rafmacalaba/datause-encoder-data huggingface dataset:
||**S**<br>|**hares of house**<br>|**hold inco**<br>|**me spent on:**<br>|<br>|
|---|---|---|---|---|---|
||Food|Education|Health|Durables|Adult|
|**A. No controls**||||||
|Migrant mother|0.026|-0.082|0.089|-0.007|0.165**|
||(0.335)|(0.112)|(0.150)|(0.019)|(0.083)|
|Size of household|-0.419***|-0.074***|-0.114|-0.005|-0.027|
||(0.102)|(0.023)|(0.070)|(0.005)|(0.022)|
|Observations|337|337|337|337|337|
|Uncentered_R_<sup>2</sup>|0.443|0.278|0.0163|0.00285|0.162|
|_F_statistic|8.604|8.604|8.604|8.604|8.604|
|Hansen J statistic|0.847|1.025|1.018|2.127|0.812|
|p-value|0.357|0.311|0.313|0.145|0.367|
|**B. Controls included**||||||
|Migrant mother|-0.293|-0.082|0.082|-0.017|0.149**|
||(0.337)|(0.102)|(0.135)|(0.020)|(0.074)|
|Size of household|-0.027|-0.034|-0.083|0.000|-0.002|
||(0.102)|(0.030)|(0.054)|(0.005)|(0.021)|
|Observations|337|337|337|337|337|
|Uncentered_R_<sup>2</sup>|0.633|0.329|0.0652|0.00462|0.230|
|_F_statistic|9.374|9.374|9.374|9.374|9.374|
|Hansen J statistic|0.723|0.775|0.771|1.938|0.637|
|p-value|0.395|0.379|0.380|0.164|0.425|

**Notes:** Controls are rural household, years of education of mother, log total household expenditure. Standard errors robust to village level clustering in parenthesis. *** p <0.01, ** p <0.05, * p <0.1

These findings confirm the predictions of the model in section I for which the difference in the share of household income devoted to investment on children, here expenditure on food, health, education, and durables, is the same in households from which the father * ** migrated and households from which it was the mother that migrated, _z_  _z_ . This would be due to the fact that the migrant spouse can use remittances as a way to offset the shifts in the allocation of the household budget that would be made by the spouse left behind. On the other hand, proposition 3 was predicting that the difference in the share of household income devoted to adult’s private consumption between the case in which the

* ** _x_  _x_  _m f_  mother migrated and that in which the father migrated, , was equal to<sup></sup> , positive if women have a stronger preference for investment on children with respect to men. The estimated coefficient thus suggests that men’s preferences are such that they would like to spend around 15% more than women on private consumption rather than on expenditure on common goods. These results also implicitly indicate that the migrant

22
questions both on the tenure status as well as on a large array of socio-demographic variables and indicators of financial burden and vulnerability which are directly relevant to our analysis.

However, there are some weaknesses in the available data: First, data for the new EU member countries are available only since 2005, which constrains a more in-depth analysis and testing of hypotheses over time. The SILC database includes a longitudinal component but this is yet to be made available in the public domain. Second, the focus of the SILC survey is not access to financial services or financing constraints, but rather on social exclusion and poverty, so that many of the variables do not correspond to exact measures as used in other country-specific studies and as required to test specific theories. For example, we have information on mortgage interest payments but not total mortgage payments (including principal repayment).<sup>9</sup> We also lack information on the access to and use of other financial services by the household. Third, we do not have the necessary information to control for the stratification, so that the standard errors in our regressions are most likely underestimated. Finally, because these are survey data, the volume of household loans and mortgages may not necessarily correspond fully to aggregate data from the banking sector. Nonetheless, they do provide a useful statistical portrait of the distribution of household debt (see, e.g., NBS 2006 and Beer and Schurz 2007).

# **3. Mortgage credit across Europe: The aggregate view**

While the new EU countries have, on average, experienced a rapid increase in household credit, there are large variations across countries. This section documents aggregate trends in household credit across the new EU member countries, contrasting and comparing them with Western Europe as appropriate.

> 9 We therefore do not include measures of debt payment-income ratios in our analysis.

9
Table 4: Import market concentration and domestic prices

||(1)|(2)|
|---|---|---|
|Dependent variable|Log|Log|
||domestic|domestic|
||price|price|
|Log HHI|0.07***|0.04**|
||(0.02)|(0.02)|
|Log nominal exchange rate with US dollar|0.92***|0.87***|
||(0.01)|(0.01)|
|Log GDP per capita||-0.04|
|||(0.02)|
|Gini coefcient||5.34***|
|||(0.31)|
|Control of Corruption||0.14***|
|||(0.02)|
|Regulatory Quality||-0.23***|
|||(0.03)|
|R-squared|0.91|0.90|
|Number of observations|19,215|15,900|
|Dependent variable mean|6.53|6.80|
|Product-year fxed efects|Yes|Yes|

Notes: The HHI is calculated at the level of the ICP basic heading level (e.g., rice) which is constructed to include at least one product in each country and maps roughly one-to-one to 2 digit HS codes. There are 88 distinct basic headings. Domestic prices are observed for 685 distinct goods (e.g., long grain rice; basmati rice) though not all are observed in each country. Standard errors clustered at the ICP basic heading-year are in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively.

26
education are stronger for daughters. Recalling that the evidence earlier also suggests stronger diminishing returns to financial investment for daughters, the daughters in urban India thus seem to face stronger diminishing returns at double margins.

We next turn to the implications of the inequality _ψ_<sup>ˆ</sup> 1<sup>_d>ψ_ˆ</sup> 1<sup>_s_.Fromthetheoryinsection</sup> (2.2), this inequality implies the following (with _δ_ 5 = 0): According to the educational investment estimates discussed above, _θ_<sup>ˆ</sup> 1<sup>_d<θ_ˆ</sup> 1<sup>_s_and</sup><sup>_θ_ˆ</sup> 0<sup>_d_=</sup><sup>_θ_ˆ</sup> 0<sup>_s_.</sup> Again, it is important to keep in mind that _δ_ 1<sup>_d_and</sup><sup>_δ_</sup> 2<sup>_d_are parameters of the true production</sup> function. The evidence on the investment equation discussed above shows that the girls face unfavorable school environment, i.e., _q_<sup>_d_</sup> _< q_<sup>_s_</sup> , implying that _δ_ 1<sup>_d< δ_</sup> 1<sup>_s_and</sup><sup>_δ_</sup> 2<sup>_d> δ_</sup> 2<sup>_s_.This</sup> implies that _θ_<sup>ˆ</sup> 1<sup>_d_</sup> _δ_ 1<sup>_d−_2</sup><sup>_δ_</sup> 2<sup>_dθ_ˆ</sup> 0<sup>_d_</sup> _< θ_<sup>ˆ</sup> 1<sup>_s_</sup> _δ_ 1<sup>_s−_2</sup><sup>_δ_</sup> 2<sup>_sθ_ˆ</sup> 0<sup>_s_</sup> _._ It is thus necessary to have _δ_ 3<sup>_d>δ_</sup> 3<sup>_s_for</sup> � � � � inequality (20) to hold. When considered along with the evidence earlier that _δ_ 4<sup>_d> δ_</sup> 4<sup>_s_,this</sup> suggests that the _marginal direct effect_ of father’s education � _δ_ 3<sup>_j−_2</sup><sup>_δ_</sup> 4<sup>_jHp_�</sup> is higher for the daughters of fathers with low education, but lower when father’s education is high enough. Since _θ_<sup>ˆ</sup> 0<sup>_d_=</sup><sup>_θ_ˆ</sup> 0<sup>_s_, the inequality of intercepts of the mobility equation, i.e.,</sup><sup>_ψ_ˆ</sup> 0<sup>_d<ψ_ˆ</sup> 0<sup>_s_implies</sup> the following (denote the common value by _θ_<sup>ˆ</sup> 0<sup>_c_):</sup> It is easy to check that bias in the school against girls found earlier i.e., � _q_<sup>_d_</sup> _< q_<sup>_s_�</sup> _,_ is necessary and sufficient for inequality (21) to hold.

# **(5.I.2) Rural India**

The estimates of intergenerational persistence in schooling in rural India are reported in Table T2.IR. The pattern of estimates from the linear CEF is similar to that in urban India; the IGRC estimate is larger for the daughters implying lower relative mobility, while the intercept is smaller. A comparison with the estimates for urban India shows that the IGRC estimates do not vary significantly across rural vs. urban areas, but the intercept for girls in rural areas is substantially lower. Also, the difference in the intercepts between sons 23
|**Table 6 (cont’d): Returns by Educatio **|**n Level (%)** (m|ales,25-65|years (dep.|var:logm|onthly earn|ings)|
|---|---|---|---|---|---|---|
|Argentina 2003|OLS|Q10|Q25|Q50|Q75|Q90|
|Primary|8.3**|5.1|8.3**|10.9**|10.4**|11.0**|
|Secondary general|7.6**|6.9**|5.0**|7.3**|8.9**|9.2**|
|Secondary vocational (vs. primary)|8.9**|8.1**|6.7**|9.4**|9.4**|8.4**|
|Higher (non-university) (vs sec general)|11.6**|11.6**|9.2**|13.0**|11.2**|14.4**|
|University (vs. secondary general)|19.5**|17.3**|17.6**|20.1**|20.3**|21.7**|
|Chile 2003|||||||
|Primary|13.0**|11.6**|8.8**|8.3**|15.9**|20.1**|
|<br>Secondary general|11.6**|7.9**|7.2**|10.8**|13.4**|16.4**|
|Secondary vocational (vs. primary)|17.6**|10.8**|12.0**|17.6**|21.8**|24.2**|
|University (vs. secondary general)|24.4**|19.9**|23.7**|25.3**|26.8**|28.2**|
|Colombia 2003|||||||
|Primary|13.2**|17.1**|11.8**|9.3**|13.3**|15.8**|
|Secondary|7.5**|7.0**|5.8**|6.8**|8.8**|9.5**|
|Higher technical (vs. secondary)|8.1**|5.1**|5.5**|11.0**|11.3**|9.6**|
|<br>University (vs. secondary)|16.1**|12.4**|18.7**|21.6**|23.1**|24.1**|
|Guatemala 2000|||||||
|Primary|5.2|6.5*|6.2*|8.0**|7.9|-6.1|
|Secondary|11.3**|7.4**|9.5**|12.3**|13.7**|14.2**|
|University|21.2**|18.5**|22.0**|21.0**|20.2**|23.5**|
|Venezuela 2002|||||||
|Primary|18.6**|19.5**|18.9**|18.4**|19.0**|20.9**|
|Secondary|6.6**|4.8**|5.8**|5.4**|7.4**|8.7**|
|<br>Higher technical (vs. secondary)|13.5**|10.4**|13.8**|16.4**|15.7**|11.0**|
|University (vs. secondary)|14.7**|9.1**|12.4**|16.7**|17.5**|17.5**|
|Mexico 2002|||||||
|Primary|12.4**|10.3**|10.4**|11.1**|11.4**|14.0**|
|<br>Lower Secondary|8.6**|10.0**|7.1**|7.4**|7.7**|9.1**|
|Upper Secondary (vs. primary)|12.0**|7.6**|9.7**|10.7**|14.5**|17.6**|
|University (vs. uppersecondary)|17.5**|18.1**|19.0**|19.3**|16.3**|13.7**|
|Bolivia 2002|||||||
|Primary|14.2*|14.5|8.5|11.6**|21.5**|25.9**|
|<br>Secondary|4.3**|3.7|3.1*|3.9**|4.8**|4.9**|
|<br>Higher technical (vs. secondary)|25.0**|19.7**|24.7**|27.4**|28.5**|24.2**|
|University (vs. secondary)|22.0**|11.2**|17.2**|23.4**|27.0**|30.4**|
|Brazil 2001|||||||
|Primary|12.4**|10.0**|14.4**|12.8**|10.3**|3.6**|
|<br>Lower secondary|6.3**|2.8**|3.6**|4.7**|6.8**|8.4**|
|Upper Secondary (vs. primary)|6.9**|4.7**|5.3**|7.0**|8.8**|10.3**|
|<br>University (vs. uppersecondary)|28.0**|24.7**|27.4**|28.6**|30.0**|30.6**|

* indicates significance at the 5% level; ** indicates significance at the 1% level

Looking at the pattern of returns by quantile, in East Asian countries (where returns to schooling were earlier found to decline by quantile), the pattern of returns at the primary and secondary level is mixed, while quantile returns at the tertiary level are generally decreasing (see Figures 3 and 4). Only for Singapore returns increase at all levels. A consistent finding, however, is that returns to university education decline through the 75th quantile and subsequently rebound at the highest quantile. In Latin America, generally speaking, returns exhibit an increasing pattern within every education level. Exceptions are fount in the case of primary education in Guatemala, Brazil and Colombia and tertiary education in Mexico.

21
In terms of geographical location, taxpayers are less concentrated than might be expected: less than 20% are registered as Nairobi taxpayers, and over 40% are outside the six largest cities/towns in the sample (Nairobi, Nyeri, Mombasa, Meru, Eldoret, and Nakuru). One possible reason for this is how tax officials’ incentives for registration vary across regions. For example, in the largest metropolitan areas of Nairobi and Mombasa, the relevant margin to increase revenues is improving taxes from large corporations. On the other hand, in smaller cities and rural regions, small businesses registered as TOT taxpayers might be a relatively more important source of revenue and, therefore, face stronger registration and enforcement efforts. In other words, the true distribution of small businesses is likely more concentrated in the largest cities, but the relative proportion registered for TOT is likely higher in smaller towns.<sup>13</sup> We also show that the total amount of tax declared and paid was close to Ksh. 90 million per year in 2016 - 2018, decreased to close to Ksh. 40 million in 2020, and reached 391 million by the end of the 2023/24 financial year. We note that the aggregates for tax due and tax payments are somewhat different since i) some taxpayers can file taxes but never pay, and ii) some taxpayers can pay back taxes and also pay their liabilities without filing, in case they use the M-service app.

# **3.3 Survey Data**

To complement the tax administrative data, we draw on specific questions from two surveys of registered small businesses in Kenya that include questions about respondents’ knowledge of the TOT regime. The first survey (hereafter the “in-person surcovered 766 small businesses across the urban areas in vey”) registered five largest Kenya (Nairobi, Mombasa, Eldoret, Kisumu, and Nakuru). It was carried out between June and September 2022. The sample frame was drawn from the list of businesses registered with the Kenya National Bureau of Statistics (KNBS), stratified by counties and sectors. To be eligible for the survey, businesses were screened based on their

> 13Using VAT records in Kenya,Wiedemann _et al._ (2024) document that over 40% of businesses and 70% of value-added are generated by businesses headquartered in Nairobi county, even though its population is less than 10% of the country.

17
<!-- Start of picture text --> Annual market prices<br>o<br>8<br>@<br>os J<br>‘= ci See oil<br>oO<br>2002 2003 2004 2005 2006 2007<br>Period<br>—*— Corn<br>—*— Soybeans ——®*— Rice<br>——*—— Wheat<br><!-- End of picture text -->
the de facto exemption of the informal sector achieves on average as much inequality reduction as the actual direct tax policies (income taxes and social security) in developing countries. Equally remarkable is that this policy achieves 75% of the inequality reduction obtained in a counterfactual world with enforcement in which can perfect governments tax both formal and informal varieties (thereby taxing a much larger base) and optimally differentiates rates between food and non-food goods. In the realistic world in which informal varieties cannot be taxed, the inequality reduction obtained by the de jure optimal rate differentiation is half that obtained by the de facto exemption of the informal sector.

We investigate the robustness of our results to changing our main assumptions. First, we obtain similar results when changing how we assign places of purchases to the formal (informal) sector. This is because the association between budget shares and household income is strongest for places of purchase which are clearly informal (non-market consumption) or clearly formal (large supermarkets). Second, we assume at baseline a zero pass-through of taxes to prices in informal stores. This assumption may not hold if, for example, informal retailers buy inputs from formal suppliers. We relax this assumption by combining our model with microdata from Mexico on formal input purchases of informal retailers. We find a slightly smaller redistributive effect of the de facto exemption of the informal sector, but our main results remain unchanged. Third, due to data limitations, we use total household expenditure to proxy for household income.<sup>2</sup> We relax this assumption by using estimates of saving rates across the income distribution and similar results. we discuss the of our find Finally, implications modeling assumption that no direct tax instrument is available to the government. An important theoretical result in public finance is that redistribution is better achieved through direct rather than indirect taxes (Atkinson and Stiglitz 1976). This result, however, is derived under the assumption of perfect enforcement of direct taxes, which is unrealistic in lower income countries (Jensen 2019). In the more realistic setting where income taxes can be partially evaded, theory suggests a substantial redistributive role for indirect taxes (Burgess and Stern 1993; Boadway et al. 1994; Huang and Rios 2016).

Our results have several main implications. First, our result that consumption taxes can reduce inequality in developing countries runs counter to the consensus view in the policy and academic literatures (Sah 1983; Shah and Whalley 1991; Gemmell and Morrissey 2005; Coady 2006). This negative prior may explain why studies of redistribution in these countries often focus on the design of government transfers (Tanzi 1998;

> 2Expenditure surveys in developing countries often do not attempt to directly measure income because of serious measurement issues - see Deaton (1997) for a discussion.

3
- (iii) Urban area boundaries;

- (iv) Rural settlement boundaries; (v) Security assessment access map.

Pre‐war region boundaries are available as shapefiles from UNDP. The boundaries of urban areas were defined by the urban enumeration areas previously used in UNFPA’s Population Estimation Survey 2014 (PESS). Boundaries of IDP settlements were provided by UNHCR’s Shelter Cluster and PESS. The IDP strata boundaries were subtracted from the urban and rural strata to prevent duplicate sampling. The remaining areas outside of the urban and IDP strata were considered as rural strata. Areas determined too dangerous through the security assessment were removed from the sampling frame.

# _Population sampling frame_

In urban and rural strata, population estimates were derived from the 2015 WorldPop data set, detailed in Linard et al. (2010). This data set uses a combination of data sources and methods, including satellite imagery, to derive highly spatially disaggregated population estimates. First, the starting point are 2005 population estimates at the district‐level from the UN Office for the Coordination of Humanitarian Affairs (OCHA) for 74 districts. Second, an Africover GIS dataset depicting 22 landcover classes was combined with 2005 Landsat satellite imagery depicting settlement outlines. Third, settlement point location data, based on the efforts of various NGOs and UN agencies, with more than 11,000 settlement points along with some population estimates, including urban and rural areas, and IDP settlements. To achieve higher spatial resolution, the OCHA estimates were disaggregated using the information contained in the settlement points data and the landcover class data. The result is a gridded population dataset at 100m‐ by‐100m spatial resolution. For each 100m‐by‐100m cell, the data set contains a population estimate, which, aggregated within the PSU, provides a population estimate for each primary sampling unit (PSU) in urban and rural strata, which was later used for sample selection proportional to size. Due to inadequacies of the population density map for the purpose of creating a sampling frame, a set of corrections had to be made to this data set. In the original WorldPop layer, the population values were not always distributed smoothly. For instance, a village might have only one pixel with a high population number creating a sharp contrast, although its coverage area is larger and the transition from sparse to dense population is more progressive. Hence, a Gaussian smoothing kernel technique with standard deviation of 500m was applied. This distributed higher values smoothly in areas surrounding a high‐ density pixel while preserving close to the total population count in the area (Figure 2).

8
- 9 -

**<u>Table 2: Levels and Changes in Mortality Rates</u>**

||||Male<br>|||||Femal<br>|e<br>||
|---|---|---|---|---|---|---|---|---|---|---|
|Age|P<br>|eriod Tab<br>|les<br>|Improvem<br>forCohor<br>|ent Rates<br>tTables<br>|Pe<br>|riod Tab<br>|es<br>|Improvem<br>forCohor<br>|ent Rates<br>tTables<br>|
||RV85|RV98|RV04<br>unbiased|Pop.<br>Projection|RV04/<br>RV85|RV85|RV98|RV04<br>unbiased|Pop.<br>Projection|RV04/<br>RV85|
|50|0.0054|0.0044|0.0044|1.40%|1.44%|0.0027|0.0022|0.0015|1.40%|3.91%|
|55|0.0082|0.0059|0.0058|1.40%|2.39%|0.0042|0.0030|0.0024|1.50%|4.04%|
|60|0.0124|0.0089|0.0091|1.50%|2.23%|0.0066|0.0047|0.0035|1.40%|4.46%|
|65|0.0189|0.0146|0.0139|1.50%|2.16%|0.0104|0.0080|0.0049|1.40%|5.22%|
|70|0.0288|0.0239|0.0219|1.50%|1.94%|0.0165|0.0137|0.0072|1.50%|5.76%|
|75|0.0447|0.0384|0.0356|1.50%|1.61%|0.0272|0.0237|0.0127|1.30%|5.30%|
|80|0.0693|0.0624|0.0593|1.40%|1.11%|0.0451|0.0401|0.0261|1.20%|3.84%|
|85|0.1070|0.0963|0.0971|1.20%|0.69%|0.0750|0.0680|0.0523|1.20%|2.54%|
|90|0.1636|0.1472|0.1511|1.20%|0.56%|0.1238|0.1115|0.0942|1.20%|1.94%|
|95|0.2459|0.2213|0.2219|1.20%|0.73%|0.2013|0.1812|0.1527|1.20%|1.95%|
|100|0.3600|0.3240|0.3097|1.20%|1.07%|0.3180|0.2862|0.2283|1.20%|2.34%|
|105|0.5064|0.4557|0.4261|1.20%|1.23%|0.4792|0.4313|0.3328|1.20%|2.57%|
|110|1.0000|1.0000|1.0000|1.20%|0.00%|1.0000|1.0000|1.0000|1.20%|0.00%|

Source: SVS and Staff Calculations Note: The values for the RV-04 tables shown here are not updated to any particular year, i.e., are representative of mortality centered around 1999.

# **_3.3. Discount Rates_**

In line with most other studies, the computation of MWRs is performed with two alternative discount rates, the interest rate on government or central bank bonds and the interest rate on corporate bonds. The MWR computed with the first rate is frequently considered to be the most meaningful to the average customer, as it excludes risk and reflects its opportunity cost more accurately. It is also used to facilitate comparisons across countries. The alternative discount rate is also computed, as it may reflect more appropriately the opportunity cost for some consumers, and because it is more relevant from the point of view of the provider.

The risk-free discount rates were obtained by the yield curve of 20 year indexed central bank bonds (the PRC-20) in March of 1999, 2002, 2003, 2004 and 2005 – as indicated below, the annuity sample consists of all annuities sold in those five months.<sup>6</sup> The yield curve for those five months was provided by the Central Bank of Chile, consisting of daily estimates of the zero coupon yield curve for maturities ranging from one month to 20 years. These curves were originally generated using interpolation and smoothing approaches developed by the RiskAmerica company, drawing on what is usually a limited number of trades on any given day in the PRC-20. The Central Bank of Chile makes some additional adjustments, based on the transactions of similar debt instruments.

> 6 March was the month selected to allow comparisons with estimates of MWRs by other researchers.