rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: IMF Data Transparency and Sovereign Bond Spreads (IV), Interactions with Trade and Financial Openness**

|Freedom of information, lag of ICRG, lag of interaction<br>||d|ependent va|riable: spread|||
|---|---|---|---|---|---|---|
|IV variables<br>between transparency and ICRG, lag of public debts and<br>lag of interaction between transparency and public<br>debts<br>|||1995-|2018|||
|Variables|[1]|[2]|[3]|[4]|[5]|[6]|
|GDP per capita (constant 2010 US$)|-1.243***|-1.200***|-0.441**|-0.254|-1.409***|-1.115***|
||(0.208)|(0.241)|(0.183)|(0.194)|(0.239)|(0.275)|
|Growth (annual %)|-0.0266***|-0.0299***|-0.0352***|-0.0377***|-0.0264***|-0.0290***|
||(0.00559)|(0.00606)|(0.00603)|(0.00655)|(0.00596)|(0.00637)|
|CPI Inflation (average consumer prices)|0.0650**|0.119***|0.0578**|0.0872***|0.0630**|0.0964***|
||(0.0257)|(0.0296)|(0.0286)|(0.0320)|(0.0280)|(0.0319)|
|Primary Balance to GDP (%)|0.0126|0.00976|0.0211**|0.0185*|0.0151*|0.0154*|
||(0.00768)|(0.00834)|(0.00872)|(0.00953)|(0.00846)|(0.00911)|
|Current Account Balance to GDP (%)|-0.00208|-0.00894*|-0.00607|-0.0141**|-0.00597|-0.0138**|
||(0.00472)|(0.00512)|(0.00501)|(0.00556)|(0.00519)|(0.00565)|
|VIX Index|0.608***|0.517***|0.648***|0.586***|0.616***|0.553***|
||(0.0601)|(0.0671)|(0.0694)|(0.0764)|(0.0679)|(0.0746)|
|US 10-year Treasury Bond Yield|-0.501***|-0.576***|-0.203**|-0.275***|-0.547***|-0.532***|
||(0.0913)|(0.0954)|(0.0933)|(0.0975)|(0.100)|(0.104)|
|ICRG Index|||||||
|Trade Openness|-0.530***|-0.417**|||-0.510**|-0.352*|
||(0.180)|(0.175)|||(0.199)|(0.192)|
|Financial Openness|||-0.0899*|-0.135***|-0.0635|-0.148***|
||||(0.0488)|(0.0448)|(0.0486)|(0.0445)|
|Transparency Dummy (IMF) Subscription|4.656**|5.166**|7.351***|6.953***|5.378**|5.718**|
||(1.883)|(2.324)|(2.054)|(2.161)|(2.168)|(2.569)|
|Transparency (IMF) Subscription x ICRG|-1.561***|-1.276**|-2.113***|-1.986***|-1.643***|-1.212**|
||(0.465)|(0.509)|(0.499)|(0.527)|(0.514)|(0.560)|
|Transparency (IMF) Subscription x public debt|0.0127***||0.0153***||0.0122***||
||(0.00141)||(0.00155)||(0.00156)||
|Transparency (IMF) Subscription x external debt||0.0175***||0.0215***||0.0169***|
|||(0.00264)||(0.00251)||(0.00291)|
|Transparency (IMF) Subscription x Trade Openness|0.171|-0.221|||0.0722|-0.434***|
||(0.160)|(0.143)|||(0.183)|(0.159)|
|Transparency (IMF) Subscription x Financial Openness|||0.0972*|0.0537|0.112**|0.102*|
||||(0.0516)|(0.0537)|(0.0519)|(0.0528)|
|Constant|17.76***|17.08***|8.436***|7.105***|19.17***|16.03***|
||(1.928)|(2.226)|(1.651)|(1.716)|(2.217)|(2.550)|
|Observations|654|529|631|512|589|476|
|No. Countries|57|45|58|46|55|44|
|Standard errors in parentheses|||||||
|<br>*** p<0.01, ** p<0.05, * p<0.1|||||||

28
deviation rise in engineering density leads to a 16% increase in income. Though not instrumented, patenting capacity leads to another 9.7%. Hence higher level human capital plausibly accounts for large differences in state level income per capita. This does not imply the unimportance of lower level capital. A 1 SD increase in literacy accounts for 51%. A comparable increase in secondary schooling leads to only a 4.5% increase although, again, the high school movement began substantially later and the variance in 1880 is very small. As a final back-of-the-envelope exercise, taking the coefficient from the well saturated US regressions and applying it to the international data in Figure 1, a one standard deviation rise in engineers in 1900 leads to an difference of 36% in 2000 income. Clearly, we cannot take this result too literally, but again, it suggests that differing endowments of higher level scientific capital potentially contribute importantly to income differences.

# **5 International sub-national engineering data**

To see whether innovative capacity retains its predictive power beyond the US, we collect engineering data at the sub-national (state) level for Argentina, Chile, Colombia, Mexico, Venezuela and the US for which census data are available. To give a feel for the disparities, Figure 2 maps this data for the US and Mexico by decile of engineering density and strikingly confirms that the border divided worlds apart. In fact, the data likely understate the true difference since our calculated stocks in Figure 1 suggest substantial overstatement in the Mexican census data. Perhaps predictably, the advanced New England states and the heavily mining dependent and generally less populated Western states show the highest density while the emerging industrial centers of the Midwest are close behind. The South is concentrated in the lower ranks with South Carolina, Georgia, Arkansas and Alabama in the bottom deciles- the lowest density in the US. What is striking, however, is that the country that was the principal mining center of the Spanish empire is almost entirely concentrated in the first and second quintiles with Sonora and the two Baja Californias appearing in the third and fourth deciles. Taking out Mexico City and the border states, Mexico is almost 21
# **6. Inequality in Opportunity for Household Welfare**

Earnings are a key component of family incomes, and an important source of individual status, self-esteem and bargaining power but, as a measure of individual wellbeing, it would be seriously incomplete. Total household income (or consumption expenditure) per capita are better measures of welfare, because they account for incomes from other sources (such as capital incomes or transfers) and for resource pooling within the household. Unless access to public and publicly provided goods (such as public safety and free public education or health care, respectively) is taken into account, household income or consumption expenditures are also incomplete and partial measures of welfare. Still, they are better measures than earnings and, for many countries, they are the best available indicators of individual welfare available.<sup>31</sup> Figure 2 depicts the conditional distributions of consumption per capita for circumstance groups defined according to mother’s education (in Panel A) and ethnicity (in Panel B), analogously to Figure 1 for earnings. In Panel A, the consumption distances between groups defined by mother’s education are larger than the corresponding earnings gaps (shown in Figure 1) for all five countries, and largest for Guatemala and Panama. Panel B exhibits greater variation across countries, with large gaps between ethnic groups in Guatemala and Panama, much more limited (or insignificant) distances in Colombia, and an intermediate pattern in Ecuador and Peru.<sup>32</sup> Tables 7 and 8 present our relative measures of inequality of opportunity for household income and consumption expenditures per capita, respectively. These tables are analogous to Table 5 (for earnings), and report θ _dN_ , θ _rN_ , θ _rP_ and Θ _rJ_ for E(0, 1 and 2), along with bootstrapped standard errors taking into account sampling weights, stratification and clustering. Gender is excluded from the set of circumstance variables since these indicators are defined at the level of the household, and the gender of the

> 31 There are two other steps in the mapping from household income or consumption to individual welfare which we overlook here, by using income or consumption per capita. First, we make an extreme assumption about the (in)existence of economies of scale in consumption within the household. Second, we assume that household resources are shared equally, which they may well not be.

> 32 Recall that there is no data for consumption in Brazil’s PNAD survey.

20
comparable framework. Building the database on which the following analysis is based was a huge job, and is itself an important contribution of this research.<sup>11</sup>

# **3.3 Estimation Strategy**

The econometric analysis explores the following three propositions for spatial or horizontal equity in service delivery.

_Proposition 1:_ Woreda block grant allocations are pro-poor, implying that β>0 in the following specification: ∆Gi, = α+βPi +γR+ δt + εj (1) where ∆Gi, is the deviation of the per capita block grant allocation from the mean (in percent), Pi is woreda poverty level based on the Ethiopia poverty map, and R and t are region and year dummies, respectively. If β>0, then the higher the woreda poverty level, the higher its allocation, and the proposition holds.

_Proposition 2:_ There is a negative association between woreda headcount poverty rate (Pi) and service delivery in that woreda (Θi), i.e., η<0 in the following equation: Θi=ηPi (2) If η<0, then the poorest woredas are also those where service delivery is lowest.

_Proposition 3:_ Following propositions 1 and 2, a block grant system that allocates proportionally more resources to the poorest woredas will also improve spatial equity in service delivery outcomes. We estimate this using the same approach as Khan et al. (2014), which showed that per capita block grant allocations were directly linked to service delivery outcomes using the following equation: ln _Oi_ =  +  ln _Ei_ +ηln _Ki_ +  _i_ (3) where _Oi_ is various outcome variables for woreda _i_ : for education, net primary enrollment rate; and for health, contraceptive use, antenatal care utilization, and births by skilled birth attendant; E is yearly expenditure per capita in the relevant sector; and _K_ is capital expenditure per capita. We additionally control for year, percentage of rural population, and percentage of certain historically disadvantaged ethnic groups, the latter two indexed by woreda.

> 11 The complete database is available at <u>http://governancefrombelow.net</u>

15
# **1. INTRODUCTION**

Accurate measurement of business outcomes is critical for understanding the dynamics of firm growth, especially in emerging market economies where self-employment constitutes the majority of income generating activities.<sup>1</sup> Such reliable measurement is also important for assessing the impact of the myriad policies and programs aimed at improving entrepreneurial performance in these economies (World Bank 2013; de Mel, McKenzie, and Woodruff 2010). Further, multinationals looking to emerging markets for customer expansion must often rely on small firms as distribution channels, despite serious issues in measuring the suitability and growth prospects of these potential business partners.<sup>2</sup> Yet, the lack of record-keeping by small-scale entrepreneurs and dearth of administrative data in government agencies make the measurement of business outcomes extremely challenging in these contexts. Relevant data on small firms, such as sales and profits, are notoriously noisy – as evidenced by the high coefficient of variation<sup>3</sup> seen in cross-sectional performance data and the relatively low autocorrelation over time seen in panel data (Fafchamps et al. 2012). Consequently, impact evaluation studies involving small firms often do not use sales and profit outcomes due to poor data quality (Drexler, Fischer and Schoar 2014; Dupas and Robinson 2009); or do not bother to collect it (Valdivia 2012; Klinger and Schündeln 2011). Studies that do examine business outcomes are often unable to detect impacts due to high variance in sales and profit values and the resultant low statistical power (McKenzie and Woodruff 2017).

In addition, studies that attempt to calculate profits from detailed questions on sales and costs often show weak correlations between calculated profits (sale minus costs) and self-reported profits (Vijverberg 1992; Vijverberg and Mead 2000; Daniels 2001). This can be caused by unreported costs and sales, likely reflecting the fungibility of resources between the business and household (Samphantharak and Townsend 2012), or by mismatching of costs and sales due to the timing of

> 1 The World Development Indicators show self-employment rates average around 40% in emerging market economies, with some as high as 75%. In comparison, the self-employment rate in the United States is 7%. See: <u>http://data.worldbank.org/indicator/SL.EMP.SELF.ZS</u> 2 The definition of firm sizes has varied with “micro” referring to businesses with 0-5 or 0-10 employees, and “small” referring to businesses with 6-20 or 11-50 employees. Throughout this paper, we will use the term “small firms” in reference to both micro and small businesses (formal or informal) with 0-20 employees.

> 3 Coefficient of Variation (CV) refers to the standard deviation relative to the mean of a business outcome for a given sample of firms: CVsales = (standard deviation of sales) / (mean of sales).

2
However, as is well-understood in the literature, multidimensional poverty comparisons are sensitive to assumptions in relation to the choice of indicators, the weights assigned to the indicators, the dimensional (deprivation) and the overall poverty cut-offs, as well as the choice of the aggregate poverty measure. For the Philippines, Balisacan (2015) already looks into some aspects of this robustness, for instance, variations in the choice of indicators across the three data sources, and sensitivity to alternative poverty cut-offs. However, the issues of alternative weighting schemes and poverty measures have remained underexplored.

Using data from six rounds of the Annual Poverty Indicators Survey (APIS) conducted by the Philippines Statistics Authority (previously, the National Statistics Office) over the period 2004-13, this paper complements Balisacan (2015) in further investigating the robustness of trends in multidimensional poverty to methodological choices for the Philippines. In particular, the paper focuses on the choice of alternative weighting schemes and aggregate poverty measures, empirical investigation of which is relatively limited even for other countries. In addition, the paper also explores the implications of including an employment-based dimension among welfare indicators. The analysis thus helps reassess evidence on the larger question of the changes in poverty in the past decade by examining whether the absence of a trend in income poverty is also mirrored in the trends of multidimensional poverty over this period. Our main finding is that there is evidence of a significant decline in multidimensional poverty measures in the country during 2004-13 that appears robust across a range of alternatives considered. Dimensional contributions to aggregate multidimensional poverty however vary enormously across alternatives.

The paper is organized as follows. Section 2 spells out the data and the methodology. In particular, it discusses the choice of dimensions and indicators, alternative approaches to setting weights for different indicators as well as alternatives for aggregate poverty measures, in particular, the use of “distribution-sensitive” measures. Section 3 presents the results of implementing these with the APIS data for 2004-13. It presents summary trends in individual indicators over this period, three sets of weighting schemes (two of which are estimated from APIS data for the initial year), and finally the estimates of multidimensional poverty measures as well as dimensional decompositions for the range of alternatives considered. Section 4 offers some concluding observations.

4
Figure 5: Real Baseline Wage <!-- Start of picture text --> 1970 1980 1990 2000 2010<br>Year<br>11000<br>10000<br>9000<br>8000<br>Real Baseline Wage (1979 USD)<br>7000<br><!-- End of picture text --> _Notes:_ The figure plots _w_ ˆ _t_ = _β_<sup>ˆ</sup> 0 _,t_ based on estimates from regression model (1) with _Xit_ = 0 for our baseline worker: a 19-year-old, white, male, full-time, routine, manufacturing worker with a high school degree, who works 50-52 weeks per year. Real earnings are based on the U.S. GDP deflator with 1979=1.

in the early 1970s (Levy and Murnane, 1992).

Based on the identity _wtL_<sup>_e_</sup> _t_<sup>= �</sup> _i_<sup>_witLit_, aggregate effective employment is then given by</sup> which allows us to compute the effective levels of routine and non-routine employment as Panel A of Figure 4 illustrates the time paths for routine and non-routine employment, both in actual and effective units of labor.

Figures 3 and 4 clearly illustrate that the increase in non-routine labor’s share in income is due to a substantial increase in both the non-routine wage premium as well as non-routine employment relative to routine employment.

12
<!-- Start of picture text --> Female-headed household Typology<br>“De jure” FHH “De facto” FHH<br>Self-reported approach Demographic approach Socio-economic approach<br>Mei Type 3: Potential FHH | Type4: Mosteducated member<br>Type 1: Self-reported Type 2: Major share of (no working-age male) wanetip i the household is female,<br>an females in HH Fuwa (2000) , ~ where no working-age<br>DeGraff and Bilsborrow(1993) Milazzo and van de Walle (2017)... employed male ispresent<br>Quisumbing et al. (2001) a ? \<br>Core FAH<br>mart seus (or Married FHH employedNumber/ femalesShai scot FHH (femae' is engoged<br>reason for spouse's Liu et al. (2017) (earners) Deere et ol generating<br>absence) Brown and Van de Bibi and Chatti (2012) activities)<br>Buvinic and Gupta Walle (2021) M Goto eral Fuwa {2000)<br>wtasenat (2025) (ois) Rogan (2013)<br><!-- End of picture text -->
18 Qaim, M., A. Subramanian, G. Naik, and D. Zilberman (2006), ‘Adoption of Bt Cotton and Impact Variability: Insights from India’, _Review of Agricultural Economics_ 28(1): 48-58, Spring.

Qaim, M. and D. Zilberman (2003), ‘Yield Effects of Genetically Modified Crops in Developing Countries’, _Science_ 299: 900-02.

Science Council (2005), _Science for Agricultural Development: Changing Concerns, New Opportunities_ , Rome: Science Council of the CGIAR, December.

Sithole-Niang, I., J.I. Cohen and P. Zambrano (2004), ‘Putting GM Technologies to Work: Public Research Pipelines in Selected African Countries’, _African Journal of Biotechnology_ 3(11): 564-71, November.

Sumner, D.A. (2006), “Reducing Cotton Subsidies: The DDA Cotton Initiative”, in _Agricultural Trade Reform and the Doha Development Agenda_ . K. Anderson and W. Martin eds., Ch. 10, New York: Palgrave Macmillan.
A significant number of women in the sample are participants in the labor market (defined as currently working). However, measures of women’s labor force participation rates declined somewhat between 2000 and 2005. Most of this decline is registered in rural areas.

Labor force participation rates between 2000 and 2005 stayed roughly the same for men. There is little variability in labor force participation across marital status, although the divorced, separated and widows are more active in the labor market than married women. Nor was there much shift across education or age groups, except in the youngest cohort where education did rise. We control for geographic trends in education to be sure differential trends in education do not account for our results.

One change between the surveys was that in 2005 the question regarding women’s current employment referred to ‘the last seven days,’ whereas there was no time limit in 2000. The inclusion of the dummy for 2005 will capture any level effect this would have. It should also be noted that all respondents in 2005 were also asked about their occupations in the last 12 months, not just those who indicated they were currently working. Thus is it possible to infer who worked in the last year. There is little decline in women’s labor participation using this 12 month measure. It is also reassuring that in the 2000 data, only 6% of respondents change their response on their employment status based on whether they are ‘currently working’ versus ‘if they have worked in the past 12 months’. Still, we present a number of robustness checks to show that the change in wording is not likely to be driving our results. One potential concern is whether there could be differential effects of the different wording on our variable of interest, namely female participation by occupations across geographic areas. If some occupations are more open to seasonal or part-time work, they could potentially have a greater drop-off in response rates. However, such a shift would be true across geographic areas; it is hard to see 17
- 27 -

influencing migration decisions. Mexico-to-U.S. migrants tend to originate from households with other adult members in the village (ADULTS in the table) who can assume the household farm duties of those who migrate. In addition, households with kinship networks in place in the United States (USNET) are significantly more likely to send additional members to the United States. The particularly large and significant coefficient on USNET for the Mexico-toU.S. migration category reflects the important role that kinship contacts play in international migration where risks are highest, labor market information is most costly and scarce, and the penalty for failure (that is, lost time and capital) is most severe. Internal migration networks (MEXNET), in contrast, do not significantly affect internal migration. This reflects the relative ease with which individuals in this sample can migrate and re-migrate internally (that is, take corrective action in case of a failure).

# III. CONCLUSIONS

The findings from Mexico reported in this paper provide evidence that, if absolute income is controlled for, relatively deprived households are more likely to engage in international migration than are households more favorably situated in their village's income distribution. In contrast, the findings suggest an interesting `income neutrality" result, unique to relative deprivation theory, in the case of internal migration. The perceived risk of a reference group substitution through internal migration is likely to be high. In this case, rural-to-urban migration may cease to be an effective vehicle for achieving either relative or absolute income gains for village