rafmacalaba/datause-encoder-data huggingface dataset:
_Table 4 Labor Income distribution by year_

|**Year**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|**2019**|**2020**|
|---|---|---|---|---|---|---|---|---|
|>200|3.0|1.2|3.7|3.5|1.4|0.2|0.0|0.4|
|200-399|6.9|5.8|6.3|5.3|4.1|1.6|1.2|1.1|
|400-799|15.4|15.9|12.3|14.2|13.7|9.0|8.3|10.3|
|800-1199|19.1|20.0|18.3|18.7|21.1|20.4|23.8|24.6|
|1200-1999|17.7|17.4|13.9|13.1|18.4|14.7|14.9|15.9|
|2000-3999|15.6|11.3|11.2|11.5|10.5|9.7|12.8|11.8|
|4000-5999|2.6|2.4|2.4|2.2|2.2|1.6|1.2|2.1|
|6000+|2.0|1.2|0.6|0.6|0.7|1.0|1.0|0.5|
|Not stated|17.7|24.8|31.3|30.9|27.9|41.8|36.7|33.2|

In this case, we face two types of problems. On the one hand, we only have access to intervalcensored data, which is insufficient to analyze changes in the distribution of earnings in the country, and, on the other hand, we have an increasing proportion of individuals who do not declare income. We apply the imputation procedure previously described to address both problems, estimating the interval-censored regression for each year, with a set of household-level characteristics and job type characteristics. The sample of interest includes all adults who declared to be employed, even if they did not state their income.

We make the simplifying assumption that people who did not state income are randomly distributed conditional on observed characteristics. To account for the fact that characteristics may differ across those who did or did not state their incomes, an inverse probability weighting strategy is used to estimate the interval regression model. Finally, the imputation procedure is implemented as discussed in section 3 but assuming no lower and upper bounds for the imputed wages. Nevertheless, the maximum imputed wage for those who do not state their income is capped at the maximum predicted among those who declare their income. In all cases, imputed earnings are adjusted by inflation.
# **1. Introduction**

Financial inclusion—providing access to financial services for the poor—has gained increasing prominence in the past few years as a policy objective for national level policy makers, multilateral institutions, and others in the development field. The United Nations designated 2005 the International Year of Microcredit, adopting the goal of building inclusive financial systems, and most other development institutions and multilateral donors have financial access on their agenda. Consequently, both private and public funds are flowing to fund various financial inclusion initiatives around the world.<sup>1</sup> The rise of financial inclusion as an important policy goal is due in part to mounting evidence that access to financial products can make a positive difference in the lives of the poor. From the field, the evidence comes in the form of rapid take-up of financial services when they are made available to poor households and the very high rates of repayment that the poor exhibit in order to maintain access. The results of the Financial Diaries Project; summarized in the recent book “Portfolios of the Poor”, by Collins, Morduch, Rutherford, and Ruthven (2009); show how dependent the poor are on various financial instruments, both informal and formal, to manage what little money they have on a day to day basis. And – though the results do not always support preconceived notions prevalent in the microfinance community – an increasing number of academic studies show that granting the poor access to financial services can make a difference in their lives in various ways [see, e.g. Burgess and Pande (2005), Karlan and Zinman (2005, 2009), Dupas and Robinson (2008), Banerjee, Duflos, Glennerster, and Kinnan (2009), Bruhn and Love (2009)].<sup>2</sup> In short, as a policy goal, developing more inclusive financial systems will continue to hold a place on the policy agenda.

> 1 For example, in 2008, there was over $11Bn in outstanding investments in MFIs, nearly 50% of which were private funds.

> 2 For example, three recent randomized control trial studies do not support the vision of microfinance’s main goal being to lend to those poor wanting to start or expand a business. Instead, they find that access to consumer credit can also improve welfare by (e.g.) keeping people in their jobs (Karlan and Zinman (2005)), that access to microenterprise oriented credit is fungible with other forms of household debt and is often used to improve risk management rather than to invest in a business (Karlan and Zinman (2009)), and that the presence of a new MFI in a neighborhood has no impact on consumption or health and education spending of micro-entrepreneur households 15-18 months later, though it does seem to improve households ability to borrow, invest, and create and expand businesses.
# **APPENDIX A: Data.**

# **A.1 Data Sources.**

Most of the data utilized in this study come from a series of Encuesta de Ingresos y Gastos de los Hogares (ENIGH), collected by the Instituto Nacional de Estadistica Geografica e Informatica (INEGI), and conducted in the third quarters of 1989, 1992, 1994,1996,1998 and 2000. ENIGH is a rich households survey built for the purpose of measuring the consumption and earnings of Mexican households. Even if the size of the ENIGH has been varying year to year, and its questionnaire updated from survey to survey, the conceptual framework remain the same. This ensures that ENIGH’s results are comparable across years. The survey is stratified according to urban and rural location. The sampling is done to assure that households are representative of geographic clusters with probability of being included proportional to cluster size. All the standard errors in the results are corrected for survey design.

The income data and especially the consumption data are very disaggregated. The survey reports 43 income categories subdivided into monetary, non-monetary and financial income. The consumption data consist of more than 600 different entries, about half of which are food items. Food and manufacturing products and services are finely disaggregated.

Since household size is not the same across income levels, and because the welfare measures are concerned with the well-being of individuals, all data were converted to a per capita basis. Since the measure of individual welfare still doesn’t have a firm theoretical and empirical basis for the construction of equivalence scales, this paper adopts the standard practice of dividing household income and expenditure by its residents, with children of age 14 or less counting as half of adults. The measure of total household income is equal to the summation of financial, monetary and non-monetary income. Non-monetary income includes payment in kind, gifts and imputed value of rent and self-consumption. Each classification of expenditures and income was converted on a quarterly basis and adjusted for regional inflation using as deflator the official consumer 35
- 31 -

Table 12: Tobit Estimates of Investment In Housing

|Varlable a\County|Gongznuling<br>(N-200)|Jurong<br>(N-190)|Tal<br>(N-200)|Xlajlang<br>(N.195)|
|---|---|---|---|---|
|I.<br>Farm Slze|8.890<br>(.210)|-67.539<br>(.486)|226.978<br>(.778)|52.380<br>(.445)|
|II. Credit|||||
|Formal credit|.612<br>|6.843<br>|2.965<br>|4.559<br>|
||(1.037)<br>|(1.324)<br>|(.329)<br>|(2.666)<br>|
|Informal credit|.803<br>|1.338<br>|1.537<br>|3.369<br>|
||(3.272)|(5.200)|(5.241)|(7.664)|
|IlI.Security Perceptions|||||
|Conf. In<br>|-763.506<br>|-550.699<br>|1233.640<br>|-1613.080<br>|
|short-term<br>|(.802)<br>|(1.407)<br>|(.432)<br>|(1.256)<br>|
|Conf. In<br>|46.281<br>|-856.118<br>|-1570.250<br>|3584.130<br>|
|long-term|(.046)|(1.030)|(1.187)|(2.886)|
|IV.nitlal Capital Stocks|||||
|Crop related capital|.008<br>|-.731<br>|11.811<br>|1.770<br>|
||(.016)<br>|(.882)<br>|(1.517)<br>|(2.100)<br>|
|Non-crop capital|7.219<br>|.981<br>|-1.379<br>|4.449<br>|
||(5.109)<br>|(.693)<br>|(.617)<br>|(1.583)<br>|
|Housing quality|-1890.98<br>|-1195.130<br>|-1140.37<br>|-948.705<br>|
||(4.710)|(1.963)|(2.822)|(1.148)|
|V. Household Characteristi|cs||||
|Family size|87.323<br>|162.489<br>|571.999<br>|265.222<br>|
||(.242)<br>|(.390)<br>|(1.269)<br>|(.616)<br>|
|No. of adults|25.197|752.566|-230.057<br>|-89.100<br>|
||(.069)|(1.697)<br>|(.501)<br>|(.203)<br>|
|Educatlon|257.485|-209.292<br>|400.977<br>|353.298<br>|
||(1.873)|(1.826)<br>|(3.019)<br>|(2.172)<br>|
|Experlence|-18.805|-29.328|-13.975|-29.625<br>|
||(.589)|(.960)|(.407)|(.585)|
|Log-likelihood|-1198.8|-957.2|-1374.3|-942.10|

a/ A constant term and village dummy<sup>variables were Included In each equation.</sup> sample sizes differ due to missing observations.
# **2.3 Empirical Literature**

Until recently there has been no harmonized migration data on the skill levels of international migrants disaggregated by country.<sup>13</sup> Many origin countries do not collect any such qualitative data, and the data collected by destination countries display much heterogeneity particularly with regards to the educational attainments of international migrants. As such much of the quantitative assessment of this much-debated phenomenon has been either anecdotal or cross sectional.

Anecdotal evidence of a potential brain gain has been strong. Kangasniemi et al (2004) have found that 40% of migrant doctors working in the UK were ’influenced’ to train in medicine by the prospect of migration. Whilst Lucas (2004) notes that despite low domestic returns on skills 72% of all students enrolled in higher education in the Philippines were enrolled in private institutions. Lucas takes this, along with the finding that the choice of field of study responds to shifts in international demand, as evidence that the domestic skill premium is unlikely to be the primary driver for the decision to invest privately in education.

Beine et al (2001a) attempted a cross-sectional empirical test of the brain drain and found that migration appeared to boost human capital formation in poor countries, and further, that the stock of human capital positively influenced growth. However the analysis was severely constrained by data problems such as the need to use gross migration rates as a proxy variable for data on the brain drain. A second attempt (Beine et al (2003)) found results in support of their earlier work other than the finding that the marginal effect of migration on human capital does not differ with the wealth of the origin country. This work, however, was reliant on a dataset compiled by Carrington and Detragiache (1998), that is itself subject to problems.<sup>14</sup>

> 13A recent World Bank sponsored study by Docquier and Marfouk has made tentative steps toward filling this void by providing emigration rates by education attainment for all countries in 1990 and again in 2000

> 14The US census data, on which it was based, does not record where the education took place; furthermore it includes foreign students, such that graduate students may account for

11
It is plain from Tables 1 and 2 that a large middle-income bulge emerged. Table 3 gives the population share of the developing world’s middle class—the proportion living between $2 and $13 a day ( _Ft_ (13) − _Ft_ (2) ). This increased from 33% in 1990 to 49% in 2005, or from 1.4 billion to 2.6 billion people. China accounts for half of this increase and East Asia as a whole for two-thirds. In 1990, only 15% of the Chinese population lived above $2 but below $13; by 2005 the proportion had risen to 62%. Though not as large (absolutely or proportionately) as for China, India saw an extra 117 million people join this middle-income group. Asia as a whole (east and south) accounts for 81% of the expansion in the number living between $2 and $13 per day. All regions of the developing world saw an increase in _Ft_ (13) − _Ft_ (2) except Eastern Europe and Central Asia where the number fell slightly over the period.

Where is the bulge within the (2, 13) interval of incomes? The empirical estimates of the CDF’s and densities for 1990 and 2005 are given in Figure 2.<sup>13</sup> While a reduction in poverty is indicated for all lines, it is clear that this has not been a simple rightward displacement of the density function. Indeed, the mode has remained almost unchanged over this period, at around $1.00 per day. The mean and median have increased, from $3.14 and $3.94 per day for 1990 and 2005 respectively for the mean, while the medians were $1.47 and $2.13. Instead of a simple rightward displacement, we have seen a marked “bunching up” due to a shift in densities from below the $2 a day line to just above it, with the bulk of the gain in the interval $2 to $6 a day. The bulge in 2005 is mostly above the median. Figure 3 gives _F_ 05( _z_ ) − _F_ 90 ( _z_ ) for 0 < _z_ ≤ 13 , i.e., the extent to which the poverty rate has fallen according to different poverty lines up to the US line. It can be seen that the reduction in the poverty rate peaks at about $1.50 a day (almost exactly the 1990 median). The impact on the poverty rate falls below 5% points at poverty lines of about $6 a day or higher.

It is clear that very little of the expansion in the developing world’s middle class was due to its “upper middle class,” namely those living above the highest poverty line found amongst developing countries, but still below the US line. Over 1990-2005, the estimated value of _Ft_ (13) − _Ft_ (9) rose from 3.1% to 4.3%, or from 139 million to 233 million. Of the extra 1.2 billion people who joined the middle class, only 95 million made it to this upper stratum.

> 13  The densities were estimated by taking the empirical derivatives of the estimated CDFs derived using _<u>PovcalNet</u>_ for each of 25 selected poverty lines.

8
Milesi-Ferretti dataset updated up to 2011.<sup>19</sup> In addition, we also consider trade openness (as measured by imports plus exports divided by GDP, taken from PWT 7.1) as a measure of real integration. We include a variable measuring real integration because, in the end, redistribution of endowments due to or any (implicit explicit) risk-sharing agreements should be materialized through flows of goods. Lastly, because domestic financial depth can also contribute to risk sharing among domestic agents (perhaps lessening the need for, or the value of, international risk sharing) we also use data on domestic financial depth, as captured by the ratio of domestic credit to GDP, taken from the World Development Indicators.

Figure 1 illustrates the trends in de jure international financial openness in the sample countries, as captured by the cross-country average of the Chinn-Ito openness measure. The figure shows both the overall mean as well as the means for advanced and developing countries separately. All three exhibit a rising trend, although with varying timing and magnitude.<sup>20</sup> The advanced-country mean rises steadily since the late 1970s, from under 0.50 to almost the maximum admissible value of 1, with little change over the final decade of the sample. In turn, the developing-country mean also displays an upward trend, but starting more than a decade later (in the early 1990s), and the extent of the rise – from under 0.30 at the beginning of the sample to just over 0.50 at the end – is also more modest than in advanced countries. The time path of the overall mean lies between those of the two group means.

# **4 Empirical results**

We proceed in two stages. First, we estimate the income process given by (19) and construct the quasi-innovations to the present value of income growth; then we use the latter to estimate the risk-sharing model (13).

## **4.1 Estimation of the income process**

As already explained, we estimate three different specifications of (16). The first one imposes **A** (L) = **0** and **β** (L) = **0** (which in turn implies that **B** = **0** in (19)), so that each country’s per capita GDP growth rate is an iid process. The second specification allows 19Specifically, from the former dataset we take the KAOPEN financial openness indicator, while from the latter we use the ratio of total foreign assets and liabilities to GDP.

> 20Foreign asset and liability positions, relative to GDP, provide an alternative way to assess the trends in de facto international financial integration. They also exhibit a marked rising trend over the sample period and, like in Figure 1, the rise is steeper among advanced than among emerging countries.

16
come from regressions with growth rate in GDP per capita as the dependent variable. In the first column, we show the results where decentralization through creation of new provinces by separation from existing provinces is examined through a dummy variable called “Separation” where Separation is 1 for the years after separation in the provinces that experienced separation and 0 before. We model this regression using the fixed effects two-stage least squares procedure since separation is likely an endogenous phenomenon. Using the Metrocity dummy and midyear population as instruments, we find that while Separation has a positive coefficient, it is not statistically significant. In the second column, we replaced Separation with the log of number of municipalities per capita as our key decentralization variable. Here, we find that decentralization is negatively and statistically significantly associated with GDP per capita. This would support the view that decentralization limits economic benefits from economies of scale in public service provision. We get a similar result in column (3) when we replace log of municipalities per capita with log of municipalities per square kilometer of land. Hence we find either no or negative effect of decentralization on level of GDP per capita. Economic growth regressions in the remaining columns in Table 6 show negative coefficients for our decentralization variables but none of these are statistically significant. Hence we cannot confirm any significant effect of decentralization on the economic growth rates in the provinces. Among other control variables in the regressions, we find that total vehicles per capita has positive and significant association with GDP per capita and GDP growth in almost all regressions. The Metrocity dummy also has a positive and significant coefficient in two of the GDP per capita regressions.

13
the daughters receive significantly less educational investment in rural India; the daughter dummy is negative and statistically significant at the 10 percent or lower levels, implying that _θ_<sup>ˆ</sup> 0<sup>_d<θ_ˆ</sup> 0<sup>_s_.Theevidenceisrobustacrossalternativedatasets(IHDS2005andNSS</sup> 1995) and consistent with existing evidence in the context of India (Azam and Kingdon (2013)).<sup>27</sup> The evidence on the marginal effect of father’s higher education in Table T3.IR also suggests a smaller effect for girls; the interaction of the daughter dummy with father’s schooling is negative, and significant at the 10 percent level in 5 out of 8 cases. However, the evidence is weaker than that in urban India, because only one of the estimates is significant at the 5 percent level, and the magnitude of the interaction effect is also smaller.<sup>28</sup> Moreover, the interaction is significant only in 4 cases when we use standard errors clustered at the district level (see Table A3.IR in online appendix).

Putting together the estimates of investment equation with those from the intergenerational mobility equation, we have the following binary relations in rural India: Since the CEF is concave for both sons and daughters, we cannot reject the null hypothesis that _δ_ 5 = 0. In what follows, the discussion assumes that this restriction holds.

# **Implications of the Investment Equation Estimates**

We first consider the evidence on the slope of the investment function. Given the uncertainty regarding the evidence discussed above ( _θ_<sup>ˆ</sup> 1<sup>_d_≦</sup><sup>_θ_ˆ</sup> 1<sup>_s_), the inference regarding gender bias</sup> depends critically on one’s interpretation. If one interprets the evidence as a lower slope for the girls (i.e., _θ_<sup>ˆ</sup> 1<sup>_d<θ_ˆ</sup> 1<sup>_s_),thenwehaveexactlythesameresultsasinurbanIndia,andas</sup>

> 27Azam and Kingdon (2013) show that the gender bias against girls in education expenditure is larger in rural areas in India. We, however, restate the caveat that the existing evidence does not relate to the question of how educational expenditure varies with the education of a father.

> 28In urban India, the interaction effect is significant at the 1 percent level in 2 cases, and at the 5 percent level in 4 cases.

25
**Table 4. Average effects, 4 to 10-kilometer buffers**

**<u>(2013, town and village level)</u>**

||||Log (emplo<br>|yment)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>4 kms|(2)<br>5 kms|(3)<br>6 kms|(4)<br>7 kms|(5)<br>8 kms|(6)<br>9 kms|(7)<br>10 kms|
|_Target_|0.6599**<br>(3.26)|0.7761***<br>(4.66)|0.7356***<br>(4.71)|0.6992***<br>(5.27)|0.7829***<br>(6.33)|0.7772***<br>(6.80)|0.8206***<br>(7.58)|
|_No. of_|195|271|350|449|530|597|678|
|_observations_|||Log (total<br>|firms)<br>||||
||(1)<br>4 kms|(2)<br>5 kms|(3)<br>6 kms|(4)<br>7 kms|(5)<br>8 kms|(6)<br>9 kms|(7)<br>10 kms|
|_Target_|0.1486|0.2687*|0.3232**|0.3578***|0.4027***|0.4126***|0.4218***|
||(0.99)|(2.14)|(2.80)|(3.63)|(4.34)|(4.79)|(5.15)|
|_No. of_|195|271|350|449|530|597|678|
|_observations_||||||||

_Note:_ All regressions include a _constant_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

**Table 5. Placebo test using a pseudo border in Uttar Pradesh**

**<u>(2013, town and village level)</u>**

|||Log (emp<br>|loyment)<br>||
|---|---|---|---|---|
||(1)<br>20 kms|(2)<br>30 kms|(3)<br>40 kms|(4)<br>50 kms|
|_Target_|-0.3611|-0.1158|-0.2009|-0.2720*|
||(-0.89)|(-0.51)|(-1.24)|(-2.22)|
|_No. of observations_|3011|4515|5691|6739|
|||Log (tot|al firms)||
||(1)|(2)|(3)|(4)|
||20 kms|30 kms|40 kms|50 kms|
|_Target_|-0.2435|-0.1021|-0.0937|-0.1603|
||(-0.72)|(-0.54)|(-0.69)|(-1.59)|
|_No. of observations_|3011|4515|5691|6739|

_Note:_ All regressions include a _constant_ , _Distance_ and _Distance X Target_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

23
# **<u>Table 2. Ebola Mortality and Rice Production</u>**

||**(1)**<br>Share of land area allocated to rice|**(2)**<br>Logriceyield[kg/ha]|
|---|---|---|
|Log all burials per 1000 population|-0.014|-0.060|
||(0.007)**|(0.024)**|
|Severely affected counties (dummy)|0.036|-0.177|
||(0.025)|(0.074)**|
|Distance weighted to nearest major road (km)|0.002|0.001|
||(0.001)**|(0.002)|
|Head of household is female|-0.008|-0.202|
||(0.016)|(0.065)***|
|Household head age|-0.001|0.003|
||(0.000)*|(0.002)|
|Head education level dummies (ref= no education)|||
|Household head has primary education|-0.034|-0.056|
||(0.018)*|(0.087)|
|Household head has secondary education|-0.041|0.002|
||(0.016)**|(0.065)|
|Household head has tertiary education|-0.092|0.069|
||(0.040)**|(0.290)|
|Total Rainfall in 2015||0.000<br>(0.000)**|
|Constant|0.217|6.409|
||(0.029)***|(0.172)***|
|Mean of dependent variable|0.212|6.676|
|Observations|3122|2387|
|Fstat|4.122|3.535|
|Prob > Fstat|0.000|0.001|
|Adjusted R-squared|0.027|0.027|

_Source_ : Authors based on HIES 2016.

17