rafmacalaba/datause-encoder-data huggingface dataset:
Nielsen, H. and M. Rosholm. 2001. ‘The public-private wage gap in Zambia in the 1990s: A quantile regression approach,’ Empirical Economics 26: 169-182.

Pratap, S. and E. Quintin. 2006. ‘Are labor markets segmented in developing countries? A semi-parametric approach,’ European Economic Review 50, pp. 1817-1841.

Phelps, E. 1972. Inflation Policy and Unemployment Theory: the Cost Benefit Approach to Monetary Planning, Macmillan: London.

Pissarides, C. 1992. ‘Loss of skills during unemployment and the persistence of employment shocks,’ Quarterly Journal of Economics, 107: 1371-1391.

Rauch, J. 1991. ‘Modelling the informal sector formally,’ Journal of Development Economics 31: 33-47 Shapiro, C. and J. Stiglitz. 1984. ‘Involuntary Unemployment as a Worker Disciplining Device,’ American Economic Review, 74:433-444.

Stevenson, G. 1992. `How Public Sector Pay and Employment Affect Labor Markets.' World Bank Policy Research Working Paper No. 944.

Stewart, M. 2005. ‘The Inter-related Dynamics of Unemployment and Low Wage Employment,’ Warwick Economic Research Papers, No 741, December.

Stiglitz, J. 1974. ‘Alternative Theories of Wage Determination and Unemployment in LDCs: the Labor Turnover Model,’ Quarterly Journal of Economics, 88: 194-227.

Terrel, K. 1993. `Public-Private Wage Differentials in Haiti: Do Public Servants Earn Rent?' Journal of Development Economics 42: 293-314.

Van der Gaag, J. and W. Vijerberg. 1988. ‘ A switching regression model for wage determination in the public and private sectors of a developing country,’ Review of Economics and Statistics 70: 244-252.

Wooldridge, J. 2000. _Econometric Analysis of Cross-Section and Panel Data_ . Cambridge MA: MIT Press.

__________. 2005. ‘Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity,’ _Journal of Applied Econometrics_ 20: 39-54.

World Bank Group. 2007. ‘Urban labour markets in Ethiopia: Challenges and Prospects,’ Washington DC, February.

26
2 capital are estimated to be higher than returns on loans (see IMF (1985)). Nevertheless, on the whole FDI is considered advantageous for the host countries and to be crucial in the process of development.

Apart from a favorable economic performance, which generally attracts investment, host countries' regulations and policies with respect to foreign investment have been pinpointed to be a major determinant of the amount of FDI a country actually receives. In contrast to host countries' policies the issue of the source countries' policies to increase FDI in developing countries is less well covered. This miglht be due to the fact that the positive effects are less obvious. Improving the investment climate in the host countries is in many cases a matter of liberalizing a rather restricted market, thus removing distorting incentives by allowing more competition. Extending the measures of the source countries to enhance FDI in developing countries is, however, mostly a matter of introducing non-market incentives in form of subsidies. This is justified only, if additional FDI is induced. The main purpose of the paper is therefore to partly fill this gap by investigating both the effects of host and source countries' measures to stimulate FDI inflows to developing countries for the case of the Federal Republic of Germany.

Germany is one of the major suppliers of FDI funds to the world; according to the balance of payments statistics of the IMF it ranks fourth after the United States, Japan and United Kingdom with a share of roughly ten percent of total world FDI outflows. Also it turned into a net creditor relatively recently. In 1976 foreign FDI stocks in Germany exceeded German FDI stock abroad (DM 48 billion) by DM 31 billion. By the end of 1988, however, German FDI stocks abroad amounted to DM 184 billion exceeding foreign stocks in Germany by DM 75 billion. The interest of German companies to invest abroad is heavily concentrated in the
their scores, which in turn will be equivalent to building state capacity, as defined by the indicators in the APAs.

The specific indicators in the APA in the Tanzania project are:

- (i) **Urban planning system:** documentation and indicators of having a General Planning Scheme in place.

- (ii) **Fiduciary or financial management system** : documentation of internal audit reports undertaken by a fully constituted Internal Audit Committee, and scores on a system of public procurement.

- (iii) **Infrastructure management** : documentation and verification of the utilization of financing to deliver physical infrastructure such as roads and sanitation services.

- (iv) **Accountability and oversight by citizens** : verifying public disclosure of information about local budgets and convening of public meetings.

In addition to these four, a fifth area of own source revenue generation (from the property taxes assigned to local governments) was targeted in the original evaluation design:

- (v) **Local property tax system** : increase in own source revenue from the collection of local property taxes. The goal was to enable local governments to gradually move away from dependence on fiscal grants to becoming self-reliant on own source revenue, of which local property taxes are typically the most important. However, in July 2016, three years into the program, the national government recentralized property tax collection, shifting it from local governments to the Tanzanian Revenue Authority (TRA). In response, after 2016 the program decided not to assign any scores to performance in generating own source revenues.

This evaluation draws on data from both the APAs and our independent surveys administered to government officials and households. Each of these sources has value. Changes in the APAs document whether project LGAs improved on the specific indicators that the project targeted. The survey data play three key additional roles. First, many of the survey indicators provide an independent check on data gathered via the APAs.<sup>8</sup> Second, the surveys with government officials complement the APA data with broader measures of governance quality. Third, the surveys with households in the LGAs provide a further measure of governance improvements, which is whether the public observes improved local governance.

A fundamental question for the impact evaluation is whether local governments that did not receive the project, and thus access to the incentives and training under the project, made similar improvements in the areas measured under the APAs. One obvious way to measure these outcomes in non-project local governments would be to engage the same firms that are scoring 8The survey data are independent in the sense that there are no rewards associated with the survey data, unlike the APA data which constitute the basis to allocate project funds to LGAs.

8
# **Who Supports Violent Extremism in Developing Countries?**

# **Analysis of Attitudes Based on Value Surveys**

**Youssouf Kiendrebeogo and Elena Ianchovichina**<sup></sup>

<u>Key words: Violent Extremism, Terrorism, Economic Welfare, Models for Ordered Outcomes.</u> <u>JEL codes: D74; D83; C35</u>

>  Elena Ianchovichina is lead economist at the Chief Economist Office, Middle East and North Africa Region, the World Bank, 1818 H Street NW, Washington, DC, USA, Tel: +1 202 458 8910, E-mail: <u>eianchovichina@worldbank.org. Youssouf Kiendrebeogo is an economist at the Chief Economist Office, Middle</u> East and North Africa Region, the World Bank, 1818 H Street NW, Washington, DC, USA, Tel: +1 202 473 7346, E-mail: ykiendrebeogo@worldbank.org. We thank Martijn Burger for his comments and suggestions at the early stages of this work. We are grateful to Aart Kraay, Jacob Shapiro, Anke Hoeffler, and Helena Schweiger for providing useful comments on previous drafts of this paper. We also benefited from the comments of participants in a World Bank seminar, organized by the Fragility, Conflict, and Violence Community of Practice, the 2016 Lisbon Meeting on Institutions and Political Economy and the CSAE 2016 Conference, held at the University of Oxford.
<!-- Start of picture text --> Discussion<br>The primary moti ation for the use of prediction in economics, health sciences, and other disci-<br>plines has been to deal with arious forms of missing data problems. e make a case for using<br>a prediction estimator to obtain more efficient estimates of the population mean by extending<br>the results of Matlof (1981). In an application to cost-effecti e double sampling, we sho<br>ho prediction estimators may be adopted to maximize statistical precision [minimize financial<br>costs] under a budget constraint [statistical precision constraint]. The approach is particularly<br>useful when the outcome ariable is relati ely more expensi e to observe than its co ariates.<br>There ha e been a number of cases in which predictions are used ex post to estimate the<br>outcome ariable of interest. or example, Elbers et al. (2003) ha e put forward an approach<br>where the prediction of household consumption per capita is made for each census record, which<br>in turn is used to compute po erty statistics for small areas. Their approach is useful because<br>household consumption is expensi e to observe and thus typically observed only for a small<br>set of households in the population of interest. _ similar approach can be used to estimate the<br>pre alence of stunting and underweight of children for small areas (Fujii, 2010). There are also<br>a number of recent case studies in which a consumption surve is combined with secondary<br>surveys (without a consumption component) to supplement existing po erty estimates (Stifel<br>and Christiaensen, 2007; Douidich et al., 2013).<br>Our study shows ho _ prediction methods may be fruitfully adopted e en ex ante by de-<br>signing surveys appropriately. recent study by Ahmed et al. (2013) on Bangladesh shows<br>that po erty can be estimated with reasonable accurac_ with a relati ely small sample (e.g.,<br>Primary Sampling Units (PSUs) with 10 households per PSU) of the outcome §ariable and its<br>co ariates when combined with a larger sample of the co ariates only (612 PSUs). Their cost<br>estimates suggest that, compared with a full scale consumption surve (complete observations<br>of both outcome ariable and its co ariates for 612 PSUs), the manpower cost for data collec-<br>tion could by cut by more than 90 percent without se erely undermining the accurac_ of the<br>national po erty estimate. If our approach is adopted, the trade-of between financial costs and<br>statistical precision can be explicitly optimized in a situation like theirs.<br>o what extent we can successfully bring down the costs while maintaining statistical pre-<br><!-- End of picture text -->
# **Appendix Table A1 (continued)**

# **Scores for 18 individual indicators (normalized to Z-scores), by donor**

||**Selec**|**tivity**|||**Align**|**ment**|||**Har**|**moniza**|**tion**|||**Spe**|**cializa**|**tion**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Donor**|**S1**|**S2**|**A1**|**A2**|**A3**|**A4**|**A5**|**A6**|**H1**|**H2**|**H3**|**Sp1**|**Sp2**|**Sp3**|**Sp4**|**Sp5**|**Sp6**|**Sp7**|
|Italy|-0.73|-0.25|-0.84|0.11|-0.88|0.31|-0.63|-0.48|0.05|-0.56|-0.12|0.77|0.97|-0.66|-0.26|-0.30|-0.62|-0.57|
|Japan|-0.07|-0.62|0.10|1.18|-0.33|-0.66|1.25|1.30|-0.16|-1.03|-0.67|-0.08|-0.09|-1.07|0.56|-0.41|-0.02|-1.77|
|Korea|-1.36|-0.90|.|-0.44|-1.08|-1.17|0.67|1.84|-1.63|-0.10|-1.57|.|.|-0.72|-0.66|-1.04|-0.46|-0.92|
|Luxem bourg|1.13|0.22|0.83|1.01|-1.12|-1.41|-0.30|-1.57|-0.69|0.43|1.23|-0.17|-0.42|-0.38|-0.78|-0.15|-0.65|-0.28|
|N etherlands|2.07|0.59|0.17|0.33|0.87|1.48|0.81|0.26|1.16|1.23|0.18|-0.15|0.10|-0.01|-0.35|0.32|-0.31|0.22|
|N ew Zealand|1.58|-0.43|0.25|0.39|-0.53|-0.42|0.28|0.46|-0.07|0.95|-1.57|-0.28|-1.07|0.01|-0.75|-0.13|0.40|0.54|
|N orw ay|0.58|0.18|0.83|-0.11|0.58|0.71|0.81|-0.50|0.43|0.15|0.74|0.30|-0.40|-0.72|-0.50|-0.05|-0.13|-0.68|
|Poland|-1.21|-0.99|.|2.27|-0.24|-0.56|1.40|-2.06|-0.37|-1.27|-1.57|2.40|2.22|.|2.36|-0.28|.|.|
|Portugal|-1.15|-0.89|-1.42|-2.24|-2.15|-1.77|0.70|-2.48|-2.78|-1.27|-1.57|-1.02|0.19|-0.55|0.53|1.40|0.15|0.35|
|Spain|-0.54|0.71|-0.44|-1.24|0.36|0.32|-0.13|-0.60|-0.57|-0.46|-0.20|-0.15|0.81|-0.77|-0.56|-0.05|-0.59|-1.09|
|Sw eden|1.24|0.15|0.64|0.48|0.59|0.22|0.30|-0.10|0.29|0.20|0.23|0.54|-0.72|-0.04|-0.59|-0.13|0.49|1.03|
|Sw itzerland|-0.14|0.01|0.83|-0.53|0.02|0.40|-0.52|-0.93|-0.29|-0.42|0.80|0.29|-1.21|-0.59|-0.94|-0.65|-0.27|0.00|
|Turkey|-2.09|-0.80|.|-1.09|-1.39|0.86|0.66|0.46|-0.15|-0.37|.|-1.20|-1.60|.|-0.06|-0.34|.|.|
|UK|-0.10|-0.19|0.83|0.11|0.59|0.25|0.26|0.49|0.90|1.51|0.57|0.21|-0.76|-0.54|0.13|-0.14|-0.25|0.92|
|UN|-1.08|-0.58|.|0.84|-1.02|-1.27|-2.27|0.22|0.18|0.73|0.64|.|.|-1.47|-0.14|-0.94|0.30|-0.83|
|US|-1.03|-0.47|-0.37|-0.39|-1.32|-1.15|-0.77|-0.38|-0.22|-0.70|-0.23|-0.06|-0.07|-1.24|1.41|-0.09|-0.15|-0.77|
|World Bank|1.60|3.00|.|0.66|0.88|0.00|0.43|1.03|0.78|0.22|0.49|.|.|1.98|0.26|1.82|-0.43|-0.64|

Note: See Appendix list (next page) for interpretation of indicator codes S1 through Sp7.

29
_Figure 1. Main sources and uses of revenues at the federal level (2002)_ <!-- Start of picture text --> 15,000<br>13,000<br>11,000<br>Non Tax<br>Revenues<br>Other<br>9,000 Federal<br>Expenditures<br>7,000 Other Tax Revenues<br>Excise Taxes<br>5,000 Other Transfers PAFEF<br> Income Tax<br>R33<br>3,000<br>State<br>Revenues<br>1,000 VAT R28<br>States and Municipalities Own<br>Revenue<br>-1,000<br>Total Assignable Taxes Current Federal, State and Municipal Resources<br>MxP per capita<br><!-- End of picture text --> _Source:_ SHCP and Bank staff calculations.

The negotiation process between the executive and congress also has several problems. First, there is not an effective set of rules for the interaction, especially on the issues of fiscal federalism. Second, congress does not have a good mechanism for making a binding agreement with the executive in budget matters, especially as the calendar moves toward December 31, at which point both sides know that the government will have to accept whatever congress passes. Consequently, the executive is reluctant to offer concessions that might then become the floor for further demands. It does seem that the _Comisión de Hacienda y Crédito Público de la Cámara de Diputados_ is taking on the role of responsible interlocutor for budget negotiations, which would be a sound way to proceed. In 2002 the deliberation and passage of the 2003 budget went more smoothly than in the previous four years.

The federal executive has repeatedly agreed to expand sub national transfers, but it needs to have some limit on SN demands in congress. For this purpose, more SN fiscal effort would help, reversing a trend that has seen declining SN taxation as the transfers have increased. The federal government also needs to have assurances that national-level fiscal effort will benefit mainly the national-level fiscal balance. This is still true now, such as with the sub national governments getting only 20 percent in revenue sharing, but proposals to raise the _participaciones_ share and to increase other transfers are undermining this situation.

To go along with its residual responsibility for public services devolved to the states (especially education, health, and roads), the federal government needs some way to monitor what the states are doing and to hold them accountable for the effectiveness of services that are still largely federally financed and are mandated by the federal constitution.

**_State governments_** lost almost all of their taxing powers in the series of measures up to and including the creation of the National System of Fiscal Coordination (SNCF) in 1980, but this gave them a secure source of revenue sharing, and they did not have much political autonomy or great spending responsibilities (except for the few larger states that developed their own large education systems to parallel the federal system).  The 1990s changed their situation dramatically—giving them substantial new spending power and service-delivery responsibilities

4
The government collects tax revenue from numerous sources: production, sales, households, and imports. The government uses these funds for purchases of goods and services at the respective markets and makes transfers to households, and engages in public savings.

Regional activities of field crops , in addition to land, labor and capital, use water as an input in production. These activities also make payments to WUAs in their respective regions. These water charges are income earned by the Water User Associations. In TACOGEM-W, it is assumed that this water income collected in each region is simply transferred back to the respective rural household in a lump-sum.

In Turkey, as of 2003, 87 percent of all agricultural land is devoted to cultivation of field crops (Table 3.4). About 23 percent of all agricultural land is under irrigation, and similarly, 22 percent of all field area sown is irrigated (Table 3.5).

**<u>Table 3.4. Agricultural land use, 2003</u>**

|(000 Hectare)|Share (%)|
|---|---|
|Agricultural land<br>26 027|100|
|Cultivated field area<br>22 554|86.7|
|Area sown<br>17 563|67.5|
|Fallow land<br>4 991|19.2|
|Area of vegetable gardens<br>818|3.1|
|Area of vineyards<br>530|2.0|
|Area of fruit trees<br>1 500|5.8|
|Area ofolive trees<br>625|2.4|

_Source: TurkSTAT (2005)_

**<u>Table 3.5. Irrigated versus rainfed land use, 2001 (hectares)</u>**

||Totalarea|Irrigated|Notirrigated|
|---|---|---|---|
|Total area|15,322,010|3,505,749|11,816,261|
|Area sown|12,253,912|2,716,529|9,537,383|
|Fallow land|2,737,560||2,737,560|
|Vegetable and flower<br>gardens|371,512|271,009|100,503|
|Fruit orchards and other<br>permanent crops|1,757,962|455,590|1,302,372|

_Source: TurkSTAT (2004b)_ For 38 percent of the irrigated area, wells are the most important source of water (Table 3.6). The second most important source of irrigation is streams, followed by the use of dams. The most common method to supply water to crops is flooding, which accounts for 84 percent of the area irrigated (Table 3.7).

26
<!-- Start of picture text --> Davido ,O., and = Haitovsk (2000) ‘Optimal design for double sampling with continuous<br>outcomes. ournal of Statistical Planning and Inference 86, 253-263<br>Douidich, M., A. Ezzrari, R. ander eide,andP. erme (2013) ‘Estimating quarterly po erty<br>rates using labor force surveys: primer. orld Bank Polic Research orking Paper 6466,<br>orld Bank<br>Elbers, C., J. Lanjouw, and P. Lanjouw (2003) ‘Micro-le el estimation of po erty and inequal-<br>ity. Econometrica 71, 355-364<br>Fujii, . (2010) ‘Micro-le el estimation of child undernutrition indicators in Cambodia. orld<br>Bank Economic Review 24(3), 520-553<br>Hansen, L.P. (1982) “Large sample properties of generalized method of moments estimators.<br>Econometrica 50(4), 1029-1054<br>Hansen, M.H., and B.J. epping (1990) “Regression estimates in federal welfare quality control<br>programs. ournal of the American Statistical Association 85(411), 856-864<br>Matloff, N. ‘Use of regression functions for impro ed estimation of means. Biometrika<br>68, 685-689<br>Neyman, J. (1938) ‘Contribution to the theory of sampling human populations. ournal of the<br>American Statistical Association 33, 101-116<br>Palmgren, J. ‘Precision of double sampling estimators for comparing two probabilities.<br>Biometrika 74(4),<br>arndal, C.-E., B. Swensson, and J. Wretman (2003) Model Assisted Survey Sampling<br>(Springer)<br>Stifel, D., and L. Christiaensen (2007) racking po ertyo er time in the absence of comparable<br>consumption data. orld Bank Economic Review 21(2), 317-341<br>amhane, A.C. ‘Inference based on regression estimator in double sampling. Biometrika<br>65(2), 419-427<br><!-- End of picture text -->
# EXITING A LAWLESS STATE

Karla Hoff and Joseph E. Stiglitz* <u>JEL codes: D02, K10, K42, P26, P37</u> <u>Key words: commitment, dysfunctional institution, coordination, Russia, transition economy, privatization, rule of law, gradualism, governance</u> _*Hoff: World Bank, Washington DC 20433; Stiglitz: Columbia University, New York, NY 10027. We thank Mayuresh Kshetramade for outstanding research assistance. We benefited from valuable suggestions from two anonymous referees, Kaushik Basu, Avinash Dixit, Phil Keefer, Gary Libecap, Norman Loayza, Branko Milanovic, Andrew Scott, Ken Sokoloff, and participants at seminars at UCBerkeley, Harvard (PIEP), Princeton, Tufts, Stanford, UCLA, and at the Joint World Bank-IMF seminar and the meetings of the American Political Science Association. Hoff thanks the MacArthur Research Network on Inequality and Economic Performance for research support. The findings and interpretations expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank._
Figure 3: The choice of how much to export: marginal effects <!-- Start of picture text -->
-1.884 -3.566<br>Subj. fin. constr. Obj. fin. constr.<br>0.127 0.129<br>Foreign shock Foreign shock<br>-0.045 -0.045<br>Domestic shock Domestic shock<br>-4.000 -3.000 -2.000 -1.000 0.000 -8.000 -6.000 -4.000 -2.000 0.000<br>(a) OLS (b) OLS<br>-19.942<br>Obj. fin. constr.<br>0.135<br>Foreign shock<br>-0.042<br>Domestic shock<br>-100.000 -50.000 0.000 50.000<br>(c) IV<br><!-- End of picture text -->

13
Table 3 Treatment Effects: Account Use

||||_Depende_|_nt variable:_|||
|---|---|---|---|---|---|---|
||Deposits<br>(1)|Withdrawals<br>(2)|Send-money<br>(3)|Total<br>(4)|Excl. withdrawals<br>(5)|Balance<br>(6)|
|Bank or mobile payroll|0.060***|0.629***|0.292***|0.683***|0.200***|440.763***|
||(0.004)|(0.008)|(0.010)|(0.008)|(0.008)|(47.377)|
||[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|Bank or mobile account|0.050***|0.046***|0.021***|0.073***|0.072***|106.719**|
||(0.006)|(0.006)|(0.004)|(0.009)|(0.008)|(47.498)|
||[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|[0.003]|
|Test: payroll = account|0.171|0.000|0.000|0.000|0.000|0.000|
|Observations|40,458|40,458|24,046|40,458|40,458|40,458|
|R-squared|0.018|0.454|0.181|0.492|0.068|0.011|

Notes: The table reports treatment effect estimates on account use, based on monthly administrative data. The dependent variable is a dummy equal to one if at least one transaction was recorded in a given calendar month. The omitted group consists of observations prior to random assignment to treatment, for which all outcomes are mechanically equal to zero. Regressions control for stratification dummies. Robust standard errors, clustered at the account level, in parentheses. Sharpened q-values in brackets. * _p <_ 0.1, ** _p <_ 0.05, *** _p <_ 0.01.