rafmacalaba/datause-encoder-data huggingface dataset:
(1 999b) "Economic Risks, the Labor Market and Older Workers in Latin America", University of Hertfordshire Business School Working Paper, No. 21 BECKER, Gary and N. Tomes, (1976), "Child Endowments and the Quality and Quantity of Children" JPE 84, pp. 143 - 162 BECKER, Gary S. & Casey B. Mulligan, (1997), "The Endogenous Determination of Time Preference". The Quarterly Journal of Economics, August 1997, pp. 729 - 758 BECKER, Gary S. & Kevin M. Murphy, (1988), "A Theory of Rational Addiction". Journal of Political Economy, Vol.96 No.4, 1988, pp. 675 - 700 BODIE, Zvi, (1990), "Pensions as Retirement Income Insurance", Journal of Economic Literature 28: pp. 28 - 49 CASE, Karl, John Quigley, and Robert Shiller, (2001), "Comparing Wealth Effects: The Stockmarket Versus the Housing Market", NBER, October CHALOUPKA, Frank, (1991), "Rational Addictive Behaviour and Cigarette Smoking". The Journal of Political Economy, Volume 99, Issue 4, August 1991, 722-742 CHAMORRO, Claudio, (1992), "La Cobertura del Sistema de Pensiones Chileno", Pontifica Universidad Catolica de Chile, Instituto de Economia, Santiago COATE, Stephen, (1995), "Altruism, the Samaritan's Dilemma, and Government Transfer Policy," The American Economic Review, Vol. 85, pp. 46 - 57 CORSETTI, G., & Schmidt-Hebbel, K. (1994), "An Endogenous Growth Model of Social Security and the Size of the Informal Sector", World Bank, Policy Research Department, Macroeconomic and Growth Division, Washington, D.C.

CORSETTI, G., (1994): "An Endogenous Growth Model of Social Security and the Size of the Informal Sector", Revista de Analisis Economico, Vol. 9, No 1, pp. 57 - 76, June CORTAZAR, R. (1997). "Chile: The Evolution and Reform of the Labor Market" In S. Edwards and N. Lustig, _Labor Markets in Latin America: Combining Social Protection with Market Flexibility._ Washington D.C.: Brookings Institute Press.

COX, Donald, Zekeriy Eser and Emmanuel Jimenez, (1998), "Motives for Private Transfers over the Life Cycle: Ana Analytical Framework and Evidence from Peru" Journal of Development Economics, Vol. 55, pp. 57-80 COX-EDWARDS, Alejandra, (2000) "Pension Projections for Chilean Men and Women: Estimates from Social Security Contributions", California State University, mimeo DEATON, Angus (1991), "Saving and Liquidity Constraints", Econometrica, September 1991, Vol. 59 (5), pp. 1221-1248 (1997), The Analysis of Household Surveys: A Microeconomic Approach to Development Policy. The World Bank, Johns Hopkins University Press, Baltimore DEATON, Angus, (1990), "Savings in Developing Countries", The World Bank Economic Review, 4 (Proceedings Volume) DIAMOND, Peter A. & James A. Mirrlees, (1978), "A Model of Social Insurance with Variable 34
**Figure 11: Number of Small Businesses in Ukraine (1,000’s)**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> Source: National Statistics, Ukraine (2005) Taxation levels have also been found to be related to new firm formation in developed countries. For instance, a study of manufacturing firm registrations in Spain’s 50 provinces between 1981 and 1995 found a correlation of -15.3% (marginally significant at the 10% level) between the number of new registrations per capita and production taxes. The correlation with provincial per capita income, by contrast, was +47.3% (Sánchez Moral, 2005, pp114-127).

# **_6.5 Multivariate Analysis Using the Panel Data_**

We use as predictors of entrepreneurial activity the country characteristics defined in Table 1. The sample for the analysis is a pooled, cross-sectional, longitudinal unbalanced panel of 197 observations across 76 countries with non-missing explanatory variables for 2003, 2004, and 2005.<sup>21</sup> We use three measures of entrepreneurship as our dependent variables: Business density, Entry rates, and Entry per capita, which is defined as new firms as a percentage of the active population. While entry rates proxy new company formation compared to the existing stock of existing companies, entry per capita captures new company formation relative to the population, thus capturing the extent to which a country was entrepreneurial during a given year regardless of the previous (or cumulative) history of net company formation. Thus, it measures a different aspect of entrepreneurial activity.

> 21 We exclude from this analysis six countries that are not included in the Doing Business database.

23
**Table 11: Decomposition of labor productivity, comparison with other studies**

|**Authors**|**Period**|**Region**|**Annual**<br>**growth in**<br>**output per**<br>**worker (%)**|**Share of contr**<br>**productivity g**<br>**Within-sector**|**ibution to labor**<br>**rowth from (%):**<br>**Between-sector**|
|---|---|---|---|---|---|
|This study|1987-1997||4.8|92|8|
||1997-2007|Mli|2.4|99|1|
||2007-2018|aaysa|2.1|113|-13|
||_1987-2018_||_2.8_|_96_|_4_|
|||Africa|1.0|54|46|
|Mi 2019|19912013|Asia|4.8|77|23|
|artns ()|-|Latin America|0.7|46|54|
|||Other|1.3|80|20|
|||Africa|0.9|248|-148|
|McMillan et al (2014)|1990-2005|Asia|3.9|86|15|
|.||Latin America|1.4|166|-65|
|||High income|1.5|105|-6|
|||Africa|1.8|94|6|
|Timmer et al. (2014)|1990-2010|Asia|3.6|85|15|
|||Latin America|0.9|113|-13|

Source: Authors’ calculations based on data from DOSM, McMillan et al. (2014), Timmer et al. (2014) and Martins (2019) Note: This study and Timmer et al. (2014) use the average annual growth of output per worker, while McMillan et al. (2014) and Martins (2019) use the compound annual growth rate of output per worker. Annual growth in output per worker reported for this study excludes the mining sector, consistent with the findings of the decomposition that are reported above.

# **4.2. Sectoral Decomposition of Economic Growth**

Building on the aggregate decomposition of economic growth, this subsection implements a series of more detailed Shapley decompositions that decompose the growth in Malaysia’s output per capita from 1987 to 2018 into the contributions from each of the seven sectors of the one-digit national account classification. In addition, the subsection discusses how these contributions relate to within-sector productivity growth, betweensector labor reallocation, changes in sectoral employment as a share of the working age population, and demographic change. Finally, it takes a closer look at the effects of between-sector shifts on output per worker in each of the seven sectors.

Table 12 decomposes the growth in output per capita from 1987 to 2018 into the contributions from each of the seven sectors and how these relate to the different components of growth. For the sake of conciseness, the contribution of changes in the demographic structure to growth in output per capita is omitted from the table. Overall, the manufacturing and trade services sectors were the largest contributors to the growth of output per capita between 1987 and 2018. Both sectors contributed 23 percent to aggregate growth. The contribution of the manufacturing sector can almost entirely be attributed to improvements in within-sector productivity while for trade services changes in the employment share also played an important role.

20
# **3. Empirical Analysis of Remittances and Labor Supply in Jamaica**

# _A. Remittances_

For every three Jamaicans living in Jamaica, there is at least one Jamaican living abroad. In other words, at least 25% of Jamaican citizens currently live overseas, mostly in the United States, Canada and the United Kingdom. The rate of population growth of legal Jamaicans in the US is almost twice as high as the population growth in Jamaica (1.5% versus 0.8%, respectively). While the inflow of Jamaicans into the US, Canada and the UK has decelerated somewhat in the last ten years, it is still ranging around 0.7% of the country’s population.<sup>14</sup> Not surprisingly, Jamaica is one of the countries in the LAC region with the highest percentage of households receiving remittances. The proportion of remittances to total household income has also been increasing. In the early 1990s remittances represented about 2% of total household income. By the early 2000s, they accounted for more than 6%. On average, about a quarter of all Jamaicans households received some remittances between 1995 and 2002. It is interesting to note that while the percentage of households receiving remittances has decreased slightly since 1995, the ratio of remittances to total income has in fact increased.<sup>15</sup> Table 2 shows selected statistics of recipient households.<sup>16</sup> The average amount of monthly household remittance reaches up to US$250 in 2001 which is a very significant amount of additional income to households. Remittances recipients are less likely to be headed by males, although the difference of share of male household heads between remittance recipients and non-recipients has decreased gradually. This fact suggests that male heads of households tend to migrate more than female heads.

Notice that household heads that received remittances, male or female, tend to work fewer hours than non recipient heads. This effect seems larger for female heads of households. A simple tabulation in Table 2 shows that remittances may have some negative impact on the labor supply in terms of weekly working hours of employees.

> 14 IDB (2003), World Bank(2006c) 15 World Bank (2006b).

> 16

> Statistics for all years are available from the author.

7
being relevant outcomes like consumption is almost systematically missing.<sup>11</sup> As a result, expected longevity cannot be estimated by consumption categories.

Last but not least, the data contains very limited information on the preference u over non-monetary outcomes. The problem is that there are no well-defined prices on nonmarket dimensions. More generally, the non-monetary outcomes cannot reveal preference u when individuals cannot (or do not) optimally select these outcomes. As a result, it is ˆ harder to estimate a preference u over J<sup>non−market</sup> . It is therefore difficult for practitioners ˆ ˆ to select appropriate weights between variables xij and xij′. This contributes to the popularity of ad-hoc methods for the selection of these weights, like the “equal weights” procedure.

# **2.5 Multidimensional indices under data constraints**

The stringent data constraints described just above help categorize the types of multidimensional indices used in practice to aggregate well-being across dimensions. This is in particular the case for the latter two constraints, namely the missing joint distributions and the scarce information on preference u. We illustrate this in Table 1, in which the two rows differ in the availability of joint distributions and the two columns differ in the availability of data on preference u.

**Table 1:** Multidimensional indices classified by data constraints.

<!-- Start of picture text --> Data on preference u<br>None Available<br><!-- End of picture text -->

|||Data o<br>None<br>(Mash-up “weights” )|n preference u<br>Available<br>(Calibrated “weights”)|
|---|---|---|---|
|Data|None|HDI|Jones and Klenow (2016),|
|on|(Synthetic index)||Boarini et al. (2016),|
|joint|Available|MPM|Decancq et al. (2019)|
|distribution|(Objective index)|global MPI|Boarini et al. (2022)|

<!-- Start of picture text --> (Mash-up “weights” ) (Calibrated “weights”)<br>Data None HDI Jones and Klenow (2016),<br>on (Synthetic index) Boarini et al. (2016),<br>joint Available MPM Decancq et al. (2019)<br><!-- End of picture text --> <!-- Start of picture text --> distribution (Objective index) global MPI Boarini et al. (2022)<br><!-- End of picture text --> Consider first the rows in Table 1. Following Fleurbaey (2009), we call “synthetic” the indices that are constructed from partial distributions only. Synthetic indices aggregate within dimension first and across dimensions second. In other words, they first construct dimension-specific statistics over the population and then aggregate these statistics across dimensions. This is for instance the case of the Human Development Index (HDI) (Anand and Sen, 1994). The HDI captures three dimensions, namely incomes, longevity and education. The partial distributions for the former two dimensions are summarized as mean income and life expectancy. These statistics are then normalized and aggregated

> 11Death is a rare event that is hard to properly capture in surveys, which partly explains why this joint distribution is missing.

10
Table 4 Descriptive Statistics: Poverty and Socio‐economic Variables

||Panama<br>(2008‐2013)|Costa Rica<br>(2001‐2013)|El Salvador<br>(2004‐2012)|Nicaragua<br>(2005,2009)|Guatemala<br>(2000,2006,2011)|Honduras<br>(2001 ‐ 2013)|
|---|---|---|---|---|---|---|
|||Poverty b<br>|elow USD4 (H<br>|eadcount ratio<br>|)<br>||
|Average|23.0%|19.4%|40.1%|55.7%|56.8%|58.4%|
|Min|20.4%|12.2%|34.8%|52.2%|52.6%|50.0%|
|Max|26.2%|26.7%|46.8%|59.2%|62.4%|64.4%|
|||Poverty b<br>|elow USD2.5 (<br>|Headcount rati<br>|o)<br>||
|Average|12.2%|8.8%|20.8%|33.3%|35.9%|40.4%|
|Min|9.9%|4.6%|14.7%|29.3%|33.4%|31.3%|
|Max|14.5%|13.8%|28.6%|37.2%|40.5%|48.0%|
|||Avera<br>|ge Total per‐ca<br>|pita income<br>|||
|Average|397.64|399.52|218.97|164.32|182.42|174.06|
|Min|364.68|321.43|197.18|160.98|156.76|148.56|
|Max|438.43|506.37|234.52|167.66|198.03|201.56|
|||Avera|ge Labor per‐c|apita Income|||
|Average|275.17|308.50|166.71|128.15|143.79|134.97|
|Min|246.26|259.94|150.86|126.74|128.06|117.60|
|Max|307.59|360.53|184.83|129.55|153.15|154.46|

# 4.2. The Model

As mentioned in the previous section, our empirical strategy exploit the natural random variation in the formation, path and intensity of storms. Given the available data, our model is an unbalanced panel data. We implement a pooled OLS model with year ( _i_ ) and sub‐region ( _j_ ) fixed effects following equation 5, where the dependent variable is the natural logarithm of household total or labor income, or whether the household is considered poor or extremely poor (dummy variable). Our identification strategy exploits the variation over time within sub‐regions. Results are clustered at the regional level.

## ���� � ������ ������ ��� ��� ������        �5�

## Where:

- ���� is labor income, total income, poverty, extreme poverty for household _i_ in sub‐region _j_ and year _t;_

- ���� is the hurricane damage index for major events for sub‐region _j_ in year _t_ (SS ≥ 1);

- ���� is a matrix of control variables at the household level;

- �� are year fixed effects; �� are country fixed effects.

The model includes rich set of time‐varying observable household characteristics ( ���� ) such as the maximum level of education of the most educated employed member of the household, access to water 16
34 Table 8: Level of Tariff Protection In the Nordic Countries and the EU in Sensitive Sectors

|||(Dutie|s%)|||(NT|Bs*)||
|---|---|---|---|---|---|---|---|---|
||Finland|Norway|Sweden|EU|Finlind|Norway|Sweden|EU|
|Lnbnr-intensive|||||||||
|Tuxtlles(63)|19|2|10|3||0.8|1,0||
|Clothing(B4)|31|17|13|3|||7.0|9.0|
|Footwcar[85)|14|7|12|6||3.0|67.8|94.8|
|Furniturm(82)|6|5|4|1|-||-||
|Resource-intensive|||||||||
|FiHIOW3)|5|0.3|0.6|10|52.2|100.0|99,9|12.4|
|Wood(24+63)|2|1|1|1|-|53.9|63.2|10.5|
|Meals(28)|1|1|1|0|1.4||-|-|
|Cnpitral-Inteuslv|||||||||
|Chemicals(S)|2|6|4|4|0.5|29.0|25.8|0.0|
|Machinery(7)<br>|S<br>|_5_<br>|4<br>|3||8.0|23.5<br>||
|Imn& steel(67+68)|4|3|4|1|||5S7.2|30.9|
|For Memo:|||||||||
|Manufacturng<br>(5 to 9 less 67.68)|9|7|6||0.1|9.4|21.0|3.4|

Source: Smrt datbase. Dudes arm simple avenges for all imports, numbers in buckets are SITC, Rev I calegories of products. *-Nontariff barriers are percentage of imporls covered by all NTBs in 1988.

resource-intensive products as wefl. Clotiing and textiles figured prominently in exports of all three Baltic countries to the Nordic EFTA members (Table 10) - up to 20-30% of their total exports. Another important export was wood, especially from Latvia. Despite the short life of the agreements so far the data shows that the agreements have enabled the Baltics to make a good start in exporting to the West. This is the more remarkable in view of the deep recession in the Nordic countries during 1992. EFTA consumers are also gainers as the trade displacement is likely to be at the cost of more protected, i.e. high cost, existing EU or EFTA producers.
Since we use fixed e↵ect estimators, it is necessary for identification to have transitions into and out of market labor. Table 4 shows the transition matrix of the market labor variable. We can see that for both boys and girls we have a sufficient number of students transiting in and out of working status. For instance, 4,500 boys and 2,300 girls change their status from not working to working in one year.

It is worth noting here that we observe only those who are enrolled in the S ̃ao Paulo municipal school system and who remain in the school system. There is no forced repetition of grades in S ̃ao Paulo municipal schools for the 6<sup>_th_</sup> _,_ 7<sup>_th_</sup> and 8<sup>_th_</sup> grades, but there can be drop-out, movement to state or private schools or movement out of the area. As drop-out and delay are likely two other important e↵ects of child labor, it is important to note that we are not examining these ancillary e↵ects of child labor.

# **3 Child Labor and Students in the City of S ̃ao Paulo**

This section describes the incidence of child labor and school attendance in the City of S ̃ao Paulo.<sup>10</sup> The data used in this study come from the _2010 Demographic Census_ collected by the Brazilian Census Bureau (IBGE). It contains information on socio-demographic characteristics, fertility, migration, and time allocation for all individuals sampled. It is a sample of the entire population representative at the municipality level. Most important for the present study, it contains information about school attendance, labor force participation and occupation in the reference month of the survey (July). The information for labor market outcomes are available for individuals aged 10 years old and above.

> 10S ̃ao Paulo is the name of both the City and the State in which it resides and there are both state and municipal schools inside the City of S ̃ao Paulo

10
<!-- Start of picture text --> me) a O<br>35 Xo PO Oo. com<br>3 3 Bo” as 6 “CO<br>2% 25 eyBO,ty, oeOne4<br>S Oo, Gree of te Nig)<br>a Cao se RPK RO<br>g ae<br>ge _ Ga =<br>05 | caer<br>O|e<br>0 .05 A 15 2 25 3 35 4<br>Predicted Ex Ante Default Risk<br>© Original Series — 45°<br>& Residualized from Sector-Municipality Effects<br><!-- End of picture text -->

<!-- Start of picture text --> _ 4<br>3 35<br>&==<br>8<br>a.25 AZ<br>3 ? ZA<br>= 15 AA<br>[7]<br>er 1 LP<br>05 |<br>0<br>0 .05 A 15 2 25 3 35 4<br>Predicted Ex Ante Default Risk<br>|) 95% Cl: Original Series — 45%<br>(_) 95% Cl: Residualized from Sector-Municipality Effects<br><!-- End of picture text -->
Rather than setting the weight on nonfood goods to zero, it is better to set a weight more appropriate to spending by poor people. Deaton and Dupriez (DD) (2008) are attempting to do this in a consistent way by re-weighting the consumption bundle to accord with consumption patterns in a neighborhood of an implicit international poverty line that is essentially a weighted average of the national poverty lines (as used in the present paper) when converted to a common currency using their PPP. In forming the common poverty line, DD used all developing countries in our data set of national poverty lines for which they had access to suitable household surveys. This means that the DD PPP’s are only available for 46 of our 75 countries. In forming an average line, DD use the national poverty lines of all of these countries, rather than focusing on the poorest as we have done. DD chose to weight by the size of the poverty population in forming their average line, although the poorest countries are not, as a rule, the largest, so there can be no _a priori_ presumption that the DD PPP’s embody consumption weights that are appropriate for the poorest countries.<sup>36</sup> However, it remains of interest to see whether the pattern in Figure 2 is robust to switching to their PPP’s. DD give estimates for the Fisher, Tornqvist and Country-Product Dummy (CPD) methods of forming the price index.<sup>37</sup> Figure 8 gives the results using their Fisher method. Again the pattern is similar to Figure 2. This was also true for the Tornqvist and CPD methods. The DD PPP’s call for a slightly smaller reference group (n=13). The estimated poverty lines for this smaller reference group are $31.72 (t=8.80), $33.37 (t=9.59) and $41.56 (t=9.70) for Fisher, Tornqvist and CPD respectively. For the Fisher and Tornqvist methods, the slope coefficients (above these lower bounds) are slightly lower than for the aggregate PPP; the regression coefficients are 0.276 (t=8.13), 0.300 (t=7.72) respectively. However, for the CPD method the slope is higher at 0.374 (t=8.83). There is little affect on the estimated elasticities.<sup>38</sup> So the pattern in Figure 2 is robust. For measuring global poverty by the standards of the poorest countries, the PPPP’s using either the Fisher or Tornqvist indices suggest a lower international line—close to $1 a day—while the CPD method suggests a line of $1.37 a day.

> 36 Using the poverty rates corresponding to the national poverty lines, we find is no significant difference between the mean number of poor for countries in our reference group and the rest (t=0.50); nor is there any significant correlation between population size and PCE per capita (r=-0.10;t=-0.88).

> 37 For further discussion of these methods see Deaton (2006).

> 38 The estimated slopes are 0.272 (t=8.73), 0.297 (8.37) and 0.368 (t=9.37) while the least squares elasticities are 0.613 (t=8.00), 0.624 (t=8.27) and 0.623 (t=8.17) for the Fisher, Tornqvist and CPD methods respectively.

22
Piketty, Thomas. 2003. “The Impact of Divorce on School Performance: Evidence from France, 1968–2002.” CEPR Discussion Paper 4146. London: Centre for Economic Policy Research.

Ravallion, Martin, and Quentin Wodon. 2000. “Does Child Labour Displace Schooling? Evidence on Behavioural Responses to an Enrollment Subsidy.” _Economic Journal_ 110 (462): C158–75.

UNICEF. 2006. _Africa’s Orphaned and Vulnerable Generations: Children Affected by AIDS_ . New York, NY: UNICEF.

Van de Walle, Dominique. 2011. “Lasting Welfare Effects of Widowhood in a Poor Country.” World Bank Policy Research Working Paper 5734. Washington, DC: World Bank.

Wooldridge, Jeffrey. 2002. _Econometric Analysis of Cross Section and Panel Data_ . Cambridge, MA: MIT Press.

Yamano, Takashi, and Thomas S. Jayne. 2005. “Working-Age Adult Mortality and Primary School Attendance in Rural Kenya.” _Economic Development and Cultural Change_ 53 (3): 619–54.

31