rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 5379

# **Abstract**

This paper focuses on the estimation of skill/industry premiums and labor force composition at the national and sector levels in seven East Asian countries with the objective of providing a comprehensive analysis of trends in demand for skills in the region. The paper addresses the following questions: Are there converging or diverging trends in the region regarding the evolution of skill premiums and labor force composition? Are changes in skill premiums generalized or industry-related? How have industry premiums evolved? The analysis uses labor and household surveys going back at least 10 years. The main trends emerging from the analysis are: (a) increasing proportions of skilled/educated workers over the long run across the region; (b) generally increasing demand for skills in the region; (c) the service sector has become the most important driver of demand for skills for all countries (except Thailand); (d) countries can be broadly categorized into three groups in relation to trends and patterns of demand for skills (Indonesia, Philippines, and Thailand; Vietnam and China; and Cambodia and Mongolia); and (e) industry premiums have increased in three countries of the region (Philippines, Thailand, and Cambodia). These trends point to several policy implications, including that governments should focus on policies promoting access to education to address the increasing demand for skills and/or persistent skill shortages; support general rather than specific curricula given broad-based increases in skill premiums in most countries; better tailor curriculum design and content and pedagogical approaches to the needs of the service sector; and target some social protection programs to unskilled workers to protect them from the “unequalizing” impact of education.

This paper—a product of the Education Unit, Human Development Sector, East Asia and Pacific Region—is part of a larger effort in the department to assess demand for skills in East Asia and its implications for skill development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at edigropello@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
transfer payments, both domestic and overseas remittances, in most analyses of trade and poverty are not well fleshed out and deserve greater attention in the future.

# **VIII.  Conclusions**

One of the most lively areas of development economics research in recent years involves assessing the micro-economic implications of macro-level policies – particularly those related to international trade. The 2003 volume edited by Bourguignon and Pereira da Silva sets out the current range of tools available for evaluating the poverty impacts of economic policies. These editors conclude that, while there is a wide range of research methodologies available for assessing the microeconomic incidence of micro-policies, and similarly many tools for assessing the impact of macrolevel policies on markets and broad groups of “representative” households, there is a gap in the current literature when it comes to eliciting the disaggregated household and firm level impacts of trade and other macro-economic policies. Recent research aims to address this gap in our knowledge and the present survey offers an overview of this literature.

Many of the most recent and innovative studies sequentially link the top-down and bottom-up approaches in a two-step procedure, such that general equilibrium mechanisms are incorporated along with detailed household survey information. While not necessarily elegant in a theoretical sense, this approach accounts for the majority of trade-poverty linkages, is based on solid empirical foundations, and is compatible with both single and multi-region trade modeling. With time, we expect the degree of integration between micro- and macro-analyses to become tighter, with fully integrated general equilibrium models eventually becoming the norm at both the national and global levels.

This review highlights the critical role of factor markets in determining the poverty impacts of trade policies. The preponderance of the evidence from the studies encompassed by this survey points to the dominance on earnings-side impacts over consumption side effects. This is problematic, since household surveys are notable for their under-reporting of income. From the perspective of the poor, it is the market for unskilled labor that is most important. The poverty impacts of trade policy often hinge crucially on how well the increased demand for labor in one part of the economy is transmitted to the rest of the economy via increased wages, increased employment or both. Further econometric research aimed at discriminating between competing factor mobility hypotheses is urgently needed.

31
**-** **<u>Table 7: Rayon level regressions for aggregate entry and exit overall and annual</u>**

||**Share of**|**area exiting**<br>|**Share of area**|**entering**<br>|
|---|---|---|---|---|
||**permanent**|**churn**|**permanent**|**churn**|
||<br>**Panel**|**A: Entry & exit ove**|<br>**r entire period (2002- **|**2011)**|
|Init. share of cult. area > 5000 ha|-0.273***|-0.360**|-0.315**|-0.385**|
||(-2.665)|(-2.423)|(-2.294)|(-2.509)|
|Initial productivity|-0.797***|-1.104***|-0.503**|-0.596***|
||(-5.408)|(-5.163)|(-2.537)|(-2.696)|
|Init. share > 5000 ha *|-0.701***|-0.841**|-0.145|-0.188|
|productivity of these farms|(-2.745)|(-2.271)|(-0.422)|(-0.490)|
|<br>Rayon fixed effect|<br>0.270***|<br>0.583***|<br>0.774***|<br>0.994***|
||(4.303)|(6.409)|(9.196)|(10.571)|
|Observations|<br>472|<br>472|<br>472|<br>472|
|R-squared|0.222|0.219|0.246|0.253|
|Init. share of cult. area > 3000 ha|-0.279***|-0.403***|-0.156|-0.246*|
||(-3.216)|(-3.197)|(-1.324)|(-1.870)|
|Initial productivity|-0.615***|-0.883***|-0.456**|-0.487**|
||(-3.914)|(-3.869)|(-2.136)|(-2.046)|
|Init. share > 3000 ha *|<br>-0.787***|<br>-0.917***|<br>-0.183|<br>-0.390|
|productivity of these farms|(-3.644)|(-2.923)|(-0.625)|(-1.192)|
|Rayon fixed effect|0.278***|0.599***|0.782***|1.003***|
||(4.422)|(6.569)|(9.177)|(10.555)|
|Observations|472|472|472|472|
|R-squared|0.233|0.229|0.240|0.250|
||**Share of**|**area exiting**|**Share of area**|**entering**|
||**permanent**|**churn**|**permanent**|**churn**|
|||**Panel B: Annual r**|**ates of entry & exit**||
|Init. share of cult. area > 5000 ha|-0.025**|-0.033**|-0.031**|-0.037**|
||(-2.172)|(-2.346)|(-2.362)|(-2.469)|
|Initial productivity|-0.072***|-0.099***|-0.058***|-0.083***|
||(-4.346)|(-4.935)|(-3.040)|(-3.799)|
|Init. share > 5000 ha *|-0.064**|-0.077**|-0.004|-0.012|
|productivity of these farms|(-2.240)|(-2.201)|(-0.112)|(-0.312)|
|<br>Rayon fixed effect|0.024***|0.053***|0.073***|0.101***|
||(3.440)|(6.127)|(9.034)|(10.883)|
|Observations|5,187|5,187|5,187|5,187|
|R-squared|0.359|0.279|0.126|0.148|
|Init. share of cult. area > 3000 ha|-0.025***|-0.037***|-0.013|-0.020|
||(-2.610)|(-3.086)|(-1.188)|(-1.511)|
|Initial productivity|-0.055***|-0.079***|-0.051**|-0.072***|
||(-3.102)|(-3.656)|(-2.503)|(-3.065)|
|Init. share > 3000 ha *|<br>-0.072***|<br>-0.084***|<br>-0.024|<br>-0.040|
|productivity of these farms|(-2.963)|(-2.824)|(-0.851)|(-1.248)|
|<br>Rayon fixed effect|0.025***|0.054***|0.074***|0.101***|
||(3.507)|(6.239)|(8.984)|(10.823)|
|Observations|5,187|5,187|5,187|5,187|
|R-squared|0.360|0.280|0.126|0.148|

_Note:_ Region dummies and constant included in all regressions but not reported.

24
(after paying SS contributions). A positive or negative productivity shock arrives at rate _λ_ , and two possible cases arise: if the shock is ‘good’, the match continues with the capital gain (or loss) _JF_ ( _x, y_ ) _− JF_ ( _y_<sup>_′_</sup> _, y_ ); if the shock is ‘bad’, the match ends, the firm pays firing costs, _s_ , and posts a new vacancy, so the capital loss suffered is _VF_ ( _y_ ) _− JF_ ( _y_<sup>_′_</sup> _, y_ ) _− s_ .

The flow value of a filled job in the formal sector with a worker of type _y_ and match-specific productivity _y_<sup>_′_</sup> (new hire) is: where _πF_ ( _y_<sup>_′_</sup> _, y_ ) is the nominal value of a job’s output in the formal sector, which can be defined as: and _pF_ is the price of a formal-sector good (price of tradable good).

The flow value of a filled job in the formal sector with a worker of type _y_ and productivity _x_ (incumbent) is: where Notice that _rJF_ ( _x, y_ ) differs from _rJF_ ( _y_<sup>_′_</sup> _, y_ ) because the wage associated with each function is<sup>43</sup> .

The flow value of a filled job in the informal sector with a worker of type _y_ and current productivity _y_<sup>_′_</sup> is as follows: where the nominal value of a job’s output in the informal sector, _πI_ ( _y_<sup>_′_</sup> _, y_ ) is defined as: and _pI_ is the price of the informal sector good (non-tradable good).

The informal-sector job yields net return for the firm firm _πI_ ( _y_<sup>_′_</sup> _, y_ ). The match may break (without any firing cost involved) if a ‘bad’ productivity shock arrives ( _x_ below some productivity threshold _RIU_ ( _y_ )), and the firm posts a new vacancy, so the capital loss suffered is [ _VI − JI_ ( _y_<sup>_′_</sup> _, y_ )]. Otherwise, the match continues and the firm gets the corresponding capital gain or loss, _JI_ ( _x, y_ ) _− JI_ ( _y_<sup>_′_</sup> _, y_ ).

> 43I will later show that _wFs_<sup>(</sup><sup>_x, y_)</sup><sup>_≥wF_(</sup><sup>_x, y_).</sup>

14
request unless it was part of the public record and would they please put their request in writing so that he could consider it and make it public. No letters came and the telephone calls eventually stopped.

Independence from short-term political influences is important because such interests lead to opportunistic behavior (Wells and Gleason, 1995). Opportunism in utility policy is evident when, for example, policymakers ex post change the rules affecting the operators’ ability to retain profits from investments made before the rule change. In illustration, regulators in the U.K. resisted pressures from the Labour Party to claw back utility profits after privatization because this would have been considered a breaking of the regulatory compact with the operators. However, once the Labour Party took control of the government, utility profits became a legislative issue rather than a regulatory issue, and the country adopted a windfall tax to capture past profits.<sup>4</sup> Some investors pulled out of the country as a result of this tax (Institute for Fiscal Studies, 2000). Because the threat of political opportunism discourages long-term investments, many countries make their regulatory agencies independent of political influence so that the agency provides a stable and credible legal environment (Goldberg, 1976; Levy and Spiller, 1996; Spiller and Savedoff, 1999).

Independence does not mean that the regulator answers to no one. An independent regulator is governed by laws, political realities, public sentiments, budgets, license provisions, and such. Independence is not absolute because trade-offs exist between certain features of regulation, such as that between independence and accountability, between certainty and flexibility, and between long-term goals and shortterm viability. This lack of absolute independence is generally considered desirable because it ensures that the regulator is not simply following a personal agenda.

# **Perils in Regulation**

As indicated, developing and implementing independence can be perilous for the regulator because finding the optimal mix of independence, accountability, flexibility, and the like is difficult. Also, making the necessary trade-offs is painful for some

> 4 Labour came into power in part because it argued that utility profits had been excessive under the Conservative Party.

4
# **Table (6): Residual Diagnostics Tests for the - 3 Lags VAR- Closed Economy**

# **Money Demand Model:**

|Single-equation diagnostics usingreduced-form residuals:|
|---|
|rm2 : Portmanteau(7): Chi^2(4)= 7.5270[0.1105]|
|rm2 : AR 1-2 test: F(2,28)= 0.32520[0.7251]|
|rm2 : ARCH 1-1 test: F(1,53)= 2.2940[0.1358]|
|rm2 : Normalitytest: Chi^2(2)= 0.32008[0.8521]|
|rm2 : Hetero test: F(30,15)= 1.5193[0.1974]|
|rm2 : Hetero-X test: not enough observations|
|rgdp: Portmanteau(7): Chi^2(4)= 6.4001[0.1712]|
|rgdp: AR 1-2 test: F(2,28)= 0.37855[0.6883]|
|rgdp: ARCH 1-1 test: F(1,53)= 0.78215[0.3805]|
|rgdp: Normalitytest: Chi^2(2)= 1.1438[0.5644]|
|rgdp: Hetero test: F(30,15)= 2.8876[0.0167]*|
|rgdp: Hetero-X test: not enough observations|
|INFL : Portmanteau(7): Chi^2(4)= 3.5036[0.4773]|
|INFL : AR 1-2 test: F(2,28)= 1.2216[0.3100]|
|INFL : ARCH 1-1 test: F(1,53)=0.00096847[0.9753]|
|INFL : Normalitytest: Chi^2(2)= 1.8518[0.3962]|
|INFL : Hetero test: F(30,15)= 0.67094[0.8287]|
|INFL : Hetero-X test: not enough observations|
|Interestrt : Portmanteau(7): Chi^2(4)= 7.8441[0.0975]|
|Interestrt : AR 1-2 test: F(2,28)= 4.3289[0.0230]*|
|Interestrt : ARCH 1-1 test: F(1,53)=0.00044828[0.9832]|
|Interestrt : Normalitytest: Chi^2(2)= 42.311[0.0000]**|
|Interestrt : Hetero test: F(30,15)= 0.58780[0.8949]|
|Interestrt : Hetero-X test: not enough observations|
|DISC : Portmanteau(7): Chi^2(4)= 7.5435[0.1098]|
|DISC : AR 1-2 test: F(2,28)= 1.1853[0.3205]|
|DISC : ARCH 1-1 test: F(1,53)= 0.021263[0.8846]|
|DISC : Normalitytest: Chi^2(2)= 17.203[0.0002]**|
|DISC : Hetero test: F(30,15)= 0.85468[0.6554]|
|DISC : Hetero-X test: not enough observations|
|Vector Portmanteau(7): Chi^2(100)= 164.09[0.0001]**|
|Vector AR 1-2 test: F(50,76)= 1.0344[0.4409]|
|Vector Normalitytest: Chi^2(10)= 44.957[0.0000]**|
|Vector ZHetero test: F(150,59)= 1.1354[0.2926]|
|ZHetero-X test: not enough observations|

46
# PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA

estimated parameters of such a regression are reported in table 6; the complete set of parameters appears in appendix 1, table 4. The estimated parameters are jointly statistically significant (the p-value associated with the computed F-statistic is less than 0.01), and account for 20 percent of the variance in the response variable. As expected, the sign of the coefficient for duration of power outages is negative and significant, and the sign of the coefficient for generator ownership is positive and significant. The size of the estimated coefficient for generator ownership suggests that, when controlling for other factors, owning a generator decreases losses from power outages by approximately 1 percent of a firm’s sales.

|**Table 5 Losses due to outages (“lost load”) for**<br>**firms with and without their own generator**|**Table 6 Marginal benef**<br>Reduction in lost sales|**it of owning a g**|**enerator**|
|---|---|---|---|
|Lost load<br> <br>Lost load<br>(with<br>|Variable|Estimated<br>coefficient|P-value|
|Country<br>(no generator,<br>$/hour)<br>generator,<br>$/hour)|Days of power outages<br>(log)|1.94<br>***|0.00|
|Algeria<br>155.8<br>52.2|Generator ownership|–1.16<br>***|0.01|
|Benin<br>38.4<br>23.1<br>|Constant|–1.71|0.11|
|Burkina Faso<br>†<br>114.1<br>13.0|_F-statistic_|26.25<br>***|0.00|
|Cameroon<br>†<br>403.6<br>12.3<br>|_R_<br>_2_|0.21||
|Cape Verde<br>†<br>177.7<br>36.4|_N_|4,254||
|Egypt, Arab Rep.<br>201.5<br>30.4|***statistically significant a|t1percentlevel.||
|Eritrea<br>31.9<br>10.2||||
|Kenya<br>113.1<br>37.1||||
|Madagascar<br>434.5<br>153.0||||
|Malawi<br>917.3<br>401.4||||
|Mali<br>390.3<br>9.5||||
|Mauritius<br>468.6<br>13.9||||
|Morocco<br>377.5<br>22.9||||
|Niger<br>†<br>81.3<br>22.6||||
|Senegal<br>166.0<br>19.2||||
|Senegal<br>††<br>12.9<br>1.9||||
|South Africa<br>1140.1<br>66.1||||
|Tanzania<br>—<br>444.3||||
|Uganda<br>27.6<br>191.4||||
|Zambia<br>286.6<br>39.2||||
|† Survey of tourism sector; †† Survey of informal<br>sector. — =datanot available.||||

# Costs vs. benefits

## **With no improvement in quality of public power supply**

Here we summarize the costs and benefits of own generation by integrating them at the firm level, assuming no improvement in quality of public power supply. The costs are computed as the sum of the annualized fixed costs of acquiring a generator and the net annual operating costs of generation. The fixed costs of own generation were annualized assuming linear depreciation. The net annual operating costs 21
# **2. India’s Labor Market**

India is predominantly rural and 71% of its more than 1 billion people live in rural areas. During the last decade Indian labor force grew at an annual rate of 2.27% in urban and 0.66% in rural areas (Chadha 2001), totaling 397 million in 1999-2000. Labor force participation (LFP) of prime-age male workers was relatively stable between 1993 and 1999 at 85%, while LFP of prime-age females declined from 34% to 28%. It seems that the increased attendance of younger women in educational institutions accounts for the majority of this decline. Unemployment rates are higher in urban (7.86%) as compared to rural areas (3.74%) and, on average, women are more likely to be unemployed than men. Unemployment rates are higher among young and more educated individuals.

In India, one of the main official sources for labor statistics – the Directorate General of Employment and Training, Ministry of Labor – makes a distinction between organized and unorganized sectors based on the size of employment. The organized sector consists of public and private components. Employment in the <u>public</u> sector consists of government civilian employees and employees in public enterprises. Public sector enterprises take three forms: the so-called _departmental undertakings, public companies_ and _public corporations_ . _Departmental undertakings_ are an integral part of government, with the same status as the government departments. _Public companies_ are incorporated under the Companies Act of 1956 and have the same status as private companies. Some of them are wholly owned by the government, while in others the government has a controlling interest. _Public corporations_ are individually created by specific legislation as separate legal entities with defined powers, management structure and duties (Roy, 1997). In the <u>private</u> sector, a distinction between organized and unorganized sectors is made on the basis of size of the enterprises. Units in the private sector that employ 10 or more workers fall into the private organized sector category, while the rest fall into the private unorganized sector category. The unorganized (informal) sector provides employment to 93% of all workers in India.

On average, there has been little change in the composition of the unorganized sector in the last decade. Agriculture and trade occupations remain nearly fully informal. In mining and quarrying; transport, storage and communication; finance, insurance and real estate; and community, social and personal services, the informal sector accounts for 5
Uhlig 2006; Winters, de Janvry, and Sadoulet 2001; Yamauchi and Tanabe 2008). The second function, _g_ ( _T_ , _H_ ) , measures the psychological costs faced by a migrant worker, which include those associated with leaving family members behind. The number is assumed to decrease with access to a landline phone ( _T_ ) and increase due to the number of left-behind family members, such as children ( _H_ ). In addition, the availability of landline phones should reduce the negative impact of the number of left-behind family members. Thus, _gT_ < 0 , _g H_ > 0 , and _gTH_ < 0 . The assumption of a negative interaction term is based on the intuition that talking and advising over the phone to the left-behind family members in the village allow the migrant to ease the pains of not seeing the family and help the migrant to react more quickly in cases of emergency.

Hence, the probability of migrating is ~ where ε _sm_ ≡ ε _s_ − ε _m_ ; _I_ (.) is the indicator function; and _J_ (.) and _j_ (.) are the cumulative and probability distribution function of ε<sup>~</sup> _sm_ , respectively.

With the properties of _f_ (.) and _g_ (.) , we obtain three propositions.

_Proposition 1_ : The effect of landline phones on the probability of outmigration is positive, _Proposition 2_ : The positive effect of landline phones on outmigration is magnified by the 11
Trade liberalization would be difficult to justify from the standpoint of poverty reduction if it adversely affects this group. This point is especially important in a country where a substantial portion of the population lives below or close to the poverty line. Aggregate growth alone is not enough to justify trade liberalization policies, particularly if poverty could worsen. Governments may not want to forgo liberalization, but must carefully choose the right mixture of policies, and be ready to implement mitigating policies when necessary.

Some argue that it is indeed possible to do so. Using a computable general equilibrium (CGE) model with a detailed panel of households, Harrison et al. (2003) argue that trade liberalization in Turkey can be designed to ensure that the poor will not lose by using direct compensation to the losers or by using limited policy reform. Their research is an improvement from previous work with very limited treatment of heterogeneity among households. However, making side payments for particular segments of households is not straightforward. As they noted, limited policy reform may induce rent-seeking.

In this study, we consider geographic targeting as a way to direct progressively more resources to areas that are least favorably affected by trade liberalization. Geographic targeting has several advantages. It is easy to understand and straightforward to implement. The distortion caused by geographic targeting is usually considered small because the cost of changing locations, especially for the poor, is often prohibitively high. Further, many countries already have some sort of programs targeted to poor areas. We only need to modify the set of areas to make the program more efficient for poverty reduction, instead of implementing a new program. Hence, given the preexistence of such a program, the political cost would also be relatively small.

Of course, the formulation of an effective policy of geographic targeting requires the knowledge of the changes in spatial distribution of the poor after market liberalization. Economic research has provided only limited guidance in this area, because socioeconomic survey data with high temporal and spatial resolution needed for poverty monitoring are usually unavailable. Although policy makers need information 3