rafmacalaba/datause-encoder-data huggingface dataset:
**Table 17 OLS: Village fixed effects regression of teacher effort, MP** (Dependent variable has four observations per teacher: Attendance=1 if teacher present, 0 otherwise: Activity=1 if teacher actively <u>engaged in teaching, 0 otherwise)</u>

||**Attendance**|**Attendance**|**Activity**|**Activity**|
|---|---|---|---|---|
|**Whether****_samvida shikshak_**<br>**(contract teacher)**|**0.06**|**0.08**|**0.07**|**0.10**|
||**(0.18)**|**(0.14)**|**(0.08)**|**(0.03)***|
|**Whether****_shiksha karmi_**<br>**(former contract teachers)**|**0.02**|**0.02**|**0.03**|**0.05**|
||**(0.66)**|**(0.62)**|**(0.32)**|**(0.16)**|
|Age|-0.01|-0.01|-0.01|-0.01|
||(0.00)**|(0.04)*|(0.01)**|(0.01)**|
|Caste (1 if non-SC/ST)|-0.01|-0.01|0.02|0.02|
||(0.61)|(0.66)|(0.18)|(0.16)|
|Gender(1 if male)|0.05|0.05|-0.01|-0.01|
||(0.15)|(0.15)|(0.81)|(0.66)|
|College educated|-0.05|-0.05|-0.06|-0.06|
||(0.08)|(0.14)|(0.04)*|(0.05)*|
|Graduate degree|-0.03|-0.03|-0.03|-0.04|
||(0.41)|(0.45)|(0.30)|(0.23)|
|Pre service training|-|-0.03|-|-0.03|
|||(0.35)||(0.38)|
|Years ofservice|-|0.00|-|0.01|
|||(0.68)||(0.04)*|
|Days of inservice training|-|0.00|-|0.00|
|||(0.55)||(0.23)|
|Village fixed effect (198<br>villages)|YES|YES|YES|YES|
|R<sup>2</sup>|.23|.23|.22|.22|
|Observations|1800|1792|1800|1792|
|Robust p valuesinparentheses|clustered at teac|her level|||
|* significant at5%;** significa|nt at 1%||||

31
2

# **1.  Introduction**

The financial crisis that emerged in the United States in mid-2007 quickly transformed into a global credit crunch that sharply reduced global trade flows. Many countries were affected by the severe global recession that followed, and millions of individuals throughout the world lost jobs, leading to increased informality and working poverty. In the aftermath of the crisis, policymakers throughout the world are questioning whether existing labor market regulations successfully balanced the competing goals of maintaining flexibility and protecting workers.

While discussions over the scope of labor market regulations are often heated, views are typically based either on the predictions of theoretical models or anecdotes. The lack of evidence is particularly acute for developing countries, where hard data are rare and mechanisms for enforcing regulations tend to be limited. Lack of enforcement is a particularly serious concern, given that significant portions of workers report earning wages that fall below the statutory minimum.<sup>2</sup> No systematic evidence, however, exists regarding compliance with other labor regulations, making it difficult to make an informed assessment of the costs and benefits of regulatory reform.

This paper contributes new evidence on the compliance of firms with severance pay regulations in Indonesia, and the extent to which changes in severance pay regulations could affect employment rigidity. This evidence sheds light on a vigorous policy debate that arose in Indonesia prior to the onset of the global financial crisis, following the passage of a 2003 labor law that increased employment protection and severance pay. The debate pitted the business community, which warned that increases in severance pay regulations would reduce employment, against organized labor, which argued that severance pay makes an important contribution to income security.

The reaction to proposed changes to severance pay regulations in 2006 exacerbated this debate. Following persistent lobbying by the business community, as well as firm surveys that showed that high severance pay obligations were a serious concern, the easing of severance pay requirements became a major part of investment-

> 2 See, for example, World Bank (2011) finds that 40 percent of wage workers report earnings below the minimum wage in Indonesia in 2007. This is broadly consistent with reported estimates that noncompliance rates are as high as 33 percent in Costa Rica (Gindling and Terrell, 2007) and 45 percent in South Africa (Bhorat et al, 2010).
<!-- Start of picture text --> Figure 3<br>China<br>Industry Share of CO2 Emissions<br>and Four Energy Intensive Industries Share<br>of Industry CO2 Emissions<br>1.0<br>0.8<br>0.6<br>CO2 Emissions from 4 Energy Intensive Industries as a Share of Industry Emissions<br>CO2 Emissions from Industry as a Share of Total Emissions<br>0.4<br>0.2<br>0.0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Source: IEA Online Data Services, 2012<br>Share (%) of CO2 Emissions<br><!-- End of picture text --> Along the way, we argue that China’s success in reducing the CO2 intensity of industrial production has not been easy or without cost. Following adoption of a new industrial policy to “grasp the large, let go the small” (Sutherland 2003: 2 and 10), attempts to create a small number of technologically sophisticated national champion enterprises that can save energy and dominate in each of these industries have not been entirely successful.<sup>8</sup> Even where successful, some (World Bank 2012) have worried about the economic costs associated with the anticompetitive effects of this strategy. Others (Allen et al 2008) argue that bank loans to China’s state-owned enterprises, including those to national champions, undermine an efficient allocation of capital thereby imposing additional significant economic losses on the Chinese economy. Finally, given China’s highly fragmented industrial structure (Oh 2012: 5) and its highly decentralized industrial governance system, it should not be surprising that the central government has not been entirely successful in “letting go the small” by closing nearly all of the small scale, energy inefficient, and polluting facilities in these industries. Yet despite these problems, as we show, China has been quite successful in reducing the CO2 intensity of production in the industries we study.

Our argument proceeds in four steps. Because improving energy efficiency in particular enterprises and industries depends on the ability of enterprises in particular industries to successfully adopt the minor process changes and the major technological changes that enable them to reap environmental technique effects, the analytical frame of technological learning/upgrading is at the center of our analysis. After delineating the major elements of such an analytical framework, we provide a historical overview of the evolution of industrial structure and technological capabilities in these four energy intensive industries in China. The focus here is on the evolution of policies and institutions affecting these industries that ultimately culminated in high speed technological catch-up in enterprises in these industries and by their domestic capital goods and services suppliers.

> 8 See discussion in the appendix.

4
five times as productive as that to insiders, and privatization to foreigners or blockholders is three times as productive as privatization to insiders. The effect of different ownership regimes varies among regions.

A second example based on the privatization experience of Central and Eastern Europe illustrates the efficiency impact of privatization. Frydman et al. (1999) draw on firm data from the Czech Republic, Hungary, and Poland for the period 1990–93 and seek to disentangle the efficiency effect of privatization according to type of ownership. Their study is also interesting because it includes a control group (a counterfactual) consisting of firms that were SOEs at the beginning of the period and remained SOEs at the end of the period. The only shortcoming of this work is that no correction has been made for the selectivity of the privatization process. Privatized firms in the sampled countries were not selected randomly. In the countries covered by Frydman et al., privatization improved efficiency when the firms were acquired by outsiders (Figure 7). Revenue increased, and employment did not change, leading to sizable productivity gains. Interestingly, when privatization entailed the transfer of ownership to firm insiders (either the managers or employees working in the SOE before privatization), the impact on efficiency was radically different. Revenue decreased in comparison with the control group, employment increased, leading to a drop in productivity. A possible explanation is that managers and workers of a privatized SOE try to extract more rent from the firm when they are in command.

Finally, the survey by Estrin et al. (2007) addresses more rigorously the issue of selection bias or endogeneity that arises when better firms may have been privatized first in transition economies. The authors present some new results, as they can draw on recent studies in transition economies that employ better methodology, as well as studies for China that were barely reviewed in the two surveys cited above. Broadly, Estrin et al. find that privatization to foreign owners increases efficiency in relation to SOEs, although this result is less clear for China. Domestic private ownership has a positive effect on efficiency in Central and Eastern Europe, but the effect is smaller than that of foreign ownership. Russia seems to be different in that domestic private ownership and mixed ownership have a negative or insignificant effect on efficiency. Worker ownership in Central and Eastern Europe and the CIS, and collective ownership in China, do not seem to have a negative effect relative to other forms of private ownership, unlike the findings in the Djankov and Murrell study. Finally, data from Central and 13
3D PRINTING AND TRADE 9 The coefficient on the indicator variable, β, captures differences in changes in export of hearing aids that are due to the introduction of the 3D printing technology. If the technology allows for the disaggregation of production, with printing performed geographically close to consumers (as it is implicitly assumed in recent reports -see the Introduction), the coefficient should be negative. On the other hand, if 3D printing, similarly to other technologies, expands quality or lowers prices, trade should expand, and the coefficient would be positive.

The econometric analysis shows that the average impact of 3D printing on trade in hearing aids is positive and statistically significant (Table 1). The results in the first column of Table 1 indicate that as hearing aids became 3D printed trade flows increased by up to 78 percent. The results are robust to using products that are high-tech and/or under HS chapter 90 in the control group. In our preferred specification, we compare trade in hearing aids to other high-tech products in HS chapter 90 – optical, photographic, cinematographic, measuring, checking, precision, medical or surgical instruments and apparatus. These are products with similar R&D and technology intensity that are likely to be subject to similar demand and supply shocks as hearing aids. In this specification, we find that 3D printing increases trade by 58 percent (from column 4, exp(0.456)-1). This effect could be interpreted as a lower bound, as there is evidence of 3D printing being used to some extent for the production of other high-tech products within chapter 90.<sup>11</sup> _Table 1: Effects of 3D printing on exports in hearing aids_

|Sector control<br>VARIABLES|(1)<br>HS Ch. 90 or<br>High-tech<br>log(exports)|(2)<br>HS Ch. 90<br>log(exports)|(3)<br>High-tech<br>log(exports)|(4)<br>HS Ch. 90 and<br>High-tech<br>log(exports)|
|---|---|---|---|---|
|(Hearing Aid 2007-)|0.580***<br>(0.158)|0.550***<br>(0.157)|0.561***<br>(0.159)|0.456***<br>(0.157)|
|Observations|476,248|234,932|360,007|118,678|
|R-squared|0.847|0.859|0.844|0.862|
|Period|1995-2016|1995-2016|1995-2016|1995-2016|
|Country-Year FE|YES|YES|YES|YES|
|Country-Product FE|YES|YES|YES|YES|
|% Zeroes|.44|.40|.44|.37|

Note: Robust standard errors, clustered at the country-product level, are in parentheses. Countries with population over 5mln.

*** p<0.01, ** p<0.05, * p<0.1

11 For instance, orthopedic appliances and other artificial parts of the body.
6 studies<sup>10</sup> . The values for these indices for the period 1981-2004, along with the corresponding mean and median incomes, are presented in Table 1 below.<sup>11</sup>

**Table 1: Brazil 1981-2004: Incomes and Summary Measures of Inequality**

|**Year**|**Mean**<br>**income**|**Median**|**Gini**|**E (0)**|**E (1)**|**E (2)**|
|---|---|---|---|---|---|---|
|1981|336.7|173.2|0.574|0.613|0.647|1.447|
|1982|348.5|178.9|0.581|0.629|0.669|1.552|
|1983|273.4|137.5|0.584|0.631|0.675|1.515|
|1984|273.2|136.3|0.583|0.626|0.679|1.464|
|1985|331.7|163.4|0.589|0.649|0.696|1.622|
|1986|483.6|249.4|0.578|0.620|0.673|1.637|
|1987|362.6|181.7|0.592|0.666|0.710|1.791|
|1988|338.9|161.1|0.609|0.714|0.750|1.742|
|1989|382.7|170.6|0.625|0.757|0.811|2.212|
|1990|347.3|167.5|0.604|0.700|0.735|1.767|
|1992|302.3|162.8|0.573|0.628|0.666|1.876|
|1993|320.7|157.2|0.595|0.678|0.743|2.308|
|1995|385.7|190.1|0.591|0.659|0.705|1.627|
|1996|393.9|194.1|0.591|0.664|0.700|1.609|
|1997|401.2|198.3|0.593|0.668|0.709|1.739|
|1998|404.0|203.7|0.591|0.658|0.707|1.672|
|1999|385.8|198.3|0.585|0.641|0.685|1.530|
|2001|393.4|199.2|0.586|0.646|0.697|1.661|
|2002|396.3|204.6|0.580|0.628|0.677|1.522|
|2003|381.2|201.7|0.575|0.619|0.663|1.474|
|2004|393.9|210.0|0.564|0.591|0.644|1.618|

Note: Incomes are monthly household incomes per capita, measured in September 2004 Reais. Source: Authors' calculations from the PNADs.

Two main features of the data jump out from Table 1. The first is the difference between mean and median income: the median-to-mean ratio ranges from 0.446 in 1989 to 0.539 in 1992. This

> 10. The Gini coefficient is only perfectly decomposable when sub-groups of the population do not overlap in the space of incomes.

> 11 While households with total incomes equal to zero are included in the distributions used to calculate mean and median incomes, as well as poverty measures, they are excluded from the calculations of inequality measures. Such households range from 0.5% to 2.0% of the sample. The Gini coefficient and E(2), which can also be computed including the zero values, are not much affected by this exclusion, as can be seen from the comparison in Appendix 3. Trends are entirely unaffected.
<!-- Start of picture text -->
#of Countries Year of Copenhagen Amendment (1992) Ratification<br>30<br>25 One Nonsignatory Party<br>20<br>15<br>10<br>5<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>MOE non-Article 5 Countries [____ Article 5 Countries<br><!-- End of picture text -->

<!-- Start of picture text -->
# of Countries Year of Montreal Amendment (1997) Ratification<br>30<br>25 One Nonsignatory Party<br>20<br>15<br>10<br>5<br>0<br>1990 1995 2000 2005 2010 2015 2020<br>MO non-Article 5 Countries [Article 5 Countries<br><!-- End of picture text -->
across the study countries suggest that on-farm trees are also a vital part of larger rural landscapes. As elsewhere in the developing world (Sayer et al., 2013), policies and practices designed to improve the management of such trees in Africa therefore hold significant promise for helping to reconcile the sometimes conflicting goals of reducing rural poverty, conserving biodiversity, and mitigating climate change.

Our second major finding is that trees on farms deliver sizeable economic benefits to rural households. Across the rural population as a whole, production from trees on farms accounts on average only for 6% of total annual gross income. Yet, this increases to 17% on average for those households growing trees on their farms. By way of comparison, these results are similar to available evidence on forest and environmental income. For example, a recent study using national-scale data from Mexico (LópezFeldman, 2014) found that forest and other environmental resources contributed 6.2% to total incomes for Mexican rural households. For those households living in or near forests in a cross-national comparative study (Angelsen et al., 2014), income from natural forests and forest plantations accounted for 21% of total household income. Together, these studies suggest that trees—in forests and outside forests—provide significant income to rural households in Africa, especially but not only to those living with trees nearby or on their land.

Finally, results from models of the determinants of the adoption of and land allocation to trees on farms highlight the importance of national institutional and policy contexts in understanding differences in onfarm tree growing. Together they account for more than 40 percent of the explained variation of the models. Proximity to forests proves also an important predictor. Households with more land tend to allocate more of their land to trees (both cash crop and fruit trees). Female-headed households tend to be less engaged in tree growing, with the effect being largest for tree cash crops (possibly linked to higher land tenure insecurity for female farmers and consistent with the higher nutritional value of fruit trees).

Despite the already non-negligible prevalence and economic contribution of trees on farms that this paper demonstrates, the numbers are likely still underestimates. The household data are unlikely to fully account for non-crop trees (e.g. for shade or different kinds of non-provisioning ecosystem services) and trees with no immediate productive function because they were not queried explicitly in the LSMSsurveys. The indirect effects of trees on farms on crops, livestock, and other productive activities are also very difficult to account for (Wunder et al., 2014) and information on them was not directly collected in 15
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE viability of public utilities. In addition, the government has recently passed a public-private partnership law to encourage investment and improve public service delivery (EIU 2010 and EIU 2011). The government has recently committed to encouraging multiple layers in the generation and distribution market and opening itself to private and public investors as Independent Power Producers (Davidson 2011).

# **Challenges**

Even with the addition of the Bumbuna plant, Sierra Leone‘s power-generation capacity falls short of meeting power demand. Sierra Leone‘s current installed capacity of 13 megawatts per million people is lower than that of other low-income and fragile states. Hydropower from Bumbuna is seasonal, producing around 50 megawatts during the wet season, but less than 20 megawatts only during the dry season. There is virtually no power generation at the Kingtom power plant due to malfunctioning of the large generators at the plant. Serious cash flow problems for NPA hindered purchase of spare parts to replace the malfunctioning units at the plant and hindered purchase of fuel required to power the diesel plants (World Bank 2007). Therefore, chronic power shortages plague the system. Existing supply meets only half the suppressed demand for Freetown, let alone the rest of the country (EIU 2009). As a result, Sierra Leone has to continue to rely on independent power producers to provide up to 40 megawatts of emergency generation.

But Sierra Leone pays high prices for this emergency generation relative to many of its neighbors. At peak 2008 oil prices, emergency generation cost $0.23–0.34 per kilowatt-hour, including fixed charges of $0.06 per kilowatt-hour and fuel charges of $0.17–$0.28 per kilowatt-hour based on international oil prices (World Bank 2007). In 2008, Sierra Leone paid as much as $0.40 per liter more for diesel than Ghana, and $0.12–0.13 more than Liberia and Guinea (table 3).

**Table 2.  Sierra Leone pays more for fuel than most of its neighbors**

<u>US cents/liter</u>

||1991|1998|2000|2002|2004|2006|2008|
|---|---|---|---|---|---|---|---|
|Sierra Leone|43|53||50|89|98|115|
|Liberia|||||77|85|103|
|Guinea|61|56|69|56|69|82|102|
|Ghana|43|30|19|23|43|84|90|
|Côte d' Ivoire|115|45|51|60|95|106|120|

_Source_ : GTZ 2010.

Power transmission and distribution systems are in the insipient stage and not able to keep up with increase in generation capacity. Sierra Leone‘s electricity sector is mainly composed of the western-area grid centered on Freetown. Years of civil war left provincial power systems, with the exception of the BoKenema power services, in a state of disrepair. Distribution and transmission networks have not expanded along with generation capacity. While generation has increased by up to 50 megawatts with the opening of the Bumbuna plant, the distribution networks, as of the end of 2009, were only able to support 20 megawatts. Despite planned expansion of the transmission network, progress has been slow and aggravated by vandalism. Measures are needed to curb the theft of overhead and underground cables (EIU 2009).

15
_Table 2- summary statistics. Mean, standard deviation, number of observations, and difference with Afghan are reported._

|**LFS**|**variables:**||**Afgha**|**n**||**Iran**|**-matche**|**d**|||**Iran**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||mean|SD|N|mean|<br>SD|N|diff|mean|SD|N|diff|
|(1)|Working hours per week|48.06|<br>17.11|<br>27956|<br>46.96|<br>17.666|<br>31170|<br>1.1**|46.29|18.32|<br>1467624|1.78***|
|(2)|Unemployment dummy|0.048|<br>0.213|<br>49166|<br>0.05|0.213|<br>49166|<br>0|0.108|0.31|1902164|-0.06***|
|(3)|Unemployed due to layoff|0.085|<br>0.278|<br>1704|0.11|0.314|<br>1685|<br>-0.026**|0.110|0.31|115124|-0.03***|
|(4)|Unemployment length|7.07|10.68|7<br>2346|9.65|13.619|<br>2348|<br>-2.6***|19.35|22.80|<br>206983|-12.3***|
|(5)|Unskilled job|0.51|0.5|49166|<br>0.50|0.5|49166|<br>0.01***|0.15|0.36|1902164|<br>0.36***|
|<br>**T**|<br>**ransition dummies (only for rotating panel sampl**|**e, not av**|**ailable f**|**or 2012,**|**2017 , 2**|**019):**|||||||
|(6)|Replaced (Sample household replaced next year?)|0.129|<br>0.335|<br>12186|<br>0.096|<br>0.295|<br>12812|<br>0.032***|<br>0.037|0.188|<br>536055|0.092***|
|(7)|Unemployed to employed<br>(For currently unemployed: employed next year?)|0.778|<br>0.416|<br>388|0.701|<br>0.458|<br>415|0.077**|0.546|0.498|<br>37271|0.232***|
|(8)|Employed to unemployed<br>(For currently employed: unemployed next year?)|0.029|<br>0.167|<br>8538|0.044|<br>0.206|<br>9499|<br>-0.02***|0.045|0.208|<br>395141|-0.02***|
|(9)|Upward mobility (For unskilled: skilled next year?|)<br>0.255|<br>0.436|<br>4604|0.313|<br>0.464|<br>4891|<br>-0.06***|0.401|0.490|<br>62419|-0.15***|
|(10)|Downward mobility<br>(For skilled: unskilled next year?)|0.259|<br>0.438|<br>3688|0.195|<br>0.396|<br>4187|<br>0.064***|<br>0.076|0.265|<br>314817|0.183***|
|**HEI**|**S variables:**|**A**|**fghan**|||**Iran-m**|**atched**||||**Iran**||
|**All v**|**ariables are logs of values at fixed price**|mean|SD|N<br>|mean|SD|N|diff|mean|SD|N|diff|
|<br>**(11 )**|<br>Total expenditure p.c. (past month)|14.67|0.50|1374|14.73|0.59|1374|-0.055**|14.92|0.67|340153|-0.243***|
|**(12 )**|Food expenditure p.c. (past month)|13.38|0.50|1373|13.39|0.56|1373|-0.009|13.59|0.59|340084|-0.207***|
|**(13 )**|Total income (past year)|18.12|0.51|1375|18.16|0.53|1375|-0.044**|18.15|0.73|340758|-0.031**|
|**(14 )**|Earned income (past year)|17.85|0.58|1336|17.65|0.68|1307|0.197***|17.68|0.85|263790|0.171***|
|<br>**(15 )**|<br>Wage rate (monthly, average over past year)|9.99|0.42|1042|9.92|0.45|930|0.074***|10.01|0.57|120896|-0.018|
|<br>**(16 )**|<br>Aid received (past year)|16.65|1.37|45|15.19|1.21|142|1.459***|15.02|0.99|56556|1.629***|
|<br>**(17 )**|<br>Transfers from other households (past year)|16.35|1.18|91|16.20|1.17|79|0.146|15.02|1.55|64636|1.324***|
|<br>**(18 )**|<br>Free goods/services received (past year)|15.42|1.50|198|14.39|1.67|729|1.027***|14.05|1.73|223442|1.374***|
|**(19 )**|Donation (past year)|12.04|1.33|927|12.18|1.33|1067|-0.137**|12.99|1.57|272461|-0.948***|

23