rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix D: Additional Results**

# **Table D1: The Impact of Survey Mode on Weekly Labor Market Reporting – with controls**

<mark>The impact of survey mode on labor market reporting Phone weekly vs in person weekly</mark> <u><mark>(omitted category) - with controls</mark></u>

|Phone weeklyv|s inperson wee<br>|kly (omitte<br>|d category)- with<br>Days-<br>|controls<br>|Hours-<br>|
|---|---|---|---|---|---|
||Working|Days|conditional on<br>work|Hours|conditional on<br>work|
|Panel A: Main effects|(1)|(2)|(3)|(4)|(5)|
|Phone weekly|-0.09***|-0.52***|-0.10|-5.81***|-3.28|
||(0.03)|(0.16)|(0.07)|(2.11)|(2.06)|
||[0.00]|[0.00]|[0.19]|[0.01]|[0.14]|
|Mean in person weekly|0.85|4.50|5.39|41.6|48.69|
|N|3,562|3,562|2,852|3,562|2,852|
|Adjusted R2|0.073|0.165|0.272|0.247|0.329|
|Panel B: Heterogeneity by self-empl|oyment status<br>|||||
||(6)|(7)|(8)|(9)|(10)|
|Phone weekly|-0.02|-0.15|-0.05|-4.71|-5.38*|
||(0.05)|(0.27)|(0.11)|(3.76)|(3.23)|
||[0.68]|[0.68]|[0.68]|[0.61]|[0.37]|
|Self-employed|0.05|0.18|-0.14|-2.60|-7.60***|
||(0.05)|(0.30)|(0.12)|(3.81)|(2.94)|
||[0.63]|[0.68]|[0.61]|[0.68]|[0.16]|
|Phone weekly*Self-employed|-0.11*|-0.62*|-0.07|-1.84|3.60|
||(0.06)|(0.35)|(0.16)|(4.51)|(3.74)|
||[0.37]|[0.37]|[0.68]|[0.68]|[0.63]|
|Mean wage employed in-person||||||
|weekly|0.82|4.40|5.35|41.75|50.78|
|Phone effect self-employed|-0.23|-1.24|-0.15|-3.68|7.20|
|p|0.05|0.08|0.64|0.68|0.34|
|Number of observations|3,562|3,562|2,852|3,562|2,852|
|Adjusted R2<br>Note: *** p<0.01, ** p<0.05, * p<0.|0.077<br>10, standard er|0.168<br>rors cluster|0.272<br>ed by household a|0.247<br>re presente|0.330<br>d in parentheses|

Note: *** p<0.01, ** p<0.05, * p<0.10, standard errors clustered by household are presented in parentheses below the coefficient. Multiple testing adjusted FDR-adjusted q-values are presented in square brackets. The omitted category are individuals who were working at baseline and completed weekly in person interviews. Regressions are attrition weighted with weights corresponding to 1/number of weeks for which we have data for each individual. All regressions control for gender, age, marital status, education, days worked per week at baseline, hours worked per week at baseline, pay per week at baseline, self-employed at baseline, wageemployed at baseline, dummies for being employed in manufacturing, trade, services, the public sector, household dependency ratio, and log household assets, and week of interview dummies.

38
_Science Study,_ Center for the Study of Testing, Evaluation and Education Policy, Boston College, Chestnut Hill, MA.

Chabbot, C. and E. Elliott (eds.) (2003), _Understanding Others, Educating Ourselves: Getting More from International Comparative Studies in Education_ , National Academies Press, Washington DC.

CONFEMEN (2015), Website: http://www.confemen.org/le-pasec/

- Ferrer, G. ( 2006), _Educational Assessment Systems in Latin America: Current Practice and Future Challenges_ , Partnership for Educational Revitalization in the Americas (PREAL), Washington DC.

- Greaney, V and T. Kellaghan (2008). _National Assessments of Educational Achievement, Vol. 1: Assessing National Achievement Levels in Education._ World Bank Publications, Washington, DC.

Eric Hanushek & Ludger Woessmann, 2012. " **<u>Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation</u>** <u>," Journal of Economic Growth,</u> Springer, vol. 17(4), pages 267-321, December

- Kamens, D. (2013) "Globalization and the Emergence of an Audit Culture: PISA and the Search for 'Best Practices' and Magic Bullets" in H.-D. Meyer & A. Benavot (eds.), _PISA, Power and Policy: The Emergence of Global Educational Governance_ , pp. 117-140, Symposium Books, Oxford.

- Kaufmann, D., A. Kraay and M. Mastruzzi (2010), _The Worldwide Governance Indicators: Methodology and Analytical Issues._ Brookings Institution, Washington, DC.

- Kellaghan, T. and V. Greaney (2001), _Using Assessment to Improve the Quality of Education_ , International Institute for Educational Planning, UNESCO, Paris.

- Liberman, J. and M. Clarke (2012), _A Review of World Bank Support for Student Assessment Activities in Client Countries, 1998-2009_ , World Bank, Washington DC.

- Lockheed, M. (2015). "PISA for Development? The Experiences of Middle-income Countries Participating in PISA", paper presented at the World Bank, June 4, 2015.

- Lockheed, M. (2013), "Causes and Consequences of International Assessments in Developing Countries" in H.-D. Meyer & A. Benavot (eds.), _PISA, Power and Policy: The Emergence of Global Educational Governance_ , pp. 163-185, Symposium Books, Oxford.

- Lockheed, M. (2010), _The Craft of Education Assessment: Does Participating in International and Regional Assessments Build Assessment Capacity in Developing Countries?_ Amsterdam: International Association for the Evaluation of Educational Achievement.

- Martin, M., I. Mullis, A. Beaton, E. Gonzales, T. Smith and D. Kelly (1997), _Science Achievement in the Primary School Years: IEA’s Third International Mathematics and Science Study_ , Center for the Study of Testing, Evaluation and Education Policy, Boston College, Boston.

18
# Becoming Legible to the State: The Role of Identification and Collection Capacity in Taxation

Oyebola Okunogbe<sup>∗</sup> Originally published in the Policy Research Working Paper Series on _November 2021._ This version is updated on _November 2023._ To obtain the originally published version, please email prwp@worldbank.org.

Keywords: Taxation, state capacity, property tax, technology, enforcement JEL codes: H24, H26, O10

> ∗World Bank Development Research Group, ookunogbe@worldbank.org. This paper was funded by the International Growth Centre and the World Bank Research Support Budget. I am grateful to many individuals and groups for helpful feedback and support: Pierre Bachas, Natalie Bau, Clare Balboni, Lori Beaman, Kathleen Beegle, Jeremy Bowles, Anne Brockmeyer, Pascaline Dupas, Jonas Hjort, Asim Khwaja, Justine Knebelmann, Anne Laski, Luis Martinez, David McKenzie, Emily Nix, Adeyemi Okunogbe, Owen Ozier, Dina Pomeranz, Ingrid Sjursen, Gabriel Tourek, Victor Pouliquen, Mazhar Waseem, Jonathan Weigel, Eric Werker, Laurel Wheeler, and seminar participants at the Central Bank of Colombia, the Centre for the Study of African Economies, the National Tax Association, the Online Public Finance Seminar, Texas A&M University, the University of Maryland Baltimore County, the University of Michigan, the World Bank Tax Conference, and the Zurich Conference on Public Finance and Development. I thank Elfrieda Tamba, Thomas Doe Naa, Darlingston Talery, Isaac Stevens, Sebastian Weah, Lasana Kromah, Robert Pyne, Scholastica Nimley, Clara Goba, Jlateh Pajibo, Titus Zoneh, Alphonso Jallah, and the management, officials, employees, and contractors of the Liberia Revenue Authority for their research partnership and kind hospitality. Aditi Kumar, Nicolas Orgeira, Alpha Simpson, Joe Lelee Sloan, and Kairat Umargaliyev provided excellent field coordination and research assistance. This study was approved by the Harvard Institutional Review Board (IRB14-1833) and Health Media Lab IRB (556WBG18). It was registered in the AEA RCT registry (AEARCTR-0004272 and AEARCTR-0011158). The findings, interpretations, and conclusions expressed in this paper, including any errors, are entirely mine and do not necessarily represent the views of the World Bank Group, its Executive Directors, or the countries they represent.
10 Thus, the same basic patterns<sup>of differentiation in education</sup> utilization incidence found<sup>in 1978 are still evident in 1987, but it also</sup> seems clear that income poverty<sup>alleviation and the governments' efforts in</sup> promoting basic education<sup>have brought benefits to the poor. The following</sup> discussion will attempt to throw<sup>light oa the relative importance of etch of</sup> these twc factors.

# _Bxplaining Changes in Incideace_

One of the problems with the<sup>kind of intertemporal incidence</sup> comparisons by quantile typified<sup>by Table 2 is that we are not comparing like</sup> with like since any given quantile<sup>is better off in 1987 than it was in 1978.</sup> Thus, observed i..iprovements may be entirely due to higher<sup>incomes in the</sup> latter period. Table 3 gets<sup>around this problem by computing proportions of</sup> school going male and female<sup>kids belonging to equivalent per capita</sup> expenditure groups in both years.<sup>The 1978 numbers are taken from Table 3 in</sup> Chernikovsky and Meesook (1985).<sup>The class intervals were updated to January</sup> 1987 prices using the 17<sup>cities CPI aid linear interpolation was used to</sup> calculate comparable figures<sup>for identical expenditure groups. Table 3 shows</sup> clear improvements at all<sup>education levels holding consumption constant. The</sup> catching up by female students<sup>at all education and expenditure levels is,</sup> once again, particularly striking.<sup>These results suggest that education</sup> incidence improvements are not<sup>just a consequence of growth. At a constant</sup> real per capita expenditure<sup>level, something else has happened. Two obvious</sup> possible factors include policy<sup>initiatives and changing tastes for education.</sup> The change in the aggregate<sup>enrollment rate can be decomposed into</sup> that due to changes in the distribution<sup>of consumption (level changes as well</sup> as changes in relative inequality)<sup>holding constant initial enrollments, and</sup> that due to changes in enrollments<sup>at each consumption level holding constant</sup> the initial distribution of<sup>consumption. Thus:</sup>
Table 3. The effect of food insecurity on vertical and horizontal trust by economic development.

|||Econom<br>|ic development<br>Lower-middle-<br>|rankings (GNI pe<br>Upper-middle-<br>|r capita)<br>|
|---|---|---|---|---|---|
||World sample|Low-income|income|income|High-income|
|Trust in national institutions||||||
|Food insecurity|-0.027***|-0.020***|-0.019***|-0.035***|-0.067***|
||(0.004)|(0.006)|(0.006)|(0.007)|(0.011)|
|Trust in friends and family||||||
|Food insecurity|-0.117***|-0.120***|-0.109***|-0.111***|-0.129***|
||(0.005)|(0.009)|(0.008)|(0.012)|(0.009)|
|Controls|_yes_|_yes_|_yes_|_yes_|_yes_|
|Number of observations|388,602|69,101|106,410|100,728|112,363|

_Notes:_ Models estimated using unweighted individual-level data from the 2014-17 Gallup World Poll. Standard errors are in parentheses and clustered by country and robust to heteroskedasticity. References categories are single or never married, elementary education, employed full-time, poorest quintile, rural area or farm, and the year 2014. Each model includes country and years fixed effects. Low-income countries are defined as those with a gross national income (GNI) per capita of $995 or less, lower-middle-income countries are those with a GNI per capita of more than $996 but less than $3,895, and upper-middle-income countries are those with a GNI per capita of more than $3,895 but less than $12,055, and high-income countries are those with a GNI per capita of more than $12,055. * Significance at the 0.10 level. ** Significance at the 0.05 level. *** Significance at the 0.01 level.

We find that each category of food insecurity is associated with a decrease in both dimensions of trust across all levels of development. For trust in national institutions, the associations increase with income ranking, ranging from a 2 percentage-point decrease for low-income countries to a 6.7 percentage-point decrease for high-income countries. This implies that food insecurity has a larger effect on trust in richer countries. For trust in friends and family, the associations are nonlinear, starting at a 12 percentage-point decrease for low-income countries, rising to 11 percentage-point decrease for lower- and upper-middle countries, but then falling back to 12.9 percentage-point decrease in high income countries.

14
coefficient is an important determinant of trust<sup>12</sup> (i.e. greater income inequality reduces trust). We find a significant and negative relation between population density and trust. But our results suggest that countries with older populations seem to trust less. This contrasts with the findings of researchers like Li et al (2005) who found that older people are more trusting than younger people are because they have higher levels of neighborhood attachment and civic participation. On the other hand, it is possible that as people grow older, their life experience make them less naïve and less trusting.

We also report (but do not present in Table 1) that we estimated specifications that included the percentage of rural population in 1996 from the World Development Indicators as a regressor. In most cases, the estimated coefficient had the expected positive sign but was not statistically significant across different specifications. We also explore different measures of “age” such as the ratio of population below 30 years old to population above 30 years old. These results consistently suggested that older populations trust more but they were not statistically significant.

The RESET test for specifications (10) and (11) rejects the assumption that the model is linear but this assumption is not rejected when we include a dummy variable for Nordic countries instead of the Scandinavian dummy variable. Apart from that, the results are very similar and we only present (11a) in table 1. We check for normality of the predicted residuals by applying the inter-quartile range test<sup>13</sup> that assumes symmetry of the distribution. The presence of any severe outlier is sufficient evidence to reject normality at the 5% level. We found one severe outlier (Iran) and two middle outliers (Indonesia and The Netherlands). In Iran, the percentage of respondents who agree that most people can be trusted is 65.3% (one percentage less than Sweden) among the interviewees who answer yes/no to the trust question. Among the countries included in the WVS, the percentage of interviewees that did not answer the question or did not know is negligible except for Iran with 24.1% and Indonesia with 11.8%. Once we exclude Indonesia and Iran from the sample, we cannot reject the Shapiro-Wilk test for normality

> 12 In specification (10), the estimated coefficient of Gini is statistically significant at the 12% significance level.

> 13 The test was written by Lawrence C. Hamilton and it is available in the Stata software.

14
threatened – if interviewed by a government official. These risks can be mitigated if enumerators are hired among FDPs from the same background. However, this can create legal challenges if FDPs do not have work permits. In some cases, it might also be necessary to have UNHCR or IOM officials accompanying enumerators or even conducting the interviews themselves.

The implementation of FDP-specific socio-economic surveys can be done in parallel with UNHCR’s verification exercises. The verification exercise is a census that visits all registered refugees to update their data. In Kenya, an additional socio-economic questionnaire was administered to a random subset of refugees participating in the verification exercise (Pape et al., 2019b). The parallel implementation reduced costs substantially but the implementation requires close coordination between the verification exercise and the socio-economic survey. Ideally, no redundant data would be collected but in reality most questions from the verification exercise are asked slightly differently than in the socio-economic survey, which usually aims to be consistent with the latest national survey, necessitating different instruments for the same concept to ensure comparability.

# Poverty measures

Poverty measures the level of deprivation in a population. The most widely used concept defines as poverty headcount the number of individuals living below a threshold, usually called the poverty line (Foster, Greer and Thorbecke, 1984). Since the poverty headcount only reflects the proportion of poor people, it is usually complemented by a measure of the depth of deprivation, the poverty gap, which estimates the average gap between poor individuals and the poverty line. All poverty measures constructed in such a way are based on an underlying welfare metric. In most cases, the metric is defined at the household level and needs to be transformed into an individual measure. The transformation either divides household welfare by household size, providing a per capita measure or, in a more sophisticated way, takes into account differences in household composition (usually age and gender), leading to a per-adult equivalent measure.

To measure household welfare, different metrics have been proposed and can generally be classified either into monetary or non-monetary metrics. Monetary metrics equate levels of well-being to a monetary indicator of utility (Samuelson, 1974), usually income, consumption or expenditure. Non-monetary metrics define normatively dimensions of deprivations, e.g., access to education and clean water, and aggregate them either by using dimension-specific thresholds or an aggregated threshold. The most commonly used approach in this class is the Multidimensional Poverty Index by UNDP and Oxford University (MPI; OPHI, 2018). While it is not uncommon to find experts with a strong preference for one measure over the other, both classes of poverty measures are in most cases complementing each other. While the monetary metric has the advantage of its theory-grounded definition without normative choices, it is complex to measure 13
<u>Table 2: Cyclical Properties of Transition Rates out of Employment: Mexico and Brazil</u>

|||Mexico|||Brazil||
|---|---|---|---|---|---|---|
|Series|St. Dev|Correlation|Elasticity|St. Dev|Correlation|Elasticity|
|O-S|0.045|-0.076|-0.127|0.040|-0.135|-0.280|
|O-I|0.032|0.353|0.420|0.043|0.442|0.972|
|O-F|0.085|0.573|1.812|0.073|0.527|1.983|
|U-S|0.094|-0.433|-1.511|0.059|-0.600|-1.752|
|U-I|0.055|0.366|0.748|0.052|0.617|1.628|
|U-F|0.120|0.798|3.554|0.128|0.652|4.285|

<u>Notes: The table shows the standard deviation, the correlation with output and the elasticity with respect</u> - <u>to output of the logged and HP filter de trended transition rates from non employment (O and U) into employment (S,I and F) for Mexico and Brazil. Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. O=Out of the Labour Force, U=Unemployment, E=Employment, S=Informal Self-Employed, I=Informal Salaried, and F=Formal Sector, all as proportions of working age population. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2.</u> 41
# **Transparent Government and Business Regulation: “Open for Business?”**

Carolin Geginat and Valentina Saltane<sup>1</sup>

**JEL:** O Economic Development, Technological Change, and Growth

Keywords: Transparency, Governance, Access to Information, Regulatory Transparency Index

> 1 2121 Pennsylvania Avenue NW, Washington, DC 20433, cgeginat@worldbank.org and vsaltane@worldbank.org. This paper would not have been possible without the extensive research of the team who collects the annual Doing Business data at the World Bank Group and the 9,600 private contributors who answered to the surveys of the Doing Business Project in 2012.  We would like to thank Sarmad Shaikh for his excellent research assistance. The full data set for each country can be found at www.doingbusiness.org. We would also like to thank -- without implication – Adrian Gonzalez, Aart Kraay, Alejandro Ponce, Rita Ramalho, Sylvia Solf and Hulya Ulku for helpful discussions in the preparation of this paper. The authors thank the United States Agency for International Development for its financing contribution to the data collection efforts.
Intuitively, -an m% rise in marginal cost due to 'a I% orise in wages is translated into quantity along the marginal revenue curve which, given constant returns to scale, implies. an equivalent percentage change in the demand for labor. Again, Le Chatelier's principle would seem to guarantee and increased elasticity with access to intermediate inputs through the first term. To see the effect of increased competition and product elasticity on _?lmr_ we draw on Bresnahan's (1989) and Porter's (1983) discussions of "conjectural variations" in firm behavior. We assume an individual firm maximizes profits: _*r=qip(q,t)-c(qi,w,Tr) p'<Op"s Oc'> O,c"< O_ where p(.) is the industry demand curve which is a function of industry output q, qi individual firm i's output, X is the impact of trade liberalization, c is the firm's constant returns to scale cost function, and w is the vector of market given factor prices. As Bresnahan notes, outside of the competitive model, firms do not have supply curves in the sense of there being a solution for q as a function of p-c'(q). Instead price and quantitysetting conduct follow more general supply relations which can be written as _mr = p(q, r) + p'(q, t)qic(r) = c'(qi, w, t)_ where a _= q._ The equation can be interpreted as narginal cost = the "perceived" **_alqt_** marginal revenue for oligopoly models where a can be considered an index of the competitiveness of oligopoly conduct. & can move from zero in the competitive case, to H (the Herfindahl index) for Cournot, or unity in the case of perfect collusion or _,Dqp(,r,q)_ monopoly (Porter 1983). Since product demand elasticity _q(r) = p(r q)q,_ it is likely to be affected by any shock to the demand curve from trade reform.

5