rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. Data and Construction of Service Export Sophistication**

We build upon the HHR framework, which uses a good export sophistication measure _Goods EXPY_ as a proxy for the most productive set of goods the country can produce at a given time. They argue that export data is the best way to reveal this production frontier as we can expect countries to export those goods in which it is most productive. Along the same reasoning and as described below we will construct a _Service EXPY_ to proxy the service production frontier of a country, by and large following the methodology developed by HHR.

We start by constructing so-called _PRODY_ ‘s for each category of goods exports, reflecting the income/productivity level associated with each good, and we do the same for each category of services. _PRODYj_ is the income value associated with the service _j_ , and is constructed by using the service export ( _x_ ) share of a country _i_ in world‘s export of service _j_ , divided by the sum of shares of _j_ in world exports of _j_ across all countries exporting that service.<sup>7</sup> These ratios are multiplied by the exporting countries‘ respective per capita income level ( _Y_ ) and the result is summed up across all countries. In other words, the _PRODY_ becomes the weighted average of per capita GDPs, where the weights represent the revealed comparative advantage in service _j_ for each country.<sup>8</sup> _PRODY_ s are constructed for each service category, for each year of available data, and are by construction the same for all countries.

_EXPY_ is then the weighted income value of services exported by a country, computed as the sum of _PRODY_ s using as weights the share of the particular service in the country‘s total service export basket. _EXPY_ s are constructed for each country and for each year with available data.

The trade data come from the IMF Balance of Payments statistics which has data available for over 190 countries from 1990 to 2007.
# **Acknowledgments**

This paper draws on contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Rupa Ranganathan on power, Carolina Dominguez on water and sanitation, Michael Minges and Rebecca Meyer on information and communication technologies, Alberto Nogales on roads, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably Marie Françoise Marie-Nelly (Country Director), Midou Ibrahima (Country Manager), Franck Bousquet (Sector Leader), Alexandre Dossou (roads), Pierre Pozzo di Borgo (railways), Michel Layec (power), Franck Bousquet (water and sanitation), Jerome Bezzina (ICT), Peter Siegenthaler and Monthe Bienvenu Biyoudi (macro), and Professor Mantsi and Afua Sarkodie (local consultants).
.4%<br>Mali_2013 1.4% Mali_2013 72.1%<br>Mali_2014 1.3% Mali_2014 61.7%<br>Sudan_2011 0.8% Sudan_2011 76.5%<br>Sudan_2013 1.5% H Sudan_2013 220.1%<br>Sudan_2014 1.4% Sudan_2014 233.3%<br>MEDIUM QFD AND WORSENING MEDIUM QFD AND WORSENING<br>Cape Verde_2011 1.3% Cape Verde_2011 29.5%<br>Cape Verde_2012 1.6% Cape Verde_2012 34.1%<br>Côte d'Ivoire_2013 1.5% Côte d'Ivoire_2013 62.8%<br>Côte d'Ivoire_2014 1.9% Côte d'Ivoire_2014 73.6%<br>Zambia_2011 0.9% Zambia_2011 38.8%<br>Zambia_2012 1.1% H Zambia_2012 47.0%<br>Zambia_2014 1.2% Zambia_2014 47.3%<br>Swaziland_2011 0.9% Swaziland_2011 30.8%<br>Swaziland_2012Swaziland_2013 1.0% 1.2% H Swaziland_2012Swaziland_2013 37.0%42.6%<br>Swaziland_2014 1.2% Swaziland_2014 39.8%<br><!-- End of picture text --> _Source_ : World Bank staff calculations using utility data.

97
**Table 3.11: Index of Real Wages per Hour by Affiliation to Social Security** **<u>(2007=100)</u>**

||2007|2008|2009|2010|
|---|---|---|---|---|
|IMSS/Salaried|100|98|94|91|
|ISSSTE/Salaried|100|99|97|97|
|PEMEX—Defense/Salaried|100|94|89|87|
|Not affiliated—employer/Self employed|100|98|92|90|
|Not affiliated/Salaried|100|99|96|94|

**Source: Own calculations using data from ENOE.**

All this evidence reveals the asymmetry of adjustment over the course of the crisis. During the recession, some sectors reduced employment quantities only, others adjusted real wages only, and others adjusted the two margins. During the recovery, all sectors (with the exception of mining) have showed job creation with lower real wages (see figure 3.1). The crisis produced a significant dislocation of the Mexican labor market. There were large flows of job destruction and creation that left the country with employment levels similar to before the crisis, but unemployment levels of around 900,000 people more than before the crisis. Average wages declined for all sectors

# **4. Policies to Cope with the Crisis**

The Mexican government announced a series of programs to cope with the crisis, which included the Program to encourage Growth and Employment (PICE) announced in October 2008, and the National Agreement to support the Household Economy and Employment ( _Ampliación del Programa de Empleo Temporal a Nivel Federal_ —ANFEFE), announced in January 2009. The expected outcome was a about a 0.9 percent boost of GDP. Additional support in the form of credit from development banks was expected to increase total investment to about 171,
postcensal<br>|Bank est.|
|||||demographic survey||
|Cameroon|Apr 76|7090 F|Bank projection<br>from census,<br>adjusted for 7.4X undercount|1980-85, world Fertility<br>Survey; 1985-90, Bank<br>projection assuming<br>reduced sterility|Bank assessment of Uorld<br>Fertility<br>Survey child<br>survival<br>rates|
|Canada|Jun 86|25309 J|UNPVSR<br>1/87 (official<br>est.)|Official<br>est.,<br>Population<br>Projections for Canada,<br>1984-2006|Official<br>est. (population<br>projections<br>for Canada,<br>1984-2006)|
|Cape Verde<br>|Jun 80<br>|296 F<br>|Bank projection<br>from census<br> <br> <br>|Bank assessment of official<br>CBR est.|Bank assessment of official<br>CDR est.|
|Cayman Islands|Oct 79|17 F|UNPVSR<br>1/88 (official<br>est.)|--|--|
|Central African<br>Rep.|Dec 75|2055 F|Bank projection<br>from census,<br>adjusted for 11.3XLradercount|Bank est.|Bank assessment of census<br>data on child survival|
|Chad|--|--<br>-|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|
|Chamnel Islards|Mar86|136 F|UNPVSR<br>4/87|Based on UNPVSR<br>7/86|Based on UNPVSR<br>7/86|
|
|**3**|Incentives Linked to Generation/Production tax-credits|
|---|---|
|**4**|Income Tax Holidays/Exemptions|
|**5**|Trade-related Incentives|
|| Customs DutyExemptions|
|| Absence of Local Content Requirements|
|**6**|Other Tax Exemptions (VAT, Excise etc)|

An examination of the presence or absence of these policies, regulations and incentives in South Asia reveals that India, as expected, scores high for the presence of both cross-cutting and sector-specific policies, regulations and incentives. Only in wind does Pakistan score above India, due to Pakistan‘s income tax holidays. India is the only country that has deployed tradable instruments to facilitate entities‘ obligation to purchase RE. Pakistan scores higher than Sri Lanka for the presence of policies, regulations and incentives in all sectors. **A higher CIRI score, however, does not mean that a country is attracting higher levels of investment or even that it offers a better climate for RE investors. It merely indicates that a country has put in place more policies, regulations and incentives to** **_attract_ investors.** Whether this contributes to creating a more attractive investment climate depends on a number of other factors, including effective implementation. As the findings from the private sector consultations reveal, many investors, particularly foreign firms, are still hesitant to invest in Pakistan owing to the prevailing political and security climate, despite the offer of very attractive terms and conditions. Hence, the binary nature of CIRI analysis reveals just one side of the story. As discussed earlier it needs to be complemented with surveys of private sector to get a complete picture.

The tables below indicate the existence or absence of policies, regulations and incentives for gridconnected RE investment in India, Pakistan and Sri Lanka. Scores are on a scale of 10 (which indicates the presence of key RE policies, regulations and incentives). The presence
<!-- Start of picture text --> Figure 3: Social assistance spending Figure 4: Cash transfer spending<br>(EAP non-HIC) (EAP non-HIC)<br>6 2<br>5<br>4<br>3 1<br>2<br>1<br>0 0<br>Percent of GDP Percent of GDP<br>Timor-Leste Mongolia Vietnam Kiribati China Thailand Cambodia Malaysia Fiji Philippines Indonesia Samoa Lao PDR Myanmar Papua New... Mongolia Malaysia Philippines Vietnam Timor-Leste Fiji China Thailand Indonesia Myanmar Cambodia Samoa Kiribati Lao PDR Papua New...<br><!-- End of picture text --> Source: CEQ and World Bank databases and World Bank calculations. Sosa and Wai-Poi (forthcoming). Note: Total SA spending excluding health fee waivers. Cash transfers include CCT and UCT. HIC = High-income country, CCT = conditional cash transfers, UCT = Universal cash transfers.

# **3. Methodology, Data, and Assumptions**

## **CEQ framework**

This paper applies an internationally recognized methodology developed by the Commitment to Equity (CEQ) Institute to assess how taxes and government spending affect poverty and inequality in Cambodia. The CEQ framework has in recent years emerged as the gold standard to answer questions concerning the implications of fiscal interventions across the income distribution.<sup>12</sup> The approach has been applied in over 60 countries, allowing comparisons of Cambodia’s performance with peer lower middle-income countries.

The CEQ approach combines detailed household survey data with tax and benefit regulations to estimate what each household paid in taxes and received in benefits. The CEQ approach then describes the distributional effect of the fiscal system sequentially through four income measures. The baseline concept is _market income_ , which is the income that an individual (or a household) earns before any taxes are subtracted and any transfers are included. Three “post-fiscal” income measures— _disposable income_ , _consumable income_ , and _final income_
**<br>Population<br>-0.018<br>-0.034<br>(0.007)**<br>(0.009)**<br>0-6 population<br>-0.070<br>-0.126<br>(0.029)*<br>(0.030)**<br>7-15 population<br>-0.003<br>0.057<br>(0.031)<br>(0.032)+<br>Observations<br>59154<br>59154<br>Municipalities<br>4748<br>4748<br>_Notes:_ An observation is a municipality - year. I instrum<br>covariates using simulated revenue and its interaction wit<br>are in parentheses, and clustered at the municipality lev<br>year fxed efects, as well as a linear time trend interacted<br>revenue, per capita transfer revenue, the fraction of publ<br>(non-municipal) schools, pre-primary enrollment, and pre-p<br>* indicates p_<_.05; + indicates p_<_.10. The mean of per c<br>capita income is 0.64, the mean of 2000 median per capita <br>0.39. Population is measured in 100,000s.<br>_Sources:_ Author’s calculations based on data from IBGE,|

46
monthly labor income assuming respondents work 8 hours per day, 5 days per week, 4 weeks per month, and 12 months per year. Business sales are reported in question 24 of Section 6. We assume all business income goes to the owner of the enterprise.

- Education: from Section 2 (Education), question 7 and 8.

## First follow-u : <u>p</u>

- Migration: we collect information on individuals leaving their household from the Post Harvest Wave 1 (February - April 2011), Post Planting Wave 2 (September - November 2012), and Post Harvest Wave 2 (February - April 2013). For each individual, we identify destination state and local government area, and assume that this remains unless a new move is recorded in a more unchanged

- recent wave. For respondents that are not indicated as leaving their household, we take household state and local government area from the most recent wave.

## Second follow-u : <u>p</u>

- Migration: we use information from the Post Planting Wave 3 (August - October 2015) and Post Harvest Wave 3 (February - April 2016). The procedure to infer movers’ location is the same as the one used for the first follow-up. In addition, individuals who migrated between baseline and the first follow-up are assumed to reside in the same location they were living in at the end of the first follow-up, unless a new move is registered in Wave 3.

# **B.7 Kagera Health and Development Survey (KHDS)**

## Baseline (1991/94):

- Self-employment: from section 7, whether the respondent says she is selfemployed during the past 7 days (part A, question 6 “During the past 7 days, have you worked for yourself or your household? For example, as an independent merchant or fisherman, lawyer, doctor, or other self-employed activity?”) in an activity not connected with farming, livestock or
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Benchmarking road indicators, 2006**

||Unit|Low-income<br>countries|Tanzania|Middle-income<br>countries|
|---|---|---|---|---|
|Paved road density|km/1000 km<sup>2</sup><br>of arable land|86.6|47.1|507.4|
|Unpaved road density|km/1000 km<sup>2</sup><br>of arable land|504.7|482.6|1,038.3|
|GIS rural accessibility|% of rural pop within 2 km from all-season<br>road|21.7|24.0|59.9|
|Paved road traffic|Average Annual<br>Daily Traffic|1,049.6|1,797|2,786.0|
|Unpaved road traffic|Average Annual<br>Daily Traffic|62.6|99.8|12.0|
|Paved network condition|% in good or fair condition|80.0|94.7|79.0|
|Unpaved network condition|% in good or fair condition|57.6|69.1|58.3|
|Perceived transport quality|% firms identifying as major business<br>constraint|23.0|14.1|10.7|
|Overengineered network|% of total network|26.0|22.0|20.0|

_Source:_ Gwilliam and others 2009. Derived from AICD database, http://www.infrastructureafrica.org/aicd/tools/data.

**Figure 8. Benchmarking the fuel levy, 2006**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>
# <u>Egypt’s Power Sector Reform Story</u>

The story of Egyptian economic reform over a long period of time has been one of short bursts of partial reform of a highly centralized, public sector-led economic model, followed by longer periods with relatively little reform, largely because of political economy challenges. The power sector has been no exception to that overall trend. Egypt has at times committed to a major role for private sector participation, the restructuring of the sector by full unbundling, the creation of a competitive market, the establishment of an independent regulator, and full cost recovery. However, as seen in figure 3, only a limited amount of these commitments have been met.

Figure 3: Egypt Power Sector Reforms<sup>4</sup> <!-- Start of picture text --> Regulator<br>operationalised<br><!-- End of picture text --> Source: Rethinking Power Sector Reform. PSP = private sector participation.

Currently there is a revived attempt at reform in the power sector, focusing primarily on the core issue of addressing the supply deficit, annual price and subsidy adjustment to reach the subsidy target of 0.5% of GDP, a partial reopening to private participation in generation (particularly in renewable energy), and a 4 In order to aggregate across the four dimensions of power sector reform considered in this study, a simple Power Sector Reform Index is constructed. The index gives each country a score on an interval of 0 to 100 on each dimension of power sector reform. The scores are based on giving equal weight to each step on each dimension of the reform continuum. The average of the four 0-100 scores is used to provide an overall summary of the extent of reform. Announced reforms are by no means always implemented, and Figure 3 disaggregates each of the four reform components into that which was implemented and that which was only “announced” but not implemented.

6
## **5.2.2 survey-to-survey imputation**

The procedure is also tested in a survey-to-survey imputations. CC yields from the 2018 survey sample are used as a test set, with the model trained with the 2017 survey sample. Table 9 summarizes the results of the survey-to-survey imputation exercise, with 100 percent of the 2017 sample used as training sample. We find that except for millet, rice, and groundnut the difference between the mean obtained with the ML model is statistically different from that of the CC means even at the national level. However, even when the difference between the mean of the ML yields and the mean of the CC yields is statistically different, it remains smaller than the difference of the SR in most cases. The statistical difference that we detect may be due to the fact that despite computing the standard errors following the MI procedure, we are getting standard errors that do not account enough for the variability across imputations. This is also visible in Figure 10 which shows the means computed for 100 simulations. Looking at the regional level estimates, we find that in general these differences are even more pronounced.

The relatively low accuracy of the survey-to-survey imputation may be somehow surprising in this framework, given that the 2017 and 2018 surveys are very similar in terms of geographical coverage, representativeness and survey instruments. Nevertheless, there are some important differences in the questionnaires between the two surveys that can explain the relatively low accuracy of the 2017 sample for imputing the 2018 yields (for example, the seeding date, which interacted with rainfall is a top predictor of yields, was not collected in 2018). (Gourlay et al., 2019) find more promising year-to-year yield predictions using CC yields in Uganda. However, their results rely on a panel of households interviewed in two consecutive years, and predictions are even more accurate when plots from the same parcel are sampled. Interestingly, in this framework predicting the logs results in lower accuracy than predicting the levels, and the difference in _R_<
Gille 18

**Table 6.** Coefficient of Proportionality

|**Group**|**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**No excluded variable**|||||
|Caste|1.34|1.27|0.92|0.79|
|Caste & occup|0.84|0.83|0.81|0.71|
|Jati & occup|0.82|0.79|0.89|0.74|
|Caste & occup, WHEAT|7.76|7.47|2.27|2.88|
|**Excluded var: Head educ and Othe**|**r group mean educ**||||
|Caste|2.05|1.93|1.35|1.14|
|Caste & occup|3.07|3.00|2.90|2.39|
|Jati & occup|1.87|1.80|2.04|1.65|
|Caste & occup, WHEAT|17.93|16.64|3.34|4.36|
|Household controls|Yes|Yes|Yes|Yes|
|Agr. controls|Yes|Yes|Yes|Yes|
|Group controls|No|Yes|Yes|Yes|
|Controls for other group|No|No|Yes|Yes|
|Add. controls|No|No|No|Yes|

_Source_ : Author’s analysis based on the 2006 round of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS) from the National Council of Applied Economic Research. _Note_ : This table reports coefficients of
of Malaysia, we use measures of output (value added) from the national accounts as published by the Department of Statistics (DOS), and employment estimates are obtained from the labor force surveys published by DOS. The labor input is adjusted for quality using a measure of the average educational attainment of the labor force (also taken from the annual labor force surveys), assuming a seven percent return per year of schooling.

We obtain a measure of the capital input from the DOS, although at the level of individual industries we must rely on estimates prepared by the Malaysian Productivity Corporation. While there is some concern about data quality because these industry-level estimates are based on limited data, we largely restrict our analysis to the level of major sector and broad industry-groupings. Finally, there is some difficulty obtaining reliable time-series estimates of total wage compensation by sector and industry. Because the growth accounts use factor income shares as weights in measuring factor contributions to output, we use the wage measure to capture the share of income attributed to labor. Currently, we have estimates of labor income only for the benchmark year of 2000, and thus we use constant factor income shares (also known as a Cobb-Douglas production function) for our analysis.

> 13 A more detailed explanation of the growth accounting framework, is provided in Bosworth and Collins (2003).

12
Policy Research Working Paper 5863

# **Abstract**

This paper examines the consequences of rapid disinflation for downward wage rigidities in two emerging countries, Brazil and Uruguay, relying on high quality matched employer-employee administrative data. Downward nominal wage rigidities are more important in Uruguay, while wage indexation is dominant in Brazil. Two regime changes are observed during the sample period, 1995–2004: (i) in Uruguay wage indexation declines, while workers’ resistance to nominal wage cuts becomes more pronounced; and (ii) in Brazil, the introduction of inflation targeting by the Central Bank in 1999 shifts the focal point of wage negotiations from changes in the minimum wage to expected inflation. These regime changes cast doubts on the notion that wage rigidity is structural in the sense of Lucas (1976).

This paper is a product of the Office of the Regional Chief Economist, Latin America and the Caribbean Region. 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://econ.worldbank.org. The author may be contacted at jmessina@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