rafmacalaba/datause-encoder-data huggingface dataset:
# **_Import liberalization and globalization measures_**

Trade liberalization can also be measured in many different ways. The primary choice is between policies – tariff reductions, elimination of non-tariff barriers, etc -- that are thought to help globalization, and outcomes such as trade volumes that are a consequence of trade policies. Both approaches have been used in the literature. Most of the studies reviewed here used trade shares as their measure of globalization. Lundberg and Squire (2003) use the Sachs-Warner Index which, although linked to policies, has been criticized on the grounds that it captures more that trade policy. Edwards (1997) uses a variety of policy measures: average tariff; average QR (quantitative restrictions) coverage; and average black-market premium. Savvides (1998) uses a specially created measure of protection covering both tariff and non-tariff barriers compiled from UNCTAD data at the four-digit level of the Customs Cooperation Council Nomenclature. The measure is only available, however, for 1988. Finally, those studying wage-inequality within a country are often able to make use of industry-specific tariff rates and quotas.

All of the various ways of specifying variables representing trade liberalization are useful and answer interesting questions. If trade volumes are chosen then the study says something about the impact of trade volumes on inequality. And for some purposes that may be an interesting question. But, in our view, it does not say much about the impact of policy on inequality, primarily because trade volumes are not determined exclusively by policy. A wide range of factors will influence a country’s trade volume: country’s geography, technology, demand conditions in importing countries, competitors’ supply conditions, weather, and so on. Even attempts to control for these other factors will inevitably leave a residual that captures more than trade policies. We suspect that widespread reliance on trade volumes in the empirical literature reflects the relative ease of obtaining data compared with the difficulty of achieving the same for trade policies. Since we are interested primarily in how pro-openness reforms affect inequality, we prefer to focus on policies and thus place ourselves squarely in the policymaker’s corner. We attempt to answer the question that many policy-makers naturally 29
# _4. Contract Responsibilitv System_ (CRS) and linking wage increase with profit

increas." The counterpart to the Household Responsibility System of the Chinese agricultural reform,12 CRS was probably intended to be the most dramatic reform for the state enterprises. Under a CRS contract, the manager had the legal right to operate the firm, and was granted some discretion to make decisions within the firm for an agreed duration, usually 3--4 years.'<sup>3</sup> Typically, a CRS contract specified the distribution of value added between the state and the firm, the performance requirements such as the minimum annual expenditure on capital maintenance, the number of new products to be developed, the volume of output and its price to be delivered to the state, the dependence of CEO compensation on the performance of the firm, and the _ex ante_ firm-level wage elasticity with respect to profit. Note that this aggregate pay sensitivity existed _only_ when the firm was under the CRS.

Most firms adopted CRS only after 1986. There was virtually no firm with a CRS contract in 1980, only 2% of them in 1984, but 42% in 1987, and 88% in 1989.

_5._ Increasing management turnover. The share of firms with management turnover generally increased over the decade, especially in 1983-85 and 1987-1989. The increase, however, is by no means smooth: it was 9% and 6% in 1980 and 1982, then jumped to 16% and 14% in 1984 and 1985; it dropped to 7% in 1986, then increased to 15% in 1987, dropped again to 10% and 9% in 1988 and 1989.

## **III. HYPOTHESES OF THE EFFECTS OF DECENTRALIZATION**

> **itAll** information about CRS is from _CRS in Practice,_ complied by the Research Group for The Chinese Firm System Reform.

> 12Lin (1993), McMilan et **al.** (1987) attributed the unprecedented increase of farmning productivity in China in the first half of the 1980s largely to the adoption of the household responsibility system.

> 13Usually, the contract for a **firm** implementing CRS was signed by the industrial bureau, which was a branch of government in charge of the industrial state enterprises, or some representatives of the firm, along with the manager--the winner among bidders for the contract. The winner of the bid was determined by a committee representing the government, and sometimes also the employees of the **firm.**

7
_Table 1 Descriptive statistics on income and poverty per Household Income Survey (HIS) round (selected years)_

|||**2004**|||**2009**|||**2016**||
|---|---|---|---|---|---|---|---|---|---|
|Mean income (2016 MYR)|974|||1,103|||1,690|||
|Median income (2016 MYR)|654|||750|||1,256|||
|Poverty headcount ratio|0.400|||0.338|||0.105|||
|_N_|36,483|||43,026|||78,297|||
|Poverty headcount (age 25–55)|0.409|||0.340|||0.107|||
|N (age 25–55)|25,893|||30,774|||54,993|||
|**_Poverty figures subgroups_**|Pop. share|Pov. ratio|Pov. share|Pop. share|Pov. ratio|Pov. share|Pop. share|Pov. ratio|Pov. share|
|Bumiputera|0.663|0.491|0.814|0.685|0.405|0.821|0.681|0.131|0.853|
|Chinese Malaysian|0.248|0.177|0.110|0.233|0.149|0.103|0.218|0.024|0.050|
|Indian Malaysian|0.084|0.334|0.070|0.077|0.305|0.070|0.088|0.073|0.061|
|Other Malaysian|0.005|0.525|0.006|0.004|0.429|0.005|0.013|0.286|0.036|
|Urban|0.639|0.284|0.454|0.663|0.237|0.466|0.774|0.071|0.527|
|Rural|0.361|0.606|0.546|0.337|0.536|0.534|0.226|0.219|0.473|
|Peninsular Malaysia|0.807|0.353|0.713|0.810|0.288|0.692|0.828|0.072|0.572|
|East Malaysia*|0.193|0.596|0.287|0.190|0.547|0.308|0.172|0.261|0.428|
|East Coast**|0.151|0.563|0.213|0.155|0.465|0.213|0.142|0.159|0.216|
|Other Peninsular|0.656|0.305|0.500|0.655|0.247|0.478|0.686|0.054|0.356|
|Head is a man|0.897|0.401|0.898|0.886|0.336|0.881|0.867|0.103|0.852|
|Head is a woman|0.103|0.393|0.102|0.114|0.353|0.119|0.133|0.117|0.148|
|Urban Peninsular|0.541|0.259|0.350|0.561|0.208|0.345|0.668|0.053|0.340|
|Rural Peninsular|0.267|0.545|0.363|0.249|0.470|0.347|0.160|0.151|0.231|
|Urban East Malaysia|0.099|0.422|0.104|0.103|0.396|0.121|0.106|0.185|0.187|
|Rural East Malaysia|0.094|0.779|0.183|0.088|0.722|0.188|0.066|0.383|0.242|
|Urban Bumiputera|0.355|0.359|0.319|0.391|0.287|0.332|0.480|0.089|0.407|
|Rural Bumiputera|0.308|0.643|0.495|0.294|0.562|0.489|0.202|0.232|0.445|
|Peninsular Bumiputera|0.512|0.432|0.553|0.531|0.345|0.543|0.550|0.092|0.480|
|Peninsular non-Bumiputera|0.295|0.217|0.160|0.278|0.181|0.149|0.278|0.034|0.091|
|East MY Bumiputera|0.151|0.692|0.261|0.154|0.612|0.279|0.131|0.298|0.372|
|East MY non-Bumiputera|0.042|0.247|0.026|0.037|0.272|0.030|0.041|0.143|0.056|

* Labuan, Sabah and Sarawak; ** Kelantan, Pahang and Terengganu. Source: Authors’ calculations based on DOSM data. Note: All figures are aggregated from household-level data using sampling and

population weights. Mean and median income are in terms of gross monthly household income per capita in 2016 Malaysian ringgit. The poverty line is defined as the 40th percentile of household income per capita in 2004, i.e., MYR 527 per month in 2016 prices. The poverty figures by subgroup give, per survey round: how large the group is as a proportion of the overall population (first column), the headcount ratio within the subgroup (second column), and what share of the total number of poor this represents (third column).

8
Technological innovation plays an important role in reducing the costs of both types of environmental protection, and thus promoting green growth. One reason that upper-middle and high income countries have been able to achieve better environmental quality for some pollutants such as local air quality is through their use of advanced pollution abatement techniques.<sup>2</sup> While the use of these technologies often entails initial costs, which can engender debate over nearterm gains relative to reduced disposable income, the benefits they provide, which may come in the form of improved health and quality of life as opposed to increased disposable income, are often substantial.<sup>3</sup> Similarly, the development of more efficient appliances, vehicles, and industrial equipment allows for greater energy efficiency and lower cost of resource use. Many of the fixed costs of technology development have already been paid by developed countries. Thus, in many cases, it is the transfer of these technologies to developing countries that is important.

Moreover, the concern is not just for the local environment in developing countries. Global environmental problems are also an issue. While emissions in high-income countries begin to stabilize, the share of emissions coming from developing countries is growing. In 2010, 75% of the growth in CO2 emissions came from non-OECD countries.<sup>4</sup> CO2 emissions from non-OECD countries are projected to be nearly double of those from OECD countries by 2035 (Energy Information Administration, 2010). Due to the growth in emissions from developing countries, designing policy that encourages the transfer of clean technologies to developing countries has been a major discussion point in climate negotiations.

This paper reviews the growing literature examining the links between technological change, environmental policy, and economic performance. Technological change proceeds in

> 2 See, for example, Dasgupta _et al_ . (2002). 3 For a recent review of such studies in a U.S. context, see Graham (2007). 4

> <u>http://www.iea.org/index_info.asp?id=1959, accessed August 5, 2011.</u>

3
**Figure 4: Evolution of average levels of efficiency of government expenditure, 1990–2002**

<!-- Start of picture text --> Mean Efficiency Transport Energy<br>0.70 0.80<br>0.60 0.70<br>0.50 High Mean 0.60 High Mean<br>0.50<br>0.40 Low Mean Low Mean<br>0.40<br>0.30 Lower middle Mean 0.30 Lower middle Mean<br>0.200.10 Upper middle Mean 0.200.10 Upper middle Mean<br>0.00 0.00<br>199019911992 1993199419951996199719981999 200020012002 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>years years<br>Education<br>1.00<br>0.95<br>0.90 High Mean<br>0.85<br>Low Mean<br>0.80<br>0.75 Lowerrmiddle Mean<br>0.70 Upper Middle Mean<br>0.65<br>0.60<br>1990 1998 1999 2000 2001 2002<br>Years<br><!-- End of picture text --> In transport, the story is driven by what happens in the road sector. Given that support for the roads sector is one of the largest sources of public expenditure in transport, the general results should not be too far from what would have been generated using a full set of information on public expenditures. The main story emerging is that there are no statistically significant differences across income groups. The richer countries do a little bit better, but this is to be credited mostly to the much higher level of air traffic picked up in our second output for transport.

In energy, the story is very different. The performance levels differ sufficiently across income groups to imply some relevance. On the one hand, the poorer the countries, the more likely it is that the expenditure composition will be dominated by investment. On the other hand, the richer the country, the more likely it is that operation and maintenance expenditures will drive the story. Ideally, we should be able to assess the efficiency of capital expenditures (Capex) and operational expenditures (Opex) separately, as is done for private utilities under a price cap regime, but the data needed to do that do not exist at this level of aggregation. There are other problems as well. For instance, the fact that the cost of fuel is picked up in the assessment may distort the story, in particular given that the expenditure levels are expressed in constant prices. But this should not be sufficient to explain the nearly three times difference between the poorest and richest countries. In spite of the data problems, the upshot of our results is that even if there seems to be a clear scope for improvements in efficiency, the poorest countries are catching up to some degree.

For education, there are differences across income groups, but these are predictable given the indicator used and that quality considerations are not well modeled. The most 17
<!-- Start of picture text --> 120 000<br>100 000<br>—— Mine workers<br>Textile workers<br>80 C00<br>60 000<br>40 000<br>20 000<br>0<br>1990 1991 1997 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text -->
24

|**Table 4: Averages by Return**|**Migrant Stat**|**us**|||
|---|---|---|---|---|
||_Return_|_Non-_|_Differ_|_ence_|
|_Variables_|_Migrant_|_Migrant_|||
|_Basic Individual Characteristics_|||||
|Male∆|0.89|0.41|0.48|***|
|Married∆|0.87|0.64|0.23|***|
|HH Head∆|0.61|0.24|0.36|***|
|Years of Age<br>|39.75<br>|36.91<br>|2.84<br>|***<br>|
|Years of Education|10.43|9.41|1.02|***|
|_Individual Employment in 2005_|||||
|Labor Force Participation∆|0.87|0.58|0.30|***|
|Employed∆|0.83|0.54|0.29|***|
|Sector of Employment: Agriculture ^∆|0.28|0.50|-0.21|***|
|<br>Sector of Employment: Low Skilled Blue Collar ^∆|0.15|0.12|0.04|**|
|Sector of Employment: High Skilled Blue Collar ^∆|0.28|0.12|0.17|***|
|Sector of Employment: Low Skilled White Collar ^∆|0.13|0.11|0.02||
|Sector of Employment: High Skilled White Collar ^∆|0.15|0.16|-0.01||
|_Individual Initial Employment_|||||
|Employed∆|0.65|0.51|0.13|***|
|Sector of Employment: Agriculture ^∆|0.41|0.54|-0.12|***|
|Sector of Employment: Low Skilled Blue Collar ^∆|0.14|0.14|-0.01||
|Sector of Employment: High Skilled Blue Collar ^∆|0.28|0.13|0.15|***|
|Sector of Employment: Low Skilled White Collar ^∆|0.04|0.04|0.00||
|Sector of Employment: High Skilled White Collar ^∆|0.13|0.15|-0.02||
|_Individual Occupational Mobility Over Time_|||||
|Degree of Mobility|0.74|0.16|0.58|***|
|Downward Mobility∆|0.16|0.19|-0.03|**|
|Immobility∆|0.41|0.57|-0.16|***|
|Upward Mobility∆|0.43|0.24|0.19|***|
|_Household Demographics_|||||
|# of Members Age [0,5]|0.53|0.31|0.22|***|
|# of Members Age [6,14]|0.75|0.73|0.02||
|<br># of Male Members Age [15,64]|1.55|1.69|-0.14|***|
|# of Female Members Age [15,64]|1.49|1.81|-0.32|***|
|# of Members Age [65+]|0.35|0.27|0.08|***|
|Female Headed HH∆|0.05|0.06|-0.02|**|
|HH Head Years of Age|49.43|50.87|-1.43|***|
|_Household Asset Position_|||||
|Wealth Index (PCA)|0.28|0.01|0.27|***|
|<br>Land Area (Hectares)|0.58|0.58|0.00||
|<br>Brick Home∆|0.62|0.62|0.00||
|Economic Status in 1990|3.64|3.53|0.11||
|Single Family Dwelling in 1990∆|0.79|0.78|0.00||
|_Household Access to Non-Labor Income_|||||
|Public Transfers**∆**|0.50|0.52|-0.02||
|Real Estate Earnings∆|0.27|0.21|0.06|***|
**Table A5: Country-Specific Proportion of Sibling Correlations Explained by Intergenerational Transmission Estimates (1970 Cohort)**

|**Country**|**Prop.**|**S.E.**|**N**|**Country**|**Prop.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.731|0.024|2271|Benin|0.867|0.037|1028|
|Indonesia|0.787|0.009|12924|Burkina Faso|0.844|0.043|1314|
|Philippines|0.816|0.021|4435|Burundi|n.a|n.a|n.a|
|Vietnam|0.829|0.022|2286|Cameroon|0.742|0.073|399|
|||||Chad|0.735|0.039|658|
|Europe & Central Asia||||Congo, Rep.|0.926|0.134|275|
|Albania|n.a|n.a|n.a|Cote d'Ivoire|0.737|0.050|507|
|Armenia|0.940|0.076|1011|Ethiopia|0.749|0.017|2106|
|Kyrgyz Republic|0.496|0.112|468|Gabon|0.890|0.096|478|
|Tajikistan|n.a|n.a|n.a|Ghana|0.853|0.046|980|
|Turkey|0.819|0.026|3375|Guinea|0.452|0.060|635|
|||||Kenya|0.656|0.036|1904|
|Latin America & Caribb|ean|||Lesotho|0.716|0.048|787|
|Bolivia|0.761|0.017|3329|Liberia|n.a|n.a|n.a|
|Brazil|0.758|0.022|1843|Madagascar|0.859|0.017|1658|
|Colombia|0.778|0.017|4009|Malawi|0.764|0.037|1285|
|Dominican<br>Republic|0.701|0.023|3097|Mali|0.855|0.053|842|
|Guatemala|0.759|0.017|2117|Mozambique|0.914|0.056|1347|
|Haiti|0.709|0.028|1272|Namibia|0.697|0.052|846|
|Peru|0.771|0.011|10144|Niger|0.767|0.093|158|
|||||Nigeria|0.707|0.054|404|
|Middle East & North Af|rica|||Rwanda|0.727|0.043|1521|
|Egypt, Arab Rep.|0.781|0.017|9541|Senegal|0.820|0.054|838|
|Jordan|0.769|0.032|4118|Sierra Leone|n.a|n.a|n.a|
|||||South Africa|0.877|0.054|1325|
|South Asia||||Tanzania|0.581|0.034|1617|
|Afghanistan|n.a|n.a|n.a|Togo|0.832|0.069|1130|
|Bangladesh|0.854|0.015|4275|Uganda|0.784|0.041|876|
|India|0.860|0.009|13288|Zambia|0.726|0.028|1710|
|Nepal|0.680|0.024|1816|Zimbabwe|0.688|0.046|1150|
|Pakistan|0.823|0.017|6202|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1970s birth cohort sample.

43
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_

# **Human Capital**

**Educational attainment is clearly associated with human capital, and is used in the literature as a proxy for human capital or “labor force quality”.** This indicator is commonly used to measure “human capital” or  “effective labor force.”

**Human capital, as measured by educational attainment, has risen in Cote d’Ivoire since independence** (Figure 12). This is evidenced by an estimate<sup>12</sup> of the average years of schooling per worker for the period 1960 to 2002<sup>13</sup> .  Since most educated persons work, most youth entering the labor force are educated, and as average years of schooling for those who do attend school is rising, the estimated average schooling of workers rises much faster than the basic literacy rate for the entire population. One important caveat: this estimate does not take into account the likely impact of AIDS on human capital, which has been particularly important since mid-1980s.

**Figure 12:  Estimate, Average Years in School Per Worker, Cote d’Ivoire, 1960-2002**

<!-- Start of picture text --> Cote d'Ivoire: Estimate of average years schooling per worker<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Years 1960-2002<br>Average years schooling per worker<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text -->

> 12 This “labor quality” estimate is described in detail in the Annex, and permits us to estimate total factor productivity in the next section.

> 13 Comprehensive data on educational attainment is lacking for Cote d’Ivoire. The best readily available measure of educational attainment in terms of completeness over time is the literacy rate, as measured in surveys as reported by the United Nations.  For the entire population over 15 years, the literacy rate climbed from 21% in 1970 to about 48% in 2000.  For youth, the rate rose from 33% in 1970 to about 60% in 2000.  See further explanations in the Annex.

14
Reallocating the resources freed up by energy subsidies removal to more productive public spending can help boost economic growth over the long run (IMF, 2013)

**Table 8: Reforms and Economic Growth**

|**_Study_**|**_Approach_**|**_Region_**|**_Data_**|**_Method_**|**_Policy impacts and_**<br>**_relevance_**|
|---|---|---|---|---|---|
|Chisari,<br>Estache and<br>Romero<br>(1999)|macro study|Argentina|performance<br>data before and<br>after<br>privatisation of<br>the argentine<br>utilities|CGE model|privatisation resulted in<br>different kinds of<br>efficiency gains with<br>significant macro-<br>economic benefits,<br>privatisation not the<br>cause for rising<br>unemployment|
|IMF (2013)|case study|global|energy and<br>economy level<br>data|multi-<br>country|subsidies removal boost<br>economic growth in the<br>long run|
|Benitez,<br>Chisari and<br>Estache<br>(2001)|macro study|Argentina|privatisation,<br>fiscal reforms<br>and regulation<br>data|CGE|country gained more<br>from subsidy cuts and<br>reforms and<br>privatisation of energy<br>utilities not responsible<br>for increased<br>unemployment|
|Nepal and<br>Jamasb<br>(2012a)|econometric|transition<br>economies|panel data: 27<br>countries, 1990-<br>2010|bias<br>corrected<br>fixed effects|power sector reform on<br>its own did not produce<br>any significant impacts<br>on T&D losses|
|Sen and<br>Jamasb<br>(2012)|econometric|India|panel data: 19<br>Indian states,<br>1991-2007|bias<br>corrected<br>fixed effects|reforms positively<br>affected the GDP|

# **_3.2.3. Impacts of reforms on poverty alleviation_**

Existing literature on infrastructure reforms and poverty linkages shows that policy changes to improve the access and quality of infrastructure services help reduce poverty through direct and indirect channels, such as providing more opportunities to generate income, improving health and educational outcomes (Estache and Fay, 1995; Brenneman and Kerf, 2002). Hence, power sector reforms aimed at improving the access and supply reliability contributes to poverty reduction. However, existing studies have not empirically examined this issue and explored the evidence to investigate this hypothesis. In fact, some studies examining this issue 35
_Figure 3: Loss of work capacity at varying heat stress levels for occupations of light, moderate and high physical intensity._ <u>Note: The figure presents the relationship of labor productivity (y-axis) with heat stress (x-axis) for occupations</u> of differing physical intensities. As heat stress increases, work capacity decreases. Heat stress is measured in degrees Centigrade on the Wet Bulb Globe Temperature (WBGT) scale. Background colors denote heat stress categories varying from “no heat stress” (green background) to “extreme heat stress” (dark red background). The blue curve denotes light-intensity activities such as office work (physical effort of 180 Watts); the green curve denotes moderate-intensity activities such as retailing and light manual work (300 Watts); the red curve denotes high-intensity activities such as construction labor (415 Watts).

# **2.3 Heat mitigation interventions: Definition and costing**

In practice, city action plans to address extreme heat tend to comprise a ‘bundle’ of diverse types of interventions ranging from planting more trees and promoting a cooler built environment to introducing heatwave emergency actions including early warnings and health sector preparedness. Indeed, past studies by the World Bank have advocated a ‘Places, People, Institutions’ approach to city heat action planning in which actions target cooler city spaces, protection of vulnerable groups during heat emergencies, and mainstreaming of extreme heat mitigation across line agencies (Jones et al, 2022; Roberts et al, 2023). Based on desk review of existing Heat Action Plans and other strategy documents, we set out to identify feasible packages of interventions that could be adopted by cities vulnerable to heat in India. For each action area, we identified ‘medium ambition’ and ‘high ambition’ alternatives. The cost of each action was estimated based on local unit costs where available and international unit costs otherwise. All costs and benefits were converted to real 2020 US dollars. International costs were adjusted using Purchasing Power Parity (PPP). Here we focus on four major actions that are widely included in Heat Action Plans: 9
25

# 1:Price Incentives Maize

<!-- Start of picture text --> Trade liberolization vs Price Guorcntee<br><!-- End of picture text --> <!-- Start of picture text --> 6-<br>5.5"'<br>5~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>45<br>4<br>3 5<br>25- \' I \<br>3<br>2 5- l :<br>1974 75 76 77 78 79 80 81 82 83 84 85<br>Years<br>3 = price guarantee + = trode lib<br>2: Production Maize<br>Trode liberoliZation vs Price Guarantee<br>15<br>13 -i 2~~ //\<br>12<br>°~ i/ / \ _<br>4!<br>1S~74 75 76 7, 78 79 80 81 82 83 84 85<br>tears<br>a pr,ce guarantee = traOe liL<br><!-- End of picture text -->