rafmacalaba/datause-encoder-data huggingface dataset:
function of agricultural productivity and other variables such as stocks of skilled labor or inflationary shocks. Because of short-run stickiness or other imperfections in labor markets, a reduced form for poverty includes agricultural wages and prices as an additional regressor. By controlling for wages and prices, Datt and Ravallion isolate a “direct” effect of agricultural productivity on poverty reduction from general equilibrium indirect effects that are felt through changes in wages and prices. Datt and Ravallion (1998) apply this framework using 24 rounds of NSS data covering a 35-year period between 1958 and 1994. In the present study our data span only three time periods, but we bring additional spatial variation to the analysis.

We build on the Datt and Ravallion framework by looking also at the role of the nonfarm sector in reducing rural poverty in India. A natural starting point would be to include a measure of non-farm productivity in the reduced form specification for poverty. Data limitations, however, mean that such a relation cannot be estimated. Some estimates of non-farm productivity are available, but these are only at the state-level and restricted to the organized manufacturing sector which leaves out the vast majority of rural non-farm jobs. Given the absence of non-farm productivity data, we proxy nonfarm growth by employment in the non-farm sector. Given the suggestion that access to non-farm sector may be rationed on the basis of characteristics that do not favor the poor – noted in the previous section where we examined individual-level probabilities of nonfarm employment – we consider inclusion of non-farm employment as a separate determinant of poverty reduction. Since overall non-farm employment may well contain important endogenous elements, we also consider separately the impacts of regular, salaried employment which is arguably less likely to suffer from endogeneity since a large share of regular jobs are in the public sector which is less affected by the local economy. In addition, we can enquire whether expansion of the sector is pro-poor on the margin. To that end, we present evidence on the marginal incidence of non-farm employment expansion based on an approach described in Lanjouw and Ravallion (1998).

Because our poverty regressions reveal a strong association between poverty reduction and agricultural wage rates we turn next to a question asked by Eswaran et al (2008), namely whether there is evidence of non-farm employment growth on 16
# **Sectors of Work**

> 17. As is to be expected, the majority of working children in the rural areas, particularly boys (95 percent), are involved in agricultural work. Ten percent of rural girls work in the service sector, with the majority (81 percent) working in domestic work. In the urban areas, on the other hand, child labor can be found largely in service and commerce sectors, the proportions being 30 percent and 25 percent, respectively. Agricultural work follows close behind with 20 percent.

> 18. Gender differences in the urban areas are noteworthy (see Table 4). About half of girls work in services compared to 20 percent of boys but boys edge out the girls in both the agriculture and commerce sectors. Again, the service-oriented jobs for girls are primarily in domestic service (77

> percent), whereas for boys, service jobs are primarily food- and drink-related services (42 percent) and auto repairs (32 percent).

> **Table 4: Percentage of Working Children by Sector, Region and Sex**

||||-~<br>|~~~~~~~<br>P,S,t<br>_C_<br>|~~w<br>l<br>_o.-_<br>|>'F,$..4,<br>|
|---|---|---|---|---|---|---|
|Agriculture|23.7|12.2|19.9|94.6|82.1|90.7|
|Industry|13.3|8.7|11.8|1.9|4.5|2.7|
|Construction|6.4|0.9|4.6|0.4|0.1|0.3|
|OtherIndustry|0.5|0.3|0.4|0.3|0.0|0.2|
|Commerce|26.3|21.5|24.7|1.5|2.1|1.7|
|Services|20.2|48.7|29.6|0.8|10.3|3.8|
|Aux.Services|2.5|3.1|2.7|0.1|0.0|0.1|
|Transportation|2.5|0.4|1.8|0.2|0.3|0.2|
|Social|1.5|2.4|1.8|0.1|0.3|0.1|
|Public|1.3|1.1|1.3|0.1|0.3|0.2|
|Others|1.7|0.7|1.4|0.1|0.0|0.1|
||100.0|100.0|100.0|100.0|100.0|100.0|

> Source: Calculations based on PNAD, 1996

7
# 4   Back-to-the-office work

## Country reports: Survey documentation

The qualitative, quantitative, and performance indicator templates described above should invariably be accompanied by a brief country report that documents each of the following issues.

### **a. General context.**

In this section the user will provide a brief description of the country‘s economic context, highlighting recent economic and/or political milestones that might help facilitate the interpretation of the data.

### **b. Data quality and coverage.**

In this section the user will provide a list of significant gaps in the data, for example, years for which expenditure figures are unavailable should be reported, and reasons given for these gaps.

The report should also include a brief assessment by the user, outlining his or her understanding of the quality and coverage of the official figures presented in the quantitative templates. This does not require undertaking an audit of the figures or carrying out a review of government systems, but merely recording any useful information that may have been acquired during the course of completing the quantitative templates. Any indications of potential underreporting of donor-financed expenditures, either in budgeted or in actual figures, will be of particular interest and must be reported.

It will be equally important to report on significant changes in the quality and coverage of data over the five-year reporting period, which may have implications for comparability. These could be due to ongoing reforms in public financial management that lead to more comprehensive budget coverage or better reporting of budget execution—for example, better budgeting and/or reporting of donor-financed expenditures. Deteriorations in budgetary systems are also possible.

Where figures for actual expenditure have not been externally audited by independent national audit institutions, this should be indicated.

### **c. Definitions, assumptions, and code mapping**

## **_c.1 Country-specific definitions_**

It is also important to document the country-specific definitions used in the economic classification, especially where these differ from those set out in the International Monetary Fund‘s (IMF‘s) Government Finance Statistics Manual 2001 (GFSM 2001). Specifically, the report should clarify the following issues regarding capital, maintenance, and rehabilitation expenditure: 53
# **Table 5.1: Employment change across broad sectors**

||All sector|Agriculture|Manufacturing|Services|Mining|Utility and<br>construction|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|**Panel A: Change in“share of number of em**|<br>**ployed mem**|<br>**bers to numbe**<br>|<br>**r of members in**<br>|<br>**working age**<br>|<br>**s”**||
|Change in provincial tariffs|0.216|1.599<sup>***</sup>|-1.155<sup>***</sup>|-0.684<sup>**</sup>|0.048|-0.047|
||(0.221)<br>|(0.320)|(0.155)|(0.271)|(0.040)|(0.115)|
|Share of working members-1|-0.579<sup>***</sup><br>(0.016)||||||
|Change in share of provincial labor between<br>2002 and 1998|<br>0.191<sup>**</sup>||||||
||(0.073)||||||
|Share of agricultural workers-1||-0.350<sup>***</sup><br>(0.010)|||||
|Change in share of provincial agricultural<br>workers between 2002 and 1998||0.012<br>(0.035)|||||
|Share of manufacturing workers-1|||-0.417<sup>***</sup><br>(0.017)||||
|Change in share of provincial manufacturing|||0.022||||
|workers between 2002 and 1998|||(0.102)||||
|Share of service members-1||||-0.361<sup>***</sup><br>|||
|||||(0.009)|||
|Change in share of provincial service<br>workers between 2002 and 1998||||0.007<br>(0.041)|||
|Share of mining workers-1|||||-0.475<sup>***</sup><br>||
||||||(0.061)||
|Change in share of provincial mining<br>workers between 2002 and 1998|||||<br>0.045<br>(0.036)||
|Share of utility and construction workers-1||||||-0.475<sup>***</sup><br>(0.020)|
|Change in share of provincial utility and<br>construction workers between 2002 and 1998||||||0.152<sup>*</sup><br>(0.084)|
|Constant|0.638<sup>***</sup>|0.436<sup>***</sup>|-0.082<sup>***</sup>|-0.043<sup>*</sup>|0.014<sup>***</sup>|<br>0.019<sup>*</sup>|
||(0.025)|(0.029)|(0.014)|(0.024)|(0.005)|(0.010)|
|_N_|18957|18957|18957|18957|18957|18957|
|adj._R_<sup>2</sup>|0.253|0.178|0.189|0.177|0.231|0.206|
|**Panel B: Changes in working hours as share**<br>|**s of total pot**<br>|**ential workin**<br>|**g hours by memb**<br>|**ers in worki**<br>|**ng ages**||
|Change in provincial tariffs|-1.832<sup>***</sup>|1.158<sup>***</sup>|-1.380<sup>***</sup>|-0.974<sup>**</sup>|0.062|-0.020|
||(0.566)|(0.285)|(0.155)|(0.473)|(0.047)|(0.085)|
|Share of working hours-1|<br>-0.607<sup>***</sup>||||||
||(0.015)||||||
|Change in share of provincial labor between|<br>0.140||||||
|2002 and 1998|||||||
||(0.235)||||||
|Share of agricultural-working hours-1||-0.505<sup>***</sup>|||||
|||(0.015)|||||
|Change in share of provincial agricultural||0.025|||||
|<br>workers between 2002 and 1998|||||||

18
For Rio Grande do Norte and the Northeast, the presence of an elder household member (above 65 years of age) is significantly and negatively correlated with poverty. That is, having an old-aged in the household implies a lower chance of falling below the poverty line. This finding may at first seem surprising, but since elder people rarely retire completely in developing countries, it is not so unexpected. Furthermore, in Brazil many old-aged receive some form of a pension that contributes positively to the household income and, therefore, reduce the likelihood of experiencing poverty.

**Figure 15a: Impact of having old-aged in the household for Rio Grande do Norte**

<!-- Start of picture text --> Rio Grande do Norte: Fam_65<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br>1<br>0.5<br>0<br>-0.5<br>-1<br>-1.5<br>-2<br>-2.5<br>-3<br>-3.5<br>-4<br><!-- End of picture text -->

## _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for fam65 dummy.

# **Figure 15b: Impact of having old-aged in the household for Northeast**

<!-- Start of picture text --> North East: Fam_65<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>-0.7<br>-0.8<br>-0.9<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for fam65 dummy.

The coefficient estimate for the 1980s is rather constant (Figures 15a and 15b). In the Northeast, the impact has marginally decreased in the 1990s, indicating that in recent years households with old-aged members are even less likely to be poor than in the early 35
Khanna, M., Quimio, R., and D. Bojilova. 1998. Toxic release information: A policy tool for environmental protection. _Journal of Environmental Economics and Managament_ , 36, 243-266.

- Koehler, D. and J. Spengler. 2007. The toxic release inventory: Fact or fiction? A case study of the primary aluminum industry. _Journal of Environmental Management_ , 85, 2, 296-307.

- Konar, S., and M.A. Cohen. 1997. Information as regulation: The effect of community right to know laws on toxic emission. _Journal of Environmental Economics and Management_ , 32, 109-124.

- Konar, S., and M.A. Cohen. 2001. Does the market value environmental performance? _Review of Economics and Statistics_ , 83, 2, 281-309.

- Li, Wanxin, 2006, Commentary, _Opening Up the Floor: Environmental Performance Information Disclosure Pilot Programs in Zhenjiang and Hohhot_ , 8 CHINA ENV’T SERIES 125, 125 (2006).

Lopez, J. G., T. Sterner and S. Afsah. 2004. Public disclosure of industrial pollution: The PROPER approach for Indonesia? _Resources for the Future_ . Discussion paper 04-34. Washington, D.C.

- Magat, W.A., and W.K. Viscusi. 1992. _Informational Approaches to Regulation_ . The MIT Press, Cambridge, MA. USA.

- Mol, Arthur P.J., 2009, _Environmental governance through information: China and Vietnam_ , Singapore Journal of Tropical Geography 30, pg 114–129, 2009

- Organization for Economic Co-operation and Development. 2001. _Why Pollutant Release and Transfer Registers (PRTRs) Differ: A Review of National Programmes_ , OECD, Paris.

Paquin, M. and C. Sbert. 2004. Towards effective environmental compliance and enforcement in Latin America and the Caribbean. Unisfera International Centre.

- Powers, N., Blackman, A., Lyon, T.P., and U. Narain. 2007. _Does Disclosure Reduce Pollution? Evidence from India’s Green Rating Project_ . Resources for the Future, Working Paper. Washington, D.C.

REPIN. 2004. Note of environmental performance rating and information disclosure scheme and its implementation in EECCA Region. Regulatory Environmental Programme Implementation Network (REPIN). 6<sup>th</sup> Annual Meeting of the EECCA REPIN, Yerevan, 26-28 September 2004.

32
orthogonal to the expectation of _y_ given _x_ ∀ _n≠m_ (see Hastie and Tibshirani 1990, Andersen 2004 for detailed explanations). The GAM essentially reduces the problem of estimating nonparametric regressions to a sequence of bivariate smoothing problems. This also allows us to make simple plots showing the bivariate partial relationships between each _xm_ and _y_ . These are presented as Appendix Figures A1-A21.

Pace (1998) presents GAM estimates of prices and housing characteristics on a sample of 442 houses with transactions data from Memphis, Tennessee. He finds that the GAM estimator consistently outperforms the OLS estimator in a variety of functional forms, obtaining a better overall fit and much less bias. The purpose of that paper is primarily to compare results across estimators, and does not attempt to derive structural demand parameters. Although we share the same first-stage estimation method as Pace, this paper is closer in spirit to that of Bajari and Kahn (2003).

# **_Recovering structural parameters_**

The restrictions placed on the utility function (3) allow us to obtain closed-form solutions to for the first order conditions (4) for _continuous variables_ . Our model of housing demand includes both continuous and categorical variables, and the derivation method must account for this distinction. For continuous characteristics, we follow Bajari and Kahn, and define the derivative as where γ ˆ _k_ = ∂ _p_ <u>(</u> _x_ _<u>j</u>_ *<sup>,</sup> η _<u>j</u>_ * <u>)</u> . In this way, the value of the characteristic is allowed to vary ∂ _x j_ , _k_ across households. For dichotomous characteristics, we must use a different method. As Bajari and Kahn point out, for dichotomous variables, there will be no first-order

> 10 Note that the linear (OLS) regression is a case of GAM with infinite smoothing.

17
Figure 6: Returns to schooling by quintiles, males: 1992-2002 <!-- Start of picture text --> 1992<br>1995<br>1998<br>2000<br>2002<br>1st 2nd 3rd 4th 5th<br>Quintile<br>Returns<br><!-- End of picture text --> Figure 7: Returns to schooling by quintiles, females: 1992-2002 <!-- Start of picture text --> 1992<br>1995<br>1998<br>2000<br>2002<br>1st 2nd 3rd 4th 5th<br>Quintile<br>Returns<br><!-- End of picture text -->

# **4. Conclusion**

In this paper we have estimated the returns to education in Venezuela for the period 1992-2002 and linked them to earlier available estimates from the 1970s and 1980s. Using consistent cross-sections from the _Encuesta de Hogares por Muestro_ we documented falling returns to schooling and educational levels until the mid-1990s, followed by increasing returns thereafter.

18
In 99 surveys in our sample, for each firm, we have a unique stratification identifier.<sup>17</sup> Hence in cols. 4-6 we restrict the sample to these 99 countries and run OLS regressions clustering standard errors by survey strata. None of our results are changed. Small firms have higher employment and sales growth but lower productivity growth.

Finally, in cols. 7-9 we use survey regression techniques that adopt a “census approach”<sup>18</sup> where in, the firms are weighed by their sampling weights. This approach gives more weight to firms in the larger countries, and thus provides a better description of the outcomes to typical firms across the world. The standard errors take weights, clustering and stratification into account. The weighted survey regressions show that the smallest firms with <20 employees have significantly higher employment growth than firms with more than 99 employees. When we look at sales growth, we find that small firms have as good or higher sales growth than large firms – the medium and large firm dummies are negative but not significant. When we look at productivity growth, we do not find any significant differences based on firm size. Overall, we find that using weighted survey regressions does not make a material difference to our results.

# **_E. Role of Firm Age_**

In this section, we examine the role of firm age, conditional on size, on total employment and job creation rates across countries. We define three dummy variables – Young Firms (≤5 years), Mid-Age firms (6-10 years) and Mature Firms (11+ years). Figure 7 reports the employment shares across the 104 countries in our sample by firm age and firm size classes.

> 17 For the remaining surveys (Afghanistan, Ghana, Kenya and Nigeria), we do not have a stratification identifier because block enumeration was used to overcome the lack of a reliable sample frame

> 18For a discussion of the census and model based approaches see Cameron and Trivedi (2005). For a practical illustration of the differences in the two approaches see Frohlich, Carriere, Potvin, and Black (2001).

17
of market timing considerations in issuance activity.<sup>3</sup> Gozzi et al. (2010) show that firms continue issuing securities at home after accessing international bond and equity markets.<sup>4</sup> In the present paper, we contribute to this literature by analyzing the characteristics of corporate bond issues in domestic and international markets, showing that firms issue bonds with different characteristics in different markets.

A second strand of the research examines market segmentation and its implications for capital market functioning and portfolio allocation.<sup>5</sup> In a frictionless world, the location of where firms issue securities is irrelevant. In practice, however, frictions might fully or partially segment domestic markets from international ones (Japelli and Pagano, 2010). For example, regulations and taxes might hinder the ability of investors to purchase securities outside their home market (Lewis, 1999; Karolyi and Stulz, 2003; Cameron et al., 2007), and information asymmetries between foreign and domestic investors might induce them to price similar assets differently (Bae et al., 2008). In this context, investors with different preferences, investment horizons, and abilities to diversify risk could dominate particular markets, so that securities with distinct traits are offered in different locations (Kim and Stulz, 1988). Securities might also differ across markets if market makers in different locations specialize in securities with particular characteristics (Pagano and von Thadden, 2004). Consequently, bond attributes might be specific to the geographic location and bond prices might not fully converge across markets. We

> 3 The value of debt issues is not directly comparable to that of equity issues because equity issues have no maturity, while debt issues must be repaid. Part of the proceeds from debt issues is typically used to repay maturing debt and, therefore, only a fraction of debt issues can be considered new financing. Henderson et al. (2006) try to adjust the data on debt issues to take this fact into account and conclude that, even with these adjustments, debt issues constitute a much larger source of new capital than equity issues at the aggregate level.

> 4 Black and Munro (2010) use unit record data for bond issuance by corporate residents of Australia; Hong Kong SAR, China; Korea; Japan; and Singapore to analyze firms‘ decision to issue debt abroad, and Eidenmüller et al. (2010) analyze the choice of issuer location in the European corporate debt market during the period 1980-2008. 5 A broader literature studies the aggregate effects of financial integration on economic growth, investment, the cost of capital, and financial development. See, among many others, Levine and Zervos (1998), Edison et al. (2002), and Bekaert et al. (2005, 2006).

6
shocks and refugee crises (World Bank, 2016). This literature remains in its infant stage but provides a baseline for further macroeconomic studies built on the very rich tradition of trade models.

# 4. Data issues

As already illustrated, the forcibly displaced are essentially represented by two large groups: Refugees and IDPs. Data collection, data management and data dissemination are historically different for these two groups.

## Refugees

Data on refugees have been collected since the creation of the UNHCR in 1950. The UNHCR’s main mandate is the protection of refugees, and since its early days the UNHCR has collected data on refugees by registering individuals and households seeking asylum and refugee status. Once granted, refugee status provides access to rights and assistance. For this reason, the UNHCR keeps and updates records of asylum seekers and refugees on a continuum basis.

These records are used for a variety of purposes, such as legal protection purposes, identification of beneficiaries of social assistance programs, or production of statistics on the people of concern to the UNHCR. Records included in the UNHCR registry are organized in different levels depending on urgency of the information required. Refugee crises often result in thousands of people crossing the border daily, and the first priority is to register large numbers of people quickly. Therefore, the first set of information recorded includes only a few key individual socioeconomic characteristics such as name, age, education, former occupation, place of origin and destination. In a second stage, the UNHCR conducts a more formal interview where existing records are verified and other records are added, trying to reconstruct, for example, family structure and relations, types of special assistance and protection needed and other information necessary for protection and assistance purposes.

Because of the very protection mandate of the UNHCR, registry data are very rarely shared. A few exceptions to this rule occur when UNHCR implementing agencies need information for running programs with refugees or if the UNHCR needs to carry out a study on a particular group of refugees. Recently, the UNHCR has also collaborated with the World Bank to carry out a study on the welfare of Syrian refugees and for this purpose the UNHCR shared some of its data with the World Bank.<sup>8</sup> However, unit records, anonymized or not, are not available online or upon request from the UNHCR. This is perhaps the main

> 8 See Verme et al. (2016) and the following section.

15