rafmacalaba/data-use-mentions-extended huggingface dataset:
where and refer to economy-wide and sectoral labor productivity levels, respectively, and is the share of employment in sector tt ii,tt . The ∆ operator denotes the change in productivity or YY yy employment shares between ii,tt and . The first term in the decomposition is the weighted sum θθ ii of productivity growth within individual sectors, where the weights are the employment share of tt − kk tt each sector at the beginning of the time period. This is called the “within” component of productivity growth. The second term is called the “structural change” component of productivity growth. It captures the productivity effect of labor re-allocations across different sectors. When changes in employment shares are positively correlated with productivity levels, the second term in the decomposition is positive, and structural change increases economy-wide productivity growth.

# _4.1.1 Structural change in Guinea during the period 2006-15_

We use Guinea’s labor data and apply the method of productivity decomposition over the period 2006-15. This is a recent period marked by several socio-political and economic shocks. Indeed, this period covers political transition before the first democratic presidential elections since the independence of Guinea, organized in 2010. It is also a period that was marked by the adoption of the new mining code, which contributes to Guinea’s attractiveness to foreign direct investments. This period was also marked by the outbreak of the Ebola crisis in Guinea in 2014. Thus, it is important to analyze how the country handled the challenges and opportunities that it faced for structural change during the period 2006-15. From a statistical perspective, the period of analysis is also relevant. Indeed, Guinean authorities recently adopted the 1993 U.N. System of National Accounts 1993 (SNA 1993), with IMF technical support, and revised the national accounts for the period 2006–15. The adoption of SNA 1993 led to more accurate estimates of national accounts data; for instance, nominal GDP was revised upward by 40 percent.<sup>6</sup> The data
- 73 -

award practices of contractors. Weather and other exogenous factors will also affect the bidding behavior, contractors’ performance and the quality of roads delivered. All these issues need to be considered when evaluating the performance of public road procurement.

<!-- Start of picture text --> Figure 44. Share of paved roads by region<br>80 73.4 70.6<br>70 64.4<br>60<br>50 47.0 47.3<br>41.3<br>40<br>30<br>20 9.9 12.9<br>10<br>0<br>Share of paved roads (%)<br>the Caribbean Sub-Saharan Africa South Asia East Asia & Pacific Middle East & North Africa Europe & Central Asia Developing countries High-income countries<br>Latin America &<br><!-- End of picture text --> Source: World Development Indicators, accessed in March 2011.

# **REFERENCE**

- Alexeeva, Victoria, Gouthami Padam, and Cesar Queiroz. 2008. ―Monitoring Road Works Contracts and Unit Costs for Enhanced Governance in Sub-Saharan Africa.‖ Transport Papers TP-21, World Bank, Washington, DC.

- Anderson, WIllam, Arthur Jacoby. 2000. Measuring economic impacts of federal-aid highway projects. _Public Roads_ , Vol. 64.

- Athias, Laure, and Antonio Nuñez. 2006. ―Number of Bidders and the Winner’s Curse in Toll Road Concessions: An Empirical Analysis.‖ CNI-Working Paper No. 2006-16, Center for Network Industries and Infrastructure, Berlin.

Blancas, Luis, Laura Chioda, Tito Cordella, Alexandre, Oliveira, Felix Vardy. 2011. Do procurement rules impact infrastructure investment efficiency? An empirical analysis of _Inversão das Fases_ in São Paulo State. Policy Research Working Paper No. 5528, the World Bank.

- Brannman, Lance,   Douglass  Klein and Leonard W. Weiss.  1987. "The Price Effects
|
|_Controls in All Panels_|||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: Mental Issues Index is constructed using the average of the variables in columns (ii) to (vi). The average is calculated with the number of variables available per individual, the index do not take into account the variables with missing values. See Appendix B for details of variable’s constructions. The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

44
-Teachrs-Non-teachrs Pay DIfferences 18-

# FOOTNOTES

(1) Hours worked can sometimes be an endogenous variable. However, in the case of Cote d'Ivoire, salaried workers rarely are able to determine autonomously and individually their hours of labor supply. Hours thus vary little within each occupation as well as among non-teacher occupations and are introduced in the equation mainly as a characteristic of occupation.

(2) We recognize that the CILSS sample is small. A census is usually preferred to a household survey as data base to do an occupation specific analysis. However, in Cote d'Ivoire available census data are too out of date to warrant current analysis.

(3) Alternative explanations for the significance of a dummy coefficient for diploma include the human capital one that degree earners are more able and therefore more productive than non-degrees earners. This is not likely to be valid here since the dummy coefficient for diploma is significant only in the government sector, where productivity matters the least.

(4) Income differentials between categories represented by dummy variables are calculated by comparing the absolute earnings predicted by the logarithmic functions. For example, the coefficient of sex in equation 2 above is -0.3352; this implies that women's earnings are below men's by exp(0.3352)-1-0.3982. (See Halvorsen and Palmquist (1980) for more on this).

(5) A reviewer suggested that the survey may have overestimated the hours actually worked by teachers. To further test the role of hours worked we reestimated the equations with all teacher hours diminished by 50X . The hour variable continued to be insignificant and the coefficient of teacher was not significantly different from the the previous ones showing the robustness of the results.

(6) This is supported by the fact that when the earnings equation No 1 of table 2 is reestimated over the subsample of employees with at least 10 years of education, the coefficient of the log of monthly hours worked is statistically insignificant.

(7) It is clear
</sup> The output of credit portfolio models can be used to verify that credit loss provisions, obtained from applying the supervisory ratios, globally match the estimated expected loss and that regulatory capital covers most unexpected losses. Academic attempts to perform such comparisons can be found in Balzarotti _et al_ (2002) - with Central Bank of Argentina PCR data -, in Cronemberger Parente _et al_ (2004) - with data from the Central Bank of Brazil PCR -, and in Jiménez and Mencía (2009) - with data from the PCR operated by the Bank of Spain.

# **III. The role of PCRs in bank supervision**

Bank supervisors around the world typically adopt risk-based approaches to bank supervision. Supervision focuses on assessing banks particular vulnerabilities and the

> 14 Examples of countries using this approach are Argentina, Brazil, Costa Rica and Panama.

> 15 However, in response to the increasingly widespread development and use of rating systems by banks, regulators are recognizing those internal ratings to compute loan loss provisions. Hence, when banks report their information to the public credit registry they include the internal rating assigned to their obligors.

> 16 For example, banks’ rating systems must be very granular (i.e. have many rating grades). This is necessary to display discriminatory rating power. In turn, to construct such a granular rating system a wide array of information is required, both of the credit operations and of the borrowers themselves as well. This is particularly important when building rating systems for corporate obligors, who for example use information on liquidity and leverage ratios as inputs. This typically exceeds the range of information registered at public credit registries, which only collect information on the credits and associated collaterals.

> 17 Colombia and Mexico pioneered the development of these supervisory rating systems.

> 18 In particular, they provide useful information of a loan portfolio loss distribution such as its median, its mean (average loss rate, to be covered with total provisions) and different quantiles from the tail of
|-0.123|-0.153|-0.127|
|(SD)||(S.E.)|(0.061)|(0.072)|(0.074)|(0.078)|(0.067)|
||2016|Comp. mean|0.131|0.137|0.127|-0.0184|0.289|
|||Obs.|2,877|1,279|1,598|1,823|1,054|

p<0.001***; p<0.01**; p<0.05* Note: Each cell-block is the result of a separate regression. Standard errors clustered at the village level are in parentheses. “Comp. mean” refers to the comparison group mean for the outcome variable. Column (1) regressions control for child characteristics (age, household size, household wealth asset index, parenting practices, mother's education and gender) and baseline child development measures (BMI, cognitive, fine motor, gross motor, and language). Columns (2) and (3) regressions use the same controls as column (1) except they exclude household wealth. Columns (4) and (5) regressions use the same controls as column (1) except they exclude parenting practices.

In Table 4, we find mixed results of the project on primary school test scores, which were measured at age 8 (2016). We find large positive effects (0.134 S.D.) on language items involved with selecting a picture whose name began with a different letter to other pictures 13
||||35||||
|---|---|---|---|---|---|---|
|Topic|Year|Title|First author|Journal / Edited volume|Citations|Rank|
|Markets in health care|1986|Taxation, Health Insurance, and Market Failure<br>in the Medical Economy|Mark V. Pauly|Journal of Economic Literature|342|151|
|Markets in health care<br>|2000<br>|<br>Is Hospital Competition Socially Wasteful?<br>|Daniel P. Kessler<br>|Quarterly Journal of Economics<br>|293<br>|204<br>|
|Markets in health care|2000|Equilibrium Price Dispersion in Retail Markets<br>for Prescription Drugs|Alan T. Sorensen|Journal of Political Economy|251|274|
|Markets in health care|1993|<br>Supply-Side and Demand-Side Cost Sharing in<br>Health Care|Randall P. Ellis|Journal of Economic Perspectives|232|304|
|Economic evaluation|1986|Measurement of Health State Utilities for<br>Economic Appraisal: A Review|George W. Torrance|Journal of Health Economics|1455|11|
|Economic evaluation|1991|Foundations of Cost-Effectiveness Analysis for<br>Health and Medical Practices|Milton C. Weinstein|The economics of health. Volume 2|1370|12|
|Economic evaluation|2004|Five-Hundred Life-Saving Interventions and<br>Their Cost-Effectiveness|Tammy O. Tengs|Classics in risk management. Volume 2|1076|19|
|Economic evaluation|2002|The Estimation of a Preference-Based Measure of<br>Health from the SF-36|John Brazier|Journal of Health Economics|774|31|
|Economic evaluation<br>|1991<br>|Economics of Coronary Artery Bypass Grafting<br>|Alan Williams<br>
657|39.1|11.7|27.1|31.9|-0.77|
|<br>Ninth Decile Products|142,627|38.4|9.0|4.0|5.4|-2.15|
|Tenth Decile Products|287,531|40.7|10.9|8.2|28.2|-21.65|

*Includes Brazil's recent restrictions on automobile imports and domestic automobile assembly regulations.

Source: Computed from SMART and the WTO Integrated Data Base.
HE operations. LGAs need to see HEs as a solution to their employment problems, as well as contributors and not temporary impediments to local economic development.

# **4.4 Operational Challenges and Livelihood Risks**

65. Boxes 4.3 to 4.7 list the many constraints HE operators grapple with in running their business. Apart from these difficulties, HEs, by their very nature of being a household business, are also vulnerable to both household as well as business risks. The most frequently cited in the literature, from the FGD study and from the recent FinScope survey (Table A1.17), is the risk of serious illness or death in the family. Faced with such shock, HEs often struggle to meet the immediate cost of treatment or burial by borrowing money from relatives and/or friends or drawing down their limited savings (Table A1.18), thereby depleting their working capital.

66. Rural HEs, in particular, are exposed to risks that stem from their overreliance on agricultural produce as a source of capital. In bad weather, low production reduces their farm earnings. In turn, their operations suffer, resulting in low turnover and further reduction in earnings. Even in times of favorable weather and good harvest, HEs are exposed to potential risks, such as instability in the market and/or prices of their products. Aggravating these risks is the perishability of some agricultural produce.

32
44 investing across borders: do fdi-specific regulations matter?

The traditional estimates obtained from log-linearized models of barriers to FDI are not consistent because they do not account for all the information contained in bilateral data, especially between countries that invest zero amounts in each other (country selection problem). These models also do not acknowledge that firms are heterogeneous and only a fraction of them are in a position to invest abroad (firm selection problem). The amount of FDI between countries i and j is not just a function of low barriers to FDI, but also the fraction of firms that invest in country j from country i. Not controlling for the latter assigns exaggerated elasticities to policy costs and gravity variables by conflating the extensive and intensive margins of investment flow.

I find that FDI-specific regulations matter significantly for attracting FDI. Using a new, painstakingly prepared data set on FDI regulations across 87 countries, I show that de facto implementation of laws related to the arbitration of commercial disputes and the number of procedures required to set up wholly-owned foreign subsidiaries are strongly associated with high levels of FDI stock. These provisions proxy for the quality of FDI-specific institutions in the country, and affect FDI more directly than measures of the _general_ quality of institutions.

I also show that it is how the targeted laws and regulation are translated into practice that is important rather than what is written in statutes. This is reflected by the fact that (the coefficient on) openness to FDI, measured by the average percentage of equity permitted to be owned by foreign investors, is not a significant determinant of FDI. Further, the correction of biases dramatically reduces the salience of bilateral distance – a proxy for transaction costs and information asymmetry – as a barrier to inward foreign direct investment from OECD countries.
i.e. those with total trade to GDP below and above the sample median, respectively). The results in both panels show a larger coecient for natural hedge among open countries (Column (2)) than among closed ones (Column (1)), but the dierence between these two groups is not signicantly dierent from zero (see the Test of Equality of Interaction row at the bottom of the table that reports the p-value for the test that the coecients in the two groups are identical).

The next two columns compare those countries where there is no forex derivatives market data and those where there is data, regardless of the size of the market (Columns (3) and (4), respectively) Surprisingly, the results clearly show that the coecient is more negative and signicant among countries where there is forex market data (Column (4)); in fact, in Panel A the coecient is one order of magnitude larger among these countries than among countries with no data on forex markets. In Panel B that looks at bilateral trade patterns the dierence between the two groups is smaller and statistically insignicant, although still the coecient is larger among countries with forex data.

The regressions in columns (5) and (6) estimate the parameters of equations (7) and (8) among those countries with data on forex derivatives. Even within this group, the coecient on natural hedge is more negative in countries with a high foreign exchange derivatives turnover, although the 23
.

– We estimate the model to a core sample of six large economies India, Indonesia, Brazil, Mexico, Canada and the United States – for which we have employment and hourly wage data spanning four to five decades. Gender barriers in each occupation-sector and country-year are quantified as “wedges” which ensure that our model equations precisely match the data on gender employment and wage gaps after accounting for economic fundamentals and individuals’ employment and consumption choices in equilibrium. For example, the model expresses the observed gender wage gap in each occupation-sector as a function of a model-implied gender gap in human capital and the wage discrimination women face relative to men. To measure gender barriers, we assume that men and women have the same productivity within each occupation-sector, conditional on their model-implied human capital levels.<sup>5</sup>

> 4See Boppart (2014); Herrendorf et al. (2014); Comin et al. (2021).

> 5In robustness checks, we relax this assumption and allow for gender-specific comparative advantage

4
to school.

There are at least two reasons to focus on expenditures made by FONCODES on education, rather than on total FONCODES expenditures. First, education is one of the two biggest sectors in the FONCODES portfolio, accounting for about one-quarter of total FONCODES expenditures made between 1992 and 1998. Moreover, as Table 1.1 shows, the bulk of these expenditures on education was made in the first few years of FONCODES operations, so that measurable changes in outcomes such as school attendance have had some time to take place (although the full benefits of investments in education may not be realized for years). An analysis of FONCODES investments in education therefore deserves attention in its own right. Second, it is possible to use available data sources to construct credible outcome measures in education; this is not easily done in other sectors, such as agriculture, transportation, or even health.<sup>2</sup>

> 2 Initial inspection of the data on health in the 1994 and 1997 Peru LSMS, for example, suggests that infant mortality went up during the period. This seems quite implausible given other changes which took place in Peru between 1994 and 1997, and is likely driven by the very small number of reported deaths in both

2
We aggregate the number of daily new cases and deaths to create weekly confirmed cases and deaths for each country. This allows us to link the weekly Google Trend data with COVID-19 cases and deaths. The number of COVID-19 cases and deaths are expected to inform consumer behavior and government policy responses to reduce the spread of the virus and the economic cost of the pandemic. These may impact demand for services through different, but related channels.

The aggregate impact of COVID-19 on consumer demand for services is the combined effects of (i) a reduction in the supply of services due to social distancing and lockdowns measures,

> 5 We have also applied the renormalization method by Stephens-Davidowitz (2014), but it makes no difference to our results. Results from this approach are available upon request.

> 6 We mass translate our search terms from English to the respective local languages using Google Translate API for all countries and terms. See also at https://github.com/MarkEdmondson1234/googleAnalyticsR

7
17 Figure 6 presents a similar exercise for Vavuniya, based on actual rainfall and irrigation tank level data.<sup>26</sup> For the Maha season, the years 1996, 2008 and 2014 were particularly bad, when crop losses were incurred due to dry spells. The simulated pay-outs (blue bars) show that the pure rainfall index insurance product would have triggered pay-outs in all these years. However, it would have also triggered pay-outs in years such as 2003, 2012 and 2015, when pay-outs would not have been necessary for farmers with access to major irrigation. Once again, a pure rainfall index product would have triggered more pay-outs than necessary, leading to a more expensive product and not addressing the lower risk profile of irrigated farming. The rainfall and irrigation index (red bars) would have triggered pay-outs only in the specific bad years, which would be a more relevant product for major irrigated farms as well as lead to a lower premium.

**Figure 6. Product Simulated Pay-outs for Vavuniya (in LKR)**

<!-- Start of picture text --> 18,000<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>Rainfall index Rainfall and Irrigation index<br><!-- End of picture text --> Source: Author constructed based on Meteorological data (Meteorological Department of Sri Lanka) and MCM data (Irrigation Department of Sri Lanka).

Based on information collected on damages for the Maha season in Anuradhapura through focus group discussions with farmers, it has been established that severe crop losses occurred in the district in the Maha seasons of 2003, 2004, 2008 and 2011. This was also validated by an analysis of damage ratios.

> 26 Based on Pavatkulam tank, from Irrigation Department, Ministry of Irrigation
expropriation? Given the profound implications for firm productivity and innovations, further research should address the impacts and the deterrninants of regulation in developing countries. To accomplish this research agenda it is important to gather panel data on, for example, pricing behavior, service coverage and quality, profitability, market structure, rent distribution (i.e., were employment and wages affected by regulatory changes), and dynamic efficiency (measured by the rate and direction of innovation and productivity).

World Bank surveys probably have focused more on regulation than any other theme, although the variation in coverage is vast across surveys. Those that cover regulation thoroughly _(the Bosnia Survey, the World Business Environment Survey, The Emergence of Private Sector: Hungary,_ and the RPED) ask questions about: the waiting period for goods to arrive, the level of government to deal with in regulation, the main problems in dealing with govermnent agencies, the frequency with which firms are required to meet with government officials, the costs of facilitators necessary for dealing with the government, the burdens of licensing requirements, costs of obtaining licenses and permits, various types of taxes, tax treatments for profits reinvested in company, and incentives for investment in new machinery and equipment.

A general problem with current Bank surveys on regulation, however, is that they focus on the barriers that regulations impose on business. They tend to ask firms "how severe" certain regulations are to firrn operations. It is true that burdensome and often unnecessary regulations are common. However, many regulations are important to the functioning of the economy (e.g., environmental regulations that force firms to internalize all costs of their production). In those cases, the regulation will be an obstacle from the firm's perspective, but efficient from the perspective of the entire economy. Surveyors need to think carefully about how to uncover the true costs of regulation, since estimates will be biased upwards by simply asking the firm whether a regulation is an obstacle.

# **