rafmacalaba/datause-encoder-data huggingface dataset:
|**Ap**|**pendix Table A6: Imp **|**act on Aspirations(I**|**TT)**||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||12 month aspirations<br>shop size (win 5%)|12 month aspirations<br>employment (win 5%)|12 month aspirations<br>customers (win 5%)|12 month aspirations<br>sales (win 5%)|
|Assigned Handbook|-1.081|-0.036|-1.162|-33.425|
||(0.746)|(0.081)|(2.867)|(20.538)|
|Assigned Handbook & Movie|-0.386|-0.014|1.590|26.043|
||(0.719)|(0.079)|(2.871)|(23.733)|
|Assigned Handbook & Assistance|0.739|0.099|1.345|-3.824|
||(0.747)|(0.078)|(2.891)|(23.801)|
|Assigned All Three|0.103|-0.019|1.553|15.174|
||(0.724)|(0.081)|(2.819)|(22.817)|
|R-squared|0.235|0.146|0.252|0.558|
|N|2204|2205|2203|2194|
|Dependent Variable Mean in Control Group|<br>15.347|1.750|51.741|416.316|
|Dependent Variable SD in Control Group|10.438|1.095|41.183|419.047|
|F-test (p-value): Book = Book & Movie|0.371|0.781|0.337|0.008|
|F-test (p-value): Book = Book & Assistance|0.023|0.090|0.385|0.185|
|F-test(p-value): Book = All Three|0.126|0.832|0.333|0.023|

**Notes:** This table presents impact analysis (Intent-to-Treat) for 12 month business aspirations (with respect to shop size (column 1), employment (column 2), number of customers (column 3) and sales (column 4)) - all winsorized at 5% level - across midline and endline survey rounds. Regressions use an ANCOVA specification, where a midline survey round dummy, baseline value of the outcome variable as well as stratification controls are included on RHSs and standard errors are clustered at retailer level. Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
concentration on access to finance is also zero. However, in countries with high government ownership, the negative impact is significant.

The average government ownership in our sample is 0.25 with a standard deviation of 0.23. These numbers suggest that in a country with average government ownership the impact of Lerner is 0.79, while in a country that is one standard deviation above the average the impact is about twice as large, at 1.5. This translates into a decline in the probability of access to finance of about 10 percentage points as a result of a one standard deviation increase in the Lerner index in a country with high government bank ownership. The same impact is only 5 percent in a country with average government bank ownership and close to zero in a country without any government bank ownership.

To summarize, we find that the impact of competition and concentration depends on the environment in which banks operate. This may explain the contradictory results observed in the previous literature, as in some countries the negative impact of low levels of competition may be mitigated by some positive factors such as availability of credit information or the overall level of financial development, while in other countries the impact may be exacerbated by factors such as high government bank ownership.

# **6. Conclusions**

The theory on the impact of bank competition on access to finance offers conflicting predictions and the empirical literature provides mixed results and suffers from a number of limitations. Combining multi-year firm-level data on access to finance with panel country-level data on bank competition, this paper offers new evidence on the link between competition and access to finance. One advantage of our dataset is that it allows us to control for unobserved differences between countries, using country fixed effects. Thus, we are able to isolate within country variation in competition and access to finance. Also, contrary to other studies that equate concentration with competition, we conduct estimations using direct measures of banks’ pricing behavior.

Our results indicate that higher bank competition, as measured by lower levels of the Lerner index, increases firms’ access to finance, while commonly used concentration measures 14
We note that our estimates concern only the U.S. and Canada, thus, providing a partial estimate of the total global value of the Amazon rainforest. These two countries represent about 28% of the total population of the OECD countries (comprising the vast majority of high-income nations) and a much smaller fraction of the global population. If one assumes that our estimates are representative of OECD countries, then the global total intrinsic value of protecting the Amazon rainforest would be approximately $11,000 per hectare. In addition, previous research suggests that preservation of rainforests in developing countries is valued also by their domestic populations (Vincent, Carson et al. 2014), adding yet another source of benefits to consider when evaluating Amazon conservation options.

To estimate the total economic value of Amazon rainforest protection, several other categories of benefits, domestic and international, also need to be considered, including global climate benefits from reduced carbon emissions. Our intrinsic-value estimate for the U.S. and Canada specifically sets aside carbon benefits from avoided deforestation in the Amazon (Supporting Information). Climate benefits are best assessed using carbon emission damage estimates (the social cost of carbon) from global integrated assessment models (Tol 2005; Council 2013), multiplied by net amounts of avoided carbon emissions resulting from Amazon protection programs, about 130 t C ha<sup>-1</sup> (Song, Huang et al. 2015). Using a recent (conservative) estimate of the social cost of carbon, $100 t<sup>-1</sup> C (Council 2013), average expected carbon benefits of avoided rainforest losses in the Amazon are $13,000 per hectare. Therefore, our results suggest that global intrinsic values, likely adding up to some $11,000 per hectare when accounted for in countries beyond just the U.S. and Canada, could be of comparable magnitude to carbon benefits from the protection of the Amazon rainforest.

25
# **Figure 2. Global Growth and Risk Factors Contribution**

_Uncertainty surrounding global growth forecasts has increased since the January 2016 Global Economic Prospects and is slightly above the historical median. Upside risk has decreased while downside risks for the current year have reached post-crisis highs. The probability of a 1 percentage point deviation from current global growth projections in 2017 is estimated at 19 percent, broadly in line with the January projection (18 percent)_

## **A. Risks to global growth**

<!-- Start of picture text --> Percent<br>5 90 percent 5<br>80 percent<br>50 percent<br>90 percent in JAN16<br>Baseline<br>3 3<br>1 1<br>2014 2015 2016 2017<br><!-- End of picture text -->

## **C. Contribution of risk factors to forecast uncertainty**

<!-- Start of picture text --> Variance<br>Oil<br>3.0 S&P500<br>Term spread<br>2.5 Other factors<br>Total<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>2016 (GEP 2016 2017 (GEP 2017<br>JAN16) JAN16)<br><!-- End of picture text -->

## **B. Uncertainty of global growth forecasts**

<!-- Start of picture text --> Standard deviation<br>Current year Next year<br>2.0 Current year median Next year median<br>1.8<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

## **D. Balance of risk of global growth forecasts**

<!-- Start of picture text --> Skewness<br>Current year Next year<br>Current year median Next year median<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>-0.2<br>2006 2007 2008 9 200 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

## **E. Contribution of risk factors to the balance of risks to global growth**

<!-- Start of picture text --> Skewness<br>Oil<br>0.2<br>S&P500<br>Term Spread<br>Total<br>0.1<br>0.0<br>-0.1<br>-0.2<br>2016 (GEP 2016 2017 (GEP 2017<br>JAN16) JAN16)<br><!-- End of picture text -->

**F. Probability of global growth being 1 percentage point below baseline forecasts**

<!-- Start of picture text --> Percent<br>20<br>Average 2006-16<br>15<br>10<br>5<br>0<br>Current year (2016) Next year (2017)<br><!-- End of picture text --> Source: World Bank. Bloomberg, Consensus Economics.

Notes: The methodology is discussed in detail in the text. “GEP JAN16” stands for Global Economic Prospects in January 2016. A. “90 percent in JAN16” is the 90 percent confidence interval of a fan chart based on data available for the January 2016 _Global Economic Prospects_ report.

B. Dispersion is measured by the standard deviation. Gray areas represent the global financial crisis of 2008-09 and the intensification of the Euro area debt crisis in 2010 and 2012.

C. “Other factors” denotes the contribution of own shocks of global growth forecast error in VAR variance decomposition. D. The balance of risk is measured by the skewness. Gray areas represent the global financial crisis of 2008-09 and the intensification of the Euro area debt crisis in 2010 and 2012.

12
**Table2  Methods Framework for Economic Evaluation (International scope)**

|**TITLE**|**WHO GUIDE TO COST-**<br>**EFFECTIVENESS**<br>**ANALYSIS**|**REFERENCE CASE GUIDELINES**<br>**FOR BENEFIT-COST ANALYSIS IN**<br>**GLOBAL HEALTH AND**<br>**DEVELOPMENT**|**THE INTERNATIONAL DECISION**<br>**SUPPORT INITIATIVE REFERENCE CASE**<br>**FOR ECONOMIC EVALUATION: AN AID**<br>**TO THOUGHT**|<br>**RECOMMENDATIONS FOR CONDUCT,**<br>**METHODOLOGICAL PRACTICES, AND**<br>**REPORTING OF COST-EFFECTIVENESS**<br>**ANALYSES**|
|---|---|---|---|---|
|**Objectives**|To provide policymakers and<br>researchers with a clear<br>understanding of the<br>concepts and benefits of<br>Generalized CEA (GCEA)|<br>To clarify important concepts, aid in<br>implementation, and provide default<br>values for key parameters including<br>options for standardized sensitivity<br>analysis|To support decisions aimed at improving<br>population health from within available<br>funding while acknowledging the relevance<br>and trade-offs associated with the<br>incorporation of social values into those<br>decisions|To improve quality and comparability of<br>CEAs|
|**Intended**<br>**audience**|Analysts with some<br>background in CEA|Practitioners with some training and<br>experience in conducting economic<br>evaluations|Policymakers|Multiple stakeholders|
|14<br>**Methodological**<br>**approach**|GCEA|BCA|Standardized principle-based methodology|CEA (with cost-consequences analysis<br>(CCA) components)|
|**Structure**|A standard set of<br>methodological choices on<br>how to perform GCEA|Stepwise analytical approach, specific<br>guidance in key areas with focus on<br>approaches implementable in LMIC<br>settings|<br>11 principles with associated methodological<br>specifications and reporting standards|<br>Central topics in economic evaluation in<br>book format|
|**Key**<br>**features**<br>**(non-exhaustive)**|<br>Maximize generalizability of<br>results across settings by<br>comparing to starting point<br>of doing none of the current<br>interventions|<br>Updated and coordinated guidance on<br>key BCA concepts including<br>valuation of morbidity and mortality<br>risk reduction, standardized VSL<br>benefits transfer approach|<br>Each principle accompanied by<br>corresponding methodological specification<br>and reporting standard|Dual perspectives – health system and<br>societal, introduction of the Impact<br>Inventory concept (cross-sectoral CCA)|

<!-- Start of picture text --> 14<br><!-- End of picture text --> Source: Authors, World Bank.
Table 5. Trends in United States Expotts of High-Tech Products to Selected Markets: 1965 to 1989

|High Technolory Expon Facts<br>|1965|1975|1985|1987|1989|
|---|---|---|---|---|---|
|Destination of U.S. Exports (Smillion)<br>Argentina/Brazil/Chile||||||
|<br>European Comnunity'|139<br>|971<br>|1,601<br>|2,459|2,976|
|<br>Developing South and SE Asia|1,434<br>|5,888<br>|18,544<br>|23,481|36,163|
|<br>Japan|338<br>324|2,063<br>|10,191<br>|13,008<br>|20,083|
|<br>World|<br>4778|1,406<br>|6,114<br>|8,019<br>|12,378|
||,|21,951|63,368|78,384|110,367|
|U.S. Hiuh-Tech Trade Balance (Smillion)<br>ArgentinatBraziUChile|135|||||
|<br>European Community|<br>|883<br>|720<br>|1,295|1,723|
|<br>Developing South and SE Asia|844<br>|3,452<br>|5,968<br>|7,986|17,199|
|<br>Japan|300<br>|597<br>|-2,714<br>|-6,878|-8,096|
|<br>World|-101<br>|-1,219<br>|-18,668<br>|-22,186|-24,313|
|<br>|3,182|12,059|-2,707|-5,258|-,444|
|Hi-Tech Trade Balance As a Share of U.S. High-Tech Exports (%)<br>Argentina/BraziUChile||||||
|<br>European Commnunit|97<br>|91<br>|45|53|58|
|y<br>Developing South and SE Ai|58<br>|59<br>|32|34|47|
|sa<br>Japan|89<br>|29<br>|-27<br>|-53|-40|
|<br>World|-31<br>66|-87<br>|-305<br>|-276<br>|-196|
|||55|4|-7|2|
|Hi-Tech Goods as a Share of All U.S. Exports (%)<br>Argentina/BrazilUChile||||||
|<br>European Conmmunity|17<br>|24<br>|36<br>|43|43|
|<br>Developing South and SE Asia|21<br>|26<br>|43<br>|44|47|
|<br>Japan|13<br>|21<br>|38<br>|39|37|
|<br>World<br>|16|15|28|29|29|
|MEMO rTEM: EEC Hi- Tech Trade Performnance<br>Hi-Tech Exportsto Jaan (Smillion)||||||
|p<br>Hi-Tech Trade Balance with J $illi|108<br>|612<br>|1,678|3,112|3,926|
|apan (mon)<br>Hi-Tech Balan^e as a Share of Hi-Tech Exorts|22<br>2|-1,024<br>|-8,064<br>|-14,615<br>|-18,88S|
|p<br>Hi-Tech Gd Sh f|-0<br>|-196|480|-470|-476|
|oos as a are o All Exports|21|22|22|20|18|

The 13 percentage point increase over 1987-89 is largely due to a $5 billion upsurge in United States aircraft cxports.
Policy Research Working Paper 6016

# **Abstract**

The crash of global financial markets in 2008 caused a ripple effect on economic demand and growth worldwide. Export-oriented economies were hit particularly hard, and many governments stepped in quickly with broad-ranging stimulus programs to lessen the effects on households of rising unemployment and falling income. To better understand the role that stimulus policy might play in softening the effects of these shocks, this paper examines recent nationallyrepresentative data from Thailand, an export-dependent economy where a large-scale stimulus program was introduced in 2009. Using monthly data spanning 2006– 2010, the paper uses sub-province-level community panel data to examine the effects of major components of the stimulus on household consumption, income, borrowing, and debt repaid. To address simultaneity of changes in government spending and household outcomes, the analysis estimates a dynamic panel regression, instrumenting the stimulus effect with second-order lagged outcome variables, and estimating the model using the Generalized Method of Moments. The results suggest that household participation in these programs helped smooth consumption. This increase in monthly consumption was not supported from household receipts from the government stimulus, but more likely through a reallocation of consumption and savings that included greater debt repayment. The paper typically finds stronger effects in urban compared with rural areas.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at skhandker@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
enue needed to fund pension debts and benefits. To illustrate this, Figure A.6 shows an advertisement used by AFIP for this purpose, encouraging Argentines to report their assets to “contribute to the country" and “achieve better pensions." Third, the amnesty program was salient. Argentina’s tax authority ( _Administración Federal de Ingresos Públicos_ , or AFIP for its Spanish acronym) led a massive advertising campaign. For instance, three large banners encouraging evaders to disclose their hidden assets were hung at the entrance of AFIP’s headquarters in Buenos Aires (Figure A.5). Moreover, AFIP’s website featured advertisements promoting disclosures of hidden assets (Figures A.6 and A.7).

Fourth, the threat of detection became more credible after Argentina signed numerous tax information exchange agreements in 2016, including treaties with its most relevant tax havens, like Uruguay and Switzerland, as well as with Brazil, Chile, and the US (Figure A.8 plots a timeline of these events). In addition, the Panama Papers were leaked two months before the adoption of the amnesty program, further raising the perceived threat of detection and its salience, as proxied by Google’s search interest (Figure A.9).

The official reports by AFIP, reproduced in Table A.2, shed light on the magnitude of disclosures. Nearly 255,000 people and firms participated in Macri’s amnesty program. Participants revealed assets worth US$117 billion under the scheme, representing 21% of Argentina’s GDP in 2016. The success of Argentina’s amnesty exceeded the government’s initial revenue projections by sixfold (Telam, 2016). Four-fifths of the disclosed assets were abroad, and the remainder in Argentina. Nearly half of the assets disclosed were foreign stock and other investments (10% of GDP). Almost one-quarter represented deposits in foreign bank accounts and currencies (5% of GDP). About 6% corresponded to undeclared cash (1% of GDP).<sup>3</sup> Lastly, almost one-fifth came from real estate (4% of GDP), corresponding to 167,000 previously hidden properties. The penalties raised US$9.5 billion in revenue, equivalent to 1.8% of GDP (AFIP, 2017). The amount disclosed and revenue collected from penalties place Argentina’s 2016 program as one of the world’s most successful tax amnesties.

# **2.3 Data and methodology**

We use information from statistical yearbooks provided by AFIP, representing detailed tabulated data from tax returns for the country’s wealth, income, value-added, and payroll taxes for FYs 2002–21. Our primary analysis uses data from the wealth tax, including the tabulations with information on the number of filers and taxpayers, the wealth value,

> 3 Participants deposited this money in a special bank account and reported it to the tax authority.

7
**Table 4: Land market restrictions, Female Labor force participation and Wages**

IV Regression Results

||Labor Force|Participation|Log(Real A|nnual wage)|
|---|---|---|---|---|
||1|2|3|4|
|**Proportion of Area Under LDO**|**1.046*****|**1.032*****|**-1.541****|**-1.778*****|
||**(2.658)**|**(2.657)**|**(-2.308)**|**(-2.672)**|
|First stage Regressions|||||
|Malaria*Average Rainfall|**-0.0439*****<br>**(-3.463)**|**-0.0440*****<br>**(-3.465)**|**-0.0433*****<br>**(-3.656)**|**-0.0425*****<br>**(-3.587)**|
|**Relevance of Instruments**|||||
|Kleibergen-Paap/Angrist-Pischke F|11.99|12.01|13.37|12.87|
|Stock-Yogo 10% max. rel. IV bias|9.08|9.08|9.08|9.08|
|**Controls**|||||
|Individual characteristics|Yes|Yes|Yes|Yes|
|Household Composition|No|Yes|No|Yes|
|Area characteristics,District FE|Yes|Yes|Yes|Yes|

(1) All regressions include full set of regressors as in columns (2) and (4) of Table 3.

(2) Column 2 includes household composition (no. of infant and kids) as controls (3) Selection term in column 3 is defined in terms of heterocedasticity in the participation equation (4) Selection term in column 4 is defined using numbers of infants and kids as exogenous controls in participation regression.

(5) Robust t statistics in parentheses. Standard errors corrected for clustering at sub-district (DSD) * significant at 10%; ** significant at 5%; *** significant at 1%
# **1.  Introduction**

To open a checking account in Cameroon, you need over 700 dollars, an amount higher than the GDP per capita of that country, while no minimum amounts are required in South Africa or Swaziland. Fees to maintain a checking account exceed 25 percent of GDP per capita in Sierra Leone, while there are no such fees in the Philippines. The fees for transferring 250 dollars internationally are 50 dollars in the Dominican Republic, but only 30 cents in Belgium. While most people in the developed world take access to banking services for granted, price and non-price barriers prevent large parts of the population in developing countries from accessing and using formal banking services. If we follow previous estimates (Genesis, 2005b) that poor people cannot afford to spend more than 2 percent of their household income on bank charges, the fees observed in many countries effectively prevent them from using such accounts. Similarly, the requirement of a physical address or of a formal sector job as eligibility criteria to open an account excludes the majority of people in many developing countries, where a large percentage of the population lives in rural areas and works in the informal sector.

This paper presents new indicators of barriers to bank access and use of banking services around the world, shows their significance for outreach and relates them to bank and country characteristics. First, through surveying the largest banks in 58 countries, we document the extent of barriers to three banking services - deposits, loans and payments - across three dimensions - physical access, affordability, and eligibility. Second, we show the importance of these barriers for access to and use of financial services. Third, we explore which bank and country characteristics are associated with these barriers, with findings that have important implications for policies to broaden access.

Market frictions such as transaction costs and information asymmetries give rise to financial institutions and markets (see Diamond 1984, 1991, Ramakrishnan and Thakor 1984, 1
# **Herder-Related Violence, Agricultural Work, and the Informal Sector as a Safety Net**<sup>_∗_</sup>

Jeffrey R. Bloem<sup>_†_</sup> Amy Damon<sup>_‡_</sup> David C. Francis<sup>_§_</sup> Harrison Mitchell<sup>_¶_</sup>

**Keywords:** Conflict, Violence, Informality, Agriculture, Farmer-herder, Safety nets, Gender. **JEL Codes:** E26, E29, I31, Q12

> _∗_ Authors are listed in alphabetical order. The authors thank members of the World Bank’s LSMS team, specifically Siobhan Murray, for assisting in the use of restricted-use unmasked GPS coordinates. The authors are also thankful for constructive feedback from colleagues attending presentations at IFPRI, the World Bank, and the 2023 Centre for the Study of African Economies (CSAE) conference. The authors are particularly indebted to Aart Kraay, Norman Loayza, Deon Filmer, and Jorge Rodriguez Meza for guidance and comments; Leora Klapper and Xavier Gine provided additional, thoughtful comments. The findings and conclusions in this manuscript are ours and should not be construed to represent any official World Bank determination or policy. All errors are our own. Replication files are available upon request. All figures use Asjad Naqvi’s white tableau scheme in Stata (v17) https://github.com/asjadnaqvi/stata-schemepack.

> _†_ Research Fellow, International Food Policy Research Institute, 1201 I St NW, Washington, DC 20005.

> _‡_ Professor of Economics, Macalester College, 1600 Grand Ave St. Paul MN 55105.

> _§_ Senior Economist, World Bank, 1818 H Street, NW Washington, DC 20433

> _¶_ Ph.D. Student, University of California San Diego.