rafmacalaba/datause-encoder-data huggingface dataset:
Distribution: Linking Macroeconomic Models with Household Survey Data at the Global Level”. Washington, DC: World Bank.

- Bussolo, M., R. de Hoyos, D. Medvedev,  and D. van der Mensbrugghe. 2008. “Global Climate Change and its Distributional Impacts”. Washington, DC: World Bank.

- Chen, S. and M. Ravallion. 2008. "The developing world is poorer than we thought, but no less <u>successful in the fight against poverty", World Bank Policy Working Paper  4703.</u>

- Cline, W. 2007. _Global Warming and Agriculture: Impact Estimates by Country._ Washington, DC: Center for Global Development and Peterson Institute for International Economics.

- de Gorter, H. and D. Just. 2010. “The Social Costs and Benefits of Biofuels: The Intersection of Environmental. Energy and Agricultural Policy”. _Applied Economic Perspectives and Policy_ . 32 (1):  4-32.

- de Hoyos, R. 2008. “Global Income Distribution Dynamics (GIDD)”. Washington, DC: World Bank.

- de Hoyos, R, and D. Medvedev. 2011. “Poverty Effects of Higher Food Prices: A Global Perspective,” _Review of Development Economics_ , forthcoming.

- Food and Agricultural Organization. 2008. “Soaring Food Prices: Facts, Perspective, Impacts and Action Required”. High-Level Conference On World Food Security. FAO, Rome.

- Habib-Mintz, N. 2010.  Biofuel investment in Tanzania: Omissions in implementation. Energy Policy 38, pp. 3985–3997.

- Hertel, T. 2009. “Analyzing the Poverty Impacts of Biofuel Mandates”. Transition to a Bioeconomy: Global Trade and Policy Issues Conference, Washington DC.

- Hertel, T., Tyner, W., and Birur, D. 2008. “Biofuels for All? Understanding the Global Impacts of Multinational Mandates”. GTAP Working Paper No. 51.

- Hahn, R., and C. Cecot. 2009. “The Benefits and Costs of Ethanol: An Evaluation of the Government‟s Analysis”. _Journal of Regulatory Economics_ . 35 (3): 275-295.

- Gal Hochman, G., Rajagopal, D., Timilsina, G.R. and Zilberman, D. 2011. The Role of Inventory Adjustments in Quantifying Factors Causing Food Price Inflation, World Bank Policy Working Paper, No. 5744, World Bank, Washington, DC.

- Horridge, M. 2008. “SplitCom: Programs to disaggregate a GTAP sector”. Melbourne, Australia: Centre of Policy Studies, Monash University.

IMF Commodity Prices. (http://www.imf.org/external/np/res/commod/index.asp)

- International Food Policy Research Institute. 2008. “Higher Food Prices: The What, Who, and How Proposed Action”.IFPRI Policy Brief, IFPRI, Washington D.C.

- Ivanic, M. and Martin, W. 2008. “Implications for Higher Global Food Prices for Poverty in Low Income Countries”. Development Research Group, Trade Group. World Bank, Washington D.C

- Keeney R., and Hertel, T. 2008. „The Indirect Land Use Impacts of U.S. Biofuel Policies: The Importance of Acreage, Yield, and Bilateral Trade Responses” GTAP Working Paper No.

32
<!-- Start of picture text --> Figure 3<br><!-- End of picture text --> <!-- Start of picture text --> China: Gross Enrollment Rate (%)<br>Gross enrollment rate (%)<br>160<br>140-<br>120 -<br>100<br>80<br>60-<br>Secondary<br>404-<br>20-<br>Higher<br>1975 1980 1985 1990 1994<br>Source: Table 1. Annex 1.<br>Figure 4<br>CHINA<br>Distribution of Cumulative Educatioin Spending: 1994<br>Percentage cumulative education spending<br>100<br>80<br>60<br>40 / o 0<br>/ / / _ ~~~~~~~~~Gini =0.395<br>20<br>0 I I<br>0 10 20 30 40 50 60 70 80 90 100<br>Percentage of school age population<br><!-- End of picture text --> ksr/c5/w70300f
Fourth, regarding international factors (see e.g. Burnside & Dollar, 2000; Demir, 2016), contrary to the lack of significant effect of the interactive terms with the variables FDI inflows and terms of trade (see Table A4), the presence of the official development assistance (ODA) reduces the tax revenues gap between Non-Cooperative and Cooperative countries (column [9]). Indeed, since ODA often comes with measures related to improved allocation and greater transparency, such measures can equally translate into various government activities including in terms of tax collection, which may ultimately improve tax revenue mobilization.

Finally, Table A4 in the Online Appendix equally reports estimations of interactive terms with other variables (see e.g. Tanzi, 1992; Sachs & Warner, 1995; Gupta, 2007; Corsetti et al., 2012; Debrun & Kinda, 2016), namely the time length since a country was included on the FATF list, the difference between the estimated propensity score and its sample average, the phase of the business cycle, education, and mineral and natural resources. These variables are not found to affect the tax revenues gap between Non-Cooperative and Cooperative countries, despite being consistent with what one may expect—for example, the coefficient of the interactive term with education is positive and with an associated p-value of 0.25. Altogether, the results presented in this section complement our previous findings, as they emphasize different environments that may affect the impact of being on the FATF list on tax revenues.

# **IX. Concluding remarks**

Recent work draws attention to the fragility of domestic tax revenues—a vital resource for the developing world—to illicit financial flows (IFFs). To cope with two major challenges in the relationship between IFFs and tax revenues—related with the mere measurement of IFFs and reverse causality—we exploited the Financial Action Task Force (FATF) data in an impact assessment analysis. Propensity score matching estimations revealed a significant tax revenue loss in countries associated with important IFFs with respect to comparable countries that are not signaled as being related to important IFFs. This causal effect—that was estimated to be economically meaningful—is supported by a large robustness analysis, and in particular remains unchanged when using several “doubly robust” estimators. Lastly, we unveiled heterogeneities in the impact of IFFs on tax revenues, related to the type of tax—significantly lower indirect taxes but not direct taxes—and the considered environment.

Our findings should be understood in two ways. First, it is not because the number of countries on the FATF list decreased that the damaging effect of IFFs on tax revenue mobilization was wiped off: although probably of a lower magnitude, IFFs still continue to 24
Table 4: Poverty and Socio-Economic Status

|_Socio-Economic Group of_<br>_Household Head_|_Bulgaria_|_Hungary_|_Poland_|_Estonia_|_Kyrgyz_<br>_Republic_|_Russia_|
|---|---|---|---|---|---|---|
||||Headcoun|t (Po, in p|ercent)||
|Wage Earner|16.4|15.7|18.7|23.1|38.8|32.5|
|Self-Employed|24.3|12.7|26.8|26.7|40.3|31.5|
|Pensioner|44.3|27.4|19.4|47.7|57.0|52.6|
|Other Transfer Recipient|63.7|57.1|64.1|54.3|61.9|68.7|
|Other|46.5|49.4|33.5|31.0|42.2|45.2|
|All|26.1|20.6|23.0|30.5|42.5|39.4|
||Poverty|Gap (in pe|rcent) 1/||||
|WageEarner|14.4|12.1|11.7|17.0|23.4|26.9|
|Self-Employed|15.3|11.1|14.4|24.6|24.8|27.9|
|Pensioner|23.7|15.9|14.4|23.1|28.6|35.8|
|Other Transfer Recipient|30.0|21.1|18.6|26.1|26.8|33.1|
|<br>Other|28.8|16.5|11.5|17.9|25.1|27.8|
|All|19.8|14.1|13.3|20.2|25.0|29.8|

_Source:_ Household Expenditure and Income Date for Transition Economies Data Set (HEIDE). _Notes:_ 1/ he poverty gap is the poor's average shortfall in expenditures from the poverty line, expressed as a percentage of the poverty linc (this measure is also known as the expenditure gap ratio).

31
**Table 2: Treatment Effect and Returns to Capital with Gender Interactions**

||Real<br>Profits<br>FE<br>(1)|Adjusted<br>Real Profits<br>FE<br>(2)|Real<br>Profits<br>IV-FE<br>(3)|Adjusted<br>Real Profits<br>IV-FE<br>(4)|Adjusted<br>Real Profits<br>FE<br>MALES<br>(5)|Adjusted<br>Real Profits<br>FE<br>FEMALES<br>(6)|
|---|---|---|---|---|---|---|
|Treatment Amount|8.04***<br>(2.90)|7.78***<br>(2.81)|||||
|Treatment Amount*Female|-8.24**<br>(4.06)|-8.60**<br>(3.97)|||||
|Capital Stock|||11.29**<br>(4.67)|11.07**<br>(4.53)|||
|Capital Stock *Female|||-11.39*<br>(6.17)|-12.05**<br>(6.03)|||
|Treatment* 1-4 quarters post treatment|||||7.75***<br>(2.79)|-0.91<br>(2.74)|
|Treatment* 5-8 quarters post treatment|||||7.84**<br>(3.78)|-0.84<br>(3.91)|
|Treatment* 9+ quarters post treatment|||||7.64*<br>(4.57)|-3.24<br>(4.51)|
|Observations|3697|3697|3518|3518|1870|1827|
|Number of firms|365|365|364|364|183|182|
|**Testing the overall female effect is zero**<br>Amount+Amount*Female=0|**(p-values)**<br>0.945|0.769|||||
|Capital + Capital*Female=0|||0.981|0.805|||
|**First-stage Coefficients:**<br>_First-stage for Capital_|||||||
|Amount|||0.733|0.733|||
|(p value)|||0.00|0.00|||
|<br>_First-stage for Capital*Female_|||||||
|<br>Amount*Female|||0.697|0.697|||
|(pvalue)|||0.00|0.00|||

Notes: Robust Standard errors in parentheses, clustered by enterprise

*, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. Results shown trim firms with extreme increases in profits (top 1% in percentage and absolute changes) Adjusted profits are real profits less the value of the owner's time, estimated in six education / gender cells using a simple production function run on the baseline data.

35

-  -
Table 7: Impact of anticipated vs. unanticipated price shocks (second-stage regression on price expectation)

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|d_lninv|d_lninv|d_lninv|
|Anticipated|7.513*|9.390**|10.01**|
||(3.964)|(3.890)|(3.921)|
|Anticipated * Ln(asset)|-0.504**|-0.566**|-0.576**|
||(0.231)|(0.229)|(0.228)|
|Anticipated * Ln(land)|0.468|0.506*|0.496*|
||(0.291)|(0.282)|(0.283)|
|Anticipated ^2||-0.973**|-1.258***|
|||(0.461)|(0.480)|
|Unanticipated|-3.507|0.417|-0.0391|
||(8.176)|(8.255)|(8.219)|
|Unanticipated * Ln(asset)|0.0458|-0.124|-0.0866|
||(0.484)|(0.489)|(0.487)|
|Unanticipated * Ln(land)|-0.0443|0.207|0.203|
||(0.467)|(0.466)|(0.465)|
|Unanticipated ^2||4.440|4.775*|
|||(2.758)|(2.796)|
|SD_prediction error|||-0.0906|
||||(0.0702)|
|Ln(asset)|0.283***|0.297***|0.304***|
||(0.0905)|(0.0898)|(0.0896)|
|Ln(land)|0.298***|0.289***|0.293***|
||(0.0991)|(0.0966)|(0.0964)|
|Land ^2|0.0636***|0.0594***|0.0603***|
||(0.0223)|(0.0221)|(0.0222)|
|Average age|-0.000654|-0.00205|-0.00236|
||(0.0110)|(0.0110)|(0.0110)|
|Average years of schooling|0.0279|0.0192|0.0179|
||(0.0240)|(0.0243)|(0.0244)|
|Househouse size|0.0655**|0.0725**|0.0708**|
||(0.0318)|(0.0318)|(0.0322)|
|Constant|7.325***|7.064***|6.891***|
||(1.527)|(1.513)|(1.522)|
|Observations|737|737|737|
|Provincial dummies included but not reported|YES|YES|YES|
|*** p<0.01, ** p<0.05, * p<0.1||||

Standard errors are corrected by two-step bootstrap estimator with 2,000 replications, which are reported in the parentheses.

Farmers with fixed price arrangement ( _Tebasan_ and _Ijon_ ) with traders are excluded from the sample.

34
# **Figure 4 PROPER rating system (Indonesia)**

|**Rating**|**Criteria**|
|---|---|
|**GOLD**|Levels of pollution control for air and hazardous waste similar to those for<br>water pollution; extensive use of clean technology; pollution prevention;<br>recycling.|
|**GREEN**|Pollution discharges 50% below regulatory standards; proper disposal of<br>wastes; good housekeeping; accurate emission records; reasonable<br>maintenance of wastewatertreatment system.|
|**BLUE **|Pollution discharges equal to or below regulatory standards.|
|**RED**|Some pollution control effort but pollution discharges exceed regulatory<br>standards.|
|**BLACK **|No pollution control effort or serious environmental damages.|

# **Figure 5 Industrial EcoWatch System (Philippines)**

|**Rating**|**Criteria**|
|---|---|
|**BLACK**|•<br>Discharge beyond standard and has no wastewater treatment system<br>and/or air emissions control system;<br>•<br>Violation of toxic and heavy metals discharge regulations;<br>•<br>Under a “Cease and Desist” Order;<br>•<br>Willful manipulation of effluent discharge or air emissions such as<br>dilution of discharge, use of by-pass or direct discharge without<br>treatment;<br>•<br>Verified obstruction of inspection activities;<br>•<br>A legitimate and verified public complaint of pollution and no effort has<br>been exerted to address this complaint.|
|**RED**|•<br>Violation of applicable effluent/air emissions standards|
|**BLUE**|•<br>Pollution level consistently within the standards within the rating<br>period of one year;<br>•<br>Full compliance with other regulatory requirements;<br>•<br>Good maintenance of pollution control/treatment system;<br>•<br>The self-monitoring reports are complete and accurate.|
|**GREEN**|•<br>Meets all DENR requirements and has been rated BLUE in the<br>previous period;<br>•<br>Pollution concentrations are consistently lower than the standards by<br>20%;<br>•<br>Well functioning flow meter and continuous emission monitoring<br>system;<br>•<br>Easily accessible discharge points.|
|**SILVER**|•<br>Meets all DENR requirements and has been rated GREEN in the<br>previous period;<br>•<br>Uses cleantechnology, energy andwaterefficiency/conservation|

26
**Table 6. Political Stability Effects in Fuel Exporters**

|Dependent variable:|Fiscal balance/GDP<br>(%)|Total revenues/GDP<br>(%)|Total<br>expenditures/GDP<br>(%)|
|---|---|---|---|
|Constant|-6.3***<br>(-7.26)|-11.05***<br>(-7.44)|-4.50***<br>(-2.66)|
|Dummy fuel > 25%|1.24|23.48***|20.75***|
|exports|(0.55)|(7.01)|(6.90)|
|Dummy ores and|0.334|0.645|0.582|
|metals > 10%<br>exports|(1.11)|(1.36)|(1.18)|
|Hard peg dummy|1.38***<br>(2.89)|-2.51**<br>(-4.15)|-3.95***<br>(-7.78)|
|Political stability|0.0475***|0.528***|0.478***|
|(0 to 100)|(3.80)|(22.86)|(18.23)|
|Political stability *|0.0594|-0.237***|-0.267***|
|fuel exporter dummy|(1.46)|(-4.05)|(-5.21)|
|Time trend|0.0155|0.324***|0.291***|
|(=0 in 2000)|(0.62)|(7.32)|(6.24)|
|n|1,378|1,392|1,378|
|R-squared|0.136|0.309|0.244|
|RMSE|4.74|7.98|7.91|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

14
|4.4.5. Swaziland............................................................................................................43|
|---|
|4.4.6. Zambia ...............................................................................................................44|
|5. Discussion..............................................................................................................................46|
|6. Conclusion.............................................................................................................................51|
|Annex 1: Country and year specific incidence and prevalence data, including sources...52|
|Annex 2: Changes to Spectrum data curves from 2012 to 2013..........................................66|
|References...................................................................................................................................69|

4
Cato, J.C. and Lima dos Santos, C.A. (1998b). European Union 1997 Seafood Safety Ban: The Economic Impact on Bangladesh Shrimp Processing. _Marine Resource Economics_ , 13 (3), 215227.

CBS (2002). _Economic Survey 2002._ Central Bureau of Statistics, Nairobi.

Delgado, C.L., Wada, N., Rosegrant, M.W., Meijer, S. and Ahmed, M. (2003). _Fish to 2020: Supply and Demand in Changing Global Markets._ International Food Policy Research Institute, Washington DC.

Delgado, C.L., Wada, N., Rosegrant, M.W., Meijer, S. and Ahmed, M. (2003). _Outlook for Fish to 2020: Meeting Global Demand._ International Food Policy Research Institute, Washington DC.

European Commission (1998). _Final Report on a Mission to Kenya: Fisheries Products, 23 to 27 November 1998_ . European Commission, Brussels.

European Commission (1999). _Final Report on a Mission to Kenya: Fisheries Products, 16 to 18 August 1999 for the Purposes of the Assessment of Control of Pesticide Residues in Fish Coming from Lake Victoria._ . European Commission, Brussels.

European Commission (2003). _Final Report on a Mission carried Out in Kenya 14 to 22 March 2002 Carried Out to Assess the Conditions of Production of Fisheries products( Council Directive 91/493/EEC) and the Verification of the Measures on Pesticides in Fish._ European Commission, Brussels.

FAO (1995). _Development of Small-Scale Fish Farming in the Lake Basin: Kenya Phase 2 Project Findings and Recommendations._ Food and Agriculture Organization, Rome.

FAO (1999). _The Importance of Food Quality and Safety for Developing Countries._ Committee on World Food Security, FAO, Rome.

FAO (2000). _The State of World Fisheries and Aquaculture 2000_ . Food and Agriculture Organization, Rome.

FAO (2002). _The State of World Fisheries and Aquaculture 2002_ . Food and Agriculture Organization, Rome.

Ferguson, A.E., Derman, B. and Mkandawire, R. (1990). The New Development Rhetoric and Lake Malawi. _Africa_ , 63 (1), 1-18.

Fisheries Department (1995). 1995 Annual Statistical Bulletin. Fisheries Department, Nairobi.

Fisheries Department (1996). 1996 Annual Statistical Bulletin. Fisheries Department, Nairobi.

Fisheries Department (2000). _Frame Survey 2000 Report._ Fisheries Department, Nairobi.

79
TABLE 1: DEFINITIONS AND DESCRIPTIVE STATISTICS OF THE VARIABLES USED IN THE ANALYSIS21

||Mea|n/Observati|ons|Coefficient/<br>||
|---|---|---|---|---|---|
|<br>||||p-value|Definition|
|Outcomes<br>Variables||||T||
||Manual|e-proc.|Total|reatment<br>vs Control||
|Ln of<br>Number of<br>Bidders|1.523<br>(0.007)<br>30626|1.362<br>(0.004)<br>69240|1.412<br>(0.004)<br>99866|-0.161***<br>(0.008)|Measured as the natural logarithm of the number<br>of tenders submitted as on tender opening day|
|Access<br>Single<br>|0.171<br>(0.002)|0.194<br>(0.002)|0.187<br>(0.001)|0.022***<br>(0.003)|=1 if the number of bidders on a tender is equal<br>|
|Bidders|30626|69240|99866||to 1|
|Non-Local<br>|0.126<br>(0.002)|0.216<br>(0.002)|0.186<br>(0.001)|0.09***<br>(0.003)|=1 if district of the winning firm is not equal to<br>|
|Winners|30147|60472|90619||the district from where the tender was invited|
|Winning|0.724<br>(0.052)|6.968<br>(0.035)|5.073<br>(0.03)|6.243<br>(0.062)|Winning Rebate was calculated as;<br>~~(~~<sup>Official estimated cost−Contract Value</sup><br>)|
|Rebate|29922|68705|98627||Official estimated cost<br><br>× 100|
|Economy<br>Time<br>O|0.305<br>(0.003)|0.191<br>(0.011)|0.299<br>(0.003)|-0.114***<br>(0.013)|Ratio of Cost Overrun is calculated as;<br><sup>Amount Disbursed−Contract Value</sup><br>|
|verrun|29251|1349|30600||~~(~~<br>Contract Value<br>)|
|Cost Overrun|-0.016<br>(0.001)|-0.017<br>(0.004)|-0.016<br>(0.001)|-0.002<br>(0.003)|=1 if the contract was extended|
||28318|1307|29625|||
|Submission<br>|25.55<br>(0.04)|19.48<br>(0.02)|22<br>(0.03)|-6.07***<br>(0.05)|Measured as the time elapsed from tender notice<br> <br>22|
|Period|30661|43137|73798||date to tender submission date<br>|
|Decision<br>|25.4<br>(0.14)|21.07<br>(0.07)|22.66<br>(0.07)|-4.33***<br>(0.14)|Measured as the time elapsed from tender<br>|
|<br>Period|24950|43108|68058||opening date to notification of award date|
|Efficiency<br>Signing<br>|17.85<br>(0.08)|16.05<br>(0.04)|16.65<br>(0.04)|-1.8***<br>(0.08)|Measured as the time elapsed notification of<br>|
|Period|29851|60068|89919||award date to contract signing date|
|Lead Time|83.58<br>(0.24)|65.91<br>(0.09)|71.82<br>(0.1)|-17.68***<br>(0.21)|Measured as the time elapsed from tender notice<br>|
||30198|60114|90312||date to contract signing date|
|Procurement<br>|1.479<br>(0.003)|1.828<br>(0.001)|1.72<br>(0.001)|0.349***<br>(0.003)|=1 if procurement methods used is Limited<br>Tendering Method and =2 if method used is|
|Independent<br>Method|31088|69240|100328||<br>Open Tendering Method|
|<br>Variables<br>Ln of<br>Contract<br>Value|14.805<br>(0.008)<br>31088|14.902<br>(0.005)<br>69240|14.872<br>(0.004)<br>100328|0.097***<br>(0.009)|The natural log of the contract price stated in the<br>original contract signed with the winner|

Note: weighted figures (for more on weights see section 6).

> 21 Additional Descriptive Statistics have been included in Appendix 9.7.

> 22 In the Bangladesh public procurement system, the tender submission date and the tender opening date are only one day apart by design. There is no variation in this under any normal circumstance.

17
affairs, made the Ministry of Cities (MOC) as the responsible agency for establishing a national housing policy. The assurance of housing rights encompasses access to land tenure, basic public services and financial services.

Our study is outlined as follows. Following this introduction, Section 2 describes the model and equilibrium conditions. Section 3 discusses the impacts of slum upgrading programs. Section 4 concludes.

# **2. Baseline Model**

In this section, we develop a general equilibrium model to examine the effectiveness of alternate slum upgrading policy instruments that can be used to improve household welfare. We first evaluate how these interventions perform in first-best settings, and then examine the effects of pre existing institutional and regulatory constraints on the effectiveness and relative rankings of these interventions (Section 3). These constraints include land supply constraints,<sup>3</sup> infrastructure bottlenecks, and credit rationing.

We start with the assumption of a monocentric-closed city with no population growth. The model has four economic agents: households, developers, financial intermediaries and the city government.

> 3 The increase in informal housing units between 1991 and 1998 (Morais 2000 based on PNAD/IBGE in World Bank Report No. 22032 BR, (2002), pp 16) shows the extent of housing deficit in Brazil. The report indicates that among 10 metropolitan regions, seven is reported to have over 50 percent increase in housing deficit with Recife and Curitiba having 52.8 and 143.4 percent respectively.  This stylized fact supports our consideration of supply side bottleneck in housing market.

6
Mass Messaging and Health Risk Reduction Evidence from COVID-19 Text Messages in Tajikistan William Seitz<sup>1</sup> Keyword: Impact evaluation; COVID-19, SMS text-messages, panel survey, Mobile Engage JEL: D8, D83, I1, I15, I18, O1

> 1 The World Bank, Poverty and Equity Global Practice, wseitz@worldbank.org. Financial support from the Korea Trust Fund is gratefully acknowledged.