rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 12. Concentration of taxes and social spending (by decile of market income) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Personal GISL Rural GIS NPPF Provident Excise Sales Tax Customs Green taxEducation Health<br>Income Insurance fund<br>Tax<br>Decile1 Decile2 Decile3 Decile4 Decile5 Decile6 Decile7 Decile8 Decile9 Decile10<br><!-- End of picture text --> _Source_ : World Bank staff estimation using BLSS 2017.

# 5. Simulating the Impact of COVID-19 and Changes in Tax Policies

The Bhutanese government has implemented important fiscal reforms in recent years, aimed at improving revenue collection and promoting equity. In this section, we will first describe the changes in tax policies applicable to households and apply the CEQ methodology supplemented with simulations to assess the potential impact on poverty and inequality.

The first reform was a reduction in personal income taxes across the board. With the aim to provide tax relief to lower-income households and to reduce the burden from the introduction of the GST, the government implemented a revision to the personal income tax schedule in 2020. The exemption threshold was raised from Nu 200,000 to Nu 300,0000, which is expected to result in about 31,000 fewer taxpayers.<sup>26</sup> A new maximum marginal tax rate of 30% was introduced and the tax brackets were adjusted. Table 6 and table 7 show the old and revised tax schedules side-by-side.

Table 6. Old Personal Income Tax Schedule Table 7. Revised Personal Income Tax Schedule

|Minimum|Maximum|Rate|Minimum|Maximum|Rate|
|---|---|---|---|---|---|
|
17 for Russia as an economy in transition with a high share of incomes generated in the shadow sector, which leads to additional distortions in regional income statistics. An alternative measure of the achieved development level is the infant mortality rate, which also has significant variation across Russian regions and is positively correlated with income. We use both the 1990 figures that reflect the level of _initial_ pre-transition development of each region and the concurrent infant mortality. Alternatively, the indicator of urban landline phone connections is employed (the number of landline phones per 1,000 urban populations). Another important indicator of development achievements is the level of education. We measured it in two ways: using the number of students per 10,000 populations (variable _students_ ) and the share of the labor force with university degrees (variable _education_ ).

Thirdly, the structure of regional fiscal revenue may matter. It is well known that in intergovernmental relations money tends to stick where it hits, leading to a so-called “flypaper effect”. Namely, the elasticity of regional government expenditure with respect to regional income is consistently estimated to be a small fraction of the elasticity of regional government expenditure with respect to central government transfers (the estimates of the latter range from 0.5 to more than unity).<sup>22</sup> This generally suggests that governments spend “earned” and “unearned” (or “windfall”) revenue in different ways.<sup>23</sup> This would suggest that “earned” revenue could be decentralized in some “normal” way, while windfall revenue is likely to be shared differently, with a much larger share retained by the regional government. In the Russian context one can identify two main sources of windfall regional government revenues: transfers received from the federal government and budget revenue derived from availability of local rents, primarily through implicit and explicit taxation of highly profitable local firms in extraction industries such as oil and gas. The former source

> 22 See the survey by Hines and
for the development outcomes.

# **5.2 Refugee Presence Does Not Lead to Backlash**

Our second set of outcomes proxy for a possible backlash. Figure 6 examines how refugees’ presence affects public opinion and insecurity. Recall that all Afrobarometer outcomes are standardized to have a mean of 0 and an SD of 1, whereas the ACLED outcome of _any violent event_ is binary (0 or 1). The top row of Figure 6 focuses on attitudes towards migrants and migration policy; _higher values_ indicate being more _pro-migrant_ . Plot 1 shows that, compared to 2011, Ugandan citizens with a higher level of exposure to refugees are no more or less welcoming of migrants than Ugandans living in parishes with lower levels of refugee presence after 2014.

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which uses consumption data to infer evasion behavior (see Pissarides and Weber, 1989; Feldman and Slemrod, 2007; Morrow et al., 2019).

# **2 Data and Measurement of Informal Consumption**

We use two data sets to present new evidence on consumption patterns in developing countries and on the redistributive of taxes. Our core potential consumption sample consists of 31 countries for which the data enable us to proxy for informal consumption at the household level. Our extended sample consists of 80 countries for which the data allow us to document food consumption patterns.

## **2.1 Core Sample**

We assemble our core dataset by combining household expenditure surveys from 31 countries that satisfy three selection criteria. First, the survey is nationally representative. Second, the survey records consumption from open diaries rather than pre-filled diaries, which only contain information on selected products. This helps to ensure that the survey covers all expenditures. Third, the diary asks households to report the place where each expenditure is purchased from - the _place of purchase_ - and that this information is in the diaries. This last criterion ensures that we can systematically reported apply our method to robustly measure informal sector consumption, as described below.

Our core sample from 31 countries contains information on over 400,000 households. Table 1 lists the countries in the core with their name alphabetically sample, survey and year, the number of households, and the average number of purchases reported per household. Countries in the sample are principally located in Latin America and Sub-Saharan Africa. Unfortunately, most household expenditure surveys in Asia do not contain information on the place of purchase. for places of purchase.<sup>6</sup> Nonetheless, the core sample covers a wide range of development levels, from Burundi (GDP pc of 250 USD) to Chile (15,000 USD). In Section 3.3, we show that this core sample is very comparable to the large set of developing countries in the extended sample,
13 branches per 100,000 adults (commercial banks), the percentage of firms with line of credit (all firms), and the percentage of firms with line of credit (small firms). When using these proxies, one needs to be mindful of their weaknesses. For example, the number of bank branches is becoming increasingly misleading with the move towards branchless banking. The number of bank accounts does not suffer from the same issue, but it has its own limitations (in particular, it focuses on banks only, and does not correct for the fact that some bank clients have numerous accounts).

Much of the data for the financial access dimension of the Global Financial Development Database came from the IMF‘s recently established Access to Finance database, which is based on earlier work by Beck, Demirgüç-Kunt and Martinez Peria (2007). A portion of the financial access data is from the newly constructed the _Global Financial Inclusion Indicators_ , or ―Global Findex‖ (Demirgüç-Kunt and Klapper 2012). The Global Findex is the first public database of indicators that consistently measures individuals‘ usage of financial products across countries and over time. It can be used to track the potential impact of global financial inclusion policies and facilitate a deeper and more nuanced understanding of how adults around the world save, borrow and make payments. It is based on detailed interviews with at least 1,000 people per country in some 150 countries about their financial behavior through the Gallup World Poll survey.

_Financial markets._ Data on access to financial markets are more scant. To approximate access to stock and bond markets, measures of market concentration are used, the idea being that a higher degree of concentration reflects greater difficulties for access for newer or smaller issuers. The variables in this category include the percentage of market capitalization outside of top 10 largest companies, the percentage of value traded outside of top 10 traded companies, government bond yields (3 month and 10 years), ratio of domestic to total debt securities, ratio of private
<!-- Start of picture text --> Fig 4 Share of total operated area by holding<br>size: all India, 1953-54 to 1982<br>100%<br>80%<br>0sp X600 ---- *>6ha<br>0 o M~40<br>0 1- 2ha<br>0<br>20% 4°0% 0.4 - 1ha<br>0% ,~~~~~~~~~~~~~< 0.4 ha<br>1953-54 1961-62 1971-72 1982<br><!-- End of picture text -->

# LANDLESSNESS AND ABSENTEE LAND OWNERSHIP

There is a widespread perception that landlessness has increased over the last few decades. While this is true in terms of absolute numbers, in relative terms the incidence of landlessness has not changed significantly since the 1960s in terms of ownership or operational holdings (see Table 3). For India as a whole, the share of households not owning any land remained at around 11 per cent in 1982, while the share of households not operating any land fell slightly from 27 per cent to 24 per cent. However, the share of households owning sub-marginal holdings - often simply homestead land - doubled between 1961-62 and 1982 (from 17 per cent to 32 per cent), in part through the redistribution of ceiling-surplus land.

Commentators vary in their interpretations of the significance of these trends. Chadha (1994), for example, recalculates NSS data on the share of households _owning_ no land to include those with only homestead land but no arable land. On this basis he finds that functional landlessness has increased in relative terms since 1953-54 in most states, particularly in agriculturally progressive states (Punjab, Haryana, Tamil Nadu, Gujarat, Andhra Pradesh, Maharashtra), and has fallen only in Assam, Kerala, and Rajasthan. On this view, land reforms have had little net impact, as they tended to redistribute
|✓|✓|
|Interaction terms|||||||✓|

_<mark>Note:</mark>_ <mark>✓ indicates variables included in each step.</mark> Equation 1 represents the null multilevel models (i.e., models with no predictor variables), which were applied to the TIMSS data, controlling for their clustering and allowing for the estimation of the proportions of the total variance in the outcome variable that is attributable within and between clusters (i.e., intraclass correlation ( _ICC_ ) coefficients). i 0 i (1) where i is the outcome variable (e.g., mathematics achievement) of student _i_ in class/school _j_ , 0 is the 0ii mean intercept, is the variation of class/school yy = ββ + uu _j_ from the mean intercept, and + ee i is the student-level residual error term. yy ββ 0ii uu ee Equation 2 represents the multilevel models, which were applied to the TIMSS data, including ν number of predictor variables while controlling for the clustering of the data. i 0 1i i (2) where i is the outcome variable (e.g., mathematics achievement) of student i in class/school j, yy = ββ + ββ1iixx + . . . + ββννiixxννi + uu0ii + ee 0 is the mean intercept, is the regression slope for the predictor variable 1 of student i in class/school j, is the regression slope for the predictor variable yy of student i in class/school j, is the variation of ββ 1ii ννii class/school j from the mean intercept, and ββ i is the student-level residual error term. xx ββ νν 0ii xx uu Reported statistics for each of the models include: proportions of variance ( ee _R_<sup>_2_</sup> ; expressed as a percentage of the total variance) in achievement explained at each level and step; intercepts with their _SEs_ ; unstandardized coefficients ( _Bs_
br>Ln (per capita GNP, 1993)|~~1.11-<br>(1.5)||-|
|---|---|---|---|
|Adults' average years of school attainment**a/**|______________(2.2)|0.84**||
|Adult literacy rate (%)|||0_(3.3)0***|
|Pupil-teacher ratio in primary education|-0.11|(011|(0.7)|
|Constant|40.6|43.7|17.7|
|Number of countries|35|33|34|
|R2|0.19|0.30|0.38|

Source: the estimates are based on country data on achievement from Lee and Barro 1997 (op. cit.); and on the other variables from the IREDU database.

33 See Krueger, Alan B. (1997) "Experimental estimates of education production functions," National Bureau of Economic Research Working Paper # 6051, Cambridge, Mass.

34 See section 2.3 above for a description of the data used in this analysis.
*|**2.50**|**2.48**|**2.20**|**2.17**|**2.32**|**1.79**|**2.44**|**1.96**|**2.41**|**2.08**|

<mark>*</mark> _<mark>Licenciaturas</mark>_ <mark>in physics, chemistry, biology, history and geography. NA: Not available (less than five observations).</mark> _<mark>Note.</mark>_ <mark>Performance in ENADE: The indicator is a result of the weighted average of the standardized scores of the graduates in the General Training test (common for all areas evaluated, weighing 25% of the grade) and in the Specific Knowledge test (75% of the grade).</mark> _<mark>Source.</mark>_ <mark>ENADE/IDD microdata, INEP/MEC.</mark> Table 3 – Value-added by type of higher education institution (2014)

||Peda<br>Face-to-<br>face|gogy<br>Distance|M<br><sup>Face-to-</sup><br>face|ath<br>Distance|Portu<br><sup>Face-to-</sup><br>face|guese<br>Distance|Oth<br><sup>Face-to-</sup><br>face|er*<br>Distance|T<br><sup>Face-to-</sup><br>face|otal<br>Distance|
|---|---|---|---|---|---|---
# **7. References**

Akter, Sonia, Pieter Rutsaert, Joyce Luis, Nyo Me Htwe, Su Su San, Budi Raharjo, and Arlyna Pustika. 2017. "Women's empowerment and gender equity in agriculture: A different perspective from Southeast Asia." _Food Policy_ 69: 270-279 Alkire, Sabina, Ruth Meinzen-Dick, Amber Peterman, Agnes Quisumbing, Greg Seymour, and Ana Vaz. 2013. “The Women’s Empowerment in Agriculture Index”. _World Development_ 52: 71-91 Allendorf, Keera. 2007. “Do Women’s Land Rights Promote Empowerment and Child Health in Nepal?” _World Development_ 35(11): 1975-1988 Alwang, Jeffrey, Catherine Larochelle, and Victor Barrera. 2017. "Farm Decision-making and Gender: Results from a Randomized Experiment in Ecuador." _World Development_ 92(4): 117-129 Ambler, Kate, Cheryl Doss, Caitlin Kieran, and Simone Passarelli. 2021. “He Says, She Says: Spousal Disagreement in Survey Measures of Bargaining Power.” _Economic Development and Cultural Change_ 69(2): 1-24 Amugsi, Dickson, Anna Lartey, Elizabeth Kimani-Murage and Blessing U. Mberu. 2016. “Women’s participation in household decision-making and higher dietary diversity: findings from nationally representative data from Ghana”. _Journal of Health, Population & Nutrition_ 35(1) Anderson, C. Leigh, Travis W. Reynolds, and Mary Kay Gugerty. 2017. "Husband and Wife Responses on Farm Household Decision-making Authority and Evidence on Intra-household Accord in Rural Tanzania." _World Development_ 90(2): 169-183 <mark>Annan, Jeannie, Aletheia Donald, Markus Goldstein, Paula Gonzalez Martinez, and Gayatri Koolwal. "Taking power: women’s empowerment and household well-being in Sub-Saharan Africa."</mark> _<mark>World Development</mark>_ <mark>140 (2021): 105292.</mark> <mark>Arugay, Aries, Forest Jarvis, Hillary C. Johnson,
her analysis.

Rosenblaum’s (2015) study paints a starker picture of the unintended consequences of inheritance law reform in India. The purpose of his study was to estimate the causal impact of the legislative amendments in the four reform states on female child mortality. For this, he uses data on children born in 1975 or later from three rounds of the NFHS, and employs a difference-in-difference approach that exploits variation in reform status across states, the timing of reform in reform states, the religious affiliation of households in reform states, and the landownership status of households in reform states. He finds that the amendments to the HSA increased the likelihood of female child mortality by a small but significant amount (0.17 percentage points).<sup>16</sup> He argues that the provisions of the law made daughters more expensive to parents who preferred handing own property to sons, and thereby increased the incentive for parents to disinvest in their daughters’ health.

> 13 Evidence suggests that the inheritance law reform had positive impacts on women’s bargaining power within the household (Mookerjee 2017) and on women’s labor participation rates in high skilled jobs (Heath & Tan 2016). 14 Bates (2004) draws on intensive field research in Bheema, a village in the Pune district, in Maharashtra state. She finds that women in the village prefer to not assert their inheritance rights out of fear of disrupting their social networks. The author argues that since improvements in female education and employment have led to positive effects on other dimensions of gender equality in the village, the government would be well-advised to continue investing in education to encourage awareness of the benefits of women’s access to property rights.

> 15 Brown et al. (2002) conducted their study in selected blocks in rural Karnataka, India. They rely on information gathered from a 400-household survey, and in-depth interviews with women and key informants for their analysis. They find that daughters do not actively assert their property rights as they continue
4.7 percent in 2013-18.

The years prior and during the global recession provided an initial encouragement to adopt business-friendly reforms, when most EMDEs improved their scores in the World Bank’s Doing Business survey. From DB2008 to DB2010, the number of business-friendly reforms undertaken by EMDEs increased from 170 per year to 243.<sup>1</sup> However, momentum for business climate reforms other than improving financial regulation stalled in DB2010, setbacks in governance appear to have returned EMDE governance indicators to their 1990s levels, and a rethink appears to be taking hold about the appropriate degree of openness to international capital flows.

EMDE growth prospects have dimmed since the global recession due to elevated debt vulnerabilities, slower momentum in structural reforms, diminished policy room to

> 1 This refers to reforms of laws, rules, and regulations relating to several activities: starting a business, paying taxes, registering property, obtaining construction permits, access to credit, getting electricity, minority investor protection, enforcing contracts, resolving insolvencies, and trade across borders. The number of reforms is calculated using the business reforms by year and by country, as listed in the World Bank’s Doing Business publications. “DB” in front of year indicates Doing Business publication year.
appearing in the data in each country. It is important to note that the respondents are categorizing the support they received or expected into these types, creating room for several complications. First, one support measure may be characterized as taking more than one form. For example, some respondents could potentially consider a grant to be access to new credit as well as a cash transfer; or wage subsidy could also be interpreted as cash transfer depending on the details of its implementation. A further complication arises from the potential miscategorization of support measures. For example, we know that the Government of Georgia did not provide wage subsidies as an explicit form of support to businesses. Still, many firms in the Georgia survey reported receiving such support. We think that cash assistance provided to newly or temporarily unemployed was interpreted this way, but it is impossible to know exactly which support measure did each respondent mean when providing Yes/No responses to this battery. We thus urge readers to interpret our findings into the effectiveness of these types with additional caution.

# **4.4. Firm Characteristics Used for Matching Estimation**

Table 2 presents the summary statistics for a long list of variables from the baseline ES datasets that we use in matching estimation. For each country, means are reported separately for two subgroups of respondents: those that received government support as of the second round (as defined above), the Support group, and those that did not, the No-Support group. The table also reports t-tests comparing means across the two sub-groups. In this subsection, we briefly describe each of these variables.

The firm size is determined based on the total number of full-time equivalent permanent workers of firms as of the reference period used in the baseline ES (i.e., December 2015 in El Salvador and December 2018 in Georgia). Following the standard ES stratification categories, small firms are those employing 5-19 workers. Medium-sized firms employ 20-99 workers, and large firms employ 100 or more workers. The firm age shows the
based on the 2013 Mexican Census of retailers and the assignment of specialized stores to the informal sector). Our three main take-aways are unchanged.

> 30 The share of formal inputs used by informal firms is likely to be an upper bound. First, the 10% number is applied to both fixed and non-fixed establishments, while the latter category (which includes street stalls) is likely to source fewer inputs from formal firms. For this reason, we maintain a 0% passthrough for home production. Second, in Mexico, it is the large informal stores which use formal inputs: The use of formal inputs for smaller stores is closer to 5% on average. Third, segmentation between formal and informal firms is likely to be even larger in poorer countries, leading to lower average pass-through in those countries than in Mexico.

> 31Calculations based on annual income may overstate the regressive nature of consumption taxes since consumption depends on lifetime income, which is less volatile than annual income (Poterba, 1989; Caspersen and Metcalf, 1994).

> 32Source: 1985 US Consumer Finance Survey. Savings rates are in the same range in the few developing countries (China and Chile) for which similar consumer finance surveys are available.

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