rafmacalaba/datause-encoder-data huggingface dataset:
the survey suggest that increasing the local presence of tax officers had a positive impact on tax morale, but this was concentrated outside Dar es Salaam and centered around limited survey measures. The impact on compliance of taxpayers in targeted wards is also not straightforward. On the extensive (paid anything) and intensive (amount paid) margins, overall the treatment has no impact. However, looking at heterogeneity across regions, increased local presence of tax officers had a positive impact on both the likelihood of payment and the payment amount in the Eastern region of Tanzania (primarily made up of Dar es Salaam and Pwani) in the first quarter of 2023 (immediately after the data collection).

The explanation for the lack of overall impact is likely due to the limited spillover effects on non-visited businesses, which is supported by an endline survey conducted in April/May 2024 in treatment and control wards with the same and neighboring businesses as the December 2022 survey. Combining findings from survey and administrative data, we try to reconcile these by exploring whether survey responses were biased (i.e., untruthful) -reflecting fear of reprimand for claiming low tax morale- or unbiased (i.e., truthful) -reflecting the increase positive view of the TRA and own obligations as a result of increased visibility. Anecdotal evidence suggests that the first hypothesis -where businesses are ‘lying to the taxman’ -is more likely, though more research is needed to validate this.

While the intervention does not provide clear results on the causal impact of a temporary, exogenous facilitation and trust based increase in the local presence of a revenue authority on compliance, the implementation itself provides many lessons learned for future experiments. This study was particularly unique since it involved embedding a field experiment within a data collection activity. To our knowledge, no studies exist where tax officials accompany enumerators in data collection to act as observers. This represents a useful exercise in understanding how responses about tax compliance and tax morale are and are not sensitive to _who_ is perceived to be conducting a data collection activity. Questions around tax compliance and morale are considered particularly sensitive; a third-party survey firm is meant to represent an objective entity that can obtain unbiased responses, even when these relate to sensitive questions like tax compliance. Our study shows that potential bias exists for 4
indeed find that significant medium-term effects on energy intake and accumulation of nonfinancial assets (as well as protein intake) emerge for scheduled castes and tribes (Table 5 top panel). Medium-term benefits to other castes are limited to higher levels of consumption (Table 5, panel 2).. Similarly, in the short term, DDD plus PSM points toward higher levels of growth in consumption, energy, and protein intake due to NREGS benefits that are exclusively concentrated among scheduled casts and tribes (Table 5, panel 3). In addition to supporting the notion of effective self-targeting, this outcome points to the need for detailed exploration of pathways through which program-induced impacts may come about.

# **4.2 Impact Pathways through Labor Markets**

As direct program effects will be transmitted through casual labor markets, we can check the plausibility of our results by using DDD methods to analyze medium- and short-term direct impacts on households with and without members primarily engaged in casual laborer in the initial period (Table 6). The top panel suggests that, in the medium term, NREGS benefits households that rely on casual labor through higher levels of consumption, intake of energy and to some extent protein, and asset accumulation. By comparison, the only medium-term impact on households that did not participate in casual labor markets was via asset creation, in line with the program’s goal to create assets that, by increasing agricultural productivity, can provide a basis for greater demand for casual labor in the long term. In the short term, we find that the only significant impact is an increase, of about 12 percent, in protein intake by participants relying on casual labor (Table 6, bottom panel) with no evidence of short-term effects on those who do not rely on casual labor.

As an additional test of the extent to which NREGS participation works through the casual labor channel, we use 2006 and 2008 panel data to estimate where ∆yi is the change in casual labor income (including NREGS wages) between 2006 and 2008 for household _i_ ; _NREGSi_ is an indicator variable that equals 1 if the household participated in NREGS between July 2007 and June 2008, the reference period for casual labor income in the 2008 survey, and 0 0 otherwise; xi is a vector of initial period control variables that includes caste, poverty category, literacy, female headship, household size, number of adults, and land holdings; D(i) denotes district dummies; and ui is a random error term. We also estimate equation (2) using 2004 and 2006 panel data as a falsification test to examine the plausibility of the DID identification. This allows us to not only assess the contribution of NREGS to total casual labor income at the household level but also distinguish households’ casual labor income by gender to explore links between changes in casual labor income and NREGS participation for male and female individuals separately.

13
<!-- Start of picture text --> 140K - fe)<br>ny<br>c<br>& 120K<br>=<br><<br>6<br>29 100K - ° Oo °<br>S 80K. °<br>3 fe)<br>Q ° ° ° °<br>is 60K - 5 2 20 0 o<br>fad Risk Lower for Risk Higher for °<br>nat Non-Poor Both: 75.5% fe) °<br>3 40K - 209%0 % 288 0,80<br>=2 oo BQ @ fe)<br>20K<br>2 9”5 CORR oScana>» Carmen pieseNO”> aD88 5¢oo So 8 °<br>OK - Risk Lower16 BO Risk Lowerfor<br>- 3.6% i Poor: 0% : . , . . : : :<br>-100K -50K OK 50K 100K 150K 200K 250K 300K 350K 400K 450K<br>Risk to Non-Poor (Relative to Current Conditions)<br><!-- End of picture text -->
Estimates of Ad Valorem Equivalents of Barriers Against Foreign Suppliers of Services in Eleven Services Sectors and 103 Countries

# Yaghoob Jafari and David G. Tarr

# 1. Introduction

Interest in estimates of the impact of services barriers has significantly increased since the early 1980s. In part this is due to the recognition of the growing importance of services as a share of national value added and as a share of global trade. By 2007, services as a share of national value-added ranged from 74 percent in high income OECD countries to 48 percent in low income countries.<sup>1</sup> Escaith (2008) estimated that cross-border trade in services accounted for almost 50 percent of total goods and services trade when trade is measured on a value-added basis; and when we include the value of sales by foreign affiliates, the share of services trade in total trade rises further. In addition, with the inclusion of the General Agreement on Trade in Services (GATS) as a major pillar of the WTO agenda and the inclusion of services in regional trade agreements, services commitments have become an important aspect of international trade negotiations. Estimates of what is at stake in these services trade negotiations depend in the first instance on estimates of the ad valorem equivalents (AVEs) of the barriers faced by foreign suppliers of services.

In this paper, we present our estimates of the ad valorem equivalents of the discriminatory barriers against foreign suppliers of services in 11 services sectors in 103 countries. The estimates are based on a recently available data set on the discriminatory regulatory barriers against foreign suppliers of services in these sectors and countries that was produced by the World Bank. The database contains significantly greater coverage of the regulatory regimes in services in developing countries than what has been available heretofore.<sup>2</sup> The database provides data on the

> 1 From the World Development Indicators database. See Francois and Hoekman (2010, table 1).

> 2 The OECD provides a database of regulatory barriers in services and services trade restrictiveness indices for 40 - countries focusing on the OECD and BRICS countries. The data are available at <u>http://www.oecd.org/tad/services trade/services-trade-restrictiveness-index.htm. See also Golub (2009) for a measures over time of restrictions on FDI</u> in services in 50 developing and 23 developed countries.

1
_Table 16: Results of model-based simulations_

||Monte C<br>|arlo sim<br>|ulations<br>|<br>||||
|---|---|---|---|---|---|---|---|
||Mean<br>Poverty|<br>Mean<br>MSE|Mean<br>CV|Corr|ARB|RMSE|Coverage<br>rate|
|Direct survey estimates||||||||
|H-T approximation|24.9|51.8|31.5|0.840|-0.005|0.060|95.7|
|EA-Clustering|24.9|44.4|29.8|0.840|-0.005|0.060|94.0|
|<br>Household-level EB model|25.1|4.7|9.2|0.978|0.012|0.020|96.4|
|Model diagnostics<br>||||||||
|Marginal R<sup>2</sup><br>|||||0.236|||
|Conditional R<sup>2</sup>|||||0.237|||
|Skewness of area<br>effect|||||0.080|||
|Kurtosis of area<br>effect|||||3.00|||
|Wilks-Shapiro P-<br>value of area effect|||||0.481|||
|Skewness of<br>household error|||||-0.002|||
|Kurtosis of<br>household error|||||3.00|||
|Wilks-Shapiro P-<br>value of household<br>error|||||0.483|||

Notes: Results reflect averages across 8000 estimates, representing 100 simulations of 80 simulated areas. Diagnostic results reflect averages over 100 simulations. H-T indicates Horvitz-Thompson approximation of variance.

7. Use the modified EMDI package to generate point and MSE estimates for each district based on the census data of 1.17 million households containing all model prediction variables and household size for these regions. The model estimates derived from the sample are not weighted, and the dependent variable is normalized welfare calculated in step 4. Census households are weighted by household size when estimating district level headcount poverty rates. As above, we use 100 Monte Carlo simulations to generate point estimates of headcount poverty and 100 bootstrap replications to estimate the mean squared error.

After repeating this seven-step process 250 times, we can calculate average evaluation statistics of the type reported in Tables 7 and 8 across the 24 districts and 250 simulations.<sup>41</sup> Since the Fay-Herriot

> 41 Poverty rates for each district are calculated based on a poverty line set at the 46.5th percentile of the national distribution of non-monetary welfare.

38
Muralidharan, K., & Sundararaman, V. (2013). _The Aggregate Effect of School Choice: Evidence from a two-stage experiment in India_ . National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w19441 Patrinos, H. A., Barrera-Osorio, F., & Guáqueta, J. (2009). _The role and impact of public-private partnerships in education_ . World Bank Publications.

Pritchett, Lant. (2013). _The Rebirth of Education: Schooling Ain't Learning_ . Center for Global Development Pritchett, Lant and Aiyar Yamini (2014). "Value Subtraction in Public Sector Production: Accounting Versus Economic Cost of Primary Schooling in India". Center for Global Development Working Paper 391 17
# **2. Background on the literature**

The literature has found that growth in average income is correlated with reductions in the incidence and depth of poverty. Looking at 67 countries, Ravallion and Chen (1997) find that inequality changes were uncorrelated with growth rates between 1981 and 1994, implying that poverty declines were strongly correlated with growth in mean incomes. They estimated that the elasticity of poverty incidence (at the “$1-a-day” line) to mean household income was about -3. Ravallion (2001) finds a lower elasticity of -2.1, when an econometric correction is made for measurement errors in surveys.<sup>7</sup> Dollar and Kraay (2002) also find that “growth is good for the poor:” in a sample of 92 countries, over four decades, the mean incomes of the poorest 20% of the population grew on average at the same rate as overall mean incomes.

Although the poor generally benefit from a growing economy, there is (of course) variation around the average benefits. The share of the variance in poverty changes that is accounted for by the variation in growth rates depends quite heavily on the sample and on the decomposition method used, with a wide range of R<sup>2</sup> ’s reported for regressions of the proportionate rate of poverty reduction on the growth rate of mean income. An early estimate with observations for only 16 countries indicated that 64% of the variance in proportionate rates of poverty reduction across countries could be explained by differences in rates of economic growth (Ravallion, 1995). Using a larger sample, World Bank (2005) finds that 52% of the variance is accountable to growth alone. Using analysis of variance and a range of poverty measures, Kraay (2006) finds shares ranging between 46% and 70% for all spells, and between 71% and 97% when only longer spells are considered.<sup>8</sup> So economic growth is strongly associated with poverty reduction and accounts for a large share of the variance in performance against poverty. But the impact of a (say) one percentage point growth in mean incomes on poverty is _not_ the same across countries or even in the same country in different periods. What makes some growth processes more pro-poor than

> 7  The measurement error problem is that both the poverty measures and mean consumption or income are calculated from the same household surveys.  Ravallion (2001) uses national accounts data on private consumption as the instrumental variable for the mean from the survey.

> 8  The higher R2 found for spells of longer duration is consistent with the empirical observation of Bruno et al. (1998) that very few developing countries have experienced significant long-term trends in inequality; the inequality measure for a given country may rise (fall) for a period, but then fall (rise). This does not (of course) mean that inequality does not change, or that the changes that occur are of little consequence for poverty or social welfare more generally.

5
Table 2: Test of balance in samples by firstborn sex

||1973-|1984|1985-|1994|1995-|2005|All years|
|---|---|---|---|---|---|---|---|
||FB<br>(1)|FG<br>(2)|FB<br>(3)|FG<br>(4)|FB<br>(5)|FG<br>(6)|FB-FG<br>(7)|
|Rural|0.68|0.68|0.64|0.65|0.59|0.58|-0.0004|
|Hindu|0.78|0.78|0.76|0.76|0.73|0.74|-0.0009|
|Muslim|0.11|0.11|0.12|0.12|0.13|0.13|-0.0001|
|SC|0.14|0.14|0.15|0.16|0.16|0.17|-0.0030*|
|ST|0.12|0.13|0.13|0.13|0.14|0.13|-0.0002|
|**Mother’s Education**||||||||
|No education|0.59|0.58|0.48|0.49|0.32|0.32|0.002|
|Incomplete secondary|0.35|0.36|0.40|0.40|0.48|0.48|-0.002|
|Secondary or higher|0.06|0.07|0.11|0.11|0.20|0.20|0.00009|
|**Father’s Education**||||||||
|No education|0.32|0.31|0.26|0.27|0.18|0.18|-0.002|
|Incomplete secondary|0.50|0.50|0.52|0.52|0.58|0.58|0.0005|
|Secondary or higher|0.18|0.19|0.22|0.21|0.24|0.24|0.001|
|**Mother’s birth cohort**||||||||
|1942-1960|0.52|0.52|0.04|0.04|0.00|0.00|0.002|
|1961-1970|0.48|0.48|0.56|0.56|0.07|0.06|0.001|
|1971-1987|0.00|0.00|0.41|0.40|0.93|0.93|-0.003|
|**Mother’s age at birth**||||||||
|12-15|0.14|0.13|0.11|0.11|0.05|0.06|0.001|
|16-18|0.35|0.35|0.32|0.32|0.26|0.26|0.002|
|19-24|0.44|0.44|0.47|0.48|0.53|0.53|-0.002|
|25-30|0.07|0.07|0.09|0.09|0.14|0.13|-0.001|
|31-49|0.01|0.01|0.01|0.01|0.02|0.02|0.0002|
|**Household wealth**||||||||
|2nd quintile|0.15|0.14|0.16|0.17|0.16|0.16|-0.00003|
|3rd quintile|0.17|0.17|0.17|0.17|0.18|0.18|-0.001|
|4th quintile|0.21|0.21|0.23|0.22|0.23|0.23|0.001|
|Richest quintile|0.23|0.23|0.24|0.23|0.27|0.27|0.003|
|N (1st births)|26,064|23,905|41,430|38,712|22,108|21,074|173,293|

NOTES: This table compares the socioeconomic characteristics of firstborn-boy (FB) and firstborn-girl (FG) families during the pre-ultrasound period and during the two post-ultrasound periods in the NFHS sample. The sample is restricted to first births as only these are quasi-random. SC and ST denote Scheduled Castes and Scheduled Tribes, respectively. Column (7) shows the differences in sample means among FB and FG families for the entire sample. The larger sample size of FB relative to FG families reflects the fact that the *** ** * sex ratio at birth is naturally above one. 1%, 5%, 10% 40
<!-- Start of picture text --> income<br>p = 0.023<br><0 >0<br>gini<br>p = 0.021<br>< 33.354 > 33.354<br>4 \<br>Node 2 (n = 598) Node 4 (n = 254) Node5 (n = 499)<br>°<br>°<br>o 6<br>0°ge 8 es<br>ona Sn ° ° r oe 4<br>0 C0 4 ei — Be. a 0 feel > —e 0 & ig sy<br>Soe og 9 ABER<br>6) oO % fo}<br>°<br>-0.2 0.2 -0.2 0.2 -0.2 0.2<br><!-- End of picture text -->
Policy Research Working Paper 9473

# **Abstract**

Policy makers are increasingly interested in strategies to promote resilience and mitigate the effects of future climatic shocks. Cash transfer programs have had widely documented positive welfare impacts. They often also aim to offer protection against shocks, but their role in fostering resilience has been less studied. This paper assesses whether the beneficiaries of a multiyear government cash transfer program in rural Niger are better able to mitigate the welfare effects of drought shocks. It analyzes mechanisms through which cash transfers contribute to resilience, such as savings facilitation, asset accumulation, or income smoothing in agriculture and off-farm activities. It combines household survey data collected as part of a randomized control trial with satellite data used to identify exogenous rainfall shocks. The results show that cash transfers increase household consumption by about 10 percent on average. Importantly, this increase is mostly concentrated among households affected by drought shocks, for whom welfare impacts are larger than transfer amounts. Cash transfers increase savings. They also help households protect earnings in agriculture and off-farm businesses when shocks occur. Few differences in household durables or livestock are observed. Overall, these findings suggest that cash transfer programs targeting poor households can foster resilience by facilitating savings and income smoothing.

This paper is a product of the Social Protection and Jobs Global Practice and the Development Impact Evaluation Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at ppremand@worldbank.org and stoeffler@itu.edu.tr.

_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
The actual forecaster’s predictive probability for the target random variable is denoted YY with and the forecaster’s reported predictive probability with **.** Both and can be pp uu pp uu probability density functions or probability mass distributions. The scoring rule is a function that assigns a score and maps into real numbers R. denotes the realization of the SS(pp, ss) ss random variable, . A scoring rule is a (weakly) proper scoring rule—in an ex-ante sense—if YY the forecaster’s expected score eliciting his real predictive probability—density or mass— function , , is smaller than his expected score eliciting any pp EEppSS(pp, YY) = ∫SS(pp, ss)pp(ss)mmss another probability function , , e.g., Equation (30) will hold for uu EEppSS(uu, YY) = ∫SS(uu, ss)pp(ss)mmss all probability functions and . Finally, the scoring rule is strictly proper when the equality pp uu holds if and only if almost surely (Gneiting 2011). pp= uu (30) EEppSS(pp, YY) ≤EEppSS(uu, YY).

If the forecaster prefers to use his true density function , instead of cheating and pp reporting any other function , the forecaster will be enforced by the proper scoring rule to uu set , i.e., the forecaster obtains his best-expected score for reporting his true predictive uu= pp probability. Besides that, the negative orientation of (30) concerning the forecast accuracy originates from a conventional change of minimizing the forecaster’s expected penalty to maximizing the forecaster’s expected score (Bunn 1984). Thus, the lower the score in (30), the better the forecaster is compensated. Hence, the desire to minimize the expected score from a strictly proper scoring rule motivates the forecaster to provide probabilities that are well-calibrated and sharp (Winkler 1986). Proper scoring rules motivate the forecasters to minimize their expected scores only by reporting individual probabilistic forecasts honestly. The use of proper scoring rules in forecast density evaluations minimizes the concerns that assessed subjective probabilities are, in some sense, arbitrary.

The objective of the scoring rule can also be interpreted in an ex-post sense. Assume that has sequential realizations, �TT 1, for a variety of horizons h∈H . Two different YY NNTT �sstt+h tt=tt prequential forecasting systems (PFSs) for collect the issued probability forecast sequences YY �<sup>pp̂</sup> �TT 1, and �TT 1, ∀h∈H . They can be compared using the scoring rule by tt+h tt=tt �uûtt+h tt=tt SS(∙) 40
Table 2: The response of output to government investment.

|||<br>**All IDA Countries**|Control for<br>|**IDA Cou**|<br>**ntries in Sub-Saha**|<br>**ran Africa**|
|---|---|---|---|---|---|---|
|||Excluding|Lagged||Excluding|Control for Lagged|
||All Observations|<br>Influential<br>Observations|<br>Dependent<br>Variable|All Observations|<br>Influential<br>Observations|<br>Dependent<br>Variable|
|_Panel A: Ordinary Least Squares(Dep_<br>|_endent Variable is_<br>|_Change in Output)_<br>|||||
|Change in Government Investment|0.190*|0.148*|0.0911|0.184|0.127|0.0982|
||(0.0994)|(0.0757)|(0.0830)|(0.122)|(0.0928)|(0.107)|
|_Panel B: Two-Stage Least Squares(D_|_ependent Variable i_|_s Change in Output_|_)_||||
|Change in Government Investment|1.418*|1.248|1.267|2.580**|1.684*|2.439*|
||(0.797)|(0.819)|(0.783)|(1.177)|(0.831)|(1.305)|
|_Panel C: First-Stage Regression(Dep_|_endent Variable is C_|_hange in Output)_|||||
|Change in Predicted Disbursements|0.290***|0.255***|0.269***|0.248**|0.308***|0.211**|
||(0.0778)|(0.0720)|(0.0786)|(0.0918)|(0.0663)|(0.0967)|
|First-Stage F-Statistic|13.94|12.57|11.71|7.3|21.59|4.74|
|Weak Instrument Consistent 95%<br>Confidence Interval for|[-0.046,3.933]|[-0.336,4.160]|[-0.293,4.035]|[0.346,11.943]|[0.109,5.713]|[-0.168,25.653]|
|Number of Observations<br>Notes: This table reports the results<br>currency units and are scaled by lagg<br>three columns). Panel A reports OLS<br>instrument-consistent confidence in<br>creditors are used as an instrument f<br>country level are indicated in parent|916<br>from a series of reg<br>ed GDP. The sampl<br>estimates, Panel B<br>tervals are based o<br>or changes in gover<br>heses. * (**) (***)|907<br>ressions of change<br>e consists of IDA-el<br>reports 2SLS estim<br>n the Moreira Likel<br>nment investment<br>indicates significan|916<br>s in real GDP on chan<br>igible countries (firs<br>ates, and Panel C rep<br>ihood Ratio statistic.<br>in Panel B. Heteros<br>ce at the 10 (5) (1) pe|611<br>ges in public investme<br>t three columns) and ID<br>orts the corresponding<br>Changes in predicted<br>kedasticity-consistent s<br>rcent level.|606<br>nt. All changes ar<br>A-eligible countri<br>first-stage regres<br>disbursements on l<br>tandard errors clu|611<br>e in constant local<br>es in Africa (second<br>sions. Weak<br>oans from official<br>stered at the|

20