rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 8 VSL transfers using alternate income elasticities</u>**

|**Country Name**|**2007 GDPPC**<br>|**GDPPC ratio**<br> **(country/USA)**|**(Y/YUS)^1.5**<br>|**VSL**<br> <br>**ε=1.5**|**VSL**<br>**ε=1**|
|---|---|---|---|---|---|
||(in PPP,US$|constant 2005)||(million|US$)|
|**High income**|34,187|0.791|0.703|3.798|4.271|
|High income: non - OECD|30,028|0.695|0.579|3.127|3.751|
|High income: OECD|34,683|0.802|0.719|3.881|4.333|
|**United States**|43,227|1.000|1.000|5.400|5.400|
|**Middle income**|5,677|0.131|0.048|0.257|0.709|
|Upper middle income|11,416|0.264|0.136|0.733|1.426|
|Lower middle income|4,318|0.100|0.032|0.170|0.539|
|**Low income**|1,437|0.033|0.006|0.033|0.180|
|Least developed countries(UN classification)|1,147|0.027|0.004|0.023|0.143|

Source: WDI, World Bank and authors’ calculations

# E. Valuing Mortality Risks to Children

The preceding discussion pertains to valuing mortality risks for adults. The life cycle model assumes a rational, adult individual who earns income and allocates consumption over the life cycle. The empirical VSL literature reviewed in sections C. and D. attempts to measure what adults will pay for changes in their own mortality risks. Valuing mortality risks to children is difficult because children are often viewed as lacking the knowledge and the judgment needed to trade money for risk of death. There is also the question of budget constraint: on whose income is a child’s WTP for mortality risk reductions based?

Two perspectives can be taken with respect to valuing children’s health (USEPA 2003). The most common is to use parents’ valuations—i.e., to ask parents what they would pay for changes in mortality risks to their children. This acknowledges that parents are responsible for their children’s welfare and assumes that WTP is constrained by the household’s budget. An alternate approach is to ask what a rational child would be willing to pay to reduce his risk of dying—often referred to as the “adult as child” approach. This approach works well when individuals are risk neutral (when β = 1 in equation (6)), implying that the VSL is equal to discounted expected earnings. In this case, the VSL can be calculated for a child, given assumptions about survival probabilities and earnings opportunities over the child’s lifetime.

In either case, the important question for policy is whether the VSL for children should differ from the VSL used for adults. The official policy of the USEPA is to use the same value for children and adults, on the grounds that there is insufficient literature to warrant setting a separate VSL for children. We briefly review this literature below.

_Parents’ WTP for Children’s Mortality Risk Reductions_ There is a small but growing literature on parents’ WTP to reduce health risks to their children, including mortality risks. In the US and Europe, revealed preference studies 21
Table 4: Pension Expenditure and its Components

||1988|1989|1990|1 1991|1 1992 1|1993|1 1994|
|---|---|---|---|---|---|---|---|
|**Actual ZUS Expenditure**|||perc|entage of|GDP|||
|**Socialinsurance fund ZUSa**|8.4{|9.7|8.5|14.0|16.3|15.8|16.1|
|Corecction coefficients|||ra|tio to 19|89|||
|ZUS beneficiariesb|0.98|1.00|1.02|1.12|1.19|1.23|1.25|
|Real net earningsc|0.99|1.00|0.85|0.97|0.91|0.90|0.93|
|Personal income taxd|1.00|1.00|1.00|1.00|1.17|1.19|1.20|
|Counterfactual ZUS expenditure|||perc|entage o|f GDP|||
|1<br>Net pension spending (a/d)<br>2<br>Net pension spending implicitly|-|**9.7**|**8.5**|**14.0**|**13.9**|**13.3**|**13.4**|
|coming to the 1989 pensioners<br>[(a/(b*d)]|_|**9**<br>**9.7**|**8.3**|**12.5**|**11.6**|**10.8**|**10.7**|
|3<br>Giving the 1989 pensioners their<br>real net pension of 1989 withy<br>GDP||9<br>9.7|9.8|12.9|12.7|12.0|11.5|
|[a/(b*d*c)]||||||||
|4<br>Giving all pensioners their real net<br>pension of 1989 with y GDP<br>[a/(c*d)]|_|9.7<br>9|10.0|14.4|15.3|14.8|14.4|
|5-Giving<br>per pensioner the same net<br>share of each 1989 GDP as in 1989<br>[ZUS 1989*b]|_|9.7|9.9|10.9|11.5|11.9|I<br>12.1|
|**RELATIVE INCOME GAIN of all**<br>**the pensioners compared with 1989 (1-5)**|**-**|**0.0**|**-1.4**|**3.1**|**2.4**|**1.4**|**1.3**|

Note:1989 is the peak year for real pensions and real wages.

- not available

- a: Rocznik, various issues, and appendix tables.

- b: Rocznik 1992, p.203, 1994, p.226, 1995, p.<sup>1 5 6</sup> .

- c: Rocznik 1994, p.228, tab. 5, 1995, p.158, use a group-specific price index, own cumulation.

d: Rocznik 1994, p.226, 1995, p.156, gross to net average monthly pension.

net pension spending in a given year on the number of pensioners in 1989. This figure can be compared with net pension spending in 1989. The comparison shows how pensions were affected by he general income decline. In 1990 pension spending fell to 8.3 percent of GDP (close to the 1988 16
> where _exit_  1 if a firm’s export share >= _ζ_ and 0 otherwise.

# **3.2 Empirical Specification**

We focus on the following equation: _exi_   0  _firmi_  _regr_  _aggr_  _Dc_  _Ds_   _i_ (4)

> where  0 designates the constant, _Dc_ country fixed effects, _Ds_ sector fixed effects, and<sup></sup> _i_<sup>the</sup> idiosyncratic error term.

Following the literature on the firm-level determinants of exporting described above, we include firm size, firm age, foreign ownership, as well as measures of workers’ skills and productivity. This leads to the following version of equation (4):

> where _D_

> _r_ denotes region fixed effects.

The firm-level indicators from the Enterprise Surveys Indicator Database combined with data from the Enterprise Analysis Unit<sup>9</sup> are defined as follows:

- _emp_ = total number of permanent and temporary employees in logarithms,

- _age_ = years of operation in logarithms,

- _fdi_ = 1 if foreign private ownership >= 10% and 0 otherwise,

- _comp_ = average real compensation per worker (including wages, salaries and bonuses) in logarithms,

- _labprod_ = labor productivity defined as real value added per worker in logarithms, and

- _tech_ = _iso_ + _tech_for_ + _website_ + _email_ ∈ {0, 1, 2, 3, 4 }, where _iso_ = 1 if firm owns internationally-recognized quality certification and 0 otherwise, _tech_for_ = 1 if firm uses technology licensed from foreign firms and 0 otherwise, _website_ = 1 if firm uses

> 9 We obtained compensation and value added data from Frederica Saliola and Murat Seker from the Enterprise Analysis Unit of the World Bank. Value added has been calculated by subtracting materials and energy costs from total value of sales. All local currencies have been converted into US dollars and deflated using a GDP deflator in USD (base year 2000). Exchange rates and GDP deflators have been obtained from the World Development Indicators.

18
Table 5.1: Households’ Income Shares in Dispersed Rural Areas Argentina, 2003 <u>(percent)</u>

||Poor|Nonpoor|
|---|---|---|
|Independent farming|28.7|50.4|
|Agricultural labor|24.9|17.8|
|**Total agricultural Income**|**53.6**|**68.2**|
|Non–agricultural labor|19.8|12.8|
|Other Sources (transfers and remittances)|26.6|19.0|
|**Total nonagricultural Income**|**46.4**|**31.8**|
|Source: Own calculation based on RHS 2003.|||

Rural dwellers have a different income source pattern dependent on the region where they live. Figure 5.1 shows that poor and nonpoor households in Mendoza receive the largest share of their income from agricultural labor. For the poor in Mendoza the share of income from farm labor is 139, 249, and 434 percent higher than in Santa Fe, Santiago del Estero, and Chaco respectively. Moreover, rural-dwellers in the poorer provinces receive a much larger share of their income from remittances and transfers than their peers in richer provinces do. Figure 5.1 also shows that independent farming is more important for the nonpoor than for the poor as the share of total income is always higher.

<!-- Start of picture text --> Figure 5.1: Sources of Income in Dispersed Rural Areas in Argentina, 2003 (percent)<br>Independent farming Agricultural labor Nonagricultural labor Other<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>e<br>10<br>0<br>Mza. Mza. Sgo. Sgo. Chaco Chaco S. Fe S. Fe<br>Poor Non- Poor Non- Poor Non- Poor Non-<br>poor poor poor poor<br>Source : Own calculation based on RHS 2003.<br><!-- End of picture text --> Wages are related to labor status. Formal sector workers, defined as workers that contribute to the pension system, earned more than their peers in the informal sector in 2003. Moreover, permanent workers earn more than temporary workers in dispersed rural areas (Table 5.2). Moreover, employers earn significantly more than self-employed and wage earners in both the formal and informal sector. In the formal sector, employers earn roughly double that of self-employed and four times more than wageworkers. Not only do protected formal sector workers receive benefits in form of pensions, but also average 18
per capita of the bottom 40 percent of the population (Indicator 10.1.1) will most likely—if not always—be measured using data from a household consumption or income survey. However, the GDP growth rate (Indicator 8.1.1) is measured using the national account. Deaton (2005) observes that the estimated growth rate of consumption based on the national account tends to be larger than that based on the household survey, both across countries and over time for major countries.<sup>17</sup> We plot in Figure 4 the growth rates of mean consumption levels from the household survey and GDP per capita over past decades for two major middle-income countries, India and China. Indeed, our estimates using more recent data are consistent with Deaton’s findings: the growth rate of the survey mean (blue bars) is almost always less than the growth rate of GDP per capita (red bars). For both countries, over the whole study period, the average growth rate of the survey mean (dashed blue line) is also less than that of GDP per capita (dotted red line).<sup>18</sup> This result also holds on average for more than 150 countries for 1981-2014 in our sample that combines the PovCalNet database and the WDI database.

It can be argued that Deaton’s finding, supplemented by our more recent estimates, applies to the whole distribution of consumption, rather than just a chunk of it, such as the bottom 40 percent. Still, this may be a cause for concern if the discussed inconsistencies extend to all parts of the consumption distribution. Consequently, at least for the purpose of comparison with estimates on shared prosperity (Indicator 10.1.1), estimates of the growth rate of consumption using the

> 17 Deaton (2005) argues that when rich households are less likely to cooperate with the survey than poor people, survey-based estimates of consumption will understate mean consumption. On the other hand, various differences with national accounts estimates can overstate the rate of growth of average consumption, both over time in poor countries, and in comparisons between poor and rich countries at a moment in time. This finding also concurs with the results from an earlier study by Ravallion (2003).

> 18 We provide a similar figure for some other countries, including Bangladesh, Indonesia, Nigeria, and Turkey (Appendix 2, Figure 2.1). All these countries, except for Indonesia, saw their GDP per capita grow faster than the survey mean in the period under consideration. Moreover, for Nigeria, the growth rate of the survey mean is even negative while that of GDP per capita is positive.

18
# **Revisiting the Impact of the Brazilian SIMPLES Program on Firms’ Formalization Rates**<sup>**1**</sup>

_Caio Piza Development Impact Evaluation Unit (DECIE) The World Bank Development Research Group_ Contact email: <u>caiopiza@worldbank.org</u>

**Keywords** : Business Environment, Business Taxation, SME Development, SIMPLES Program, and regression discontinuity design.

**JEL** : C26, K34, L20.

1 I would like to thank Joana Monteiro and Gabriel Montes-Rojas for sharing their do-files. I would like to thank Guadalupe Bedoya, Astrid Zwager, Miriam Bruhn, David McKenzie, Aart Kraay, Bill Maloney, Gabriel MontesRojas, Florence Kondylis, Aidan Coville, Arndt Reichert, Arianna Legovini, Tulio Cravo, Prashant Bharadwaj, Owen Ozier, Eeshani Kandpal, Clement Joubert, Pedro Olinto, and the participants of the World Bank DECRG’s half-baked seminar for comments and suggestions. All conclusions are mine.
- [5] Auerbach, A. and Y. Gorodnichenko (2010), ‘Fiscal Multipliers in Recessions and Expansions’, NBER Working Paper No. 17447.

- [6] Auerbach, A. and Y. Gorodnichenko (2011), ‘Measuring the Output Responeses to Fiscal Policy’, NBER Working Paper No. 16311.

- [7] Azrai, E. M. and A. G. Zeufack (2011), ‘Malaysia: Postcrisis Growth Prospects Depend on Restoring Fiscal Discipline and Private Sector Con...dence’, ed. M. K. Nabli in The Great Recession and Developing Countries: Economic Impact and Growth Prospects, The World Bank.

- [8] Bachmann, R. and E. Sims (2011), ‘Con...dence and the Transmission of Government Spending Shocks’, NBER Working Paper No. 17063.

- [9] Barro, R. and C. J. Redlick (2011) ‘Macroeconomic e¤ects from government purchases and taxes’, forthcoming Quarterly Journal of Economics.

- [10] Baxter, M. and R. King (1993), ‘Fiscal Policy in General Equilibrium’, American Economic Review, 83(3): 315 - 334.

- [11] Blanchard, O. J. and R. Perotti (2002), ‘An empirical characterisation of the dynamic e¤ects of changes in government spending and taxes on output’, Quarterly Journal of Economics, 117(4): 1439 - 1469.

- [12] Bouakez, H. and R. Norman (2003), ‘Why does private consumption rise after a government spending shock’, Bank of Canada Working Paper No 2003-48.

- [13] Christiano, L., M. Eichenbaum and S. Rebelo (2009), ‘When is the government spending multiplier large?’, NBER Working Paper No. 15394.

- [14] Cimadomo, J., S. Hauptmeier and M. Kirchner (2010), ‘Transmission of government spending shocks in the euro area: Time variation and driving forces’, European Central Bank Working Paper Series No. 1219.

- [15] Cochrane, J. (1994), ‘Shocks’, Carnegie-Rochester Conference Series on Public Policy, 41: 295 - 364.

- [16] Corsetti, G., A. Meier and G. J. Müller (2010), ‘What determines government spending multipliers’, mimeo

- [17] Eggertsson, G. and P. Krugman (2010), ‘Debt, Deleveraging, and the Liquidity Trap’, mimeo

- [18] Erceg, C. J. and J. Lindé (2010), ‘Is there a Fiscal Free Lunch in a Liquidity Trap’, CEPR Discussion Paper No. 7624.

- [19] Federal Reserve Bank of Dallas (2009), ‘International Economic Update’

- [20] Galí, J., J. D. Lopéz and J. Vallés (2007), ‘Understanding the E¤ects of Government Spending on Consumption’, Journal of the European Economic Association, 5: 227 - 270.

22
**Table 33 Household Expenditure Per Capita, 1993 and 1995** _(in 1985 CFAF per year)_

||_Consumption_<br>_of home_<br>_products_|_1993_<br>_Purchased_<br>_food_|_Total_<br>_expenditure_|_Consumption_<br>_of home_<br>_products_|_1995_<br>_Purchased_<br>_food_|_Total_<br>_expenditure_|
|---|---|---|---|---|---|---|
|East Forest|||||||
|Export crop farmers|32397|21420|96768|31066|27186|99169|
|Food crop farmers|32837|27998|98157|21905|30278|83564|
|West Forest|||||||
|Export crop farmers|48114|18864|110082|26419|21959|94840|
|Food crop farmers|40418|16233|92117|33089|22790|90038|
|Savannah|||||||
|Export crop farmers|39654|12096|82372|33986|19605|89929|
|Foodcropfarmers|38645|13305|81088|31354|23793|87115|

**Table 34 Regional Estimates for Food Expenditure Budget** _(t-values in parentheses)_

|_Independent_<br>_Variables_|_Abidjan_|_Other Cities_|_East Forest_|_West Forest_|_Savannah_|_West Forest_|
|---|---|---|---|---|---|---|
|LOGHI-HEXP|-0.112|-0.110|0.020|0.0314|0.019|0.031|
||(-36.368)|(34.899)|(-4.037)|(5.686)|(3.623)|(3.553)|
|LOGHHSZ|0.061|0.051|0.037|0.097|0.083|0.098|
||(5.030)|(4.925)|(2.456)|(4.843)|(4.894)|(4.900)|
|LOGHHSZ2|-0.007|-.006|0.016|-0.037|-0.034|-0.037|
||(-1.990)|(-2.158)|(-4.044)|(-6.527)|(-7.608)|(-6.554)|
|YR85|0.126|0.058|0.035|-0.009|0.013|-0.004|
||(20.161)|(-.326)|(4.131)|(-0.789)|(1.328)|(-0.307)|
|YR86|0.073|0.031|0.027|-0.039|-0.015|-0.034|
||(11.804)|(5.277)|(3.153)|(-3.174)|(-1.710)|(-2.683)|
|YR87|0.047|0.045|0.047|-0.020|-0.073|-0.015|
||(7.316)|(7.651)|(5.548)|(-1.642)|(7.330)|(-1.117)|
|YR88|0.030|0.042|0.051|0.013|0.016|-0.008|
||(4.625)|(7.098)|(6.132)|(-1.237)|(1.808)|(-0.674)|
|YR95|-0.022|0.009|0.007|-0.050|-0.012|0.011|
||(-2.995)|(1.046)|(0.648)|(-3.809)|(-0.998)|(0.542)|
|1993 Export farrmer||||||-0.0069<br>(0.873)|
|1995 Export farmer||||||-0.0918<br>(-3.604)|
|Adj. R-Square|-0.3255|0.2171|0.0499|0.0387|0.0587|0.0403|
|Source: Estimated fro|m CILSS 1985-|1988, Priority Surv|ey 1993 and 1995.||||

37
Table 4: Impact of migration on children’s education

||Current|ly attends|school|Complet|ed years o|f schooling|
|---|---|---|---|---|---|---|
||All|Rural|Urban|All|Rural|Urban|
||(1)|(2)|(3)|(4)|(5)|(6)|
||**Chil**|**dren age**|**d (5-14)**||||
|has Gulf-Malaysia migrant|0.422**|0.377**|0.539*|1.072|0.762|2.032|
||(0.168)|(0.190)|(0.322)|(0.811)|(0.907)|(2.042)|
|First-stage F-stat|14.72|10.69|3.90|14.72|10.69|3.90|
|Observations|7194|5322|1872|7194|5322|1872|
||**Male c**|**hildren a**|**ged (5-14)**||||
|has Gulf-Malaysia migrant|0.128|0.060|1.482|0.135|0.116|-1.020|
||(0.138)|(0.134)|(2.055)|(0.846)|(0.830)|(7.371)|
|First-stage F-stat|14.40|14.58|0.65|14.40|14.58|0.65|
|Observations|3558|2626|932|3558|2626|932|
||**Female **|**children **|**aged (5-14**|**)**|||
|has Gulf-Malaysia migrant|0.866***|1.131*|0.354*|2.614*|2.377|2.898*|
||(0.324)|(0.611)|(0.188)|(1.526)|(2.415)|(1.683)|
|First-stage F-stat|7.53|3.47|5.59|7.53|3.47|5.59|
|Observations|3636|2696|940|3636|2696|940|

Source: Author’s estimates using the NLSS-III data. Note: This table shows the impact of household migration on educational outcomes of children aged 5-14. Reported coefficients are the estimates of _β_ from Equation (1). The column headings indicate the outcome variables and sample restriction. Dependent variable is an indicator of whether the household has a migrant in Gulf-Malaysia, and is instrumented with the village level migration rates to Gulf-Malaysia in 2001. Standard errors, clustered at the ward level, are reported in parentheses.<sup>_∗∗∗_</sup> : _p <_ 0 _._ 01;<sup>_∗∗_</sup> : _p <_ 0 _._ 05;<sup>_∗_</sup> : _p <_ 0 _._ 1 24
mator in order to account for the presence of heteroskedasticity in the trade data and to take advantage of the information contained in the zero trade ows, cf. Santos Silva and Tenreyro (2006, 2011). We use three-year interval data, cf. Cheng and Wall (2005) and Egger et al. (2022).<sup>13</sup> Finally, we cluster the standard errors by country pair.

Next, guided by equation (15), we specify our estimating gravity equation for FDI as follows: Here, _FDIij,t_<sup>_value_</sup> is the value of FDI stock from origin _i_ to destination _j_ at time _t_ . Capitalizing on the developments in the bilateral trade and FDI literatures, and for consistency with our estimating equation for trade ows, we specify our FDI econometric model to be as close as possible to our estimating equation for trade ows given in equation (17). Specically, we use the same estimator (i.e., PPML), we include the same set of xed eects (i.e., origintime xed eects ( _ψi,t_ ), destination-time xed eects ( _φj,t_ ), and directional pair xed eects ( _μij_ )), and we employ the same set of time-varying policy covariates (i.e., indicators for WTO membership ( _WTOij,t_ ), for bilateral investment treaties ( _BITij,t_ ), and for sanctions ( _SANCTij,t_ )). Finally, just as in our trade specication, we rely on three-year interval data and we use the same clustering (i.e., by country pair).

Even though, from an econometric perspective, we will use exactly the same set of exporter-time and importer-time xed eects as in our trade equation, the country-time xed eects in the FDI model would proxy and account for dierent variables. Following the existing empirical FDI literature,<sup>14</sup> possible robust determinants of FDI in the country of

> 13Cheng and Wall (2005) note that `[f]ixed-eects estimation is sometimes criticized when applied to data pooled over consecutive years on the grounds that dependent and independent variables cannot fully adjust in a single year's time.' (footnote 8, p. 52). Treer (2004) also criticizes trade estimations pooled over consecutive years. He uses three-year intervals. Baier and Bergstrand (2007) use 5-year intervals. Olivero and Yotov (2012) provide empirical evidence that gravity estimates obtained with 3-year and 5-year lags are very similar. Most recently, Egger et al. (2022) show that gravity models with three-way xed eects deliver similar estimates of the common estimates of FTAs.

> 14The two leading empirical FDI studies are Eicher et al. (2012) and Blonigen and Piger (2014). The objective of both studies is to identify a set of robust FDI determinants. Both papers utilize Bayesian Model Averaging and each of them comes up with a set of covariates which vary across the four dimensions that

15
The Global Consumption and Income Project (GCIP) database<sup>3</sup> also uses per capita measures of wellbeing, and Lahoti et al. (2016, p.7) similarly argue that “...per capita surveys are easier to understand” and assert that “...limiting our focus to per capita surveys greatly aids comparability...” Both papers make the case for per capita allocation based on arguments of simplicity, transparency, and enhanced comparability.

The assertions of simplicity and transparency are difficult to argue against. In contrast though, the claim of improved comparability is problematic. Under the very restrictive assumption that all goods consumed by people in the household are private (i.e., none is shared), and strictly in the money metric, per capita allocation may indeed be comparable across countries and people. But once we acknowledge that there may be economies of scale in household consumption, emanating for several reasons including the existence of within-household public consumption goods (such as shelter and other shareable goods like light, radios, televisions, and many durable goods), a per capita allocation of household consumption or income will neither be comparable across households nor countries. Similarly, if we consider measuring wellbeing in the metric of meeting needs (i.e., proportion of basic needs met with the money allocated), and acknowledge that men, women, and children have differing biological needs, the per capita metric will not be comparable across households or countries if household demographic composition differs. These issues are noted by Lahoti et al. (2016, p.7) in discussing the per capita allocation in the context of the GCIP, “...differences in the real value of resources arising from variations in household size and composition are not taken [into account].” Both of these issues – economies of scale in household consumption and adjusting for differences in needs based upon age – will matter for cross-country comparisons if there is

> 3 http://gcip.info/

4