rafmacalaba/datause-encoder-data huggingface dataset:
We use the specification in Equation (1) to run a set of multivariate OLS regressions to estimate earnings differentials that control for observed potentially confounding variables: l (E 0 1T 2P P 3 4 2 5 6 ii (1) ii ii ii ii ii ii EEllEEllEE ) = γγ + γγ EETThTTEE + γγ _PPT PPllssE ss + γγ AAEETT + γγ AAEETT + γγ MMEEllTT + γγ UUEEUUEEll ii where _ln(Earning)_ is the log of total reported earnings (monthly or hourly, depending on the + εε where _ln(Earning)_ specification), _Teacher_ is an indicator variable for teachers, _Post-secondary_ is an indicator for having post-secondary education, _Age_ is the individual’s age, _Male_ is an indicator for male individuals, and _Urban_ is an indicator for urban residents. In order to further ensure that we are comparing teachers to comparable workers, the regression sample is limited to wage workers with secondary or post-secondary education.<sup>14</sup> Even though we control for several potentially confounding observed variables, the estimated coefficient may still not represent the isolated impact of being a teacher on earnings, as there may be unobserved factors that affect both earnings and the probability of being teacher. If this is the case, the coefficient on the teacher variable would capture both the actual impact of being a teacher and the impact of these unobserved factors. Therefore, the analysis should be viewed simply as a summary of the observed premium (or deficit) of being a teacher controlling for the variables included in the model.

When calculating total earnings, we combine reported payments in cash as well as payments in kind (if any), such as food and allowances.<sup>15</sup> However, five of the 15 countries in our sample (Côte d’Ivoire, the Democratic Republic of Congo, The Gambia, Namibia and Tanzania) did not explicitly report the existence and value of in-kind payments. It is unclear from the questionnaires whether in these cases in-kind payments are included in the reported values or not.<sup>16</sup> If in-kind payments are included in these cases, our measures of earnings can be interpreted as the total earnings of an employed person from their primary job. We note, however, that teachers who

> 14 While this resembles the model of wage determination proposed by Mincer (1974), we employ it here with a descriptive rather than a causal interpretation.

> 15 Appendix Table A2 shows the proportion of respondents who reported in-kind payments (median is 16.9%) and – for those who did – how much of their income they reported as such (median is 24%).

> 16 This could affect our results if the reported earnings in surveys from those countries did not include in-kind payment, and teachers in those countries were systematically more (or less) likely to receive in-kind payment relative to employees in other professions. Appendix Table A21 reports regression estimates using monthly earnings excluding the in-kind payments for countries that report in-kind payments. The results are consistent with estimates using earnings with in-kind payments in our main analysis.

10
In addition, there is a methodological concern in estimating agglomeration e↵ects based on wages in developing countries, where a majority work in self-employment instead of wage employment (Gindling and Newhouse 2014).<sup>1</sup> Equally important but understudied is the link between urbanization and the way in which workers are allocated to tasks. As well described by Fafchamps (2012), agglomeration potentially has a profound e↵ect on the mechanism by which workers are allocated to tasks by extending gains from specialization and circulating information conducive to innovation in technology, organization, and business practices. Of particular relevance is the transition from specialization driven by the market-based task allocations to hierarchical task allocations within firms. The former is dominated by self-employment in small towns, while the latter prevails in large cities in the form of wage employment.<sup>2</sup> The shift from informal/self-employment jobs to formal/wage employment potentially helps low-skilled workers escape from poverty. However, this transition to wage employment does not automatically take place as self-employment remains prevalent in many large cities in the developing world.

Our study examines the nexus between town population size and job allocation mechanisms in one of the least-urbanized Sub-Saharan African countries: Ethiopia. We focus on urban Ethiopia, where approximately a quarter of the country’s population currently resides. This study reveals whether the transition of self-employment to wage employment, as well as other labor outcomes, is associated with the town population at the early stage of urbanization. This paper focuses on the following key questions. First, we examine whether and to what extent larger towns create more wage and/or formal jobs than smaller towns. Second, we assess who are likely to benefit from labor transitions due to the larger population size (or agglomeration). Finally, we investigate how other labor outcomes, such as labor force participation and unemployment/underemployment, change in tandem.

Our empirical approach to cross-sectional labor force surveys is an instrumental variable (IV) regression of the town population. To reduce an endogeneity bias, we instrument the current town

> 1Teal (2011) puts this well: “[i]n almost all the literature the empirical analysis has been confined to wage employment. In extending it to developing countries that is a rather serious limitation as most employment in such countries is... not wage but self-employment.”

> 2A study of Nepal by Fafchamps and Shilpi (2005) examines the relationship between the proximity to urban centers and various labor outcomes. They find that the transition to wage employment took place mainly in the public sector.

3
35 _MR. VERSLUYSE: I have a question on this impact of technology. It was mentioned that there Is a new pattern of concentration In Industry. I thought that the main beneficlal Impact of micro-computer technology In the 1980s was to enable firms to reap the benefits of Increasing returns to scale without having to have a large-scale industry._ _If you look at a lot of statistics, particularly in Japan, but I think also In the U.S., the momentum for the increase of employment in industry has in fact been in small companies. This may diminish the volatility of employment. Unemployment will continue to Increase in the major smokestack Industries, out will b6 partly offset by modernized new small enterprises. The increments in productlon come from small companies._ _MR. MARRIS: I think the one thing I am impressed by is that the electronic technological revolution is, it seems to me, the nicest one we have ever had. It is so general. It is both product Innovation and process Innovation. it can be used anywhere. It has this characteristic which is that it can stimulate competition and decentralization. You know, it Is not like most of the big driving forces which would displace geographically -- where people have to shift from agriculture to industry or move from Europe to America._ _It seems to me we are on the leaf of the lucky with this one, and, as long as we keep the world going at a reasonable pace, this one will work out extremely well, and probably be pretty highly productive._ _MR. ARMINGTONV: If we do wind up with, say, 2.5 percent growth characterizing the whole of the 1980s, that performance, in retrospect, may turn out to be considerably better than we would have expected without reference to a technological foundation for it. In the 1980s, the demand side, the policy side, and the monetary side have not been conducive to growth. Given that, the question is how did we manage to do as well as 2.5 percent, if this is near our view of the long-term growth trend for the future? It may be that we will wind up looking for some difficultto-measure reason why we managed to get as much as 2.5 percent annual growth in the 198Cs, and hence come back to the technological revolutlon as a supportive factor. If so, well, then, of course, there Is a potential for 3.5 percent, or even 4 lorcent growth In the 1990s, if you can get through some of the problems that Mr. Marris is talking about._

**_More on_**

**_More on_** _MR. LINN: How much of a normative dimension is chere :echnology... to the discussion of technology? I am a bit worried that It doesn't_

WP/80LTHO/SF:Ig
# **6 Conclusions**

This paper has developed a multi-sector, small open economy Dynamic Stochastic General Equilibrium (DSGE) model based on Melina _et al._ (2016), to include the accumulation of human capital, built via public expenditures in education and health expenditures. We have calibrated the model to Kenya and examined four possible fiscal rules for total public investment in infrastructure, education and health in the context of a sustainable resource fund. There are two dimensions to this exercise: the scaling effect which describes the level of total investment and the composition effect which defines the structure of investment between infrastructure, education and health. In terms of the impacts on the non-resource economy, efficiency of spending and sustainability of fiscal outcomes, we find that the the PIH approach provides the best balance between effects on non-oil growth and fiscal sustainability in the case of Kenya. A balanced composition is the preferred structure of investment given the PIH allocation of total investment over time.

Our DSGE modeling approach to these issues offers an internally coherent framework for making conditional (i.e., ceteris paribus) forecasts of the effects of different fiscal policies. For the MF or PIH approaches these are defined in terms of parameters _k_ 1 and _k_ 2 that define the scaling effect, while the composition is driven by the share parameters at the beginning and end of the simulation period. Instead of restricting the choice to a limited and finite number of scenarios, future work could examine a welfare optimal choice of these parameters. Our exercise has been purely deterministic, ignoring uncertainty in crucial areas such as the world price of oil. A further line of research could study welfareoptimal state-contingent rules that indicate how policy would be adjusted in the face of unanticipated changes to the price of oil and other relevant exogenous macro-economic variables.

# **References**

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Adam, C. and Bevan, D. (2014). Public investment, public finance, and growth: the impact 30
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101
odds of being assignment to treatment. Let πi be the probability that unit _i_ receives treatment, and X _i_ the observed covariates. Then the log odds ratio is given by Under the hypothesis of ignorability, γ=0 (or equivalently, Γ=1, where Γ≡e<sup>γ</sup> ). With higher values of Γ, propensity score matching will be less precise. Rosenbaum shows how to obtain significance levels (using a Wilcoxon sign-rank test) and new confidence intervals (if the treatment effects are additive) for different values of Γ. The results of estimating these Rosenbaum bounds for the log of expenditure per capita are shown in Table 8, and show that our results are not especially robust; if an unobserved variable were to cause the odds ratio of treatment assignment to vary by a factor of about 1.07, then our finding of a impact would no longer be statistically significant at even the 10% level. Although our results are sensitive to the assumption of ignorability, this potential loss of significance is, as DiPrete and Gangl (2004) rightly point out, just a worst-case scenario.

Our final robustness check is to estimate the effect of borrowing on income and consumption using a common impact model (Haughton and Khandker 2009). Let _Yi_ measure the outcome, X _i_ be a vector of covariates, _Ti_ measure the treatment under consideration (i.e. VRF borrowing), and εi represent a zero-mean error term, and estimate Then the estimate of the coefficient αTC should be able to measure the impact of the borrowing. The results are shown in Table 9, and use the same other covariates as in the propensity score matching (see Table 3). When the treatment is measured as a binary variable, set equal to 1 if the household borrows from the VRF, then the impact is to raise expenditure per capita by 2.3% - broadly in line with the 3.3% impact as measured using propensity score matching (Table 4); however, the estimated effect on income is nil. One may also measure the impact of the amount of borrowing, _given that one is a borrower_ . The middle section of Table 8 shows that an additional 100 baht of borrowing is associated with 83 baht more spending and 143 baht more income, figures that are on the high side, particularly for income, unless “lumpiness” in investment is a commonly binding constraint. The estimates in the bottom panel of Table 8 imply that a 10% higher loan is associated with 1.4% more consumption or 1.8% more income, again rather large effects. The common impact model is less compelling than propensity score matching because it does not confine the estimates to a region of common support, and does not try to tailor comparisons for each treated case.

_Village Fund, draft of June 29, 2009_ _Page 14 of 34_
Table 2: Classification of PoTec

|**Occupational group**|**Professionals**|
|---|---|
|**Researchers**|Researchers|
|**Engineers**|Mechanical and electrical engineers<br>Civil engineers, among others<br>Agronomists and fshing engineers|
|**R&D directors and**<br>**managers**|R&D directors<br>R&D managers|
|**Scientifc professionals**|Biotechnologists, geneticists, metrology researchers,<br>and specialists in meteorological calibrations<br>Mathematicians, statisticians, among others<br>IT and computer professionals<br>Physicists, chemists, among others<br>Biologists and other professionals|

Source: (Ara ́ujo et al., 2009) (respectively, primary school, middle school, high school and college degree or higher), a dummy for whether the employee has a temporary contract, firm’s number of employees (firmsize), and a dummy for public servants. _θt_ and _σj_ represent common time and region effects. The parameter of interest in this study is _α_ 1, the semi-elasticity or wage premium of working in an innovative firm.

One challenge when estimating Equation 1 is that if worker characteristics are not captured adequately by the control variables, and more productive or higher ability workers self-select into innovative firms, then OLS estimates of the innovation wage premium are likely to be biased. In order to control for these unexplained worker factors, we use worker where _λi_ represents time-invariant worker effect. The error term is now decomposed into a time-invariant, person-specific quality component ( _λi_ ) and a purely random element ( _εijft_ ). Since we control for individuals and year effects, we omit information on some of workers’ characteristics due to additive worker and year fixed effects, remaining with age squared and a dummy for temporary contracts.

A final challenge is the fact that there may be unobserved firm characteristics that are not controlled and are correlated with the decision of becoming an innovator and, therefore, bias the coefficient on the wage premium. The problem, however, is that given that identification 10
Figure A1: Share of China’s Processing Exports, 2000-2007 <!-- Start of picture text --> .56<br>.53<br>.55<br>.55<br>.55<br>.55<br>.55 .55<br>2000 2001 2002 2003 2004 2005 2006 2007<br>year<br>Total Exports Processing Exports<br>Share of Processing Exports<br>1200<br>1000<br>800<br>Billion USD 600<br>400<br>200<br><!-- End of picture text --> 12
Table 2: _Average Treatment Effect on Signatories - Non-Binding Provisions_

||||**All agr**|**eements**|**E**|**U**|**Rest of**|**the World**|
|---|---|---|---|---|---|---|---|---|
||**Variable**||**ATT**|_δ_ **pct.**|**ATT**|_δ_ **pct.**|**ATT**|_δ_ **pct.**|
||CO2|(-)|**0.141**|**48.17%**|-0.007|-2.33%|**0.116**|**39.84%**|
||Forests|(+)|-0.766|-2.35%|0.314|0.96%|**-0.878**|**-2.70%**|
||**SO2**|(-)|3.870|0.80%|10.261|-2.13%|3.695|0.77%|
||Black Carbon|(+)|-2.350|-4.56%|-5.995|11.65%|-1.083|-2.10%|
||Greenhouse Gases|(+)|**2.315**|**3.93%**|-1.059|1.80%|**2.408**|**4.09%**|
||Nox|(+)|-2.809|-5.98%|6.296|13.42%|-3.340|-7.12%|
||Ozone Exposure|(+)|**-4.556**|**-9.10%**|1.467|2.93%|**-3.807**|**-7.61%**|
|**Environment**|Protected Areas|(+)|**-1.779**|**-6.64%**|0.823|3.07%|**-1.523**|**-5.68%**|
||PM2.5|(+)|-1.105|-2.72%|1.264|3.11%|-1.212|-2.98%|
||Species Habitat|(+)|-0.557|-0.60%|2.334|2.51%|1.813|1.95%|
||Nitrogen Management|(+)|**-2.563**|**-7.68%**|4.698|14.08%|**-3.393**|**-10.17%**|
||Species Protection|(+)|0.487|0.74%|-0.583|-0.89%|-0.282|-0.43%|
||Tree coverage loss|(+)|-3.854|-9.68%|**-4.321**|**-10.86%**|-2.336|-5.87%|
||Wetland loss|(+)|-3.985|-6.96%|5.184|9.06%|**-7.130**|**-12.45%**|
|**LbMkRli**|Labour Rights|(+)|-0.770|-3.35%|||-1.172|-5.10%|
|**aour aret eguaton**|Workers Protection|(+)|0.094|9.86%|||-0.0668|-6.98%|
||Polyarchy|(+)|0.020|4.29%|||||
||Freedom of Expression|(+)|0.001|0.15%|||||
|**Civil and Human rights**|Women Political Participation|(+)|0.000|0.07%|||||
||Political Liberties|(+)|-0.004|-0.65%|||||
||Freedom of Association|(+)|0.096|8.55%|||||

_Notes: Average Treatment effect on the Treated (ATT) of non-binding provisions in PTAs on signatories. Percentage change refers to the relative change compared to the outcomes’ sample averages. Bold changes signal statistically significant effects. Boldface font indicates a statistically significant effect. The sign in brackets indicates the direction of change denoting an improvement in the related outcome. Table A-5 reports the sources and summarises the sign indicating an improvement for each outcome variable considered. Data constraints preclude estimation of the ATT for EU NTPs addressing labor and civil and human rights. US agreements do not include non-binding provisions on Civil and Human Rights.
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