rafmacalaba/data-use-mentions-extended huggingface dataset:
We use as exchange rate regimes variable, the classification by Ilzetzki et al. (2017). The classification ranges from 1 to 15, with lower values representing more fixed exchange rate regimes and higher values representing more flexible regimes (See Appendix 4).

As aforementioned, the set of control variables that we employ includes an inflation targeting dummy, the number of years prior to elections, an index for government polarization, the output volatility, the logarithm of GDP per capita, the ratio of public debt to GDP and the ratio of natural resource rents to GDP.

The use of inflation targeting is motivated by the fact that this institutional reform may influence the conduct of fiscal policy by limiting the scope of seigniorage revenues and placing other constraints on expenditure (Combes et al. (2017)). To address this point, we use a dummy variable equal to 1 if the country has implemented inflation targeting at time t and 0 otherwise. The data are taken from Aizenman et al. (2011), Samarina and De Haan (2014), and Laurens et al. (2015).

In addition, we take account of the electoral cycles to explain fiscal discipline since the main theoretical explanations of fiscal indiscipline emerge from the political budget cycle models. We use the variable _elections_ which is the number of years prior to executive elections. We construct this variable using the database of Hyde and Marinov (2012): The National Elections across Democracy and Autocracy (NELDA) data set.

The literature on government fragmentation suggests that fragmentation has harmful effect on public finances (Kontopoulos and Perotti (1999); Volkerink and De Haan (2001)). Thus, government polarization may be associated with higher fiscal discipline. We use the index _Polariz_ from the World Bank Database of Political Institutions to control for government polarization. It represents the maximum polarization between the executive party and the four principle parties of the legislature. The higher the index, the more polarized the government is. The index is zero if the chief executive’
Chelminski, K. 2018. “Fossil Fuel Subsidy Reform in Indonesia: The Struggle for Successful Reform.” In J. Skovgaard and H. Van Asselt (Eds.), The Politics of Fossil Fuel Subsidies and their Reform (pp. 193-211). Cambridge: Cambridge University Press.

Cheon, A., J. Urpelainen, and M. Lackner. 2013. “Why Do Governments Subsidize Gasoline Consumption? An Empirical Analysis of Global Gasoline Prices, 2002–2009.” Energy Policy 56: 382–90.

Coady, D., V. Flamini, and L. Sears. 2015. “The Unequal Benefits of Fuel Subsidies Revisited: Evidence for Developing Countries.” IMF Working Paper WP/2015/250, International Monetary Fund.

Cruces, G., R. Perez-Truglia, and M. Tetaz. 2013. “Biased Perceptions of Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment.” Journal of Public Economics 98: 100–112.

Dabla-Norris, E., T. Helbling, S. Khalid, H. Khan, G. Magistretti, A. Sollaci, and K. Srinivasan. 2023. “Public Perceptions of Climate Mitigation Policies: Evidence from CrossCountry Surveys.” Staff Discussion Notes 2023/002, International Monetary Fund, Washington, DC.

Dechezleprêtre, A., A. Fabre, T. Kruse, B. Planterose, A. Sanchez Chico, and S. Stantcheva. 2022. “Fighting Climate Change: International Attitudes toward Climate Policies.” NBER Working Paper 30265, National Bureau of Economic Research, Cambridge, MA.

de Quidt, J., J. Haushofer, and C. Roth. 2018. “Measuring and Bounding Experimenter Demand.” American Economic Review 108 (11): 3266–3302.

50
the ability of the state to generate revenues from income and domestic sales taxes, prompting the state to obtain revenues through both the inflation tax and taxes on imports and exports, which can be more easily monitored. In the late 1980s, many countries reformed taxes to diminish the distortionary effects of taxation and to increase revenue, thus allowing trade liberalization and disinflationary policies and reducing the tax burden on the formal sector.

> 37 Comparison with OECD countries is problematic, given that the available investment figures for this group of countries ~ do not disaggregate corporate investment into its public and private components. Therefore, the “private” investment figures reported for OECD countries in the text and in the appendix include public enterprise investment.

22
# Appendix 1. Variable Definitions

Table A1.1. Variable definitions

|**Variable**|**Definition**|
|---|---|
|Age of PA|Age of theprincipal applicant(PA)|
|Children under 5|1 if there are one or more children below the age of 5 (inclusive) in the<br>household|
|Disable|1 if there are one or more disabledpersons in the household|
|Education|Categorical variable. We classified education of the PA in three groups: below<br>years,6-11years,and more than 12years of education|
|Elderly|1 if there is one or more persons above the age of 65 (inclusive) in the<br>household|
|Entry status|Categorical variable. ProGres reports 5 entry statuses of which we selected th<br>three categories with the largest number of PAs: Informal, formal, and<br>smuggled.|
|Expenditure|Raw addition of all expenditure categories, which include rent, bills, food,<br>healthcare, education, and others.|
|FamilyType|Categorical variable. For a detailed definition see section 4|
|Household size|Number of people included in the case records of each PA in Individual<br>ProGress dataset|
|Wage Income|1 if the household receives income from employment and/or daily or irregular<br>work|
|Income from remittances|1 if the household receives income from remittances|
|Income per capita|Raw sum of household income from all sources; work, pension, assets in Syria<br>transfers, donations, other organizations' humanitarian aid, and other divided<br>by household size|
|Male Adults|<br>Number of males above 18(inclusive)in the household|
|Marital Status|Categorical variable. The classification includes married PAs with spouse in th<br>household, married PAs without spouse in the household, widowed
we find that only the CCT intervention has a positive and significant impact, and we reject equality of the CCT and UCT coefficients. However, using the school-based enrollment measure (column 4), we find positive and significant impacts for both the CCT and UCT interventions and no statistical difference between the coefficients.

In Table 4 (columns 2 and 5), we present results using the Equation 2 difference-indifference strategy. As previously discussed, results show no impact of the conditional or unconditional transfers at round 2 for school year 2008-2009, the program’s first year, because the transfers were delivered too late in that school year. However, the results show significant impacts of transfers at round 3 for school year 2009-2010, when the transfers were delivered on time. More precisely, columns 2 and 5 show significant positive impacts in the CCT villages for children ages 7 to 15, using both enrollment measures, while there is a positive but not significant coefficient for the UCT villages. At round 3, the CCT and the UCT coefficients are significantly different from each other using the self-reported but not the school roster measure. These results are confirmed in columns 3 and 6 when we use the Equation 3 difference-indifference specification using only the baseline and last follow-up surveys (rounds 1 and 3).<sup>23</sup> Overall, when looking at all school-age children, across the two measures of enrollment and the three alternative specifications, Table 4 allows us to conclude that the cash transfer intervention had no impact on school enrollment in the first year (2008-2009), but CCTs had a positive impact on enrollment in the second year. The impact of the UCT intervention on enrollment for all children is less clear and often not statistically significant. The remainder of our analysis extends our discussion of Figure 1 to investigate how the impacts of the conditional and unconditional cash transfers vary with the type of child.

> 23 The point estimates in columns 1 and 4 are larger than in the difference-in-difference specifications in the other columns suggesting that controlling
= FF�ββ + ββ XX zero otherwise. We use the strictly green job definition for the first round of estimates and the broadly green job definition for a second round of estimates. F is the logit function link while the individual characteristics (age, race, gender, education level) are represented by . α is a location dummy for urban residence, η is a vector of province dummies and δ are ii quarter of year dummies. θ is a vector of industry dummies. is the error term. Our sample XX pp ll tt includes working-age individuals (age 15-65) who are employed. ss ii εε To create the variables, we adapt some variables in the PALMS data. We convert the continuous education variable into a four-category variable:<sup>22</sup> 1=less than grade 12 (those without a high school qualification), 2=matric (complete high school), 3=other tertiary (those with more than a high school qualification but less than university), and 4=degree (those with a university degree). For the age variable, we construct 5 categories from a continuous age variable as follows: age 15-24, age 25-34, age 35-44, age 45-54 and age 55-65. Gender is defined as a binary variable (1=female, 0=male). For ethnicity group or race,<sup>23</sup> we use the

> 21 This final caveat underlies one of the key differences between the text analysis and O*NET tags a much larger share of managerial jobs as green, as compared to the text analysis.

> 22 As we used categorical variables to show green employment by education and age categories in the descriptives sector, for consistency, we maintain the same categories for the regressions. The South African literature (Branson and Leibbrandt 2013) has also found that the relationship between education and labor market outcomes is convex and for this reason it is more informative to use education categories.

> 23 Race is included
countries such as Indonesia, Morocco, Thailand and Tunisia. Cameroon’s per capita GNP in 1988 dollars was estimated at US $1,010 (World Bank 1990). These positive achievements in economic growth were generally attributed to fiscal prudence and political stability. The World Development Report of 1988 did praise Cameroon along with Indonesia for managing cautiously the windfall from the 1979-1981 oil boom<sup>2</sup> .

The fact that Cameroon did enjoy high and sustained economic growth throughout 1965-1985 has been abundantly documented (Bradford et al. 1994, World Bank 1995). However, little is known about trends in inequality and poverty during those “good” times for lack of data. Based on the 1983 Household Expenditure Survey, the World Bank (1995) found evidence of high levels of inequality in the distribution of income and rural poverty. The same report discusses factors indicating that the situation may not have been much better in years prior to the 1983 survey. While acknowledging that many urban residents did benefit from this growth episode, the report points to the following factors as contributing to high rural poverty: (1) an incentive structure that favored capital-intensive methods of production over labor-intensive ones; (2) an urban bias in the selection of public investment; and (3) the lack of human capital development in the rural areas.

In 1985, the economy was hit by a collapse of world prices of the country’s major export commodities, namely oil, cocoa and coffee. This was further complicated by a 40 percent appreciation of the CFA franc between 1985 and 1988, and gains in competitiveness by Nigeria since 1985. The export price index fell by 65 percent for oil, 24 percent for cocoa, 11 percent for coffee and 20 percent for rubber (Bradford et al. 1994). Faced with this difficult international environment, the government adopted initially a strategy of internal adjustment<sup>3</sup> between 1985 and 1993. This entailed cutting back on public spending (mainly investment
of respondents’ demographic characteristics such as gender, years of schooling, and age. While LAPOP allows the identification of respondents living in rural and urban areas, Latinobar ́ometro is exclusively conducted in urban areas (albeit it allows to distinguish between small and large urbanization). Importantly, both survey provide information regarding the municipality of residence for each respondent which allows to link income data discussed below as well as other time varying municipality-level characteristics (e.g., crime rates or national government transfers).

I focus my analysis in 10 outcome variables. From LAPOP I focus on 1) trust in the president, 2) trust in the municipal government, 3) trust in the national legislature, 4) confidence in the judicial system, 5) trust in mass media, and 6) trust in the Catholic church. All trust questions asked about trustworthiness on the aforementioned institutions. Specifically, LAPOP asks “To what extent do you trust the President/Municipality/National Legislature/Mass Media/Church”. Respondents provide ordinal answers from 1 (not at all) to 7 (a lot). I construct dichotomic variables taking value of 1 if the answer is 5 or more, 0 otherwise. For the case of confidence in judicial system the question asked is slightly different: “If you were a victim of a robbery or assault how much faith do you have that the judicial system would punish the guilty?” The answers go from 1 (a lot) to 4 (little). I coded confidence in the judicial system as 1 if the answer is “a lot” or “some”, 0 otherwise. I dichotomize ordinal dependent variables for three main practical reasons: to ease the interpretation of the results, to employ more reliable measure of trust/confidence, and to facilitate the computation of my econometrics models.<sup>3</sup> Nonetheless, it is important to note that none of the main results depends on this dichotomization approach (see Table A.2 in
0.427|0.223|
|Observations|2370|4386|8828|2872|1296|978|510|
|_Panel B:_|_Samp_<br>ultra<br>poor|_le of House_<br>other<br>poor|_holds Not_<br>middle<br>class|_Giving Tran_<br>upper<br>class|_sfers to Ul_<br>other<br>poor|_tra-Poor at_<br>middle<br>class|_Baseline_<br>upper<br>class|
|Treatment|0.026***|0.007|0.024|-0.001|0.028***|0.020***|0.004|
||(0.006)|(0.011)|(0.019)|(0.003)|(0.006)|(0.004)|(0.003)|
|Mean in control|0.059|0.117|0.164|0.024|0.048|0.026|0.005|
|Adj. R-squared|0.102|0.104|0.058|0.019|0.269|0.130|0.080|
|Observations|11094|9078|4636|10592|13384|12506|3920|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. In columns 1-4, the sample includes observations from ultra-poor households; while in columns 5-7 non-ultra-poor households. In particular, the sample in column 5 includes other poor, in column 6 middle class and in column 7 upper class households. All specifications control for the baseline level of the outcome, survey wave and subdistrict (strata) fixed effects. Standard errors are clustered at the
6). When looking at total taxes over GDP, the SSA region lags behind ECA, EAP and LAC, but it is better positioned compared to MENA and SA.

**Figure 6. Trends in taxation (total taxes as % GDP) by region**

<!-- Start of picture text --> EAP ECA LAC MENA SA SSA<br>26%<br>24%<br>22%<br>20%<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>2 0 0 0 2 0 0 2 2 0 0 4 2 0 0 6 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> _Source:_ Government Revenue Database (GRD) – United Nations University UNU - WIDER.

<mark>Regional-level analysis hides considerable country heterogeneity. Public sector performance indeed varies substantially across countries (Table 1). CPIA scores show that a small subset of countries stands out, such as Rwanda and Cabo Verde which have been among the better-performing countries in terms of public sector performance for more than a decade. Similar trends emerge when zooming into budget and PFM performance: Public Expenditure and Financial Accountability (PEFA) assessments of budget execution, for example, show that some countries, such as Rwanda, Ghana and Zambia, outperform their regional neighbors with regards to the level of predictability and control over budget execution (Figure 7).</mark>

**_Table 1. How have African countries performed from a governance perspective in the past decade?_**

||**CPIA Public Sector Management and Institutions**|
|---|---|
|Top5performers(2021)|Cabo Verde,Rwanda,Kenya,Senegal and Ghana|
|Bottom 5 performers (2021)|South Sudan, Somalia, Sudan, Guinea-Bissau, Central African Republic|
|
OZIER - REPLICATION REDUX – p. 24 of 41 Dunn/Bonferroni style of correction simply involves multiplying the p‐value by two; other corrections that take into consideration correlations between outcomes generally require smaller corrections.<sup>23</sup> Allowing for both the test of the coefficient 0.0387 (yielding a conventionally insignificant p‐value of around 0.2) and the test yielding of the coefficient 0.0848 (yielding a conventionally very significant p‐ value of less than 0.00001) to be considered, then, is there a significant result –after correcting for performing two tests—that agrees qualitatively with the original paper? There is. The p‐value associated with the 8.48 percentage point increase in school attendance, when doubled, is still significant by the conventional standards of both economics and epidemiology.

This is the first place where drawing a conclusion from the replication exercise proved to be divisive. MK’s own replication files include all these calculations, old and new. Following the corrected data construction, but without any adjustment to analytical decision‐making in light of the data, one could conclude that the 7.5‐percentage‐point finding drops to insignificance. Allowing for such adjustments, either via a minimum mean‐squared‐error approach or a multiple‐testing‐adjustment approach, one would conclude that the 7.5‐percentage‐point finding is now an 8.5‐percentage‐point finding, and is stronger than before.

Either way, everyone agrees that the study shows (and the corrected data can be replicated to show) an effect of deworming treatment on direct recipients of medication, on children in the same school, and on children within three kilometers; this is true both in terms of moderate‐to‐heavy worm infections and in terms of school attendance. In fact, in the published version of the pure (verification)

> 23 The intuition behind this correction is that, whatever vanishingly small probability there is of
WPS 3877

# **_PROPENSITY SCORE MATCHING AND POLICY IMPACT ANALYSIS A Demonstration in EViews_**

B. Essama-Nssah<sup>*</sup> Poverty Reduction Group (PRMPR) The World Bank Washington, D.C.

# **_Abstract_**

Effective development policymaking creates a need for reliable methods of assessing effectiveness. There should be, therefore, an intimate relationship between effective policymaking and impact analysis. The goal of a development intervention defines the metric by which to assess its impact, while impact evaluation can produce reliable information on which policymakers may base decisions to modify or cancel ineffective programs and thus make the most of limited resources. This paper reviews the logic of _propensity score matching_ (PSM) and, using data on the National Support Work Demonstration, compares that approach with other evaluation methods such as double difference, instrumental variable and Heckman’s method of selection bias correction. In addition, it demonstrates how to implement _nearest-neighbor_ and _kernel-based methods_ , and plot program incidence curves in EViews. In the end, the plausibility of an evaluation method hinges critically on the correctness of the socioeconomic model underlying program design and implementation, and on the quality and quantity of available data. In any case, PSM can act as an effective adjuvant to other methods.

Keywords: EViews, Double Difference, Impact Analysis, Instrumental Variables, Kernel Function, Matching, Propensity Score, Sample Selection.

JEL: C14, C81, C87, I38.

World Bank Policy Research Working Paper 3877, April 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent
King, R., Levine, R., 1993. Finance and growth: Schumpeter might be right. The Quarterly Journal of Economics, Vol. 108, No. 3, pp. 717-737.

Kreiner, C. T., Lassen, D. D., Leth-Petersen, S., 2012. Consumer responses to ...scal stimulus policy and households’cost of liquidity. CEPR Discussion Papers 9161. Lusardi, A., Schneider, D. J., Tufano, P., 2011. Financially fragile households: Evidence and implications. NBER Working Paper No. w17072.

McKay and Reis, 2016. The role of automatic stabilizers in the US business cycle. Econometrica, Vol. 84, No. 1, pp. 141-194.

Mehrotra, N. R., 2018. Fiscal policy stabilization: Purchases or transfers? International Journal of Central Banking, Vol. 14, No. 2, pp. 1-50.

Monacelli, T., Perotti, R., 2011. Redistribution and the multiplier. IMF Economic Review, Vol. 59, No.4, pp. 630-651.

Oh, H., Reis, R., 2012. Targeted transfers and the ...scal response to the great recession. Journal of Monetary Economics, Vol. 59, pp. S50-S64.

Parraga-Rodriguez, S., 2018. The dynamic e¤ects of public expenditure shocks in the United States. Journal of Macroeconomics, Vol. 56, pp. 340-360.

Pennings, S., 2020. Cross-region transfers in a monetary union: Evidence from the US and some implications. World Bank Policy Research Working Paper No. 9244.

Ramey, V. A., 2016. Macroeconomic shocks and their propagation. NBER Working Paper No. w21978.

Ramey, V. A., Zubairy, S., 2018. Government spending multipliers in good times and in bad: evidence from US historical data. Journal of Political Economy, Vol. 126, No. 2, pp
]|[3.83]|[3.65]|[3.54]|
|GDP growth|-0.002|-0.002|-0.005|-0.001|-0.006|-0.009|-0.006|-0.010*|
||[0.31]|[0.24]|[0.66]|[0.09]|[1.06]|[1.56]|[1.10]|[1.83]|
|Investment environment||-0.008||||0.051**|||
|||[0.27]||||[2.43]|||
|Chinn-Ito index of capital account openness|||0.019||||0.040||
||||[0.69]||||[1.29]||
|Bank assets, % of GDP||||0.002||||0.000|
|||||[0.90]||||[0.01]|
|Country fixed-effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,173|2,151|2,069|1,921|2,055|2,033|1,957|1,807|
|R-squared|0.047|0.046|0.060|0.062|0.021|0.025|0.023|0.021|
|# of countries|29|29|29|29|28|28|28|28|

_Notes:_ The dependent variable is Portfolio debt inflows as % of trend GDP in Columns 1-4 and portfolio debt outflows as % of trend GDP in Columns 5-8. The dependent
Figure 5. Title: Growth in livestock and weather microinsurance in Andhra Pradesh <!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>2005 2006 2007 2008 2009<br>livestock weather<br><!-- End of picture text --> Note: Figure presents total nominal premia (in Rs.) paid on livestock and weather microinsurance policies since 2005. Data for rainfall insurance is for Andhra Pradesh only, while data for livestock insurance is for all states. Indexed so that 2005 = 100. Source: BASIX.