rafmacalaba/datause-encoder-data huggingface dataset:
to this question. This is a very high rate compared to similar questions in the literature.<sup>13</sup> In Uganda, people do not hesitate to state in public their support for the ruling party, the National Resistance Movement (NRM), as this is highly encouraged by the government. However, people are generally much more reluctant to report support for opposition parties, which include the Democratic Party (DP), Forum for Democratic Change (FDC) and Uganda People's Congress (UPC). This is in part due to perceived and actual government action against supporters of these parties, including harassment, arrests and torture, although torture is much less common. Nevertheless, it is reported in some international press and is believed by many opposition supporters.<sup>14</sup> The distribution for the answers to the political opinion questions are reported in Table 4. Average openness to vote for opposition parties is approximately 1.9 (on a 1-4 scale). This is half of the average value for the ruling party. Voting preferences are highly correlated within communities: stated preference for opposition parties has an intra-class correlation between 0.30 and 0.33.

# **4. Empirical methods and results**

> 13 Non-response to political questions is very common (see for instance, Blair et al. 2012). The political question was developed carefully through field piloting to minimize the refusal rate. Note that from the answer to this question it is not possible to know which party the respondent has voted for. This helps to explain why the response rate is much higher than in the usual surveys on political opinions. Yet, this comes at the cost of asking a less precise question. However, this formulation of the question - because the respondent is invited to express a separate and not comparative judgment on each political party - reduces the possibility of an enumerator effect because the respondent’s political view is not uniquely identified. In this sense, the estimates should be interpreted as a lower bound of the true effect.

> 14 Uganda can best be characterized as a semi-authoritarian regime. There is significantly greater political competition at the local than at the national level, though the ruling party, which initially came to power in a coup in 1987, holds 2/3 of parliament seats and the majority of local elected positions. Tripp (2010) provides a detailed discussion of Ugandan democracy.

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children finding indicates that single mothers with small children are far more likely to experience poverty than, for example, male-headed households with no children.

**Figure 14a: Impact of having children between the age of 5 and 15 for Rio Grande do Norte**

<!-- Start of picture text --> Rio Grande do North: Fam5_15<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>-0.1 1981 1985 1988 1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for fam5_15 dummy.

# **Figure 14b: Impact of having children between the age of 5 and 15 for Northeast**

<!-- Start of picture text --> North East: Fam5_15<br>0.5<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>1981 1985 1988 1990 1993 1995 1997 1998 1999<br><!-- End of picture text --> _Source:_ Authors’ calculations based on PNADs 1981 to 1999.

Note: Figures are probit coefficients and confidence intervals for fam5_15 dummy.

Also, households with members between the age of 5 and 15 have a large probability of being poor compared to the average, albeit their likelihood is lower than for families with small children. For Rio Grande do Norte and the Northeast, the impact seems rather constant across the 1980s and 1990s, but with a tendency to increase in recent years (Figures 14a and 14b).

34
Friedman, M. (1957) A Theory of the Consumption Functions, Oxford University Press, London Gardner J. and Oswald, A.J. (2007) Do Divorcing Couples Become Happier by Breaking Up? Journal of the Royal Statistical Society: Series A (Statistics in Society), 169(2): 319–36.

Graham, C. and Felton, A. (2006) Inequality and Happiness: Insights from Latin America, Journal of Economic Inequality, 4(1), 107–22.

Graham, C and Pettinato, S. (2002) Happiness and Hardship: Opportunity and Insecurity in New Market Economies. Washington, D.C.: Brookings Institution Press.

Grund, C. and Sliwka, D. (2003) The further we stretch the higher the sky – On the impact of wage increases on job satisfaction. University of Bonn, Bonn Econ Discussion paper 2003-1 Grund, C. and Sliwka, D. (2007) Reference-Dependent Preferences and the Impact of Wage Increases on Job Satisfaction: Theory and Evidence, Journal of Institutional and Theoretical Economics, 163(2): 313–35.

Hamermesh, D. S. (1977) Economic Aspects of Job Satisfaction. In Essays in Labor Market Analysis, ed. O. Ashenfelter and W. Oates. New York: Wiley, 53–72.

Helliwell, J. F. (2003) How’s Life? Combining Individual and National Variables to Explain Subjective Well-Being, Economic Modelling, 20(2): 331–60.

Helliwell, J.F. and Huang, H (2005) How’s the Job? Well-Being and Social Capital in the Workplace, NBER Working Paper, no. 11759.

Inglehart, R. (1990) Cultural Shift in Advanced Industrial Society. Princeton: Princeton University Press.

Johansson-Stenman, O., Carlsson, F., and Daruvala, D. (2002) Measuring Future Grandparents’ Preferences for Equality and Relative standing, Economic Journal, 112, 362-83.

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meter (pl) – both with and without infrastructure, to estimate the cost of land and cost of infrastructure per house. Using land prices, both with and without infrastructure facilities, we estimate the price of infrastructure amenities per unit of land (in square meters) and then convert this into corresponding average costs per house. These estimates are provided in Table 1. The average construction cost per house (Btpt) and total cost per house (ph), have been taken from World Bank (2002).<sup>10</sup> That report also shows state level per capita expenditures on urban development in Brazil. The amount of land, infrastructure and building structure per house is considered as the units of respective assets and unit prices have been adjusted accordingly (see Table 1 for details).

From Equation (17), housing quality is directly related to price, given the parameter values α , β and loan rate, rt. This corroborates our consideration of the share of each resource in total cost as their respective shares in quality. From this, we can assume that the share of land ( ξ 2), building ( ξ 3) and infrastructure ( ξ 1) in housing quality will be similar to their relative contributions to housing price. Table 2 presents the estimated parameter values from our model. Based on the parameter values given in Table 2, we estimate the equilibrium values of the following variables (presented in Table 3).

The above steady state values indicate the requirements of each resource to maintain the equilibrium housing quality at the city center (D=1 mile around the center). In the following section we calibrate our model and discuss the demand for each intervention in a first best setting, and then evaluate the change in demand for these interventions in the presence of various market distortions. For our numerical analysis, we consider three different situations with sub optimal housing quality. The steady state

> 10 Table 36: Cost Break Down of Urban Upgrading, Recife, 1998 (pp. 71)

15
Borchert, I. , B. Gootiiz, and A. Mattoo (2012), “Policy Barriers to International trade in Services: Evidence from a New Database”, Policy Research Working Paper 6109, The World Bank.

Caves, D.W., L.R.Chistensen and W. E. Diewert (1982), “The economic theory of index numbers and the measurement of input, output and productivity”, Econometrica, Vol. 50, No. 6, pp. 1393-1414.

Eick, S.(2007) “A History of Indonesian Telecommunications Reform 1999-2006”, Proceedings for the 40<sup>th</sup> Hawaii International Conference on System Sciences. Mimeo.

Eschenbach, F. and B. Hoekman (2006b) , “Services Policy Reform and Economic Growth in Transition Economies”, Review of World Economics/Weltwirtschaftliches Archiv, Vol.142, No. 4, pp. 742-764.

Fernandes, A.M. and C.Paunov (2012), “Foreign direct investment in services and manufacturing productivity: Evidence for Chile”, Journal of Development Economics, Vol. 97, No.2, pp. 305-321.

Fink, C., A. Mattoo, and I.C. Neagu (2005), “Assessing the Impact of Communication Costs on International Trade”, Journal of International Economics, Vol. 67, No.2, pp. 428-445.

Fink, C., A. Mattoo, and R. Rathindran (2003), “An Assessment of Telecommunications Reform in Developing Countries”, Information Economics and Policy, Vol. 15, No.4, pp. 443-466.

Francois, J., M. Manchin, and A. Pelkmans Balaoing (2009), “Regional Integration in Asia: the Role of Infrastructure.” In Pan-Asian Integration: Linking East and South Asia, ed. J.Rancois, P. B. Rana and G. Wignaraja, 439-486, Houndmills, U.K. and New York: Palgrave Macmillan.

Goswami, A., A. Mattoo, and S. Saez (2012), “Exporting Services: A Developing Country Perspective”, The World Bank.

<u>Hoekman, B. (2006), Trade in Services at 25: Theory, Policy and Evidence, World Bank,</u> mimeo.

Kalinova,B., A. Palerm and S.Thomsen (2010) “OECD’S Restrictiveness Index: 2010 Update, OECD Working Papers on International Investment No. 2010/3, OECD Investment Division, www.oecd.org/daf/investment/workingpapers Magiera, S. (2011a) “Indonesia’s Investment Negative List: An Evaluation for Selected Services Sectors”, Bulletin of Indonesian Economic Studies, Vol. 47, No.2, pp. 195-219.

Mattoo, A., R. Rathindran, A. Subramanian (2006), “Measuring Services Trade Liberalization and Its Impact on Economic Growth: An Illustration”, Journal of Economic Integration, Vol. 21, No. 1, pp. 64-98.

Nielson, J. and D. Taglioni (2004), “Services Trade Liberalisation: Identifying Opportunities and Gains”, OECD Trade Policy Working Paper 1.

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# **TABLE 7. Estimation results including trend**

||**Aluminum**|**Zinc**|**Copper**|**Oil**|**Coal**|**Gas**|**Rice**|**Wheat**|**Maize**|**Soybeans**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Long run**|||||||||||
|Log per capita<br>|4.23***|2.20***|11.06***|1.90***|4.16***|0.71***|0.52***|3.42***|1.37***|1.03***|
|income|(0.45)|(0.22)|(0.95)|(0.47)|(0.99)|(0.09)|(0.14)|(0.24)|(0.24)|(0.21)|
|Squared log per|- 0.19***|- 0.08***|- 0.57***|- 0.06**|- 0.23**|||- 0.21***||- 0.03***|
|capita income|(0.03)|(0.02)|(0.05)|(0.03)|(0.05)|||(0.01)||(0.01)|
|Lo real rice|- 0.21***|- 0.16***|- 0.26***|- 0.01***|- 0.02|- 0.25***|0|- 0.04|- 0.03|- 0.02|
|g p|(0.04)|(0.03)|(0.04)|0.00|(0.06)|(0.03)|(0.02)|(0.03)|(0.06)|(0.02)|
|**Short run**|||||||||||
|Adjustment|- 0.27***|- 0.28***|- 0.16***|- 0.07***|- 0.06***|- 0.17***|- 0.24***|- 0.27***|- 0.12***|- 0.33***|
|coefficient|(0.03)|(0.03)|(0.03)|(0.07)|(0.02)|(0.03)|(0.03)|(0.03)|(0.02)|(0.04)|
|Log change in per|- 18.98*|11.17|2.13|3.69**|- 1.78|0.60**|0.19***|11.32**|0.80*|- 2.56|
|capita income|(9.81)|(14.16)|(8.04)|(2.23)|(3.22)|(0.21)|(0.03)|(5.73)|(0.43)|(6.79)|
|Squared log<br>change in per|1.06**|- 0.45|- 0.04|- 0.14|0.13|||- 0.76*||0.08|
|capita income|(0.49)|(0.71)|(0.42)|(0.11)|(0.16)|||(0.41)||(0.37)|
|Log change in real|0.08*|0.05*|- 0.03|- 0.01*|0|0.03|- 0.09|- 0.02|- 0.07|0|
|price|(0.04)|(0.03)|(0.03)|(0.06)|(0.02)|(0.02)|(0.02)|(0.01)|(0.10)|(0.02)|
|Constant|- 5.40***|- 3.39***|- 8.00***|- 0.92***|- 1.34***|- 1.29***|1.41***|- 2.94***|- 0.71***|- 0.55***|
||(0.64)|(0.41)|(1.32)|(0.08)|(0.37)|(0.27)|(0.19)|(0.37)|(0.15)|(0.08)|
|Joint Hausman<br>test- statistic|4.46|5.45|5.01|2.99|11.07|0.82|1.21|1.2|6.34|6.27|
|_p_ - value|0.22|0.14|0.17|0.39|0.01|0.66|0.55|0.75|0.1|0.1|
|Log likelihood|889.59|694.75|755.16|3067.8|1546.83|1146.19|1529|1978.46|47.31|1696.66|
|Observations|1,668|1,680|1,275|1,683|1,428|2692|2,372|2,775|1,500|1,781|
|Number of<br>countries|33|33|25|33|28|33|47|55|32|35|

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses.
# **Box 1.1: Main Data Sources**

This study used both existing data and literature available in Tanzania, as well as a special qualitative analytical work commissioned for the report.

- The only household survey which contains data on HEs is the Integrated Labor Force Survey (ILFS). Using available data drawn from the two rounds of the ILFS survey conducted by the National Bureau of Statistics (NBS, 2000/01 and 2006), a quantitative analysis was conducted to identify the key features of the HE sector and its economic role, and to determine what drives or constrains their productivity. The two rounds of survey enabled understanding of the dynamics in the HE sector in general, and in several types of HEs differentiated by spatial location (Dar es Salaam, other secondary urban, and rural areas), gender (male- and female-operated), industry group (trade, manufacturing, services), and other key characteristics. However, the coding of the spatial locations is not consistent in both surveys, so comparisons between the two surveys by area are biased and should be regarded with care.

- To complement the quantitative analysis, a focus group discussion (FGD) survey was conducted to learn from HEs and from their experiences with running their business. The FGD survey was done in two phases. The first phase, conducted in March and April, 2009, focused on urban clusters in 9 regions -- Dar es Salaam, Morogoro, Dodoma, Singida, Kigoma, Mtwara, Kilimanjaro, Arusha and Mwanza.  The second phase, launched in September, 2009, covered the 3 districts of Kilosa, Kwimba, and Masasi located in Morogoro, Mwanza and Mtwara regions, respectively. The narratives from the in-depth interviews of individuals and groups during the FGDs provide rich insights into the HE operators‟ perception of their needs, constraints, and coping strategies, as well as the impact of government policies, programs, and projects on them (see Kessy, 2010).

- The study also drew on existing literature on the informal sector, as well as interviews with Government officials, donors and NGOs involved in the sector.

4
the model, only a subset of the _X_<sup>_h_</sup> variables are interacted with a linear time trend (Nguyen and van der Weide (2018)). The _X_<sup>_d_</sup> vector consists of district level variables calculated as means of household variables for each district. This vector also includes a measure of rainfall shocks, which is the district’s deviation from mean historical rainfall. District level characteristics are included both to improve the accuracy of model predictions, and to generate more accurate estimates.<sup>28</sup> Area means are calculated at the district rather than the PSU level due to measurement error arising from insufficient numbers of observations per PSU. As with household characteristics, a subset of district characteristics are interacted with time trends ( _Z_<sup>_d_</sup> _t_ ).

The regressions are weighted using population weights.<sup>29</sup> In addition, because of important differences in consumption patterns in urban versus rural areas, and because poverty rates are reported separately for urban and rural India, separate models are estimated for each sector.

Taking _X_ as the vector of all variables and _β_ as the vector of coefficients, equation (1) can be rewritten as: An initial estimate of _β_ is obtained using weighted Ordinary Least Squares (OLS) (equation 2). The consumption spending of households within a cluster are assumed to be correlated. To allow for this within-cluster correlation, the random disturbance term _uch_ has a cluster, _ηc_ , and a household component _εch_ . Therefore, the random disturbance term can be written as, The variables _ηc_ and _εch_ are assumed to be independent of each other and uncorrelated with the explanatory variables. We do not allow for heteroskedasticity in the cluster component of _uch_ , due to the small number of clusters.<sup>30</sup> However, the household error term, _εch_ , is assumed to be heteroskedastic reflecting unequal variances in the error terms across households.

Because of heteroskedasticity in the error term and spatial correlation from the introduction of _ηc_ , we re-estimate equation (2) using Generalized Least Squares (GLS).

> 28Elbers, Lanjouw, and Leite (2008).

> 29Household weights are multiplied by household size to calculate population weights.

> 30Elbers, Lanjouw, and Lanjouw (2003).

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dents are self-employed. Of these, 88 percent have no employees: common self-employment occupations in KLPS2 include fishing, hawking assorted wares, and working as a “boda-boda” bicycle taxi driver. On the other hand, among the employed respondents, the degree of skill varies among unskilled (loader of goods onto vehicles), semi-skilled (factory worker, carpenter, mechanic), and high-skill professional occupations (electronics repair, teachers, and other government and NGO employees).

As in other labor market studies of relatively young men (Griliches 1977, Zimmerman 1992), I use sector of employment rather than wage to estimate the impact of secondary schooling. Clear patterns emerge when I measure the effect of education on (implicitly low-skill) self-employment, shown as a reduced form graph in the lower left panel of Figure 5, and presented in the second row of Table 4. While secondary education and self-employment are negatively associated in the cross-section (columns 1 and 2), the causal impact of secondary schooling on low-skill self-employment is much larger; marginal effects from IV probit and bivariate probit estimation are in broad agreement with the 2SLS coefficients: a 40-50 percent lower probability of being self-employed among those who go to secondary school because they pass the KCPE cutoff.

# **4.3.3 Fertility**

While labor market outcomes are of interest for the men in this sample, fertility and health outcomes are of more importance for the women: women are less than half as likely to be employed as men in each of the six KLPS2 24
More advanced forms of text analysis can use machine-learning techniques to improve skills matching and enable workers to seek training in high-demand skills. By creating algorithms to alert suitable candidates to newly posted jobs, online job portals can reduce search costs for both employers and job seekers. Algorithms can also help employers describe in greater detail both the technical skills and non-cognitive skills required for the position, and portals can identify suitable job seekers based on characteristics that go beyond observable qualifications such as academic degrees and years of experience. This type of contentbased matching can also be useful to job seekers, especially those whose interests and qualifications are not restricted to a single occupational category.

Collecting additional information on non-cognitive skills could improve the quality of job seeker information. Employers typically rely on interviews to assess non-cognitive skills, which tend to be abstract and subjective. Encouraging employers to identify the type of non-cognitive skills that they require and then assessing the non-cognitive skills offered by job seekers could reduce employers’ reliance on timeconsuming interview processes. LinkedIn and other job-matching services are already pursuing similar initiatives and have achieved some preliminary success (e.g., Gee, 2015; Horton, 2016).

# **_Limitations and Caveats_**

There are a number of important limitations and caveats to the use of big data in general, and online jobsearch portal data in particular. These include compliance and privacy concerns, sample representativeness and selection biases, and overall data quality. Askitas and Zimmermann (2015) identify privacy issues and data ownership as major challenges involved in big data analytics. While data collected by private firms offer key insights that can inform better public policies, the legal and ethical frameworks for obtaining and using such data are not always clearly established. Moreover, big data analytics often involve micro-level data associated with individual transactions, which can give rise to serious privacy concerns.

The representativeness of the data is also a major issue, as online job-search platforms are subject to selfselection biases and other forms of sampling error. Carnevale et al. (2014) argued that online job-portal data are vulnerable to systematic errors arising from how employers and job seekers use them. For example, online job advertisements tend to be skewed toward upper-level and professional positions, while lowerlevel and unskilled positions are often filled through other channels. Babajob was selected for this study due in part to the fact that its postings tend to include more entry-level and informal jobs than other jobsearch portals. In addition, while barriers to internet access can restrict the type of job seekers who apply for online positions, Babajob also provides phone-based access to job seekers, eliminating the necessity of 26
Flyer and Rosen (1997) examined the effects of the rising labor force participation of women on both demand for and supply of teachers. They concluded that in the US, the rising demand for teachers during the past few decades is attributable to the growing market opportunities for women and thus higher demand for a market substitute for home schooling. Widened occupational choice for women has also attracted women away from the traditionally female dominated occupations. Nonetheless, the flexibility of teaching and less wage penalty for temporary leaves remain important attractions for women to work in teaching jobs during their productive years.

Using the US longitudinal survey of the High School Class of 1972, Van der Klaauw (1997) estimated a dynamic utility model of occupational choice and occupational mobility, accounting for first and subsequent occupation choice. He found that teacher salaries and opportunity wages are important determinants of the supply and retention of teachers. Csellack (2002) also used a dynamic structural model and panel data (NLSY 1979, US) looking into teacher occupational choice. The simulation results show that a 2 percent increase in teachers’ wages increases teacher supply by 2.6 percent.

Hanushek and Pace (1995), on the other hand, find that in the US, participation in teacher training is not significantly affected by relative teacher earnings. In addition, teacher candidates perform lower on tests than other graduates, and teacher training completion is lowered by state requirements for courses and teacher tests.

Using UK data, Dolton (1990) and Dolton and Chung (2004) focused on looking into the problem of recruiting graduates into the teaching profession and retaining them in the UK. Dolton (1990) found that relative earnings in teaching and non-teaching occupations and the corresponding growth in earnings in these two choices have a 3