rafmacalaba/data-use-mentions-extended huggingface dataset:
but does not change the direction or statistical significance of the relationships we identify between participatory institutions and tax outcomes in the broader sample. Municipal tax rates are statistically significant determinants of some tax outcomes (sales, per capita tax revenue) in Arellano-Bond models, but participatory institutions also remain statistically significant. We take these results as good omens for the independence of relationships between participatory institutions and tax outcomes, though we would be more certain with full data on sales tax rates for all Brazilian municipalities.

Next, we address the possibility of non-linear relationships between participatory institutions and tax outcomes at different levels of taxation and tax shares of revenue. This is because participatory institutions might promote tax collection differently in different socioeconomic environments. For example, participation could be associated with tax compliance in places where tax revenue is low, but face diminishing marginal returns in areas where tax collection is already relatively high.<sup>10</sup> We find that the average treatment effects of participatory institutions on tax outcomes are consistent across municipalities with different levels of tax collected. The one exception is the number of years that PB has been in place, which is not statistically connected to the municipal tax share of revenue or per capita tax revenue in municipalities where tax collection is more than one standard deviation above the mean.

Finally, we address concerns for our matching exercise in general. We use matching because it provides highly-targeted hypothesis tests connecting participatory institutions to tax compliance. However, our goal is not to build comprehensive models of tax compliance because we lack strong, direct measures of local enforcement and facilitation. Instead, we build theory and test hypotheses surrounding alternative, complementary ways to generate compliance using propensity score matching to isolate participatory institutions’ independent effects. Nevertheless, we also provide the results of estimation for tax outcomes using Arellano-Bond Dynamic Panel Estimation, allowing a full range of covariates to vary across our models. Participatory institutions remain statistically significant determinants of tax outcomes in these models, along with measures of local administrative capacity and local GDP per capita.
baseline and what was registered in the underlying databases (PDMM, MERNIS). As described in detail in Section 2, such discrepancies could exist due to the absence of documentation, spelling differences in existing documents, a backlog of cases to be processed, or difficulties in linking registrations of household members. This means that households that appear _de facto_ eligible at baseline could be deemed _de jure_ ineligible by the program administration and vice versa. We exploit these discrepancies to match households that appear very similar on observables but have discordant treatment status.

> 20 In Appendix Table 4, we present baseline balance of covariates by quintiles of the propensity score, which is recommended as a test of balance in propensity score matching (Imbens 2004).

14
In India, the evidence in section (5.1) shows that the long-term variance is substantially higher in the nonfarm households because of a large family background multiplier. The estimates of the long-term (steady state) means also show a higher mean for the nonfarm households. When we plug in the estimates from sections (5.1) and (5.2) for rural India in equation (18) above, we get (using _ω_ = 0 _._ 40 from the summary statistics table in online appendix): Thus, the evidence suggests that structural change in favor of the nonfarm sector contributed to higher cross-sectional variance in rural India during our study period.

# **(6) Economic Mechanisms: Towards an Explanation of the Differences between Rural China and Rural India**

A major concern in the literature has been whether the observed pattern of intergenerational linkages is primarily driven by omitted variables bias due to unobserved genetic correlations between parents and children. An obvious approach to this question is to try to correct the estimates for possible positive bias due to genetic correlations in cognitive ability. We develop a simple but plausible approach by taking advantage of the recent evidence on intergenerational correlation in cognitive ability from economics and behavioral genetics. There is substantial evidence that intergenerational correlation in cognitive ability (denoted as _ρ_ ) falls in a narrow interval, _ρ ∈_ [0 _._ 20 _,_ 0 _._ 40]; see, for example, Black et al. (2009), Bjorklund et al. (2010) on economic literature, and Plomin and Spinath (2004) on behavioral genetics literature. We use this information in a biprobit sensitivity analysis as developed by Altonji et al. (2005) to check if the estimates of intergenerational persistence in schooling remain positive and statistically significant for plausible values of intergenerational correlation in ability. We call this approach augmented AET (AAET) sensitivity analysis, and it requires binary indicators of educational attainment instead of years of schooling. We use a dummy
|Yes|Yes|
|Sample|<15 km|<30 km|<60 km|All SA|onlyHL|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS regression where the sample is restricted to communities within _X_ kilometers of the former homeland borders (on both sides), as indicated in the sample row. Column 5 restricts the sample to communities inside former homelands. The dependent variable is the difference in log per capita income for 2011-1996. All regressions include 1996 controls and municipality fixed effects. 1996 controls include: linear distance to former homeland border, share of white, colored and Indian/Asian population, share by age group (0-9, 10-19, 20-39, 40-59, 60+), household head education, share employed, share male, per capita income, total population, share connected to electricity grid, share no refuse, share with piped water. All control variables are interacted with the homeland indicator. Regressions are weighted by community population in 1996. Heteroscedasticity robust standard errors in brackets clustered at the municipality-homeland level. ***p<0.01, **p<0.05, *p<0.1
necessarily the heads of households, though the data include various employment information about household heads.

We use the Ethiopia HFPS data for our nation-wide analysis of urban areas, while relying on the Kinshasa HFPS data for our city-level analysis. We focus on the first six rounds of the Ethiopia HFPS, spanning April through October 2020 (Table 1). We restrict the sample for our analysis to 1,704 (Ethiopia) and 880 respondents (Kinshasa) who were interviewed in all included HFPS rounds.<sup>7, 8</sup> Summary statistics of the samples are reported in Table A1 for Ethiopia and Table A2 for Kinshasa in Appendix A.

**Table 1. Survey samples: Ethiopia and Kinshasa**

||||Survey rou<br>|nds<br>|||
|---|---|---|---|---|---|---|
||1|2|3|4|5|6|
|**Ethiopia**<br>|||||||
|Period|APR 22 to|MAY 14 to|JUN 4 to|JUL 27 to|AUG 24|SEP 21 to|
||MAY 13|JUN 3|JUN 29|AUG 21|to SEP 18|OCT 14|
|Total respondents in urban areas|2,233|2,113|2,064|1,978|1,934|1,885|
|Interviewed in all rounds|1,704|1,704|1,704|1,704|1,704|1,704|
|(numberoftowns)|(103)|(103)|(103)|(103)|(103)|(103)|
|**Kinshasa**|||||||
|Period|JUN 16 to|JUL 23 to|AUG 25 to|OCT 16|NOV 7
boundaries).</sup> The targeting differential can be interpreted<sup>as a measure of absolute progressivity,</sup> namely the difference between per capita<sup>spending on the poor and that on the non-poor. A</sup> TD of zero indicates that there is no difference<sup>in Trabajar spending on the poor versus non-</sup> poor. A positive TD means that the program<sup>favors poor areas; a negative coefficient means</sup> it favors non-poor areas. Poverty is measured<sup>by the proportion of the population deemed to</sup> have unmet basic needs (IJBN), based<sup>on the 1991 census.</sup>

**1Z** Equation (12) indicates that the error term will<sup>not be homoskedastic. Standard errors of the</sup> targeting differential were corrected for<sup>heteroscedasticity.</sup>

18
paper, however, we address sensitivity of our findings to the inclusion of non-owners with use rights (which is only possible to do in Ethiopia and Tanzania). Most individuals who provided answers on rights and decision making were owners instead of users of land.

For each parcel, respondents are asked about different types of ownership (reported, economic, and documented); rights (to sell, bequeath, use as collateral, rent out, and make improvements/invest); as well as decision-making in the case of agricultural parcels.<sup>13</sup> A detailed list of land rights and ownership variables in the LSMS+ surveys, as well as additional decisionmaking variables, are presented in Table 1. All countries had the same questionnaire structure and implementation, except for Ethiopia where the right to sell was not asked because land is legally

> 10 Other types of assets were also covered, including financial accounts, and mobile phones. Ownership of livestock was covered in Ethiopia.

> 11 The module on land specifically covers all land owned or accessed via use rights and follows the recommendations of FAO, World Bank and UN Habitat (2019).

> 12 Ethiopia and Malawi looked at all non-residential land used for agriculture. Tanzania was not restricted to agricultural use only due to missing data on current use. 13 Along with rights/ownership, respondents reported on how each parcel was acquired; identified the individuals from whom the asset was inherited or received as a gift, as applicable; and provided the current hypothetical sales value for each asset (and the construction costs specifically for the dwelling) and limited information on their knowledge of asset transactions in their communities.

6
# **Table 2. Descriptive Statistics for Students in Brazil and Ecuador**

**_Based on Individual-Level Data for Students Matched to Surveyed Programs_**

|**Variable**|**Br**|**azil**|**Ecu**|**ador**|
|---|---|---|---|---|
||**Mean**|**Std. Dev.**|**Mean**|**Std. Dev.**|
|**Panel A.Student characteristics**|||||
|Age|24.53|5.65|24.66|3.40|
|Female|0.59|0.49|0.62|0.49|
|Mother's education:|||||
|Less than primary|0.12|0.32|-|-|
|Primary school|0.16|0.36|0.15|0.36|
|High school|0.19|0.39|0.28|0.45|
|Higher education|0.07|0.26|0.52|0.50|
|Unknown|0.46|0.49|0.05|0.22|
|Student has children|-|-|0.27|0.44|
|Socioeconomic index (std)|-|-|0.01|0.97|
|Student administrative variables(PCA Score)|0.01|1.25|0.07|1.32|
|**Panel B. Peer(average) characteristics**|||||
|Age|24.59|1.70|24.66|1.81|
|Percentage of female peers|0.56|0.
**Figure 2.1. Tariff rates have dropped sharply, especially in manufactured products.**

**Figure 2.2. Trade flows have increased sharply.**

Cambodian tariff rate of all products in percentage, 2000-2020 Exports and imports of goods and services, constant millions 2015 US$ <!-- Start of picture text --> 20 25,000<br>Exports of goods and services<br>15 (constant 2015 US$) 20,000<br>Imports of goods and services 15,000<br>10 (constant 2015 US$)<br>All products (%) 10,000<br>Manufactured products (%) 5 5,000<br>Primary products (%)<br>0 0<br>2001 2002 2003 2005 2006 2007 2008 2010 2011 2012 2014 2016 2020 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from Our World in Data (https://ourworldindata.org/)

**Source:** Authors’ elaboration using data from Our World in Data (https://ourworldindata.org/)

As Figure 2.3 shows, export growth has primarily been driven by an increase in trade volume rather than a price effect. Notably, while exports have grown more than imports in volume, imports are relatively more expensive than Cambodia's export basket, resulting in a negative current account balance (Figure 2.5). Despite the rise in trade value, Cambodia’s share of exports and imports as a percentage of GDP remained relatively constant in the second half of the 2000s.

**Figure 2.3. Exports are driven by increases in volume rather than price.** Unit and volume index, 2000-2021

<!-- Start of picture text --> 250 1,200<br> 1,
Table 2: WBES-De Loecker Comparison: Non-Parametric Estimates of Exporting on TFPR (in <u>percent)</u>

|SectorDescrition|Median Productivity Premium from Exporting|
|---|---|
|p|WBES<br>De Loecker|
|Food & beverages|5.953<br>2.280|
|Textiles|4.949<br>1.980|
|Garments|3.696<br>1.660|
|Leather|-1.577<br>1.830|
|Wood|7.186<br>1.920|
|Publishing, printing and|5.732<br>4.880|
|reproduction||
|Chemicals|6.541<br>3.930|
|Rubber & plastics|6.122<br>4.500|
|Other non-metallic products|5.246<br>2.730|
|Basic metals|5.141<br>3.190|
|Fabricated metal products|6.071<br>3.320|
|Machinery and equipment|4.218<br>3.450|
|Electrical machinery|3.687<br>4.640|
|Furniture|1.862<br>1.990|

Note: Table 2 presents the median TFPR-premium from exporting following De Loecker (2013) method. The latter is based on the estimation of a value-added log-linearized Cobb-Douglas production function based on the Control Function approach by Ackerberg, Caves, and Frazer (2015) and assuming an endogenous (cubic) Markovian (AR 1) productivity process, which is a function of learning by exporting. WBES data covers a sample of manufacturing enterprises from 82 developing countries during the period 2002-2019; while De Loecker (2013) study focuses on 7,915 manufacturing firms in Slovenia during the period 1994-2000. Data for WBES come from the World Bank, while data from De Loecker (2013) come from the
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data

# _B. First Measurement Equation for Tax Office Data : Tax Compliance Model_

There are two available types of indicators for the latent variable true sales, one from the official tax data and one from the firm-level survey data. Both indicators are required for identification purpose (see below). Reported sales to the tax office is a potential valuable indicator for unobserved true sales. However, it does not form an unbiased estimator of true sales because there are strong incentives for firms to reduce the tax burden by underreporting.

Allingham and Sandmo (1972) is one of the earliest and best known models of tax evasion, in which the individual taxpayer’s decision on the level of tax compliance is subject to an exogenous and positive audit probability and a penalty when evasion is detected. Later extensions of this tax evasion model include endogenous labor supply decisions and audit probabilities, allowances for repeated interactions between tax payer and tax office, and taxpayer attitude and social dynamics (Reinganum and Wilde 1985, Graetz and Reinganum 1986, Grasmick and Bursick 1990, Erard and Feinstein 1994a). Also there is a small literature on corporate tax evasion (Crocker and Slemrod 2005, Chen and Chu 2005).

We formulate a simple tax compliance model to derive a relationship between the sales reported to the tax office and the true sales.<sup>16</sup> Firms have incentives to underreport taxable incomes through underreporting in sales and/or over-reporting in raw material costs. As for labor costs, over-reporting lowers taxable incomes but increases payroll taxes. So

> 16 The model is constructed purely from a firm’s perspective without modeling the strategic interaction between a firm and the tax authority because information on the tax authority is lacking.

15
. This refers to a hypothetical crop and relies on climatic inputs only, not taking into account the specific crop or soil conditions. See Allen et al. 1998. We use the monthly values and compute an annual value from this.

> 32There are 421 districts in the ICRISAT file, compared to a total of 640 in India 2011 NSS. So about 35% are missing. However, our final dataset has about 575 districts (because we don’t use all the states), so according to this 27% districts are missing in ICRISAT.To deal with the missing, we assigned the value of the nearest non-missing neighbor out of 4 closest neighbors.

55
**Fig. 2: New Zealand and United States**

<!-- Start of picture text --> 1<br>1<br>0.95<br>0.9<br>0.8 0.9<br>GDP<br>Priv Employment +<br>0.7 Consumption 0.85 Unemp with Jobs<br>Investment Priv Employent<br>Q1-19 Q3-19 Q1-20 Q4-20<br> 1<br>1<br>0.95<br>0.9<br>0.8 0.9<br>0.7 0.85<br>Q1-19 Q3-19 Q1-20 Q4-20 Q1-19 Q3-19 Q1-20 Q4-20<br>Jan-19 Jun-19 Nov-19 Apr-20 Sep-20 Feb-21<br><!-- End of picture text --> Source: Aggregate macroeconomic data for the United States is from FRED. New Zealand data from https://www.stats.govt.nz/ and data on unemployed with jobs from the Current Population Survey as reported in Hall and Kudlyak (2020). The dotted black lines in the top right panel are the monthly averages for each 2020 quarter of the sum of US private employment and unemployed persons with jobs.

5
1988. It was one of the bloodiest and longest battles that occurred in the Middle East in the 20<sup>th</sup> century. On September 22, 1980, Iraqi armed forces launched a full-scale invasion of neighboring Islamic Republic of Iran. The war was ended by a 1988 cease-fire. The conflict caused devastating effects on these two nations and their people, with total casualties of both sides estimated to range from 1 million to 2 million.

To quantitatively evaluate the long-lasting effects of the war on children aged 6 to 12 during the war during the 1980-1988 period, this study uses the Iraq Household Socio-Economic Surveys (IHSES) 2006-2007 and the event study method. The treatment event cohort is 1968 because the children would reach the age of 12 in 1980 when the Islamic Republic of Iran–Iraq war started. The treatment cohorts include individuals born between 1969 and 1981.

We find that the Islamic Republic of Iran–Iraq war has adverse impacts on labor market performance. The war decreases the social welfare of individuals who were exposed to the war during the school-age years. Specifically, the war decreases the probability of men accessing social security, pension plan, health care, paid leave and job permanence while we find little impact on women. However, we find significant and negative impacts of the war on wages for both men and women. Furthermore men, who were exposed to the war during their school-age years are more likely to work in a hazardous job and without air conditioning; we find no evidence for the women. We document the education channel through which the war affects labor market outcomes. We expect that the war would decrease educational attainment, leading to negative impacts on labor market outcomes. Indeed, the war decreases the educational level of the cohorts of 1971-1981 for both men and women. The Islamic Republic of Iran–Iraq conflict had a greater impact on female education than on male education. The illiterate rate of women increases due to the conflict while we find no evidence of the impact of the
public schooling system, often with innovations like double-shift instruction, while health services have been made available through primary health care centers run by ministries of health, as well as through non-governmental organizations. Infrastructure services – electricity, sanitation, water supply – are by and large publicly provided to refugees and host communities. There are three immediate implications of this model of service delivery. First, there is no doubt that the addition of hundreds of thousands of people in need has put these public service-delivery systems under tremendous strain. Second, the expansion of resources – both national and international – has mitigated the strain by allowing for a greater supply of services and expansion in personnel. And finally, the extent of direct local competition with host communities depends on the degree to which the latter were themselves relying on public services as opposed to privately provided services.

In this paper, we document rates of access to infrastructure and social services among hosts and displaced persons, and related perceptions of quality of service delivery, including changes in these perceptions, where the data permit. If displaced populations crowd out host communities in access to services, negative (positive) changes in perceptions about services will be more (less) frequent in areas that receive a larger (smaller) influx. Where the data allow, the analysis compares two similar individuals

> 4 Lebanon and Jordan ranked first and second in the world in 2018 in number of refugees received relative to national population. Lebanon counted 156 refugees per 1,000 national population and Jordan 72 refugees per 1,000 residents (UNHCR, 2018). These figures are based on official UNHCR registration numbers and do not reflect the unknown number of unregistered refugees.

2
we consider the outreach of the financial system. An overview of the state of retail networks, including branches, ATMs, and POS terminals as well as urban vs. rural, and bank vs. non-bank divides are presented.

Note that due to limitations in data availability, especially in time dimension, it is not possible to control for potential endogeneity problems between financial service penetration and other indicators of growth and development. Hence, the econometric results reported below should be interpreted with caution, as they are likely not to point to causal effects but are rather partial associations. Furthermore, the data are from a crisis year, which may bias the results.

## **3.1 Deposits**

In 2009 as the global financial crisis unfolded, about 60 percent of countries experienced a decline in real per capita GDP, and for those that went through expansions, median growth was only 2.1 percent. Deteriorating macroeconomic conditions affected the deposit volume around the globe as individuals and firms had to tap into their savings. Seventy-seven percent of countries in our sample experienced a decline in deposit-to-GDP ratio, with an average decline

> 4 Kendall _et al_ . (2010) use household survey data together with _Financial Access_ data to obtain this estimate.

6
the fifth column of Table 1. The five indicators covering the largest number of countries (DRI, EIU, HF, PRS and WDR) are substantially more representative according to this measure than the others, with a value of the coverage index of less than 0.25. In our subsequent empirical work we will refer to these as representative indicators, and the remainder as non-representative indicators.

Finally, we note that each of these sources of governance data uses different units to measure governance. Most polls of experts report discrete categorical responses (e.g. the prevalence of corruption on an integer scale from one to four), while 6
Secondary 500<br>5<br>400<br>4<br>300<br>3<br>200<br>2<br>1 100 Mathematics<br>Science<br>0 0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> _Sources:_ World Bank calculations using UIS, IMF online databases; Altinok, Angrist, and Patrinos (2018).

Learning outcomes have improved alongside the continual increase in public education spending as a share of GDP. While information on secondary school learning outcomes only goes back to 1985, the data show an upward trend that corresponds to increases in spending on secondary education. More recently, average learning outcomes have remained relatively unchanged, even while public education spending on secondary education has continued to increase rapidly. While learning outcomes only measure one objective of the education system, this situation points to a potential lack of efficiency in the education system.

a. The secondary gross enrollment rate is the number of children enrolled in secondary school divided by the number of children of official secondary school going age.

12