rafmacalaba/data-use-mentions-extended huggingface dataset:
# **I. Introduction**

Although most workers in developing counties are self-employed, relatively little is known at a broader level about their characteristics and prospects, and how types of employment evolve as economic development occurs. This paper uses a comprehensive set of household surveys to document the heterogeneity of the self-employed, by which we mean both employers and own-account workers. In developing countries, self-employed workers are often classified according to their perceived prospects for growth. A small minority of self-employed are innovative, successful entrepreneurs with further growth potential and ambition (de Soto, 1989; Bennet and Estrin, 2007). On the other hand, the majority of the self-employed work for themselves and earn little, either because they are rationed out of wage jobs (Fields, 1975; Tokman, 2007, de Mel, et al, 2010) or because they prefer the autonomy and flexibility of selfemployment (Maloney, 2004).

These less successful self-employed workers, whether self-employed by choice or not, are also heterogeneous. For example, Grimm, Knorringa and Lay (2011) distinguish between two types of unsuccessful entrepreneurs in several West African cities. The first type has the profile, in terms of age, education, and sector of work, of more successful entrepreneurs, but has yet to acquire significant capital. Although it is impossible to know exactly why these entrepreneurs’ enterprises have failed to grow, the authors assume that their lack of success is partly attributable to personal and environmental constraints, such as inadequate skills and experience, access to capital, or physical infrastructure. The second group of unsuccessful selfemployed, on the other hand, does not share the same characteristics as successful entrepreneurs, and are therefore assumed to be more likely to be constrained by their age, education, and sector of work than unobserved features of their skill set or external environment.

In this paper, using data from nationally representative micro-level household surveys from almost 100 countries, we examine the characteristics of the self-employed throughout much of the developing world. Building on our
higher availability will report an index=100%).

# _c. Measuring safety_

## **Definition**

The third and last indicator of the public transport network looks at a different aspect of transport, that can affect the decision to use public transport – namely the safety of the built environment at the public transit stops. This should be interpreted as a broader neighborhood level measure of safety.

## **Data source**

We rely on the built environment audits conducted in the three cities and described in section 2.1.

45
technology-based poverty traps could exist even in the absence of increasing returns in the aggregate production function. In the first example we discussed, we assumed that total factor productivity was a function of capital per capita. In this case, the aggregate production function has constant returns to scale, but a poverty trap could nevertheless exist. Moreover, in this case existing evidence on the degree of returns to scale is not helpful in assessing the empirical relevance of this particular mechanism. In the remainder of this section, we directly examine the relationship between estimates of total factor productivity and capital per worker using firm-level and cross-country data.

The three panels of **Figure 9** show the relation between (log) TFP and (log) capital per worker, total capital, and output across countries. Total factor productivity is obtained as residuals 29
household members who had worked on the plot during the previous week were identified, and the hours for each day they worked on the plot during the previous week were reported.<sup>9</sup>

- **_Weekly phone:_** _Weekly phone surveys for the duration of the masika_<sup>10</sup>

- For weekly phone households, a face-to-face baseline survey was conducted in January 2014 (during which households were provided with mobile phones to respond to subsequent surveys), followed by weekly phone surveys through the end of June 2014 and a face-to-face end line survey in July–September 2014 to collect farm production information. For each plot, household members who had worked on the plot during the previous week were identified, and the hours for each day they worked on the plot during the previous week were reported.

- **_Recall NPS:_** _Face-to-face survey at the end of the masika, standard NPS module_ For recall NPS households, a face-to-face end line survey was conducted after the harvest (July–September 2014), during which both labor and farm production information was collected. The agricultural labor module was identical to the respective module in the Tanzania NPS, waves 3 (2012/13) and 4 (2014/15). For each plot, the household members who worked the plot at any point during the season were identified, and the following information was reported: (a) total days spent on the plot over the season in each of four activities (land preparation and planting; weeding; ridging, fertilizer application, and other nonharvest activities; and harvesting) and (b) typical hours per day worked in each of these four activities.

- **_Recall alternative (ALT):_** _Face-to-face survey at the end of the masika, alternate survey module_

- For recall ALT households, a face-to-face end line survey was conducted after the harvest (July–September 2014), during which both labor and farm production information was
_Fareed M. A. Hassan, ECSPE_ 20 Table 5: Tax **Rate/ Base and Tax Bracket Relative to Average Income**

# (a) The **1992 Personal Income Tax System**

|Tax rate (%)|Tax base (No. of<br>households)|As percent of<br>total households|Ratio of tax<br>bracket to<br>average income|
|---|---|---|---|
|0|277|13||
|20|433|20|_0.5_|
|24|1416|64|0.7|
|28|73|3|2.1|
|32|2|0|4.3|
|36|1|0|7.1|
|40|0|0|14.3|
|Total|2202|100||

# (b) The **Proposed Personal Income Tax System**

|Tax rate<br>11 % flat|Tax rate<br>20 %<br>flait|Tax rate<br>3-bracket<br>progressive|Tax base (No.<br>of households|As percent of<br>total households|Ratio of tax<br>bracket to<br>average<br>income|
|---|---|---|---|---|---|
|0|0|0|440|20||
|11|20|20|881|40|0.60|
|1 1|20|30|661|30|0.99|
|11|20|40|220|10|1.60|
|Total|||2202|100||

_Source: Author's estimates using 1992 Individual Budget of households Survey, NSI._
; Trussell and Menken 1984). The Peruvian National Statistical Institute (INEI) used one such method--the preceding births technique--to estimate infant mortality in Peru.

The preceding births technique requires women to report the total number of children ever born and surviving at two different points in time. This information was available in Peru from the 1981 and 1993 population censuses. However, because the population of many districts is very small, applying the preceding births technique to individual districts would have produced highly uncertain results. INEI therefore followed a two-stage procedure. First, it estimated the infant mortality rate in every department, where the sample size was large enough. In addition, INEI regressed its estimate of the infant mortality rate in every department on some of its 3
focuses on explanations in terms of labor market imperfections by testing the relevance of family labor and separability of labor supply and demand. Section five concludes with implications for policy and research.

# **2. Motivation and relevance**

This section draws on conceptual and empirical literature to document strong empirical evidence in support of a negative farm size–productivity relationship and to discuss potential channels underpinning it. If an inverse relationship between productivity and farm size can be attributed to labor market imperfections, one would expect the relationship to weaken as better labor market functioning and increased demand for non-agricultural labor lead to choice of more capital-intensive modalities of agricultural production. Longitudinal data spanning a long enough period should uncover such patterns.

# **2.1 The farm size-productivity relationship**

A negative relation between farm size and output per unit of land in un-mechanized agriculture has been confirmed many times in the literature (Eastwood, Lipton, and Newell2010). It was first noted in Russia (Chayanov 1926), in Indian farm management studies (Bardhan 1973; Srinivasan 1972), and in a host of other countries using simple ordinary least square (OLS) regressions (Berry and Cline 1979) that likely failed to fully control for land quality (Bhalla and Roy 1988). Yet, even when these shortcomings are addressed, the inverse relationship has remained surprisingly robust. Correcting for measurement errors may weaken the relationship, but it does not invalidate it (Lamb 2003). In fact, use of panel estimation techniques (Assuncao and Braido 2007) or careful controls for soil quality affected the magnitude but not the existence of the relationship (Barrett, Bellemare, and Hou 2010). Although biased estimates of area cultivated by farmers may reinforce the inverse relationship, it persists even when objective, GPS-based measures of area cultivated are used (Calogero, Savastano, and Zezza 2011). Use of profits rather than output per hectare in India has the same result—namely, the relation weakens but does not disappear (Rosenzweig and Binswanger 1993).
Vietnam, children are entitled to a place in a kindergarten from the age of 3 through just before attending primary school. School, including kindergarten, in the country starts from September each year. Thus, for example, if a child was born in 2000 (regardless of birth

> 4 We acknowledge that if the VHLSSs do not capture the top-income population groups, our analysis may not be relevant to these groups.

> 5 Unless noted otherwise, our estimates are based on the 2016 VHLSS. Vietnam includes 63 provinces and provincial-level cities. Each province (and provincial-level city) is split into districts, and each district is split further into communes. Communes are the smallest administrative units in Vietnam. In 2016, there were 713 districts and 11,164 communes in Vietnam. Each commune contains around 3-15 villages. In urban areas, communes are called wards.

6
<mark>top 10 percent’s income level increased by roughly 8.7 percent cumulatively despite an average GDP cumulative collapse of roughly 8.4 percent. This is based on a reduced sample of 8 cases for which data on income shares by percentile were available from surveys.</mark> <mark>Our results echo with parts of different strands of the literature studying the relationship between output declines and sovereign default. Some works have found defaults and debt crises to be succeeded by significant and lasting economic contractions (Reinhart and Rogoff 2009, Furceri and Zdzienicka 2012, Medas et al. 2018, Rewilak, 2018, Kuvshinov and Zimmermann 2019, Esteves et al. 2021, Arellano et al. 2018), others find the impact of sovereign default on output to be less so (Borenzstein and Panizza 2009; Sturzenegger and Zettelmeyer 2005; Tomz and Wright, 2007) or find sovereign defaults to occur following a decline in economic output or at the trough of a recession (Levy-Yeyati and Panizza 2011). Trebesch and Zabel (2017) build on an earlier literature differentiating between types of default (Eichengreen, 1991; Obstfeld and Taylor, 2003) and show that part of this divergence is likely driven by fundamental differences between defaults. We find that the output collapse in level terms is relatively short-lived with growth returning in short order, yet, and importantly, our study also finds declines relative to the counterfactual to be of a staggering magnitude on average.</mark> <mark>Our findings thus have important consequences for the theoretical literature of default. The vast majority of the dynamic equilibrium models on sovereign default assume the cost of default to be lump-sum, typically calibrating models assuming an output loss of 2 percent for each year of default (Arellano and Ramanarayanan, 2012; Aguiar and Gopinath, 2006; Yue, 2010;</mark> <mark>Hatchondo and Martinez, 2012; Chatterjee and Eyigungor, 2012; Aguiar et al., 2013; Cole et al., 2016,
<mark>We label these developed market institutional investors and emerging market sovereign investors the “cross-over investors,” because they are crossing over from other asset classes into the emerging market corporate debt asset class. In the cross-over corollary in Section 2, we hypothesize that these cross-over investors will tend to invest more in indexeligible bonds relative to specialists.</mark> <mark>In this section, we explicitly test the cross-over corollary, using data on different funds’ holdings of emerging market corporate bonds. Figure 7 presents two pieces of evidence that connect investor interest with changes in the composition of emerging market corporate bond issuance. Panel A plots the cumulative flows into mutual funds that invest in emerging market sovereign and corporate debt from 2003 to 2016. It also plots the number of $500 million bonds issued by emerging market firms, as a fraction of all bonds issued by these firms. The correlation between the two is very high (0.93), showing a clear connection between the growing investor interest in emerging market debt and the growing relative importance of issuances that just meet the threshold of $500 million. Panel B plots the percentage of the portfolio invested in emerging markets by developed market debt mutual funds from 2005 to 2016. We observe a sharp increase in the weight dedicated to emerging market securities within these funds, which is consistent with a crossing-over of developed market debt funds into emerging market securities. Together, these figures show the increase of investor interest in emerging market debt securities, from ultimate investors and from asset managers from developed countries.</mark> <mark>We complement the evidence in Figure 7 with additional evidence showing that investors less familiar with the emerging market corporate asset class tend to hold a greater proportion of index-eligible emerging market corporate debt. We assemble data from Morningstar Direct on debt funds that we categorize into emerging market corporate specialists and cross-overs, using fund categories provided by Morningstar. Within the cross-over category, we also classify funds into emerging market mixed (those that invest</
* Not all datasets in UNHCR MDL are micro-level datasets or sample explicitly from refugees or IDPs and should be manually removed.

**Most of the relevant datasets in WB MDL prove false positive because while having some references to relevant topics such as migration and displacement in the metadata (which is typically tagged as relevant in nlp4dev), they do not specifically mention sampling explicitly from refugees or IDPs.

Third, we identify micro-level datasets from the WB MDL that have a representative sample of refugees or IDPs. A large majority of datasets in the WB MDL do not sample specifically from refugees or IDPs, calling for a methodology to systematically filter these datasets and identify those that do have a representative sample of FDP. To this end, we apply three different filters to first identify “potentially relevant” datasets from the WB MDL:
The representative household faces the real budget constraint at every period t: where the income of the representative household comes from the provided labor service _h_<sup>_s_</sup> with a wage _w_<sup>_s_</sup> (in each sector s), and real risk-free domestic bonds _B_ remunerated at a rate _r_ . The household’s expenditures go to its consumption basket, bonds, and the lump-sum taxes _T_ charged by the government.

The representative household allocates total consumption between non-agricultural and agricultural consumption goods. The CES consumption bundle reads as follows: where _μ_ denotes the substitution elasticity between the two corresponding types of goods, while _φ_ is the fraction of agricultural goods in the total consumption basket. The consumption price index is determined accordingly by: Finally, demand for each type of good is a function of the total consumption index and its relative price:

# _Agriculture sector_

The economy is populated by a unit mass _i ∈_ [0 _,_ 1] of entrepreneurs, where a fraction _n_ operate in the agricultural sector (farmers).<sup>4</sup> The agricultural output is produced with the Cobb-Douglas production function: where _y_<sup>_A_</sup> is the outcome of farmers’ activity that combines an amount of land _l_ , physical capital _k_<sup>_A_</sup> , and labor _h_<sup>_A_</sup> inputs. The production is subject to a weather shock through a function Ω � _ε_<sup>_W_</sup> _t_ � described in detail below and an economy-wide technology shock _ε_<sup>_Z_</sup> that follows an AR(1) process. The parameter _ω_ is the elasticity of output to land, _α_ stands for the physical capital share in the agricultural production, whereas _κA_ is a technology parameter endogenously determined in the steady state.

The farmers’ land _l_ is subject to both economic and meteorological conditions. The agricultural production process between _t-1_ and _t_ might
[0.0508]|[0.0305]|[0.0319]|[0.0227]|[0.0163]|[0.0159]|
|See Table 6 notes.QR represents unc|onditionalquanti|le regression e|stimation.||||

**Table 10. UQR results for 10th and 90th quantiles**

||_10thquantile_|_90thquantile_|
|---|---|---|
|L Rl Vl f Mthl P Cit Fd Cti|-0.129**|-0.725***|
|og ea aue o ony er apa oo onsumpon|[0.0521]|[0.0762]|
|LogDailyPer Capita Calorie Intake|0.00402|-0.382***|
||[0.0444]|[0.0486]|
|LogFood Consumption Score|-0.248***|-0.183***|
||[0.0576]|[0.0331]|
|See Notes for Table 6.|||

32
research design in our application. In the context of our analysis, an important issue is whether the high Inpres intensity districts were experiencing higher growth in educational outcomes in the pre-program period because of factors unrelated to school construction. Mazumder et al. (2019) use 1985 intercensal population survey (SUPAS 1985) data and show that there are no signicant dierences in the trend of primary completion rates across districts for the cohorts that completed primary schooling before Inpres school construction. This allays the concern of dierential underlying trends across districts. In addition, we nd that treatment intensity is not correlated with father's education which is informative about whether the program

> 28See, for example, Martinez-Bravo (2017), Mazumder et al. (2019), Jung et al. (2021), Ashraf et al. (2020), Akresh et al. (2018).

11
jections and actual deprivation levels, these results corroborate the results from Section 4 and further illustrate that the link between economic activity and well-being in PNG is likely one of the most tenuous among resource-intensive economies. One potential consequence of these findings is that additional growth in the resource sector alone will likely be insufficient to improve well-being in the country for the majority of the population. Other analyses have similarly come to that conclusion by demonstrating, to the degree that one is able to in such a data deprived environment, that there is little evidence that previous resource booms have benefitted the population (e.g., Pandey and Howes 2022; World Bank 2023).

# **Section 7. Conclusion**

We update key well-being estimates in PNG and estimate the degree to which strong resource-led growth might have benefitted the overall population. We find that there was extremely little change in well-being in response to the growth that occurred, and that this performance was extremely poor even compared to several other comparison regions. We also utilize the richness of newly updated global databases to illustrate how the link between economic activity and well-being is more tenuous in PNG than in many other resource-intensive countries.

These findings raise important questions regarding the composition of growth and issues of governance in PNG. As discussed in the Introduction, the results more concretely illustrate that none of the economic activity seemed to have spurred demand for other goods and services that could have benefited the population. Even if the growth that occurred did not trickle down to the population, the outcomes illustrate that the government did not effectively redistribute the substantial government revenue from that activity to the population in the form of transfers or availability of essential services. The lack of effective government transfers is even more striking given the substantial total assistance received by the government that has potentially been growing. Combined, the stagnation of well-being outcomes above illustrates the urgent need to spur engines of growth that better benefit the population.

19
Survey (MxFLS), carried out in 2002 and in 2005, and the 1999 National Survey on Nutrition (ENN)—to examine whether climatic variability, namely the incidence of rainfall and temperature more than one standard deviation from their respective long run means, have significant impacts on the wellbeing of rural _households_ and vulnerable _individuals_ . Well-being or welfare is defined by two (of many) important dimensions—household consumption expenditures per capita, and individual health outcomes.

Second, we attempt to shed light on the channels through which climatic variability can impact the two different dimensions of welfare examined. On the one hand, erratic weather may affect agricultural productivity which, depending on how effective was the portfolio of _ex ante_ and _ex post_ risk management strategies employed, may translate into reduced income and reduced food availability at the household level.<sup>5</sup> Such reductions in food availability may not affect all household members equally. On the other hand, both temperature and precipitation may affect the prevalence of vector borne diseases, water borne and water washed diseases, as well as determine heat or cold stress exposure (Confalonieri _et al._ , 2007). Many parasitic and infectious species have very specific environmental conditions in which they survive and reproduce, and a slight change in precipitation or temperature could render previously uninhabitable areas suitable for a particular parasitic and infectious species. Specifically in Mexico, several studies have shown positive correlations between temperature, and vector- and food-borne illnesses (Ministry of Environment and Natural Resources, 2007).

It is also the case, that changes in the environmental conditions do not uniformly affect the health of household members. Children are more likely to contract or die from vector borne diseases, more likely to suffer from diarrhea, more likely to suffer psychologically from extreme weather events, and more likely to suffer from maltreatment due to household economic stress (Bartlett, 2008). Early childhood health not only affects children‘s current wellbeing but may determine their adulthood quality of life including their productivity and cognitive
Table 7: Imports of intermediates versus other goods

||BEC clas|sifcation|Schott (2004)|classifcation|
|---|---|---|---|---|
||OLS|IV|OLS|IV|
|Dependent variable:|_log_(1 +_imp_)|_log_(1 +_imp_)|_log_(1 +_imp_)|_log_(_imp_)|
||(1)|(2)|(3)|(4)|
|**A. Intermediate goods**|||||
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1094|0.6815**|0.2132***|0.8566**|
||(0.0788)|(0.3240)|(0.0573)|(0.3124)|
|First stage :|||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||0.0027***||0.0027***|
|||(0.0003)||(0.0003)|
|Observations<br>46|888,813|888,813|888,813|888,813|
|_R_<sup>2</sup>|0.5137|0.5027|0.5278|0.5101|
|Kleibergen-Paap rk Wald-F-stat||111.795||111.795|
|**B. Other goods**|||||
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1507***|0.5627***|0.1136*|0.4929**|
||(0.0511)|(0.