0.015)|||(0.126)|
|Household Size|||0.000|||1.003|
||||(0.003)|||(0.014)|
|Constant|0.454***|0.454***|0.393***|0.892|0.892|0.636***|
||(0.018)|(0.018)|(0.025)|(0.073)|(0.073)|(0.079)|
|Observations|6,779|6,779|6,779|6,779|6,779|6,779|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.176|0.177|0.181||||

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Logit model coefficients report odds ratios. Country binary variables are included in each regression but not shown.

The summary results from pooled regressions maintain the same patterns at the national level for Kazakhstan. In Table 6, columns 1 and 4 show that assignment to any hypothetical incentive is associated with a more than 22 percent lower intention to vaccinate than the control group, with results consistent using either OLS or Logit regression. Examining the results for each treatment arm (Table 6, columns 2 and 5) suggests that the effect remains uniformly negative across all offer amounts with an inverted-u shape in average reductions with rising compensation amounts (moving from the first to the fourth treatment arm). Including additional controls (Table 6, columns 3 and 6) does not make any meaningful change in the estimated effect.

**Table 6: Main results table Kazakhstan**

||OLS: Agree t|o Vaccinate =|1|Logit(odds r|atio): Agree to|Vaccinate = 1|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Any Treat|-0.221***||-0.223***|0.331***||0.329***|
||(0.048)||(0.047)|(0.064)||(0.063)|
|T-Arm1||-0.262***|||0.243***||
|||(0.051)|||(0.064)||
|T-Arm2||-0.207***|||0.360***||
|||(0.056)|||(0.093)||
|T-Arm3||-0.188***|||0.375***||
|||(0.061)|||(0.097)||
|T-Arm4||-0.228***|||0.348***||

11
which is much lower than its employment share, 61 percent. Thus shifting out of agriculture likely improves TFP growth.

Some caveats exist when making an inference between structural transformation and TFP growth in SSA. The assumption that agriculture is a low productivity sector can be challenged due to the high degree of underemployment in agriculture. Agriculture is highly seasonal and agricultural workers may not be continuously employed, making measurement of their productivity difficult. With many African households engaged in non-agricultural activities this can be especially problematic.<sup>37</sup> In addition, TFP growth in agriculture is not necessarily slower than in other sectors. The limited studies that compare TFP growth between agriculture and non-agriculture sectors provide inconclusive evidence.<sup>38</sup> Studies such as Block (2010) suggest that TFP growth is highly non-linear over time, and TFP in agriculture in SSA largely improved since 1980s. Therefore TFP growth can take place even without structural transformation.

While admittedly imperfect, the trend of structural transformation suggests its association with TFP growth. Figure 8A shows the share of each sector in GDP over time. It shows that structural transformation in SSA had been slow up until the mid1990s, with the contribution of the agriculture, industry, and service sectors to GDP being quite steady. Since the mid-1990s, however, structural transformation accelerated; the share of agriculture to GDP has significantly declined and that of industry largely increased. When the changes in TFP and the share of agriculture to GDP are plotted, a negative association between the two is observed (see Figure 8B), indicating that sectoral shifts from agriculture to the more modern sectors are related to the gains in factor productivity.

Structural transformation in employment may not be as evident as in output if the increase in the share of industry in output is driven by extractive industries where employment generation is believed to be small due to the capital intensive nature of their work. In such case, TFP growth coming from reallocation of labor may not be as large as the output transformation suggests. Policies to promote transition from

> 37 See Gollin (2002) for the empirical evidence for this.

> 38 See Christiaensen et al. (2011) for a discussion.

17
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 16. Water prices in Burkina are among the highest in Africa**

<!-- Start of picture text -->
1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>$ per cubic meter<br><!-- End of picture text -->

_Source:_ Banerjee and others 2009. _Note:_ DRC = Democratic Republic of Congo.

# Power

## **Achievements**

Burkina has taken firm steps to improve the legal and regulatory framework of the energy sector. In November 2007, an electricity law was adopted encouraging private sector –via the involvement of independent power producers—and donor participation in this sector. Also, in recent years, there has been a systematic effort by the Burkinabè authorities to increase power trade with neighbors in the regional West African Power Pool (WAPP). Investment has been carried out to integrate the national power system into a unified regional electricity market by building interconnections with Côte d’Ivoire (the Bobo-Dioblasso-Ouagadougou while facing few delays is already planned to be functional by 2014) and with Ghana (expected to commence in 2011). Nowadays, Burkina imports 15 percent of the electricity consumed. The power capacity from neighbors comes at much lower prices.

27
<!-- Start of picture text --> Hungary<br>1992 2003<br>Education 17.9 Education 27.0<br>Employment 15.1 Employment 14.0<br>Location (U/R) 6.0 Location (U/R) 12.0<br>Age 3.1 Dependency 5.0<br>Dependency 2.9 Age 3.0<br>Gender 1.9 Gender 1.0<br>Poland<br>1994 2002<br>Education 13.0 Education 19.1<br>Region 7.9 Region 12.2<br>Employment 4.0 Employment 6.4<br>Age 1.7 Age 1.7<br><!-- End of picture text --> Decompositions is Hungary using Mean Log Deviation index and income based on CSO and TASKI data, Poland –consumption per equivalent adult and HBS data, Russia – disposable resources per equivalent adult and RLMS data.

Sources: Toth, Sultz, World Bank (2002) – Poland PA, Popova

# **_Summing Up_**

The decomposition of changes in inequality over time by income source and socioeconomic group help identify the forces behind the direction and magnitude of changes in inequality across the transition countries. Although the theoretical framework developed to explain changes in inequality in transition does not allow rigorous testing of hypotheses and exact identification of various effects, it allows broad qualitative conclusions to be drawn. These conclusions, which are summarized in Table 8, show that each of the drivers of inequality, especially the ones specific to transition (1-5), operates through a specific channel and can be mapped by looking at the components of inequality in a particular way. However, the role and, in some instances, the direction of influence of each effect differs across countries depending on how far advanced they are 32
Policy Research Working Paper 5315

# **Abstract**

This paper estimates the impact of a change in procurement strategy of a private buyer in the central Indian state of Madhya Pradesh. Beginning in October 2000, internet kiosks and warehouses were established that provide wholesale price information and an alternative marketing channel to soy farmers in the state. Using a new market-level dataset, the estimates suggest a significant increase in soy price after the introduction of kiosks, supporting the predictions of the theoretical model. Moreover, there is a robust increase in area under soy cultivation. The results point towards an improvement in the functioning of rural agricultural markets.

This paper—a product of the Agriculture and Rural Development Team, Development Research Group—is part of a larger effort in the department to explore the impact of technological innovations in improving the functioning of agricultural markets in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at agoyal3@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Typical Wage in Past<br>Three Months|V|Husband|No|4|Typical wage the husband reports earning in<br>the past three months. It takes on a value of<br>zero if the husband earned nothing.|
|=1 if Currently Employed|V|Husband|No|4|=1 if husband currently employed.<br>The husband's sexual activity and marital<br>fidelity standardized index is constructed from|
|Sexual Activity and|||||three variables: number of sexual partners|
|Marital Fidelity<br>(Standardized)|V|Husband|No|4|ever, number of sexual partners in the past 12<br>months and an indicator for concurrent<br>multiple partners. Constructed so that higher<br>values are better.<br>=1 if husband does not suffer from|
|=1 if Does Not Suffer<br>from Psychological<br>Distress|V|Husband|No|4|psychological distress. Psychological distress<br>is equal to one if the summed General Health<br>Questionnaire- 12 score is equal to three or<br>higher, and is zero otherwise.|
||||||=1 if husband is HIV positive. Biomarker data|
|=1 if HIV Positive|V|Husband|No|4|for HIV was collected through home based<br>voluntary counselling and testing (HCT).|
|Attitudes Towards<br>Women's Empowerment<br>Super Index<br>(Standardized)|S9|Husband|Yes|4|Standardized super index of five indicators<br>listed below: preferences for children's<br>education, attitudes towards wife's autonomy,<br>attitudes towards abuse, divorce prospects, and<br>desired fertility.|

71