rafmacalaba/datause-encoder-data huggingface dataset:
economic growth via the savings rate and overall labor productivity. We have adopted this approach for a first-stage regression that relates national income growth since 2010 to the age-dependency ratio. We substitute the regression prediction for income per capita in our EKC model that excludes all other righthand variables except the time trend and seasonal variations by hemisphere. The resulting EKC estimate is included in Figure 8 (labeled “IV”). Its peak occurs at a lower CO2 anomaly than its OLS counterpart, and at an income that is substantially higher. We should emphasize that this IV result is far from the last word, and future research should use satellitemeasured CO2 data to address the simultaneity issue in more depth.

We should also note that the income-related results in this study are only intended to provide a benchmark for judging urban performance in reducing CO2 emissions. The same would be true if our results had rejected the EKC (β12 > 0, β13 = 0), yielding a relationship in which CO2 emissions increase continuously with income per capita. To avoid any misunderstanding, we should emphasize that _our EKC results have no normative or policy implications in themselves_ . They do not imply that additional public resources are not needed for reducing CO2 emissions because “the problem will take care of itself” with continued economic growth. In fact, the opposite is true. The most recent IPCC report (IPCC (2021), Figure SPM.10) affirms a near-linear relationship between cumulative CO2 emissions and the increase in global surface temperature. Our EKC results (Figure 8) show that even high-income countries have not approached zero CO2 emissions, and growing industrial giants like India and China are still on the rising portion of the curve. The clear implication is that waiting for the EKC to reduce emissions from all countries would produce an enormous increase in cumulative CO2 and a potentially-catastrophic global temperature increase. This conclusion will simply be compounded if future research finds insignificance for the EKC regression parameter (β13 = 0).

Finally, we should note that our EKC results only provide a descriptive “snapshot” of the income/emissions relationship for our sample urban areas during the period 2014-2019. Even if the EKC specification survives future econometric tests, policy changes and technology improvements may lower the EKC peak significantly while shifting it to a much lower income level. Indeed, it is possible that recent changes in policy and technology in wealthier economies have already steepened the post-peak decline in CO2 emissions at higher incomes. Tracking changes in the emissions/income relationship and its determinants should be an important topic for future research.

18
single enterprise, it is not at all clear whose human capital is most relevant to the success of the business. The choice here of using the age of the oldest and the education of the most educated family worker may not be optimal (although other specifications were tried and proved even less successful than this).

Secondly, even if the earnings model is correctly specified, the problem of measuring business earnings is considerably more difficult than that of measuring an employee's wage. This is especially true in the case of small businesses in developing countries, where written records are not kept and where those who request such information are often suspect. The PLSS was carefully designed and conscientiously pretested; one of its principal objectives was the collection of data on small-scale enterprises comprising Peru's informal sector. Undoubtedly, the PLSS achieved this objective as well as any national survey has done to date. Still, the state of the art, it seems fair to say, is primitive.

Thirdly, there is a question of aggregation across sectors of selfemployment, which we have discussed briefly already. There may be important differences -- say, between a weaver and a beautician -- in the amounts and types of physical capital and materials required, the amounts and typeP of human capital required, and how such human capital is typically acquired. Recognition of these differences -- and the results of a Chow test of sample homogeneity [Chow (1960)] -- prompted us to run separate analyses for textile workers (out of all manufacturing enterprises) and those engaged in personal services (separately from other services). Still, differences remain within what we have defined to be a "sector." The "other manufacturing" sector is especially diverse, and this fact could account for the absence of significant 31
adverse direct effect of India’s higher land inequality on the elasticity of poverty to agricultural output.

# **4. Allowing for different parameters across provinces**

The various tests on provincial data reported in the last section confirm the finding of Ravallion and Chen (2007) on national-level data that it is the primary sector that has been the main driving force of China’s poverty reduction, rather than the secondary or tertiary sectors. However, in the previous section we only considered regressions with constant elasticities across provinces for each sector. As we argued before, and was shown by Ravallion and Datt (2002) for the case of the states of India, the composition of output and the geographical distribution of growth are potentially important for the analysis of the elasticity of poverty reduction to economic growth. As a starting point for investigating this issue, we use a similar specification to that of Ravallion and Datt (2002) for India, in which all parameters are now allowed to differ across provinces: Note that this specification includes a state-specific time trend and a state fixed effect.

Equation (5) can be interpreted as postulating a separate regression for each province. We use this regression to test for the poolability of the coefficients on the various explanatory variables. We exclude from the sample the municipalities since we learned in the previous section that the rural areas of the municipalities are special and have an important effect on the estimation. We should also notice that the definition of the rural part of a municipality is subject to geographical changes over time, which recommends their exclusion from the sample in any serious analysis of rural poverty in China.

Table 6 contains the test results. It shows that the coefficients on inflation and GDP per capita in the primary sector can be pooled across provinces while the rest of the explanatory variables (specially the trend) should not be pooled. Notice that this result is not very surprising since we are dealing with rural poverty and we showed before that, when the coefficient are forced to take a common value across provinces, only the growth rate of GDP in the primary sector has an effect on changes in poverty. The same results are found if we use the overall headcount index (rural plus urban areas) instead of the headcount index for the rural areas alone.

Table 7 presents the estimates of the restricted model with common coefficients for GDP in the primary sector and inflation. The elasticity of poverty with respect to GDP per 13
Policy Research Working Paper 0

# **Abstract**

Using the management and operational practices survey in the Russian Federation, this paper finds that an average Russian manufacturing firm adopts 43 percent of the structured management practices (a score of 0.43), a value that is far from the frontier (for example, the United States scores 0.62). This average mask the wide heterogeneity in practices, where a large share of firms adopt few structured management practices and only 3.5 percent of them have a score over 0.75. Consistent with the findings in other countries, better managed firms in Russia show stronger firm performance, measured as gross revenue per employee, value added per employee, total factor productivity, and employment growth. Improving the management score from the 10th to the 90th percentile is associated with an increase in sales per worker by 87 percent, value added per worker by 30 percent, and total factor productivity by 13.5 percent. What drives better management capabilities? Russian firms are similar to those in other countries, such that exporters and firms with foreign linkages are better managed. Switching from operating purely in the domestic market to being globally linked is associated with a significant increase in management capabilities. However, unlike the results in other countries, management capabilities in Russia are not associated with firm age, implying that firms do not learn to be better managed over their life cycle. This result points to the possibility of inefficient allocation of resources, such that learning and selection mechanism does not weed out the badly managed firms, perhaps due to the lack of pro-competitive forces.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at agrover1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
First, Pitt and Khandker (1998) use household survey data to assess the impact of microcredit on household welfare across several programs in Bangladesh. However, as in the case of cross-country regressions, omitted variable bias and reverse causation would bias the result of a simple OLS estimation, as illustrated by the following system: where _y_ is a measure of household welfare of household _i_ in village _j_ , _f_ is the amount of credit obtained by a household, _C_ is a vector of household characteristics, and _Z_ is a set of household or village characteristics that serve as instruments for the endogenous credit variable. μ and η are unobservable village characteristics, that are correlated with household welfare and credit, respectively. Correlations between μ and η and between ε and ν can result in a biased OLS estimate of β in (28). These correlation can arise because microcredit program placement is nonrandom, often related with specific village characteristics, such as poverty levels. Further, unmeasured household and village characteristics can influence both the demand for microcredit and household outcomes _y_ . Pitt and Khandker (1998) therefore use the exogenously imposed restriction that only farmers with less than a half-acre of land are eligible to borrow from microfinance institutions in Bangladesh as an exclusion condition to compare eligible and non-eligible farmers in program and non-program villages. Using survey data for 1,800 households and treating landownership as exogenous to welfare outcomes, they exploit the discontinuity in access to credit for households above and below the threshold and find a positive and significant effect of credit on household consumption expenditures. Morduch (1998), however, shows that mistargeting, i.e. allowing farmers with landholdings above the threshold to access microcredit, violates the exclusion condition, and that different econometric techniques exploiting the landholding restriction lead to different findings.

Coleman (1999) exploits the fact that future microcredit borrowers are identified before the roll-out of the program in Northern Thailand and can thus exploit the 30
observed contract labor intensity are those in which it is easier to use contract labor for core, industry-specific tasks. We then examine if the impact of the IDA amendment was larger in the high contract intensity group by including an interaction of a dummy for high contract intensity with the _RJ ∗ Treatment ∗ Post_ term in the difference in differences regression.<sup>36</sup> The results, presented in Table 9, show that one of the two main impacts of the IDA amendment, the switch towards contract labor, was felt more strongly in industries with inherently higher contract labor intensity in India. Specifically, the treatment impacts on contract and permanent employment, share of contract workers and incidence of contract workers (columns 2-5) were all significantly higher in the high contract labor intensity group. This suggests that the amendment may have eased binding regulatory constraints on contract labor hiring in firms with the potential to replace permanent workers with contract workers in core tasks.

Table 9: Pre-post DiD: Relative impact by Contract Labor Intensity

|VARIABLES|(1)<br>Log1 work|(2)<br>Log1perm work|(3)<br>Log1 cont work|(4)<br>Share - cont|(5)<br>Usage - cont|(6)<br>Implicit cost work(All inp)|
|---|---|---|---|---|---|---|
|RJ * Treatment L3 * Post * High Contract Intensity|0.177***|-0.363***|1.323***|0.180***|0.247***|0.019|
||(0.067)|(0.123)|(0.270)|(0.034)|(0.059)|(0.087)|
|RJ * Treatment L * Post|-0.075|0.041|0.014|-0.010|-0.008|-0.147*|
||(0.075)|(0.124)|(0.294)|(0.033)|(0.063)|(0.087)|
|Observations|66,909|66,909|66,909|66,909|66,909|66,909|
|R-squared|0.025|0.024|0.010|0.008|0.006|0.083|
|Number of fact<br>num|10,470|10,470|10,470|10,470|10,470|10,470|
|Firm FEs|Y|Y|Y|Y|Y|Y|
|Year Dummies|Y|Y|Y|Y|Y|Y|

Note: Sample restricted to firms with more than 100 workers. Treatment refers to firms with 100-300 workers. Firms are defined to have a high contract intensity if the average share of contract workers used in the corresponding sector at the national level before 2015 was more than 0.35. All dependant outcome variables represented in log forms here have been assigned an underlying value of 1 when they take a value of zero. Share of contract workers is defined as the share of contract worker in total worker hours used by the firm for that year. Implicit cost is defined using the expenditure on all raw materials as a base. Standard errors in parentheses, clustered at the firm level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.)

> 36This is a quadruple difference term, with all lower degree interactions also included but not shown for the sake of concision.

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13 and Mining dataset only starts in 1994 and thus limits the sample period for this part of our empirical analysis. Similar to the data on discoveries, data on exploration spending are broken down by country, type of natural resource and year.

# **Measure of market orientation**

Our prime explanatory variable in our empirical model (I) is market orientation or openness. For this we use data on the timing of economic liberalization for 133 countries during the years 1950 to 2004, originally constructed by Sachs and Warner (1995) (SW thereafter) and revised and extended by Wacziarg and Welch (2008). Following SW, the following criteria are used to classify a country as open: (i) the average tariff rate on imports is below 40%; (ii) non-tariff barriers cover less than 40% of imports; (iii) the country is not a socialist economy (according to the definition of Kornai (1992)); (iv) the state does not hold a monopoly of the major exports; and (v) the black market premium is below 20%. The resulting indicator is a dichotomous variable. If in a given year a country satisfies all of these above criteria, SW define it to be open and set the indicator to 1. Otherwise, SW set the indicator to 0.<sup>14</sup> While this indicator was originally designed to capture openness to trade, we follow Rodriguez and Rodrik (2001) and Buera et al. (2011) by viewing the SW indicator as proxy for broader economic openness and market orientation. Trade liberalization is usually just one part of a government’s overall reform plan for integrating an economy with the world system. Other aspects of such a program almost always include price liberalization, budget restructuring, privatization, deregulation, and the installation of a social safety net (Sachs and Warner, 1995). For our purposes, we capture a broad measure of market orientation with policy implications which often reverberate on the “openness” of the resource sector. Indeed, investment in exploration is worthwhile only if there are prospects for further extractive activities. Such a generic measure of market orientation allows us to capture a combination of factors such as favourable business climate including fiscal terms, political risks and access to relevant equipment and financing.<sup>15</sup> We thus use the indicator as proxy for country’s degree of market orientation.

> 14 Unfortunately, the disaggregated data for the components of the SW indicator are not available.

> 15 We prefer this to a resource-specific measure of market orientation, because generic measures typically capture de facto conditions and/or multilateral commitments (e.g., to the World Trade Organization) which are more
15 prevailing legal minimum in each year. The results appear in the first row of Table 3. We also applied the tails' method explained above, the results of which appear in the second row of Table 3, allowing a comparison with the L-C method. In general, our estimates show larger proportions of persons affected by MWs than those associated with the L-C method.<sup>16</sup> These results clearly indicate that the L-C method introduces an important underestimation of the actual coverag,. of the VW. Our methodological approach can be also used for calculating the expected increase in the population affected associated with a certain given increase in MWs.<sup>17</sup> B. Effect of MWs upon Specific Groups Table 4 presents the percentage of different population groups that would have been affected by MWs (i.e., potential 'covered, population) Table 3 Proportion of Persons Affected by the MW

|Method|1968|1977|1980|1985|
|---|---|---|---|---|
|Linneman-Castaneda|<br>0.69|0.00|10.36|12.34|
|Tails|10.44|0.01|24.44|25.57|

Source: Table 2 during the years 1980 and 1985. In general, the groups most affected by MW laws are the young and the less educated. This information also shows that the impact of MWs on unemployment (and probably also disguised unemployment), is important despite of the fact that MWs declined substantially in real terms in
# **The Political Economy of Teacher Management in Decentralized Indonesia**

_Andrew Rosser_<sup>_1_</sup> _Mohamad Fahmi_<sup>_2_</sup>

**Keyword** s: political economy, education, teachers, teacher management, Indonesia

**JEL Classifications:** D73, H75, I28, P16

1 University of Adelaide (University of Melbourne from February 2017). 2 Padjadjaran University.
private, and peer experience—is positive. We also assume, without loss of generality, that . That is, in the absence of any additional information, individuals are indifferent about the entry choice. vv̅ = cc **Skilled and Unskilled Investors:** The only _ex ante_ difference between potential investors is that a fraction of them have _skill_ , meaning their private signals about the value of entering the stock market are more informative. More concretely, we assume that the precision of the private signals of skilled individuals ξξ is higher than the precision of signals from those who are unskilled, . Since we are comparing SS UU individuals who self-selected into the training program, we do not model the explicit choice to acquire ττ > ττ information. In our setting, all potential investors already decided to become more informed by paying a registration fee and participate in a stock trading course. Instead, we model the investment choice of skilled and unskilled individuals, subject to observing different peer outcomes.

and unskilled individuals, subject to observing different peer outcomes. **Market participation:** The expected payoff from stock market participation, given signals , , pp ii and , for a student with skill level _K={S,U}_ is )ss̃, with ss̃ ee pp ii ee vv pp pp KK ii ee ee Σ<sup>−1</sup> ss̃ . An investor decides to start trading (EE[vv� |ss̃ , ss̃ , ss̃ ] = Σ(ττ) if the expected value under the vv̅ + ττ ss̃ + ττ ss̃ + ττ ss̃ vv pp KK ee posterior is above the fixed cost of investment, = ττ + ττ + ττ + ττ yy= 1. In terms of the private and market pp ii ee signals, an individual chooses to start trading actively if EE[vv� |ss̃ , ss̃ , ss̃ ] ≥cc . When we bring the pp pp KK ii ee ee model to the data, we observe peer outcomes from transaction records, but we do not observe the private ττ ss̃ + ττ ss̃ ≥−[ττ (ss̃ −cc)] information of potential investors. In turn, we calculate the probability of entry unconditional on the private and market signals by integrating over all possible and . Consequently, the probability of market ii pp participation given the peer signal and the skill level is: ss̃ ss̃ (1) where Φ() is the cumulative distribution of the standard normal, Pr(yy= 1 | ss̃ee, KK) = Φ[ββKK(ss̃ee −cc)] and = KK ee KK KK 1 2 . Since is always positive, this implies the following: HYPOTHESIS 1: ββ = ττ /TT _Under_ TT ~~�~~ ττvv �ττpp<sup>2</sup> + ττKK�+ ττpp + ττKK ββKK _accurate communication, stock market entry is monotonically increasing in peer outcomes : >0._ ee ee ss̃ ∂∂Pr/∂∂ss̃

11
municipalities. Attanasio et al. (2010) provide an evaluation of the program’s main impacts.

# **<<B>>** _<mark>The CCT Program and Divorce</mark>_

Before studying the interaction between CCTs and the permanent absence of the father, we address the potential concern that the absence of the father—particularly in the case of divorce—may be affected by CCTs. Indeed, there is direct evidence of positive effects of the PROGRESA CCTs on the divorce rate in Mexico (Bobonis 2011) and indirect evidence that the _Familias en Acción_ CCTs may have increased the bargaining power of women (Attanasio, Battistin, and Mesnard 2012). Therefore, women receiving CCTs might be expected to transit more readily out of relationships.

To estimate the effect of CCTs on divorce, we use data from the first and second follow-ups only (because there is no variation in the outcome of divorce at the baseline; see note 3).<sup>22</sup> We estimate the following regression at the household level on our sample of households: pooling _t_ = 2 and _t_ = 3, where _yjt_ is a dummy variable indicating whether the parents living in household _j_ divorced between periods _t_ – 1 and _t_ and _Tjt_ is an indicator equal to one if household _j_ lives in a municipality that is receiving CCTs at time _t_ and zero otherwise. Note that _Tjt_ = 1 for {(early-treat = 1 or mid-treat = 1) and _t_ = 2,3} and for {late-treat = 1 and _t_ = 3}. _Xjt_ are time-varying measures of the composition of children in the household in period _t_ ; _Ijt_ – 1 is a vector of dummies indicating whether the household 21