rafmacalaba/datause-encoder-data huggingface dataset:
A further hypothesis relates to potential sample selection bias in the data underlying <u>Figure 1</u> and similar figures in the literature. We will be using panel data on SP spending across countries and over time. However, it is an unbalanced panel, in that not all countries have data in all years. The potential for a non-random pattern in the availability of data suggests another reason we see a rising SPEC. It is a plausible assumption that the latent selection process increases the observed mean share of income devoted to SP spending conditional on mean income; all that is required for such positive selection is that higher SP/GDP values are more <mark>likely to be reported. However, that is not sufficient to explain why we see a rising SPEC. For</mark> that step in the argument, we draw on the literature on estimating models with sample selection bias.<sup>19</sup> Following that literature, let g(p) denote an additive control function for the selective <mark>reporting, which is taken to be some suitably smooth function of the probability of being selected</mark> ′ <mark>into the sample, denoted p.</mark><sup>20</sup> <mark>Then, with positive selection (g (p) > 0), the key issue in the</mark> present context is the sign and size of the correlation between the control function for selection and GDP per capita.

In thinking about that correlation, it is instructive to consider a simple economic model of how selective reporting arises in this context. Consider the costs and benefits of providing public spending data. (Without loss of generality, these can be taken to be the costs and benefits as perceived by public decision makers charged with this task.) It is a plausible assumption that the marginal cost (MC) to a country’s government of collecting, processing and disbursing such data rises with the amount of data made available. The marginal benefit (MB), on the other hand, can be expected to fall if anything, as diminishing returns set in to collecting and processing extra <mark>data. It is also plausible that the MC of providing data tends to be higher for low-income</mark> countries than for high-income ones. This reflects the lower levels of state capacity generally in poorer countries, stemming from weaknesses in both governance and ICT. The government chooses a level of data availability equating MC and MB, with the implication that low-income counties tend to make less data available as the MC function shifts upwards.

Then we can expect the control function for selection, g(p), to be positively correlated with GDP per capita. However, if one ignores the non-random selection then the control function

> 19 See Vella’s (1998) survey.

> 20 More precisely, <mark>p(Z) = Prob(d = 1|Z) is the propensity score for selection, where d = 1 if the country, year combination is observed while d = 0 otherwise, and Z is a vector of covariates for selection.</mark>

12
# **1. Introduction**

The share of workers in alternative work arrangements has increased dramatically in developed countries. In the United States, all the net employment growth between 2005 and 2015 occurred in alternative work arrangements (Katz and Krueger [2016]). In Europe, the share of workers in full‐time, permanent salaried jobs has decreased for most countries since 2008 (World Bank [2016b]). At the same time, 17 percent of Europeans have used online platforms, with 23 percent of them also providing services through these new technologies (European Commission [2016]). Empirical evidence regarding the role of the Internet powering the growth of alternative work arrangements is scarce. Scholars have speculated that the rise of information and communications technologies (ICT) ‐ which allow for better monitoring, remote work, standardization of job tasks and making information on workers’ reputations more widely available – may have contributed to the observed increase in alternative work arrangements in the United States (see, for instance, Katz and Krueger [2016]). These aspects of ICT would facilitate the rise of self‐employment as well as more flexible forms of work — such as temporary and part‐time work— as firms can more easily break jobs down into smaller tasks.

Alternative work arrangements include several forms of employment, such as part‐time work, working from home, temporary work, on‐call jobs, and self‐employment or freelancing. Unlike traditional wage employment, alternative work arrangements are associated with greater schedule flexibility and less job security. Individuals in alternative work arrangements are also less likely to have access to health or unemployment insurance, as well as old‐age pensions, as these are typically covered by employers (Eurofound [2015]). Understanding the drivers of alternative work arrangements is important for several reasons. First, from the point of view of the worker, greater schedule flexibility and lower job security are likely to affect job satisfaction and compensating wage differentials (Mas and Pallais [2016]). Second, both greater schedule flexibility and working from home may boost the labor force participation of individuals 2
# **2 Distribution of Deaths by Age**

COVID-19 mortality data were gathered from official government sources. See the Appendix for a complete listing. Data were used for countries with at least 100 reported COVID-19 deaths and for which a breakdown of mortality by age was available.<sup>3</sup> In nearly all high-income countries, people age 70 or older constitute the large majority of deaths attributed to COVID-19. For the set of high-income countries considered, on average 87 percent of COVID-19 deaths are of those age 70 or older. In contrast, in the low- and middleincome countries, on average just 37 percent are age 70 or older (Figure 1).

High-income countries have on average younger populations than low- and middle-income countries. Individuals over age 70 constitutes on average 15 percent of the population in the highincome countries analyzed in this paper vs. just 4 percent of the population in the low- and middleincome countries. To what extent can the wide variation in the age distribution of deaths be explained by the fact that high-income countries have substantially older populations who are more at risk from the disease? To consider this question, age-specific mortality rates were calculated by dividing the number of deaths attributed to COVID-19 for each 10-year age group by the population in each age group. Population data by age were taken from United Nations (2019), using the estimated populations corresponding to July 1, 2020. Because most countries group all COVID-19 deaths for those ages 80 and above in their reporting, 80+ is shown as a single category.

Next, for each country, a standardized population was created. The standard was generated using the mean population shares for each age group across high-income countries. Finally, a hypothetical distribution of deaths for each country was calculated by multiplying the standard population shares by the corresponding age-specific COVID-19 death rates. The resulting distributions are shown in Figure 2. A comparison of the true and hypothetical distributions is summarized in Table 1.

> 3 The data used in this paper are the most currently available as of June 22, 2020.

3
# **References**

<u>Alesina, A., S. Hohmann, S. Michalopoulos, and E. Papaioannou (2021). “Intergenerational</u> Mobility in Africa.” _Econometrica_ , 89: 1 and 35.

- <u>Arellano, M., and O. Bover (1995). “Another look at the instrumental variable estimation of error-</u> components models.” _Journal of Econometrics_ , 68: 29–51.

- <u>Aydemir, A., and H. Yazici (2019). “Intergenerational education mobility and the level of</u> development.” _European Economic Review_ , 116: 160–85.

- <u>Azomahou, T. and E. Yitbarek (2016). “Intergenerational education mobility in Africa: Has</u> progress been inclusive?” _World Bank Policy Research Working Paper_ 7843.

- <u>Batana, Y., and J. Duclos (2010). “Testing for Mobility Dominance.”</u> _SSRN Electronic Journal_ . 10.2139/ssrn.1549806.

- <u>Bárcena-Martín, E, and O. Cantó (2024). “The three ’I’s of downward income mobility: A</u> directional subgroup decomposable measure.” _Review of Income and Wealth_ , 71 (1): e12686

- <u>Becker, G., Kominers, S., Murphy, K., and J. Spenkuch (2018). “A Theory of Intergenerational</u> Mobility.” _Journal of Political Economy_ , 126(S1): S7–S25.

- <u>Bell, A., Chetty, R., Jaravel, X., Petkova, N., and J. Reenen (2019). “Who Becomes an Inventor</u> in America? The Importance of Exposure to Innovation.” _Quarterly Journal of Economics_ , 134(2): 647–713.

- <u>Björklund, A., M. Lindahl, and E. Plug (2006). “The origins of intergenerational associations:</u> Lessons from Swedish adoption data.” _Quarterly Journal of Economics_ , 121, 999–1028.

- <u>Blundell, R., and S. Bond (1998). “Initial conditions and moment restrictions in dynamic panel</u> data models.” _Journal of econometrics_ , 87: 115–43.

- <u>Bolt, J., and J. van Zanden (2020). “Maddison style estimates of the evolution of the world</u> economy. A new 2020 update.” Maddison Project Working Paper WP-15. University of Groningen.

- <u>Blyth, C. (1972). “On Simpson’s Paradox and the Sure-Thing Principle.”</u> _Journal of the American Statistical Association_ 67: 364-366.

- <u>Causa, O., and A. Johansson (2010). “Intergenerational social mobility in OECD countries.”</u> _OECD Journal: Economic Studies_ , (1): 1–44.

- <u>Chevalier, A., K. Denny, and D. McMahon, (2009). “A multi-country study of inter-generational</u> educational mobility.” In P. Dolton, R. Asplundh, and E. Barth (Eds.), _Education and inequality across Europe_ . London: Edward Elgar.

- <u>Christoph, B., Spangenberg, H., and H. Quast. (2024). “Tertiary Education, Changing One’s</u> Educational Decision and the Role of Parental Preferences.” _Research in Higher Education_ , 65: 283–302.

- <u>Clark, G., with N. Cummins and Y. Hao and D. Diaz Vidal (2014).</u> _The Son Also Rises: Surnames and the History of Social Mobility_ . Princeton, NJ: Princeton University Press.

- <u>Corak, M. (2013). “Income Inequality, Equality of Opportunity, and Intergenerational Mobility.”</u> _Journal of Economic Perspectives_ , 27(3): 79–102.

21
Given the inadequacy of safety-net support from government-run programs during monga, vulnerable households adopt a variety of coping mechanisms. As Table 7 suggests, some 57 percent of safety-net program recipients adopt some type of coping measure, compared to 49 percent among non-recipients. Among members of government safety-net programs, about 55 percent adopt informal coping mechanisms (e.g., advance sale of labor or crop) and 6.5 percent adopt formal means (e.g., borrowing from formal sources). Seasonal migration is the major informal coping mechanism for the poor in the greater Rangpur region: 38.6 percent of safety-net program recipients and 34.8 percent of non-recipients adopt out-migration as a coping measure, suggesting that both groups remain equally vulnerable to monga.

# **{TABLE 7 HERE}**

We next examine the extent of the hardship that households in the greater Rangpur region are subjected to during the monga period. The InM survey lacks data on household food consumption; rather, it has an indicator variable reporting the status of meal consumption of the sampled households, as follows: (i) starvation (households go without meals for a day or more), (ii) meal rationing (households consume less than what they normally would in a day), and (iii) full meal consumption (households consume desired quantity of meals, which is usually three a day). This information was collected from all households for both monga and non-monga periods.

A high percentage of the rural ultra-poor in the greater Rangpur region, with the exception of Nilphamari District, experienced starvation during the 2006 monga season (Table 8). Overall, about 47 percent of poor households experienced starvation during monga, while only 4.4 percent had full meals during the same period. Predictably, the rate of starvation during the non-monga period declined; however, it did not fall to zero; even during the non-monga season, 8.5 percent of households experienced starvation. On the other hand, the share of households that consumed full 13
Table A.4: Descriptive relationship between self-employment and migration for different levels of administrative divisions, all survey waves

||**Level 3**<br>**migration**<br>(1)|**Level 2**<br>**migration**<br>(2)|**Level 1**<br>**migration**<br>(3)|**Level 3**<br>**migration**<br>(4)|**Level 2**<br>**migration**<br>(5)|**Level 1**<br>**migration**<br>(6)|
|---|---|---|---|---|---|---|
|Self-employed|-0.008***|-0.012***|-0.008***|-0.008***|-0.012***|-0.008***|
||(0.003)|(0.003)|(0.001)|(0.003)|(0.003)|(0.001)|
|Aged 25 to 34|-0.013***|-0.027***|-0.024***|-0.014***|-0.027***|-0.024***|
||(0.005)|(0.005)|(0.003)|(0.005)|(0.005)|(0.003)|
|Aged 35 to 44|-0.024***|-0.043***|-0.037***|-0.025***|-0.042***|-0.037***|
||(0.005)|(0.005)|(0.003)|(0.005)|(0.005)|(0.003)|
|Aged 45 to 54|-0.024***<br>(0.005)|-0.042***<br>(0.005)|-0.039***<br>(0.003)|-0.025***<br>(0.005)|-0.042***<br>(0.005)|-0.039***<br>(0.003)|
|Aged 55 to 65|-0.027***<br>(0.006)|-0.038***<br>(0.006)|-0.043***<br>(0.003)|-0.027***<br>(0.006)|-0.038***<br>(0.006)|-0.043***<br>(0.003)|
|Female|0.001***|0.003***|0.001***|0.001***|0.003***|0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Years of education|-0.004|0.003|-0.003**|-0.003|0.003|-0.003**|
||(0.003)|(0.002)|(0.001)|(0.003)|(0.002)|(0.001)|
|arcsinh(income)||||0.000|-0.000|-0.000|
|||||(0.000)|(0.000)|(0.000)|
|Wage worker mean|0.056|0.069|0.039|0.056|0.069|0.039|
|Sample size|32,549|44,089|78,594|32,301|43,841|78,346|

_Notes:_ The sample is the set of individuals aged 18-65 surveyed in the CFPS 2010 and 2016, ELMPS 1998, 2006 and 2012, IHDS 2005 and 2011/12, IFLS 2000, 2007/08 and 2014/15, MxFLS 2002, 2005/06 and 2009/12, LSMS-ISA 2010/11, 2012/13 and 2015/16, KHDS 1991/94, 2004 and 2010, and PSID 2001, 2007 and 2017. We drop respondents who are not self-employed or paid workers in the baseline wave of each survey. Level 1 administrative divisions include provinces for China and Indonesia, governorates for Egypt, states for India, Mexico, Nigeria and US, and regions for Tanzania. Level 2 administrative divisions are defined as counties for China, aqsam for Egypt, kabupaten for Indonesia, municipios for Mexico, local government areas for Nigeria, and the Kagera region for Tanzania. Level 3 administrative divisions detail sheyakhat’ for Egypt, kecamatan for Indonesia, localities for Mexico, and villages for Tanzania. Refer to online appendix section B for more details on variable construction in the different surveys. Each column reports marginal effects from a probit regression of an indicator for migration on an indicator for self-employment at baseline. Each regression controls for country-wave fixed effects. Columns (4)-(6) additionally control for the inverse hyperbolic sine (arcsinh) of the respondent’s income in the baseline wave. Standard errors in parenthesis are clustered at the respondent level.

43
chart draws attention to relative low variance in _Service EXPY_ versus _Goods EXPY_ . In the next section, we discuss our empirical methodology for testing the relation between growth and S _ervice EXPY_ .

**Figure 8. Service EXPY for selected countries**

<!-- Start of picture text --> 18000<br>Brazil China<br>16000 India Ireland<br>Singapore Sri Lanka<br>14000<br>United States Indonesia<br>12000 Paraguay<br>10000<br>8000<br>6000<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Source_ : Authors‘ calculations.

15
These arguments have clearly not exhausted the theoretical possibilities. For example, I have said nothing about the possibility of the poor forming a coalition with a subset of the non-poor so as to protect the poor from cuts.

However, the above discussion provides adequate warning against presuming that the poor will necessarily bear the brunt of cuts in situations in which the non-poor hold the balance of power over the budget. The need for specific actions to protect the poor must then be deemed an empirical question.

The rest of this paper addresses that question using data for Argentina. Evidence will be drawn from very different sources. In the next section, time series evidence on aggregate budget allocations across types of spending will be studied. After that, the paper turns to evidence for just one anti-poverty program. Although the data will be very different, in both the aim will be to examine how the composition of spending varies with total spending i.e., to estimate empirical models motivated by equation (4).

# **3. Social spending in Argentina during fiscal expansions and contractions**

While methods of measurement differ, it is widely agreed that in the mid-1990s less than 30% of the population is poor by Argentinean standards (World Bank, 1999). The level of "social spending" averaged 56% of total government spending in the period 1980-97 (Government of Argentina, 1999). Less than half of this went to "social services" (education, health, water and sewerage, housing and urban development, social assistance, and labor programs); the remainder can be labeled "social insurance" (pensions, public health insurance, unemployment insurance). Spending on social services is believed to be pro-poor, in that the poorest x% of households receive more than x% of spending, but this is not so for social insurance (Gasparini, 1999; Llach and Montoya, 1999). Table 1 reproduces recent estimates of the incidence of public spending on social insurance as distinct from the social services. The results confirm that social service spending is more pro poor than social 8
3 Lopez-Cordova (2005) in Mexico, Lokshin et al (2010) in Nepal and Adams, Cuecuecha and Page (2008) in Ghana all find that international remittances reduce poverty.

On the issue of how international remittances are spent or used by households, the literature is not as clear. There are at least three views on how remittances are spent and their effect on economic development. The first, and probably most widespread, view is that remittances are fungible and are spent at the margin like income from any other source. In other words, a dollar of remittance income is treated by the household just like a dollar of wage income, and remittance income is spent just like any other source of income. The second view argues that the receipt of remittances can cause behavioral changes at the household level and that remittances tend to get spent on consumption rather than investment goods. For example, a review of the literature by Chami, Fullenkamp and Jahjah (2003:10-11) reports that a “significant proportion, and often the majority” of remittances are spent on “status-oriented” consumption goods. A third, and more recent, view arising out of the permanent income hypothesis is that since remittances are a transitory type of income households tend to spend them more at the margin on investment goods -- human and physical capital investments – than on consumption goods, and that this can contribute positively to economic development (Adams, 1998). For instance, in a study of remittances and education in El Salvador, Edwards and Ureta (2003) find that international remittances (mainly from the USA) have a large positive impact on student retention rates in school. In a similar study in the Philippines, Yang (2008) reports that positive exchange rate shocks lead to a significant increase in remittance expenditures on education.

The purpose of this paper is to extend the debate concerning the impact of international remittances on poverty and how remittances are spent or used by households by analyzing the results of a large, panel household budget survey in Indonesia. Indonesia represents a good case
38

**Table 10: Product Structure of Baltic Exports** to **the Nordic Countries** (1992, % **of total)**

||Estonia|Latvia|Lithuania|
|---|---|---|---|
|**Fmland**||||
|Food|6|0|0|
|Textiles|20|15|31|
|Wood|14|61|24|
|Metals|36|2|1|
|Oil|1|_5_|-|
|Other|23|17|44|
|Value (mio. US$)|119.3|16.7|9.6|
|Sweden||||
|Food|3|1|4|
|Texdles|19|10|11|
|Wood|18|18|24|
|Metals|36|4|3|
|Oil|7|59|54|
|Other|17|8|4|
|Value (mio. US$)|97.3|90.5|53.9|
|Norway||||
|Food|1|0|0|
|Textiles|19|3|20|
|Wood|3|0|0|
|Metals|6|60|4|
|Oil|56|10|60|
|Other|15|27|16|
|Value (mio. US$)|8.1|10.5|2.9|

Source: UN Comtrade data from importer data. The categories in SITC terms are Food=0+ 1+22+4, Textiles=26+65+84, Wood=24+62, Metals=28+68+67, Oil=3.
**A2: Evolution of maize production in Burkina Faso since 1984 (tons)**

<!-- Start of picture text --> 450000<br>400000<br>Boucle Du Mouhoun<br>350000 Cascades<br>Centre<br>300000<br>Centre-est<br>250000 Centre-nord<br>Centre-ouest<br>200000<br>Centre-sud<br>Est<br>150000<br>Hauts-bassins<br>100000<br>Nord<br>Plateau Central<br>50000<br>Sahel<br>0<br>Sud-ouest<br>1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010<br><!-- End of picture text -->

# **_<u>Source: COUNTRYSTAT data downloaded on July 05, 2013</u>_**

27
Table 2: Italy: 2002–2006–2010 dominance conditions

|quantiles/types rank|GIC||type OGIC|c|um. type OGI|C<br>i|ndividual OGI|C<br>cu|m. individual OG|IC|
|---|---|---|---|---|---|---|---|---|---|---|
|1|10.5691|***|2.6484|***|2.6180|***|3.9839|***|3.9744|***|
|2|4.6810|***|11.5799|***|7.3428|***|2.6985|***|3.3317|***|
|3|4.1114|***|-1.5181||4.3373|***|2.6562|***|3.1058|***|
|4|4.4694|***|0.5413||3.3125|***|2.5996|***|2.9757|***|
|5|3.6610|***|5.9404|***|3.9061|***|3.4201|***|3.0512|***|
|6|3.3625|***|1.3937||3.3944|***|1.6977|***|2.7881|***|
|7|3.2277|***|7.8721|**|4.0511|***|2.8942|***|2.8017|***|
|8|2.8174|***|6.0244|***|4.3561|***|5.4506|***|3.1885|***|
|9|2.5479|***|1.6141|**|3.9883|***|1.8843|***|2.9947|***|
|10|2.4750|***|-1.1908||3.3700|***|2.1158|***|2.8801|***|
|11|2.3956|***|5.7042|***|3.6263|***|1.5239|***|2.7224|***|
|12|2.7012|***|1.4691||3.3977|***|1.3037|***|2.5751|***|
|13|2.8946|***|7.2706|**|3.8027|***|2.6333|***|2.5808|***|
|14|2.7802|***|5.3008|**|3.9270|***|2.6164|***|2.5835|***|
|15|2.4743|***|-7.5717|**|3.0613|***|1.8334|***|2.5185|***|
|16|2.9552|***|1.4023||2.9156|***|2.6758|***|2.5292|***|
|17|1.8412|***|1.9006||2.8161|***|3.4850|***|2.6090|***|
|18|0.3548||4.2672|**|2.9169|***|2.5781|***|2.6063|***|

_Source: Authors’ calculations on SHIW (Banca d’Italia)._

*=90%, **=95%, ***=99% are significance levels for the difference between curves obtained from 1,000 bootstrap replications of the statistics.

Table 3: Italy: 2002–2006–2008 Complete rankings and inequality

||2002|2006|2010|||
|---|---|---|---|---|---|
|_μ_(_y_) eq.|20116.82 (4735.42)|21692.12 (5275.08)|21117.34 (5445.91)|||
|mld (all)|0.1422 (0.0026)|0.1301 (0.0021)|0.1437 (0.0027)|||
|mld (between)|0.0256 (0.0006)|0.0274 (0.0001)|0.0313 (0.0007)|||
|||||’02-’06|’06-’10|
|_GY s_||||1.821 (0.0145)<br>|-0.9532 (0.0155)<br>|
|_OGY s_||||-0.0946 (0.0080)|-2.869 ( 0.0244)|
|_GY μ_||||-0.2340 (0.3707)|-1.2618 (0.0197)|

_Source: Authors’ calculations on SHIW (Banca d’Italia). mld_ = mean logarithmic deviation or generalized entropy index with parameter 0, _GY s_ = EOp consistent aggregate measures of growth (eq. 9), _OGY s_ = EOp consistent aggregate measures of growth progressivity (eq. 10), _GY μ_ = Aggregate measure of between-type inequality of growth (eq. 11); 95% bootstrapped standard errors are reported in parenthesis.

The type population share and the anonymity implicit in the individual OGIC explain why a regressive individual OGIC is coupled with a non-regressive type OGIC. The smoothed distribution, constructed to evaluate distributional phenomena from an EOp perspective, ranks the types according to their average income at each point in time. Hence, growth is evaluated by comparing the average of different types whenever there is a reranking of types over time. In contrast, the type OGIC tracks types over time. Hence, types are ranked according to their average income 25