rafmacalaba/data-use-mentions-extended huggingface dataset:
2006, from Demographic Health Survey (DHS) data. _Note:_ The continuous dark line represents the mean infant mortality rate in each country, while the endpoints of the whiskers indicate the infant mortality rates by different levels of mother’s education.

Parental education is not, unfortunately, the only predetermined circumstance that affects the basic opportunity for life. There is some evidence that gender does too, at least in parts of Asia, where juvenile sex ratios are unusually high. A juvenile sex-ratio simply measures the number of 0-4 year-old boys in a population, relative to the number of 0-4 year-old girls. Because slightly more boys than girls are born in a typical population at any given time, that ratio oscillates between 1.00 and 1.05 in most countries. Remarkably, in the Indian states of Punjab and Haryana, the 2001 ratio was above 1.20. In China, it reached 1.17 in 2000. While some recent work suggests that the patterns of incidence of hepatitis B – and the fact that it leads to more male births – may account for
is determined by At the steady state, the condition for a self-selection equilibrium to emerge (i.e., skilled workers only emigrate) is : In this case, migration prospects impact on the critical ability level required for investing in education; the condition for investing in education becomes: which is higher than cn = h ¡ 1 providing that the self-selection condition holds.

There is a great deal of evidence that migration prospects indeed impact on people’s decisions to invest in higher education. For example, in their survey on medical doctors working in the UK, Kangasniemi et al. (2004) evaluate that the migration premium in the medical profession lies between 2 and 4 (in PPP values); about 30% of Indian doctors surveyed acknowledge that the prospect of emigration a¤ected their e¤ort put into studies; furthermore, the doctors surveyed estimate that migration prospects a¤ect the e¤ort of about 40% of current medical students in India. Focusing on the software industry, Commander et al. (2004) estimate that the migration premium for Indian IT workers contemplating emigration to the US lies between 3 to 5 (depending on the type of job) in PPP values.<sup>10</sup> According to the IOM (2003), the prospects of working abroad have increased the expected return to additional years of education and led many people to invest in more schooling, especially in occupations in high demand overseas.

Migration prospects stimulate domestic enrollment in education but actual emigration deprives the country of its educated citizens, the proportion of educated in the remaining population falls to zero, and the average level of human capital of remaining members falls to 1. In the presence of a minimal threshold for consumption, migration can be limited by an additional liquidity constraint. Liquidity constraints are due to the monetary fraction of the migration costs only (as psychological costs of leaving and assimilation costs are incurred only once migration has occured). Let us denote by k0w¤ < kw¤

>
<sup>9</sup> as are households with missing values in our variables of interest (0.04 percent). All these adjustments reduce the sample by an additional 6.3 percent, which leaves us with a balanced panel of 30,343 households. Table 2 shows the size of the sample by year, and the number of expanded observations using panel-adjusted weights.<sup>10</sup>

# _3.1. The distribution of income and consumption growth_

The period of 2005-2012, according to IHDS data, shows an interesting pattern of distributional change. In looking at how income and consumption growth have been distributed across the population, the distinction between anonymous and non-anonymous measures is important. Anonymous growth incidence curves (GIC), which show the annual percentage change in income or consumption for every centile of the distribution (dotted lines in Figure 1), are flat for both consumption and income, at a level well above zero. This implies that growth has been significant with almost no impact on the overall distribution of consumption and income, which is also consistent with negligible change in the Gini coefficients for these indicators (Table 3).

> 8 For capital gains we convert all negative income sources to zero because these might reflect losses in a particular year but not in the long-run. All incomes and consumptions are converted to 2005 prices and adjusted for spatial price differences (at the State level).

> 9 Households with high consumption income ratios are likely to be those under-reporting income, or experiencing losses in business incomes.  Most are in the top decile of the consumption distribution, and their exclusion has little impact on decompositions focused on the lower tail of the distribution.

> 10 Even though the restrictions imposed by data reduce the full sample by around 13 percent, in Table A3 in the Appendix we show that that the poverty estimates we obtain using the full sample are consistent with those obtained from using our balanced-panel sample. Therefore, the results we present here are indicative of what would
. The remaining 11 percent of firms were mostly in manufacturing and construction. Although reporting to RAIS is mandatory, not all firms report to RAIS consistently year after year. Indeed, we only find 782 of the 866 study firms in the RAIS data at follow-up, in 2019. Some firms may have closed, while others may simply not have reported to RAIS that year.

Third, to verify which firms closed after the intervention, we consulted the registry of firms maintained by the Federal Tax Authority (RFB). This data shows that 32 firms had officially closed by the end of 2019. From the RFB data, we also obtained firms’ starting capital as an additional background characteristic reported in table 1 (data on sales is not available for confidentiality reasons). The average value of starting capital was about USD 10,000.<sup>2</sup> Finally, we conducted a follow-up survey about one year after the intervention, between July and September 2019. This survey collected information on business performance (sales and profits), as well as business practices. The original plan was to revisit study firms during the 2019 round of NaN to reapply the questionnaire on 29 business practices. However, NaN was discontinued after 2018 for budget reasons. We applied a different questionnaire on business practices, based on McKenzie

> 2 Using an exchange rate of 0.19 BRL per USD.

8
ownership drops below 20%. These findings highlight the stark gender disparities in high-value property ownership, which are important to consider in the wealth distribution and gender equity literature.

Second, we conduct a detailed analysis of property tax compliance by gender and find that men and women are equally likely to pay their property tax bills. A key advantage of studying property taxes is that evasion is directly observed: owners are billed based on cadastral valuation, and we observe whether they pay or not. We characterize tax payment rates for women and men, examining how these rates vary across the wealth distribution, proxied by the properties’ assessed value. We document substantial evasion, with slightly lower rates for women-owned properties (46%) compared to men-owned (48.5%).<sup>1</sup> The property tax gap is economically significant, amounting to roughly 8% of total municipal spending or the equivalent of the entire local public safety budget. On-time payment rates increase with property value, ranging from about 35% in the first percentile to 60% in the top percentile, though they remain similar for men and women across the distribution. However, women face slightly higher effective tax rates than men, driven by ownership of lower-value properties and a _de jure_ regressive tax schedule. We also document a small but statistically significant difference in payment methods: women are slightly less likely to use electronic means to pay property taxes, even after controlling for age.

Third, we examine gender-specific responses to (soft) tax enforcement measures. We estimate the effects of personalized tax letters on current, past, and subsequent property tax payments for women and men by leveraging a large-scale field experiment conducted by Cruces, Tortarolo, and Vazquez-Bare (2023) in October 2020.<sup>2</sup> The intervention, launched amid the COVID-19 pandemic, sought to address a sharp decline in property tax payments driven by economic disruptions and lockdown measures. It consisted of sending a one-page personalized letter to randomly selected residences, providing
the bottom panel presents them in contract terms, that is, the number of fields (plots) under a given contract. Both panels are in percentages of rentedin land. The use of fixed rent has increased from 45% to 58% as a percentage of the total acreage rented in, over the course of twenty years, while sharecropping arrangements have decreased from 45% to 31%. The pattern is similar, albeit less striking, in contract terms. Fixed rent arrangements have increased from 35% to 49% while sharecropping arrangements have decreased from 49% to 38%.<sup>14</sup> We further split up fixed rent by fixed monetary rent (rent paid in money), and fixed crop output (rent paid in produce), a distinction we explore in the analysis.

We focus our analysis on the acreage terms data. Using acreage terms instead of contract terms gives us a more accurate representation as to the amount of land, and by extension, the effect on input decision-making, and regional yields.

Due to the presence of other contract forms, the total percentages in Table 1 (that is, fixed rent plus sharecropping) do not add up to 100%. In Appendix Figure A1 we display the percentage of contracts under various contract types, including: ‘for fixed money’, ‘for fixed produce’, ‘for service contract’, ‘for share of produce together with other terms’, ‘under usufructuary mortgage’, ‘from relatives under no specified terms’, and ‘under other terms’. Fixed and sharecropping represent the majority of the rental contracts. Again, one notes the increase in the various fixed terms (as opposed to sharing terms).

Table 1 presents data from the kharif (rainy) season. The trends observed are similar for the rabi (dry) season (see Appendix Table A1). Although crop types and irrigation levels tend to differ across seasons, contractual forms may be similar across them because multi-season and multi-year contracts
Time to obtain antenatal care and DTP immunization <!-- Start of picture text --> 8<br>7<br>7<br>6<br>6<br>5<br>5<br>4<br>3<br>3<br>2 2 2 2 2 2 2<br>2<br>1<br>1<br>0<br>Obtain combined oral Obtain an antenatal care Obtain DTP immunization<br>contraception visit<br>Chile Ghana India Vietnam<br>Source: Living Life data<br>Calendar days<br><!-- End of picture text --> While the time frame to obtain HIV/AIDS testing reported by contributors in all four economies ranged from 2 to 5 days, the total time it takes to start HIV/AIDS treatment can vary from 2 days in Ghana up to 59 days in Vietnam, where patients undergo pre-therapy adherence counseling sessions before receiving their ART medications (Trinh et al. 2011). Figure 3.6 shows the time it takes to obtain both HIV/AIDS treatment in a public health facility in all four economies.

**Figure 3.6: Time to Obtain HIV/AIDS Treatment in a Public Health Facility (calendar days)**

<!-- Start of picture text --> Time to obtain HIV/AIDS testing and treatment in a<br>public health facility<br>70<br>59<br>60<br>50<br>40<br>30<br>20<br>10 5 3 2 3 7 2 6<br>0<br>HIV/AIDS testing HIV/AIDS treatment (ART)<br>Chile Ghana India Vietnam<br>Source: Living Life data<br>Calendar days<br><!-- End of picture text --> The timeframe to obtain tuberculosis testing and treatment reported by contributors
<br>‐0.031|0.009|0.130|‐37387|
|(0.052)|(0.014)|(0.485)|(36636)|
|Group Treatment*During Intervention|‐0.001|‐0.041|‐36|
||(0.008)|(0.404)|(25805)|
|Group Treatment*Post Intervention<br>0.051|‐0.002|‐0.272|36526|
|(0.056)|(0.010)|(0.467)|(33202)|
|Sample Size (N*T)<br>135|8502|8502|8502|
|Sample Size (N)<br>135|146|146|146|
|P‐value: Individual=Group During|0.472|0.985|0.339|
|P‐value: Individual=Group Post<br>0.170|0.500|0.482|0.117|
|Control Mean<br>0.47|0.74|43.0|1087335|
|Control S.D.<br>0.20<br>Notes:<br>Regressions use PILA data on formal employment, and are for samp<br>Column 1 is a cross‐sectional regression for firms with employment<br>Columns 2, 3 and 4 include firm and time fixed effects, and cluster s<br>*, **, *** denote significance at the 10, 5, and 1 percent levels resp|0.14<br>le of survivin<br>data in both J<br>tandard error<br>ectively.|4.97<br>g firms.<br>an 2013 a<br>s at the fir|418941<br>nd Dec 2017.<
sup>2</sup> ), household size, wealth, livestock, bank account, distance to rainfall station, experience of past weather shocks, and risk preferences.<sup>26</sup> Table A2, taken from our pre-analysis plan, specifies the data we use to derive each covariate. Table A3 shows

> 23Using data from seven studies on microcredit, Meager (2019) finds that microcredit only affects the profits of households with prior business experience, and that the effects for this subpopulation vary largely across contexts. This pattern suggests that business experience of microcredit beneficiaries is a necessary but not sufficient condition for positive effects.

> 24We also specified in our pre-analysis plan that we would analyze heterogeneity with respect to household head’s gender. However, very few households are in fact headed by females, as shown in Table A3.

> 25We stratify the dummy variables by studies. However, for covariates using standardized scales across studies (household head age and household size), we show that estimates are similar with no stratification (Figure A5).

> 26More specifically, we regress each outcome on its predictors using the full sample of control observations, and then use the coefficients from this regression to predict the outcomes of all sample units. In our pre-analysis plan, we specified that we would estimate treatment effects using a repeated split sample approach to limit the bias resulting from overfitting (Abadie et al., 2018), however this is not possible using the _baggr_ R package. Our results on predicted outcomes should therefore be interpreted with caution. In particular, treatment effects for farmers with low predicted outcomes are likely to suffer from an upward bias, whereas treatment effects for farmers with high predicted outcomes are likely to suffer from a downward bias (Abadie et al., 2018).

12
changes in poverty and inequality? Figure 4 presents the distributional impact of different components of the tax and benefit system as a share of market income (including pensions). Most components of the system are progressive, with the bottom 50 percent of the distribution being net receivers of social benefits. However in cash terms, households beginning in the second decile were net payers to the treasury in 2014, as the share of taxes paid exceeded the cash benefits received for all but the poorest 10 percent of the population. This coincides with the results presented above, particularly once indirect taxes are included.

Since the influence of specific interventions may be different from that of the overall system, a fundamental question in the policy discussion is whether a particular fiscal instrument (or a particular combination of them) is equalizing. If there were a single fiscal intervention, using the typical indicators such as the Kakwani index<sup>14</sup> to determine whether a particular intervention is progressive or regressive would be sufficient to unambiguously determine whether that intervention was equalizing. Given there is more than one fiscal intervention, this one-to-one relationship between the progressivity of a particular intervention and its effect on inequality breaks down. As Lambert (2001) demonstrates, depending on certain characteristics of the fiscal system, a regressive tax—for example—can exert an equalizing force over and above that which would prevail in the absence of that regressive tax. This is because each fiscal intervention interacts with all the others. For instance, the proceeds of a regressive indirect tax could be used very

> 14 The Kakwani index for taxes is defined as the difference between the concentration coefficient of the tax and the Gini for market income. For transfers, it is defined as the difference between the Gini for market income and the concentration coefficient of the transfer. See, for example, Kakwani (1977).

19
taxes and social security) in developing countries.<sup>45</sup> We note however that the redistributive potential of direct tax systems in these countries is constrained because they only cover the small share of the workforce which is not selfemployed (Jensen, 2019). Direct tax systems that do not face this constraint have a much larger effect on inequality.<sup>46</sup>

> 44Optimally differentiating rates when all varieties are taxed reduces inequality substantially, despite the relatively small progressivity achieved by differentiating rates on food and non-food described in section 4.3. It occurs because this (unrealistic) scenario assumes the government can tax the entire consumption base, which yields much higher effective tax rates.

> 45Since direct taxes are generally considered most strongly suited for redistribution, our results are large in magnitude among tax policy impacts. They are, however, smaller in magnitude than the inequality reduction achieved in CEQ-countries by in-cash and in-kind transfers (Lustig, 2018).

> 46Using data from the OECD Income Distribution Database, we calculate that direct taxes in developed countries achieve a 11.2% Gini-reduction in inequality.

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# **Box 1.1: Main Data Sources**

This study used both existing data and literature available in Tanzania, as well as a special qualitative analytical work commissioned for the report.

- The only household survey which contains data on HEs is the Integrated Labor Force Survey (ILFS). Using available data drawn from the two rounds of the ILFS survey conducted by the National Bureau of Statistics (NBS, 2000/01 and 2006), a quantitative analysis was conducted to identify the key features of the HE sector and its economic role, and to determine what drives or constrains their productivity. The two rounds of survey enabled understanding of the dynamics in the HE sector in general, and in several types of HEs differentiated by spatial location (Dar es Salaam, other secondary urban, and rural areas), gender (male- and female-operated), industry group (trade, manufacturing, services), and other key characteristics. However, the coding of the spatial locations is not consistent in both surveys, so comparisons between the two surveys by area are biased and should be regarded with care.

- To complement the quantitative analysis, a focus group discussion (FGD) survey was conducted to learn from HEs and from their experiences with running their business. The FGD survey was done in two phases. The first phase, conducted in March and April, 2009, focused on urban clusters in 9 regions -- Dar es Salaam, Morogoro, Dodoma, Singida, Kigoma, Mtwara, Kilimanjaro, Arusha and Mwanza.  The second phase, launched in September, 2009, covered the 3 districts of Kilosa, Kwimba, and Masasi located in Morogoro, Mwanza and Mtwara regions, respectively. The narratives from the in-depth interviews of individuals and groups during the FGDs provide rich insights into the HE operators‟ perception of their needs, constraints, and coping strategies, as well as the impact of government policies, programs, and projects on them (see Kessy, 2010).

- The study also drew on
|**Table 1. S**|**ummary Stati**<br>Mean|**stics**<br>Median|Std Dev|# Obs|
|---|---|---|---|---|
|**Panel A. Loan level data- working capital**|**loans of recipi**|**ent firms**|||
|Spreadlfbt|27.13|20.69|22.97|397,427|
|Volumelfbt|11.11|10.90|1.53|397,427|
|Provision ratelfbt|1.75|1.00|1.56|397,427|
|Earmarkfbt|0.61|1.00|0.49|397,427|
|Collaterallfbt|0.79|1.00|0.41|397,427|
|Maturitylfbt|16.06|12.00|12.48|397,427|
|BF Relationfbt|3.03|2.45|2.47|397,427|
|WC Loan Ratingft|8.13|8.00|0.95|259,993|
|First Ear Ratingf|7.73|7.00|1.03|353,224|
|HHfy|15.82|14.58|5.79|397,427|
|**Panel B. Firm-bank-year level data**|||||
|Probability of new earmarked loanfby|0.04|0.00|0.19|1,990,576|
|Firm agefy|1.83|1.91|1.02|1,990,576|
|Firm sizefy|1.84|1.61|1.45|1,990,576|
|BF Relationfby|1.72|0.94|2
it is difficult to monetize. (Barrett and Constas, 2014) propose a definition of _development resilience_ that focuses on the capacity of people to avoid such poverty traps.

Also, the ability of individual firms to cope with the shock and continue to produce in the disaster aftermath – the _static resilience_ of (Rose, 2009) – depends on many factors that would need to be included in the analysis. Various methodologies have been proposed to assess these parameters, using input‐ output or general equilibrium models (Santos and Haimes, 2004; Rose and Wei, 2013; Hallegatte, 2014b) or explicit modeling of supply‐chains (Battiston et al., 2007; Henriet et al., 2012). But more work is needed to assess this resilience based on the data and indicators that are available in all countries.

We have disregarded the impact on natural capital, in spite of its importance for the income of poor people across the world (Angelsen et al., 2014) and the impact of natural disasters on soils (through salinization or erosion), fish stocks, or trees. Including natural capital in the assessment would meet many data related issues, on the local importance of natural capital in income and on the vulnerability of natural capital to disasters.

Further, our framework does not address the ability to “build back better” after a disaster and the possibility for reconstruction to lead to an improved situation. It also takes the current exposure and vulnerability as a given, and investigates policy options without accounting for feedback in terms of risk‐ taking decisions. Better ability to manage risks – e.g., through access to insurance and social protection – could indeed have further positive economic impacts through more risk‐taking, innovation, and specialization (Elbers et al., 2007; World Bank, 2013). It can also have negative impacts through moral hazard and excessive risk‐taking (Michel‐Kerjan, 2010). These feedbacks and relationships have to be 39