rafmacalaba/datause-encoder-data huggingface dataset:
Policy ReseaRch WoRking PaPeR 4713

# **Abstract**

Households in rural Colombia are confronted with a variety of violent threats: attacks and displacement threats by guerrillas and paramilitaries, gang violence among drug traffickers, and high common delinquency. In this context, households have to adjust their day-today decisions, including saving and portfolio choices, in order to be less vulnerable. The authors test the hypothesis that households, when confronted with exogenous violence, reduce their investment and, moreover, shift it from fixed to mobile assets, which would be safer in the case of displacement, and choose the opposite strategy under higher common delinquency associated with property crimes. Empirical evidence from a rich Colombian micro-data set strongly supports the hypothesis. The results shed new light on the economic impact of violence. The immediate reduction in capital stock might be much less severe than more permanent damage via the savings function. This has implications for the appropriate political answer to chronic violence in Colombia as well as in other areas of chronic conflict.

This paper—a product of the Human Development Group, Middle East and North Africa Region—is part of a larger effort in the department to understand the impact of violence on household decisions. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at rgrun@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
# **4.2.2 Policy Experiments**

Our next task is running five policy experiments. The first two consider an acceleration of public capital formation. IBGE data imply that gross electricity infrastructure formation has decreased in Brazil from a 3.28 percent average share of GDP in the 1980s to an average of 0.9 percent for the period 1990 to 2007. As a consequence, as Calderón and Servén (2010) and Frischtak (2013) among others argue, Brazil’s infrastructure investment has fallen in the past few decades below the levels shown by other Latin American and emerging nations such as Chile, China, and India. We now ask our model, what would have been the impact on the different sectors and the economy as a whole if Brazil had maintained the public-capital formation levels of the 1980s during the 1990s and 2000s? This would have implied an average investment in electricity infrastructure of 3.0 percent for the period 1970-2007.

We conduct two different experiments to achieve a stock of _Gt_ similar to the one that the increase in the ratio of _Igt_ to GDP from 1.81 to 3.0 percent would deliver. In the first experiment (the “ _λt_ up” case), the rise comes as the result of stronger incentives to invest in grid capacity induced by an increase in the political ideology parameter _λt_ . Put differently, the policy maker increases the public capital formation perceived as valuable by her supporting group. For this, starting from 1990, we increase each period the value of _λt_ so that _Gt_ rises at a constant rate that implies the desired average ratio _Igt/GDP_ of 0.03 for the whole period 1970-2006. All other parameters take on their benchmark numbers.

The results are in Figure 6 and the fourth column of Table 7. The Figure shows values of the different variables from 1970 to 2006. However, the quantities reported in the Table – this time refer to the differences for the interval 1990-2006 this will be the case in all the policy experiments. The bottom right panel depicts the different expenditure categories. We can see that the counterpart to the increase in the electricity infrastructure investment share is a decline in private consumption of 7.70 percentage points. The fixed cost share falls very little, namely by 0.07. Finally, the one for private investment increases by 5.22 percentage points triggered by the complementarity of the two capital goods.

49
-33-

job description, and through training. This should enable the Municipal Economist in future to systematically carry out a review of rates and charges, evaluate fairness and efficiency of taxes, cost recovery strategies and revenue collection efficiency.

**Table 4.3: Major Revenue Items for the Eight Project Towns**

||(Percent)|||
|---|---|---|---|
|SW-Ge<br>td Revenues|1q92-|1993|1994|
|Annual Increase in Total Self-||51%|10%|
|Generated Revenues||(1992-93)|(1993-94)|
|Property tax|13|19|22|
|Trade licenses|19|15|15|
|Market rent|18|16|18|
|Development levy|9|7|7|
|Hotel levy|_5_|4|_5_|
|Service charges|_5_|3|3|
|All others|32|36|30|
|Total Self-Generated Revenues|100|100|100|

_Excluding water_ _Source: PMO_ 4.16 The installation and use of activity based budgeting and the new accounting system (through LAAM; para. 4.17) would facilitate these and other necessary reviews. Tlhe inclusion of rate and tariff adjustments in the annual budget approval exercise and greater autonomy for tariffs and rate adjustments by the municipalities during the budget approval are measures that the Government will take into consideration when submitting draft legislation to Parliament, arnending the applicable local governnent acts (para. 1.28). The Government will also revise the by-laws for tax and tariff adjustments by the urban local authorities by the time of the Mid-Term Review.

# **Expenditure Control**

4.17 In order to overcome the shortcomings of financial management practices, the Government has promulgated a new accounting and budgeting system for local authorities (through LAAM; para. 4.10). Financed under USEP, the LAAM implementation was piloted in two of the eight secondary towns (Tanga and Mwanza) and in Dar es Salaam and is being put in place in the other six project towns. Similarly, the new activity-based budget format which was piloted in those towns has been replicated for all eight project towns for FY96. In addition, external financial audit of towns has started and FY92-FY94 accounts have been audited, and the audit of FY95 accounts is underway.
# **References**

Ackerberg, Daniel, Benkard, C. Lanier, Berry, Steven, and Ariel Pakes. “Econometric Tools for Analyzing Market Outcomes.” _Forthcoming chapter in Handbook of Econometrics_ , Vol. 6.

Andersson, Agnes and Magnus Jirström. 2007. “Rural-Urban Linkages in Amhara.” Department of Social and Economic Geography. Lund University: Lund. Processed draft report.

Bardasi, Elena and Abbay Asfaw Getahun. 2007. “Gender and Entrepreneurship in Ethiopia.” _Background paper to the Ethiopia Investment Climate Assessment._ Washington DC: World Bank. Processed.

Barret, Christopher, Reardon, Thomas and Patrick Webb. 2001. “Nonfarm Income Diversification and Household Livelihood Strategies in Rural Africa: Concepts, Dynamics and Policy Implications” _Food Policy_ 26(4): 315-331.

Bartelsman, Eric, Haltiwanger, John, and Stefano Scarpetta. 2004. “Microeconomic Evidence of Creative Destruction in Industrial and Developing Countries,” _Institute for the Study of Labor Discussion Papers_ 1374.

Cabral, Luis. 1995. “Sunk Costs, Firm Size and Firm Growth.” _Journal of Industrial Economics_ 43(2): 161-172.

Central Statistical Agency in collaboration with World Bank (2008). _The 2006/2007 Rural Investment Climate Survey: Basic Information Document_ . Addis Ababa: processed.

Davis, Junior and Dirk Bezemer. 2003. “Key emerging and conceptual issues in the development of the rural nonfarm economy in developing countries and transition economies.” _Development and Comp Systems_ 0510017 (EconWPA).

Deininger, Klaus, Songquing Jin and Mona Sur. 2007. “Sri Lanka’s Rural Non-Farm Economy: Removing Constraints to Pro-Poor Growth.” _World Development_ 35(12): 2056-2078.

Escribano, Alvaro and J. Luis Guash. 2005. “Assessing the Impact of the Investment Climate on Productivity Using Firm-Level Data: Methodology and the Cases of Guatemala, Honduras, and Nicaragua.” _World Bank Policy Research Paper_ 3621.

Guenther, Isabel, Olapade, Markus and Josef Loening. 2007. “A Review of the nonfarm sector in rural Ethiopia: Characteristics and Dynamics.” _Background paper for the Ethiopia Rural Investment Climate Assessment._ Washington DC: World Bank. Processed.

Haggblade, Steven, Hazell, Peter and Thomas Reardon. 2007. _Transforming the Rural Nonfarm Economy._ Baltimore: Johns Hopkins University Press and International Food Policy Research Institute.

Jovanovic, Boyan. 1982. “Selection and the Evolution of Industry.” _Econometrica_ 50(3): 649-670.

Lanjouw, Jean O. and Peter Lanjouw. 2001. “The rural non-farm sector: issues and evidence from developing countries” _Agricultural Economics_ 26(1): 1-23.

Liedholm, Carl. 2002. "Small Firm Dynamics: Evidence from Africa and Latin America," _Small Business Economics_ 18(1-3): 227-42.

-22-
purchased HYV maize is about 2.2 tons per ha, which is 0.5 tons higher than the yield of the local/recycled HYV maize. The difference between the two groups is statistically significant and partly driven by the differences in the quantities of the input applications. For instance, 86 percent of the purchased HYV maize plots receive at least some inorganic fertilizer, while only 58 percent of the local/recycled HYV maize plots do so. In terms of the quantity, the average amount of inorganic fertilizer applied on the purchased HYV maize plots is about 119 kilograms per ha, which is about twice as much as the amount applied on the local/recycled HYV maize plots.

In contrast, in Uganda, the maize yield is low for the two maize seed groups, and the difference between the two groups is small, at about 0.2 tons per ha. The small difference between the two maize seed groups may be due to the low applications of external fertilizer on both seed groups in Uganda. For instance, only three and six percent of the maize plots receive inorganic and organic fertilizer, respectively. Although the purchased HYV maize plots receive more inorganic fertilizer than the local/recycled HYV maize plots, the average quantity of the inorganic fertilizer application on the HYV maize plots is only nine kilograms per ha.

Among the maize plots with soil data, we find that the average carbon content is 2.5 percent in Kenya and 2.4 percent in Uganda (Table 1). Thus, the average carbon content is about the same in the two countries. In Kenya, the purchased HYV seeds are cultivated in better soils than the local/recycled HYV seeds, while in Uganda the purchased HYV seeds are cultivated on poorer soils than the local/recycled HYV seeds in Uganda. The average carbon content is not so different across seed types within country and across countries. To examine the relationship between the soil fertility and the maize production further, we divide the samples based on the soil carbon content next.

12
23 sector is reported to account for 743 percent of total employment, whereas the secondary and tertiary sectors account for 5.1 percent and 20.6 percent, respectively. Total modem sector emplovment therefore appears to be almost seven times the employment in industry (35 percent of the total vs. 5.1 percent of the total). A notable feature is the large size of the public sector (15.5 percent in 1980).

The unemployment problem appears to be most serious in the predominantly urban area of Cap-Vert. The 1979 labor migration survey, for example, reported that the unemployment rate in CapVert was 19.4 percent; the national average stood at 6.8 percent in that year. Moreover, Cap-Vert was the only region in which the unemployment rate exceeded the national average, according to the official data.

TABLE 41 Senega: Urban Labor Fofce, 1960 - 190

|||Number<br>(in tho|of Wor<br>usands)|kers|Parent<br>labor f|age of<br>orce|
|---|---|---|---|---|---|---|
||1960|1970|1976|1980|1960|1980|
|Moden Seocor|**53-1**|**122.4**|**1587**|1955|**_3S5_**|343|
|Productive Sector<br>AgricuL & Fishing<br>Nfanufacturing|42.2<br> <br>1.5<br>11.6|60.6<br>49<br>15.0|869<br>5.7<br>20.8|107.1<br>85<br>23.6|16.7<br>0.6<br>4.6|18.<br>1.5<br>4.1|
|Public Sector'|46.1|61.8|71.7|88.4|183|LS5|
|**Informa Sectori**|147.0|205.2|**2411**|**275.7**|**582**|**484**|
|**Openly Unemployed**|<br>17.2|41.1|69.8|**96.9**|6B|173|
|Urban Labor Force|252.5|368.8|469.6|570.1|100.0|100.0|

* Ciil Service and Non-Commercial Public Enterprises. * Presumably calculated as a residual. Ihe source does not identiry methodology. Source: Statistical Directorate, Ministy of Finance, Senegal.

The urban unemployment problem is also underscored in Alain Mingat's 1982 study. Table 4.1, which is assembled from Mingt's
ing refined Brain Drain indicators, which, in comparison with the existing literature, provide superior estimates of gross and net human capital levels across the world.

There have been several efforts to analyze bilateral migration patterns. The Eurostat database<sup>2</sup> provides data on the size of migration flows, by age, gender and country of citizenship, but solely between EU member states and numerous missing observations exist. More broadly, Ozden<sup> ̈</sup> et al (2011) referred to as OPSW henceforth, construct five 226x226 comprehensive matrices of origin-destination stocks that correspond to the last five completed census rounds, thereby extending the work of Parsons et al. (2007). However, while OPSW significantly broadens the time, gender and geographical coverage of the available data, different skills or education levels are not distinguished.

Another set of studies investigates the education structure of migration, but only for a limited set of destination countries for which data are more readily available. Docquier and Marfouk (2004, 2006) and Dumont and Lemaitre (2004) collect detailed census and register data on immigration from all the host countries of the Organization for Economic Cooperation and Development (referred to as OECD henceforth). Aggregating these numbers allows them to characterize the size and structure of low-skilled and high-skilled emigration stocks to the OECD from all the countries of the world. Docquier, Lowell and Marfouk (2009 - referred to as DLM henceforth) and Dumont, Martin and Spielvogel (2007) introduce the gender breakdown in the above analyses.

Existing data sets of bilateral migrant stocks disaggregated by education level only cap2See http://epp.eurostat.ec.europa.eu/portal/page/portal/population/data/database 3
# **OWNERSHIP, COMPETITION, AND CORRUPTION:**

# **BRIBE TAKERS VERSUS BRIBE PAYERS**

George R.G. Clarke and Lixin Colin Xu* Key words: Corruption, bribes, ownership, competition. **JEL codes:** Illegal behavior, K4; market structure, LI; industry studies, L9.

Some of the data used in this paper are from the World Business Environment Survey (WBES) 02000 The World Bank Group. We are grateful to Luke Haggarty and Andrew Stone for their help with the WBES data. _Contact informnation:_ L. Colin Xu, The World Bank, 1818 H Street NW, MSN MC3-300, Washington, DC 20433. Phone: (202) 473-4664. Fax: (202) 522-1155. E-mail: lxulworldbank.org.
<!-- Start of picture text --> 10.00 Non-Election Period<br>Trend Line Pre-, Current- or Post-<br>Election Year Trend Line<br>0.00<br>-10.00 Pre-, Current- or Post-<br>Election Year Observation<br>Non-Election Period<br>Observation<br>-20.00<br>-30.00<br>0 5 10 15<br>Average Sovereign Rating (Moody's, S&P, Fitch)<br>Budget Balance (% of GDP)<br><!-- End of picture text -->

**Figure 1.** _Results from analyses of linear trends for budget balances during election and nonelection periods, 1989-2004_

# **3.2. Regression Results**

Regression results in Table 1 confirm trends observed in Figure 1. GMM estimation of current year election effects ( _Electionit)_ in Columns 3-9 is consistently negative and statistically significant, again documenting the link between election periods and fiscal expansion through government borrowing. But the interaction of this election dummy with current-year rating ( _Rating*Electionit_ ) is positive and significant, indicating that governments with higher ratings borrow relatively less in election years –fiscal discipline in the face of opportunistic borrowing pressure. The current-year rating ( _Ratingit_ ) is negative and significant, indicating that countries 7
|||M3/M4<br>|M2/M3 Bridge–|||||Launched in May 2018 and|||
|---|---|---|---|---|---|---|---|---|---|---|
||42.|Multan<br>Highway|<br>Faisalabad–Multan|Pakistan|New road|(60)|Operational|<br>now in use.|27-May-18|(71)|
||43.|Lahore-Abdul<br>Hakeem road<br>upgrade|Lahore–Abdul<br>Hakeem|Pakistan|Road<br>upgrade|(60)|Operational|By October 2018, all<br>upgrades were completed,<br>and the highway is ready<br>for opening to traffic.|9-Nov-18|(72)|
|||||||||The first section of the<br>two-way six-lane road was|||
||44.|Multan-Sukkur<br>road|Multan–Sukkur|Pakistan|Road<br>upgrade|(60)|Under<br>Construction|launched in 2018 and is<br>operational. The rest is<br>under construction and to<br>be completed by 2019.|17-Sep-18|(73)|
||45.|Gwadar-Surab<br>road|Gwadar–Panjgur–<br>Surab|Pakistan|New road|(60)|Operational|Launched in 2016 and now<br>operational.|10-Sep-17|(74)|
||46.|Surab-DI Khan<br>road|Surab–Quetta–DI<br>Khan|Pakistan|Road<br>reconstructio<br>n|(60)|Operational|Launched in 2017 and now<br>in use.|26-Nov-17|(74)<br>(75)|
||47.|M8 Sukkur-<br>Besima road|Sukkur–<br>Shahdadkot–<br>Besima|Pakistan|New road|(60)|Operational|Construction completed in<br>early 2018. Work was<br>finished by the Pakistan<br>Army after Chinese<br>contractors refused to work<br>out of security concerns.|9-Apr-18|(76)|
|||||||||Marked as “medium to|||
||48.|Shahdadkot-DI<br>Khan road|Shahdadkot–DI<br>Khan|Pakistan|New road|(60)|Planned|long-term” on CPEC<br>maps. No evidence of<br>concrete steps.|25-Nov-18|(77)|
|BANGLADE<br>SH|49.|Kunming-<br>Calcutta HSR|Kunming–<br>Mandalay–|Bangladesh,<br>China, India,<br>Myanmar|New hicap<br>rail|(78)|Proposed|Chinese officials reiterate<br>support for the idea, but no<br>concrete plans.|13-Sep-18|(79)|

60
**Figure 2. The Oakland-San Francisco Bay Bridge, which was closed for one month following the 1989 Loma Prieta earthquake. Source: Dan Bluestein, Wikimedia Commons.**

# **2.1.1. Potential positive spillovers depending on demand absorption capacity**

Not all indirect impacts are negative. Disasters reduce production capacity, but also increase demand for outputs from the reconstruction sector. Thus, reconstruction can act as a stimulus. However, the resulting dynamic depends on pre-existing economic conditions, such as the phase of the business cycle and the existence of distortions that lead to under-utilization of production capacities (Hallegatte and Ghil, 2008). If the economy is efficient and in a phase of high growth, in which all resources are fully used, the net effect of a stimulus on the economy will be negative, for instance through diverted resources, production capacity scarcity, and accelerated inflation. If the pre-disaster economy is however depressed, the stimulus effect may in some cases (e.g. when there are substantial aid flows) yield benefits to the economy by mobilizing idle capacities.

For instance, the 1999 earthquake in Turkey caused direct destruction amounting to 1.5-3% of Turkey’s GDP, but consequences on growth were limited, as the economy had significant unused production resources (labor and capital) available at the time, with Turkish GDP contracting by 7% in the year preceding the earthquake. The reconstruction demand from the earthquake aftermath allowed to use this under-utilized production capacity and acted as a stimulus, increasing economic activity (Ewing et al., 2004). In 1992, when Hurricane Andrew hit south Florida, the region’s economy was sluggish, with 50% unemployment among construction workers (West and Lenze, 1994). Reconstruction had a large stimulus effect in the economy, which would have been impossible in a better economic situation such as the one in 2004 when four hurricanes hit Florida during a housing construction boom (West and Lenze, 1994).

7
F igure 1 F lood impacts in Bangladesh <!-- Start of picture text --> 0 depth<br>effect<br>1 2 3 4<br>‐0.2<br>durati<br>‐0.4 on<br>effect<br>‐0.6<br>repair<br>‐0.8 cost<br>effect<br>C ase 1: Direct effect, C ase 2: Maximum educ = 8 years<br>C ase 3: Household size = 10, C ase 4: Livestock = 4148 (mean)<br>Y ears<br><!-- End of picture text --> F igure 2 Impacts of very‐severely flood exposure in Bangladesh 2 <!-- Start of picture text --> 0<br>1 2 3<br>‐0.5<br>‐1<br>‐1.5<br>‐2<br>Y ears<br><!-- End of picture text --> C ase 1: Direct effect; C ase 2: Maximum educ = 8 years; C ase 3: Household size = 10 F igure 3 Drought impact in E thiopia <!-- Start of picture text --> 0<br>‐0.1 1 2 3<br>‐0.2<br>‐0.3<br>‐0.4<br>‐0.5<br>Y ears<br><!-- End of picture text --> C ase 1: Direct effect; C ase 2: Maximum educ = 8 years and Distance from town = 9 km; C ase 3: Household size = 10 and Distance from town = 9 km 21
beneficiary selection method, all households in both treatment and control communities were classified as eligible or non-eligible for participation in the program. A second survey took place in March 1998 before the initiation of payments in July 1998. The third round of the survey took place in October 1998, which was well after most households received some cash transfers from the program. The next round of the survey took place in June 1999, and the fifth round took place in November 1999. Soon after November 1999, the benefits of the program began to be distributed to eligible households in the control communities. We limit our analysis to rounds 1, 3 and 5 of the survey, since these three surveys took place at the same months in time within each calendar year.<sup>16</sup>

# _Performance of the RDD estimator_

Tables 3a and 3b present the RDD and experimental estimates of program impact by round and gender on the sample of boys and girls between 12 and 16 years of age. Our benchmark experimental impact estimates are the cross-sectional difference (CSDIF) estimates obtained by comparing the post program differences in the means between treatment and control groups. Using the sample of beneficiary/eligible households ( _B=1_ ) in treatment and control localities (groups A and B in Table 2), the program impact on the binary outcome indicator<sup>_Y_</sup> _i_<sup>pooling together the three rounds, is specified by a linear probability model (LPM)</sup> of the form: market work of children only up to age 16 (inclusive).

16 The means of all the variable used in our analysis for boys and girls in groups A,B, C and D separately are provided in Appendix table A.

14