rafmacalaba/datause-encoder-data huggingface dataset:
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a result of a contracted public sector. Measures for them have comprised compensatory wage increases to counter price increases, and expansion of facilities for vocational training to prepare them for alternative labor market opportunities.

10. Annex I shows that the types of interventions proposed in the various countries have varied widely. The diversity of approaches is illustrated by the contrast between Ghana (SAL I) which included a wide range of measures for both the newly unemployed and the poor, and Dominica (SAL I) which focussed on a Vocational and Technical Education project to reach school leavers who may face a contracted public sector job market after adjustment. The same Annex also shows that more recent SALs include a greater number of interventions to address the social impact suggesting that this issue is receiving more attention.6 11. The use of tranche release conditions related to the adoption of social measures has not been common. Out of the twenty-four countries reviewed, tranche release conditions for social measures were found in the following six countries:

*In Chile, SAL I and SAL II second tranche release required satisfactory progress in studies to assess the relative efficiency of the employment programs and to review major issues in the health sector.<sup>7</sup>

> 6/ **The 'Report** on Adjustment Lending IIO (RAL **II,** 1990) found that the shar, of loans with **at least** one social policy reform had increased from **16** percent in FY79-86 to **26** percent ir, FY66-88 to 34 **percent** in FY89.

- 7/ Th- second trenche release **was** contingent only upon satisfactory progress in **the** studies, not upon **th.** adoption of follow-up actions.
the success of non-farm enterprises. Their findings would suggest that a policy focus on agricultural productivity growth should alleviate poverty across the board as long as nonfarm enterprise constraints from infrastructure and human capital are mitigated. However, it might be harder to raise agricultural productivity growth in certain regions of the world than it is to alleviate the constraints facing non-farm enterprises.<sup>5</sup> In summary, the literature suggests that agricultural growth has had an important role in poverty reduction in many countries. However, with more open trade possibilities, low prices for staple cereals in world markets, and population growth slowing, the importance of agricultural growth for overall economic growth and poverty reduction is no longer so clear. Furthermore, in most developing countries rural non-farm output now accounts for roughly half of rural income. Despite the fact that some nonfarm activity is surely of the low-productivity insurance type (and important to the poor for that reason), it is equally clear that the rural non-farm sector often enjoys greater potential for growth in income than the agricultural counterpart. This is especially true for rural regions that enjoy high levels of physical infrastructure and human capital, as such regions can reach higher productivity levels due to effective demand for their goods and services.

Thus, whether the farm or the non-farm sector has been the most important pathway out of poverty is ultimately an empirical question, as is the relative importance of inter-sectoral transitions and rural-urban migration. The answer for any given country depends both on its factor endowments as well as its policy and institutional history. We therefore explore the empirical evidence about the pathways out of poverty for Indonesia over the last two decades.

# **3. EVIDENCE ABOUT PATHWAYS OUT OF POVERTY**

To understand what have been the most important paths out of poverty it is necessary to know how many people there are in each of the cells of Figure 1, how many of them managed to move from one cell to the other, and by how much they improved

> 5 Several recent studies explore the difficulties of improving growth in the rural non-farm sector by improving the rural investment climate – see World Bank (2006a) on Indonesia, Deininger, Jin and Sur forthcoming on Sri Lanka, and World Bank (2006c) for a cross-country review of the evidence.

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The next section describes our novel data. Section III presents our empirical strategy and findings. A final section concludes, including a discussion of some relevant policy considerations. In summary, we find that economy-wide, internet adoption has no statistically significant net effect on aggregate employment and has a negative effect on average wages, with a reduction in measures of wage dispersion. The absence of economy-wide significant net employment effects masks important shifts in employment from sectors with more limited expansion opportunities—wholesale and retail trade, public administration and largely publicly-owned utilities that comprise almost half of the formal workforce in 2010—to sectors with more output expansion possibilities. Employment effects are positive and most pronounced in manufacturing, transport and storage, finance and insurance, and hospitality industry groups. Importantly, internet adoption in manufacturing industries is inclusive, with positive employment and wage effects across high and medium-skill workers.

# **II. Data**

We combine the rich, comprehensive data from Brazil containing annual matched employee-employer records with annual municipality-level information on the availability of internet to investigate the effects of technology intensity on the labor market outcomes of workers.

Labor market metrics are derived from the **Annual Social Information Survey (RAIS),** which contains annual wage earnings and occupation information for all formally-employed workers in Brazil in all sectors, including agriculture, manufacturing and services. Each record is comprised of annual employer-reported data on the firm’s industrial classification and each worker’s hours, earnings, hiring/dismissal dates (if applicable), occupational classification, education level and age. The RAIS data importantly contain identifiers for both workers and employing firms; that is, because this is a longitudinal data set both for firms and workers, we are able to analyze worker employment histories for the entirety of their duration in the formal sector within our sample period, and within and across firms, occupations, and industries. Using the RAIS microdata, we construct an annual municipality-level dataset that contains aggregate indicators of employment, wages, worker turnover (based on hires and fires), number of firms and firm size.

To map the internet rollout in Brazil, we construct a new data set based on answers to questions on internet availability from the **School Census** , an annual survey of all schools in the country. We use this seemingly unrelated data-source because other more common sources—such as official telecoms data, Census, household and ICT surveys—are either too recent or do not allow municipal level disaggregation. Official data on internet availability at the municipal level (ANATEL) are only available from 2007 onward, by which time most municipalities had access. PNAD, the Brazilian household survey, only allows for spatial disaggregation at the state level. TIC Empresa, a specific survey of internet use by households and firms, 5
3 lacking male-earned income simply has a much higher probability of being poor. In the final section we show that Brazilian female-headed households appear to be replicating the trend observed in the United States. Even after controlling for income, children in these households are less likely to attend school and are more likely to work. While work experience per se is not a bad thing, as the Brazilian economy seeks to compete in the hightech end of manufacturing and services, workers without sufficient education will suffer increasingly limited opportunities. Thus, the growth of female-headed households raises questions about the intergenerational transfer of poverty.

The data used in this study are from the Brazilian annual hodsehold sample survey data, PNAD<sup>2</sup> , from various years. This survey is performed annually (except in the census years). On the whole, these data are reasonably good, but they contain a few of features that limited our analysis. First, although the questionnaire contains a number of items regarding household income, most of these are designed to measure steady income flows. The survey does a poor job of recording agricultural income. For this reason, we concentrated our analysis on urban households (covering roughly three-fourths of Brazil's population). Second, the survey contains both standard questions (asked each year) and special questions designed to explore a particular topic in-depth. For example, while sex of head is asked each year, only in 1984 were detailed questions on marital status asked. For this reason, the bulk of the data presented here are from 1984. Finally, Brazil suffered high and highly variable levels of inflation in the 1980s. The increase in inflation has made regional price differentials, already known to be a problem in the 1970s, even greater, vastly complicating the task of performing intertemporal and interregional comparisons of household income.<sup>3</sup> Given these data weaknesses, we proceeded as follows. In Part 1, the descriptive section, we look broadly at the characteristics of female-headed households in all urban 2 Pesguisa National por Amostra de Domicilios. conducted by the Brazilian National Institute for Geography and Economics.

3 See Fox (1990) for a discussion of the methodological problems associated with trying to measure differences in living standards in Brazil.
are comparable to those of reforms observed in real world settings.

Our paper is closely related to an existing literature that studies police searches of civilians for contraband, and in that context formalizes conditions that would constitute racial or other kinds of discrimination in police search behavior. Knowles _et al._ (2001) and Persico (2002) use an expected utility framework to formalize the decision to search an individual and/or his property (e.g., automobile).<sup>3</sup> An implication of expected utility theory is that – the police officer is assumed to know, with certainty, the probability conditional on an – observable characteristic or characteristics such as the civilian’s race that a search of an individual or her automobile will find contraband. Our primary departure from this literature is formalizing a role for ambiguity about the underlying probability distribution. It is likely that ambiguity is more important in the international trade context given (a) the large number of characteristics of an import shipment, and (b) the fact that many import shipments have bundles of characteristics that are only rarely observed together.<sup>4</sup> We also – focus on overall search intensities and compliance rather than discrimination with respect – to characteristics since the equal protection issues important for police searches are less relevant in the context we study.<sup>5</sup> In the border management context, risk management generates very different search intensities for shipments with different characteristics (e.g., product type and/or country of origin), an outcome that the police search literature would likely attribute to bias. In this respect, our paper is also related to a small but growing literature on inaccurate statistical discrimination. Bohren _et al._ (2020) show that statistical discrimination that is based on inaccurate beliefs about decision-relevant characteristics can

> 3A user-friendly introduction to those papers can be found in Persico and Todd (2008), which also discusses some caveats to the theory. Another related paper is Eeckhout _et al._ (2010), which shows that random crackdowns can be an optimal policing strategy. Lazear (2006)’s paper on high-stakes testing in education is also relevant for its potential applicability to crime.

> 4Armenter and Koren (2014) show that, even in U.S. data, trade data are extremely sparse. The most common number of shipments for a product-country-firm observation is one. Related facts have been observed in data from other countries. Our data from Serbia offer an example.

> 5Furthermore, while we focus on the planner’s problem (i.e., the inspecting agency), both Knowles _et al._ (2001) and Persico (2002) look at the optimal behavior of a single officer (among many) to determine if there is any discrimination in equilibrium. This distinction is important since when deciding on search intensity, the agency takes into account the effect of its decision on the overall compliance rate, whereas the individual does not.

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definitely a positive case, and is second only to the most positive case of both reduced poverty and vulnerability and expanded middle class (Table 2).<sup>13</sup>

# _United States_

The year 2008 marks the Great Recession in this country, where subprime housing mortgages had a detrimental domino effect on other sectors of the economy. Petev, Pistaferri, and Eksten (2011) document that while real per capita consumption declined monotonically until the middle of 2009, the decline of real per capita disposable income was significantly smaller. This finding was corroborated by De Nardi, French, and Benson (2012), who also found that the drop in income for poorer households was in fact lower than that of other households (thanks to means-tested transfer programs from the government).<sup>14</sup> Both the negative growth rate for mean income per capita, and the negative growth for the bottom 40 percent (shown in Table 1)—even though the latter is slightly larger—are consistent with these findings.

Our estimates (Table 3) suggests that growth in the US in this period has been least pro-poor, with the population share of the Poor category increasing by 12 percent. At the same time, the Vulnerable category expanded by 9 percent, while the Middle class contracted by 2 percent. The growth scenario for the US is in fact the most negative case for pro-poor growth according to the typology shown in Table 2.

# _Vietnam_

> 13 Analysis of mobility for the more recent period (2009/10- 2011/12) for India is provided in Dang and Lanjouw (2015).

> 14 In addition, Saez and Zucman (2014) document that over the 1986-2012 period, wealth per family averaged a growth rate of 1.9% per year but did not grow at all for the bottom 90% of U.S. families.

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outlined above, suggest the following list of main individual determinants of informal (rather than formal) dependent employment (conditional on being an employee)<sup>7</sup> : (i) low skills (as measured by educational attainment, occupation, experience, etc.) and/or low unobserved productivity; (ii) strong preference for flexible working time and/or substantial volatility of desired working hours over the course of the year; (iii) low value placed on job security; (iv) large endowment of social capital relevant for the informal sector (belonging to a group or groups which is known to be over-represented in the informal sector and/or to have difficulties in the formal labor market: ethnic or linguistic minority, first or second generation immigrants, students, pensioners, persons with disabilities);

- (v) low level of tax morale and/or trust in state institutions.

Apart from the standard prediction that informal workers are likely to be less skilled, it follows that the age-informality profile is likely to be U-shaped. Indeed, younger and older workers are usually less productive than middle-aged ones and less prepared for a stable fulltime work; the young ones, especially students, and those in retirement age are also less concerned about job stability. Students and persons with disabilities, also are more likely than others to receive informal job offers (and, plausibly, to be less productive) than other workers, so we expect these groups to feature higher informality rates, other things equal.

Minorities, workers with immigrant background, as well as workers in less developed regions, are more likely to hold informal jobs because of large informal social capital which, in addition, might interact with low trust in institutions and in some cases with productivity problems caused by insufficient language skills.

With respect to gender and family status, the predictions are ambiguous because those whose family status suggests a strong preference towards flexible working time, are also likely to place high value on job security and be more risk averse in general.

# **2 Prevalence and dynamics of informal employment in Europe, 2004-2009**

In this section we use the ESS data to compare prevalence of informal employment (in the main job) across 30 European countries and years 2004 to 2009. For 25 countries we will also show (in Table A3) that ESS-based results for 2004-2006 are well in line with the results of the Fourth European Working Conditions Survey (EWCS) conducted in 2005 (European Foundation for the Improvement of Living and Working Conditions, 2007); it is worth noting

> 7 See Hazans (2011a) for econometric analysis of agents‘ sorting across all six labor market states.

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We could allow preference parameters to vary with household characteristics, but as our analysis suggests that it does not make much difference to the computation of elasticities, we keep fixed coefficients for simplicity.

Under each nest, the worker’s choice set is generated according to the rules described in the previous section. For example, under nest (1), the commute mode choices available are walk, bus, rail and bus + rail if the worker’s residential location is far enough from his work location, while nest (2) contains the choices in nest (1), plus a two-wheeler. In the model of vehicle ownership, some households simply cannot afford some of the options. We assume that if the sum of ownership cost and operating cost exceeds a household’s monthly income, the option is not available to the household.

# **_C.  Data_**

The data for estimating both sets of models come from our Mumbai household survey (Baker, Basu, Cropper, Lall and Takeuchi 2005). For the mode choice models, journey-to-work data come from the descriptions of the usual commute trips of the two main income earners in the household questionnaire. We asked each respondent to identify the two main income earners in the household and to describe their job locations (in terms of section and pin code),<sup>13</sup> earnings, and a typical journey to work (modes taken, out-of-vehicle time, in-vehicle time, out-of-pocket cost). Information was reported for 6,666 income earners from 4,979 households. In estimating commute mode choice models the following workers were dropped from the sample: (1) workers with no fixed

> 13 Mumbai is divided into 88 sections.  A commuter’s workplace location was considered to be the centroid of the intersection of the section and pincode (analogous to zip code) in which he worked.  The geographic coordinates of residential locations were recorded for all households in the survey.

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# S

Unfortunately, there is no up to date research on the labor market outcomes of vocational secondary schooling in Brazil. The only research available refers to nonformal training, and covers SENAI graduates In the early 1970s (do Moura Castro, 1979). This study yielded high estimates of social rates of retum: 24 percent for those who received SENAI training .- ter four years of primary schooling, 12 percent for those who received SENAI training after eight years of schooling, and 23 percent for those who received SENAI training after secondary school.

## 3. Labor Market Outcomes of Secondary Schooling

## Data

The data source Is a nationally representative subsample of males drawn from the 1980 Brazilian Population Census. The sample was restricted to males between the ages of 15 and 65, who attended secondary and were working full time (36 hours per week or more In the week prior to the census) in private sector wage employment". The Census questionnaire gathered Information on years and type of terminal schooling. This Information was used to distinguish between academic and vocational secondary school leavers (i.e. those that terminated full time education at secondary level). While the Census questionnaire did not gather explicit information on the type of school attended by secondary school leavers (this is inferred from courses of study), available Information presented In Table 2 enables us to believe that the majority of the Individuals who received vocatlonal schoollng In our sample come from state/municipal schools. In all, 2,743 individuals were included In the final sample: of these, nearly 407 (some 15 percent) received vocational schooling.

Table 3 presents the characteristics of the sample of secondary school leavers as a whole, and by type of schooling attended. It appears that most personal characteristics (age, experience, migrant origin) are similar for vocational and academic leavers. The two groups differ in the amount of schooling attained, where vocational school leavers have over 1.5 more years than their academic counterparts. There are also some differences between the two groups In terms of the distribution by occupation and eamings. Clerical work, followed by professional/technical work, are the main occupatlons for vocational leavers while production and clerical work are the main occupations for those of academic fields. On average, vocational school leavers appear to earn about 6,000 cruzados more per month than their academic counterparts.

> 5 Originally we worked with the full sample of salaried workers employing a dummy variable 1 if employed In the public sector. However, the estimation of F test Indicated that the samples of private and public sector workers come from different populaftons, which conform with available evidence on the operation of labor markets in Brazil.