rafmacalaba/datause-encoder-data huggingface dataset:
_6(WP-Wm)=(rM-rp)/2+am-ap._ With g identical across stationary points, _aM - ap = rp - rM and_ Wp _- WM = (rP - rM)I_<sup>_2 6_</sup> _._ The discounted value of the recipient's welfare (using the sarne discount rate and applying a zero shadow price to aid) is /3S/8 at any stationary point. Since _- ,6(Sp - SM) = (rM rp) /_ 26, the recipient is better off at the M equilibrium.

# References

Arrow, K. 1962. "The economic implications of learning by doing." _Review of Economic Studies_ 9: 155-73.

Bauer, P. T. 1971. "Foreign aid forever?" _Encounter_ (March).

Botchwey, K., and D. Brautigam. 1998. "The impact of aid dependence on governance and institutions in Africa." Paper prepared for .AERC/OCD Collaborative Research Project on Managing the Transition from Aid Dependency in Sub-Saharan Africa, May 21-22, 1998.

Bruton, H. 1998. "A reconsideration of import substitution." _Journal of Economic Literature_ 36: 903-36.

de Long, J. B., and B. Eichengreen. 1991. "The Marshall Plan: History's Most Successful Structural Adjustment Program." Paper prepared for the Centre for Economic Performance and Landeszentralbank Hamburg conference on Post-World War II European Reconstruction, Hamburg, September 5-7, 1991.

Frank, A. G. 1966. "The underdevelopment of development." _Monthly Review_ 18(4).

Friedman, M. 1958. "Foreign aid." _Yale Reviewv_ 47(4): 500-16.

Kaldor, N. 1963. "Will underdeveloped countries learn to tax?" _Foreign Affairs_ (January).

Kimbrough, K.P. 1986. "Foreign aid and optimal fiscal policy." _Canadian Journal of Economics_ 19(1, February): 35-61.

McKinnon, R. 1964. "Foreign exchange constraints in economic development and efficient aid allocation." _Economic Journal_ (June).

United Nations Industrial Development Organization. 1972. Guidelines for project appraisal. New York: UTNIDO.

World Bank. 1998. "Addressing the challenge of reforming public institutions." Public Sector Board, PREM Network. Draft.

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**The greater reduction in mobility among women than men during the pandemic was partly due to women’s greater reliance on public transport that saw its services reduced for several months** . However, the disproportionately greater shift away from public transport among women – with a drop in the number of trips of about 76 percent compared to October 2019, against 69 percent among men (Figure 6) – was also driven by their greater aversion to the risk of exposure to the virus, which was a bigger concern when using public transport compared to other modes. For example, the survey implemented by the team with current car and motorized vehicle users suggests that about 12 percent of them had previously used another mode for that same trip (11 percent of men and nearly 17 percent of women), and about 77 percent of these had previously used public transport. When asked about the main factors determining mode choice, the risk of COVID-19 was much more salient for women than men, with over 40 percent of women naming it the single most important factor determining travel choices, compared to less than 32 percent of men. On the other hand, comfort and travel time were more likely to be named as the most important travel attributes by men than women, while the importance of travel cost is roughly similar for men and women, with 12 percent of women and 11 percent of men ranking it as the most important attribute.

**_Figure 6:_** _Change in the number of trips in October 2020 vs. October 2019 (%), by mode and gender_

<!-- Start of picture text --> NMT Private motorized Public transport Total<br>10<br>0<br>Women<br>-10<br>-20 Men<br>-30<br>-40<br>-50<br>-60<br>-70<br>-80<br><!-- End of picture text -->

**Similarly, a statistical model developed using the collected survey data suggests that the odds of becoming a** **_new_ user of private motorized vehicles just since the onset of pandemic were significantly higher for women – by about 31 percent compared to men** . The estimated odds of being a new user of a car/van or motorcycle are also higher for people with at least some university education and people who had at least some type of change in their occupation status (e.g., work becoming remote or hybrid). The odds decrease with the person’s age, meaning that the older the respondent, the more likely he or she was already using the private motorized vehicle already before the pandemic as opposed to becoming a new user. The odds of recently switching to private motorized modes are also higher if the start of the trip is in CABA rather than in PBA, likely explained by the higher financial ability of households residing in CABA. As people already before the pandemic relied disproportionately more on private motorized modes to go on trips for ad-hoc reasons such as recreation or to see a doctor, the odds of _switching_ to those modes since the start of the pandemic were lower compared to other (more regular) types of trips such as to work or school. The results are also intuitive regarding the statistical association with the person’s stated most important travel attributes: the odds of becoming a new user of private motorized modes are
skills or an obstacle to effective job matching. In an era of rapidly changing technology, policy makers require up-to-the minute information on new job types and emerging trends in the demand for workforce skills. Online job-portal data can be used to facilitate demand-driven training, and policy makers should explore ways to link this information to skills development programs or job-placement services.

Second, policy makers can use big data analytics to develop a more detailed and timely understanding of the characteristics of the labor supply. Online job-portal data can provide information on the interests and qualifications of recent graduates, as well as the features of specific labor market participants, such as women, young people, or workers in the informal sector. Identifying the dynamics that drive labor force participation among women and young people, as well as their unique job-search constraints, could also contribute to greater labor productivity and support the achievement of key social development goals. Moreover, understanding the differences between formal and informal sector jobs and workers, how wages are determined, how job matching takes place, and what skills are in shortage or surplus in the informal sector are particularly crucial to the development of effective labor market policies in developing countries.

Third, online job-portal data are more valuable when used to complement other sources of information. As issues with representativeness are a major limitation on data quality, periodic surveys of the entire labor market are essential for normalizing samples and verifying data reliability. In addition, hybrid statistical techniques using both big data analytics and more traditional methodologies can reveal dimensions of the labor market that are otherwise unobservable.

Fourth, analytical results from online job portals and their policy implications should be made public, so long as privacy concerns can be effectively addressed. Ever-expanding access to internet technology and social networks is generating a wealth of information on human behavior, raising important questions about data confidentiality and ownership. Once an appropriate legal framework has been established and sound privacy controls are in place, information on current labor market trends, including job availability, wage growth and the demand for workforce skills, should be provided to the public in order to better inform personal and firm-level employment, education and training decisions. Students in particular can use online job-portal data to determine the skills that they may wish to acquire. Data visualization can assist in conveying complex information on employment dynamics to the public.

Fifth, the analytical results from online job portals can inform training programs to make them more demand-based. Training programs are often unable to catch up with the speed of changing labor market demands driven by fast technological change, and skills training institutions continue to provide outdated training with outdated training materials and curricula. The real-time big data analytics will provide the 28
matches fulfilling the data criteria for 1,622 out of the 5,401 unique parties, which represent 582 out of the 2,154 distinct firms.<sup>5</sup> The discrepancies in matching success across tax data sources are due to revenue thresholds (as well as lower compliance) for monthly filing regimes and the exclusion of banks and insurance companies from the monthly revenue analysis (see last footnote). We perform robustness to differential attrition in Section 6.3. While our primary focus is on the impact of court proceedings on firm revenues, for some specifications we leverage the monthly revenue time series of all 8,575 firms in the VAT filings data without a case over our study period that meet analogous data criteria (complete time series of at least 12 months).<sup>6</sup> Finally, between August 2016 and February 2017 we conducted a survey of firms involved in commercial cases over our study period. We recovered addresses and/or phone numbers in the Dakar region for 1,709 of these 2,154 firms.<sup>7</sup> Out of the remaining 445 firms, 218 were located outside the survey area (abroad or in a different region of Senegal), while for 227 no contact information could be obtained. We successfully located 812 of the 1,709 firms for which we had recovered some contact information,<sup>8</sup> and completed 277 surveys. Conditional on being located, we obtained a response rate of 34%. These 277 firms correspond to 925 parties that are firms; they were involved in 884 different cases. Again, we discuss robustness

> 5The universe of monthly VAT filings includes 15,652 distinct firms with revenue data for at least one month within our study period. Of these, 939 were party to at least one court case (3,441 parties in total) over our study period, while 14,713 were not. Of the former, the 12-month complete time series criterion was met for 2,410 parties (606 distinct firms). The 12-month window is chosen as a compromise between event study time series length and sample size, given the presence of data gaps in the monthly VAT filings. We further exclude all case parties that are banks or insurance companies (788 parties, for 24 distinct banks/insurance companies) to avoid assigning excessive weight to sectors where court cases are a frequent occurrence of regular operations and cannot be expected to affect revenues.

> 6These observations remain in our analysis sample, contributing to the estimation of (month _×_ year) fixed effects to improve the precision of our event study and difference-in-differences estimates. Note that 6,138 of the 14,713 firms in the universe of monthly revenue data that were not party to a case did not meet the data criteria.

> 7Tracking of these firms was done through a combination of court records, name, and tax ID merged with a national registry of firms operating in Senegal which contains contact information fields ( _R ́epertoire National des Entreprises et Associations_ , RNEA), and searches in public address books and web search engines.

8Another 133 firms were found not to exist anymore and the remaining 743 did not have sufficient contact information.

8
Estimation results for the second definition of the vulnerability line ( _P_<sup>_2_</sup> ) are provided in Table 2. As discussed earlier, we start iterating from a minimum sample size of 500 households whose consumption is between the poverty line and vulnerability line, which yields a maximum vulnerability index of 19 percent and 22 percent respectively for the US and Vietnam. If we are to apply the same automatic scaling of 30 percent to the poverty line for Vietnam as before, this would result in a vulnerability index of 22 percent. Reducing these vulnerability indexes to, say, 10 percent would entail increasing the poverty line by 177 percent and 114 percent respectively for each country. As discussed earlier, since the poverty lines do not change much for both periods with both the US and Vietnam, the vulnerability index provides a larger range of values compared to the insecurity index.

# **3.1.2. Welfare Dynamics over Comparable Time Periods**

Both Tables 1 and 2 also offer potential ranges of values for the vulnerability line that can work as a middle class income line. For example, if we use the second definition and a vulnerability index of 10 percent, it would translate into a middle class income line (or vulnerability line) of $US 36,905 and D 5,480,000 for the US and Vietnam. These lines are respectively 71 percent and 92 percent of the median incomes for each country in the same year (i.e., $US 52,163 for the US (DeNavas-Walt et al., 2009) and D 5,986,000 for Vietnam (our calculations)). We can then update these middle class lines with the appropriate consumer price indexes in the second period and use these to estimate different measures of welfare transitions.<sup>21</sup> Table 3 shows the welfare transition matrices respectively for the US based on the poverty line and middle class line defined above. Estimation results suggest that the lower income groups

> 21 These consumer price indexes are 7 percent for the US (Census Bureau, 2013) and 33 percent for Vietnam (our calculations based on data provided by Vietnam’s General Statistical Office).

20
2012; Wagstaff _et al_ ., 2014). We focused on those MDG indicators that are relevant to maternal and child health. These include Goal 4 (reducing child mortality) and Goal 5 (improving maternal mortality). Goal 1 is relevant as well, owing to its role in reduction of undernutrition and hunger.

**Table 1. List of maternal and child health indicators analyzed in this study**

|**Indicators**|**Definition**|
|---|---|
|Stunting|Percentage of children with a height-for-age z-score <-2<br>standard deviations from the reference median *|
|Wasting|Percentage of children with a weight-for-height z-score <-2<br>standard deviations from the reference median *|
|Underweight|Percentage of children with a weight-for-age z-score <-2<br>standard deviations from the reference median *|
|Neonatal mortality rate (NMR)|The number of neonates dying before reaching 28 days of<br>age per 1000 live births|
|Infant mortality rate (IMR)|The number of deaths among children under 12 months of<br>age per 1000 live births|
|Under-five mortality rate (U5MR)|<br>The number of deaths among children under 5 years of age<br>per 1000 live births|
|Full immunization|<br>Percentage of children aged 12 to 23 months who received<br>BCG, measles and three doses of Polio and DPT #,+|
|Measles vaccination|Percentage of children aged 12 to 23 months who received<br>measles +, #|
|Contraceptive prevalence (Modern<br>method)|Percentage of currently married women aged 15 to 49 who<br>currently use a modern method of contraception|
|<br>Antenatal care visits- 4 or more<br>(ANC4+)|<br>Percentage of mothers aged 15 to 49 who had a live birth in<br>the past 5 years who received at least 4 antenatal care visits<br>from any skilled personnel during pregnancy for the most<br>recent birth|
|Skilled birth attendant (SBA)|Percentage of mothers aged 15 to 49 who had a live birth in<br>the past years who were attended by skilled health<br>attendant|

Notes: * Based on WHO 2006 growth standards are used to calculate z-scores. + Immunizations are either verified by card or based on recall of respondent. # Data not collected in 2014 mini-DHS survey.

We selected six health status indicators and five health intervention indicators.

The five health status indicators include three child undernutrition and two mortality indicators, namely stunting, wasting, underweight, neonatal mortality rate (NMR), 6
# **5. Conclusions**

In light of the rarity of wage cuts, even in the face of quite severe economic shocks, this paper examines responses by firms’ managers ranking the relevance of a number of theories put forward in the labour economics literature for why cuts tend to be avoided. To do this, we use a large specially commissioned survey of firms across fourteen European countries asking managers directly about their experiences with wage cuts.

Just over 2% of firms had cut wages over the last five years at the time of the survey. We document the relative importance of eight possible reasons for avoiding wage cuts, with firms being asked about the effect of labour regulations and collective agreements, the existence of implicit contracts, efficiency wage considerations in terms of negative effects on worker morale or effort, whether firms had concerns about losing key staff or having difficulties in future recruitment, whether the costs of future recruitment and training would be higher, and whether they felt employees would be concerned with how their wage compares to that of similar workers in other firms.

Across all countries and sectors, the two most important causes for avoiding base wage cuts are the belief that this would result in a reduction in morale or effort and the danger that the most productive workers would leave as a consequence. The greatest variation across survey respondents from different countries was in the importance attached to labour regulations and collective bargaining, which we found to be almost twice as high in the EU-15 countries as in the CEE countries. This difference can be attributed to varying coverage of collective bargaining agreements, which tends to be much higher in EU-15 countries than in most of the CEE countries.

We find certain worker and firm characteristics to be strongly related to the relevance of different theories. For example, firms that employ higher proportions of blue-collar and low-skilled white-collar workers rank labour regulation highly but are less likely to lay importance on concerns about losing the best employees, or the potential costs of later recruitment and training. Larger firms are more likely to be aware of the potential 30
43

**PHN** 1991 **Sector Review**

# **Figure 1:** PHN Projects **under** Supervision

<!-- Start of picture text --> ASIA 35%<br>1547.1 ~~~~~LAO 18%<br>AFRICA 46% 17<br>EMENA 15%<br>683.7<br>EMENA 14%<br>AFRICA 25% 14<br>L/ AC" 21092.7<br>LAO 25%<br>1120.2 ASIA 22%<br>21<br>Lending in Number of<br>US$ Millions Projects<br><!-- End of picture text --> .
admit the possibility that trade reform could have adverse long-run consequences for employment. Turning to the empirical evidence, however, there is no support for such a view. Marquez and Pages-Serra (1998) suggest that firm-level declines in employment per unit of output (increased efficiency) are offset by increases in firm sizes or numbers. IADB (2004), in a review of ten countries’ household data, suggest that trade liberalization increased employment and left unemployment unchanged—i.e., increased participation.

In a macroeconomic study, Kee and Hoon (2005) show that increasing openness lay behind much or all of a dramatic decline in the natural rate of unemployment in Singapore. Between 1966 and 2000, over which period the openness ratio, (X+M)/GDP, increased from 224 percent to 298 percent, the relative prices of export goods increased and there was a rapid accumulation of capital in the export sector. Both phenomena increased the marginal product of labor (and hence the wage) in terms of nontradeables and expanded overall employment fourfold (as population doubled). The direct effects of the accumulation were larger than those of relative prices, although the latter, which is the natural consequence of trade liberalization, is probably the exogenous driver variable. Kee and Hoon show their results are robust to whether either or both are exogenous or endogenous.<sup>6</sup> Rodrik (1995), on the other hand, argues for Korea and Taiwan that their investment booms were exogenous (government-led) and that these induced the export growth, the price changes being too small to produce such strong export growth themselves. Even if this is true, however, openness was still a critical component of the policy mix, for without openness the import of capital goods (and, subsequently, intermediates) would have been impossible, as would the huge growth of exportables output, for without access to world markets with huge potential demand, the expansion would have induced strongly declining prices.

These cases demonstrate strong macroeconomic links between trade policy and aggregate employment. Openness may or may not be sufficient to drive up employment, but, particularly in small and medium-sized economies, if booming sectors do not have access to supplies of inputs from abroad and to the large world market with its high elasticities of demand their growth is

> 6 Fields (2001) similarly argues that all four East Asian tigers show enhanced employment as their openness-induced growth has proceeded

5
This exercise demonstrates that cost-effectiveness analysis, even when only considering errors in impact estimates, can be quite sensitive, and that simple comparisons of the costeffectiveness of different programs can be very misleading. Given that, it is crucial that policy makers and advisors take into account the sensitivity of cost-effectiveness estimates. Simulations like this can illustrate the sensitivity in an intuitive way and guide policy makers in comparing different programs.

# **III. Context-Specificity of Costs**

There is an extensive and growing literature on the external validity or context specificity of impact estimates. How impact estimates vary across contexts and time is crucial to adapting cost-effectiveness analysis across locales, but as it has been much discussed elsewhere, we do not discuss it in detail here.<sup>8</sup> However, there has until now been little discussion of the context-specificity of costs in the context of development. In this section, we explore three context-specificity issues related to costs that complicate cost-effectiveness analysis, associated with (i) cost variation across contexts, (ii) cost extrapolation as a function of program complexity, and (iii) cost extrapolation as a function of pilot bias.

## **Cost Variation Across Contexts**

Costs associated with development projects vary dramatically across contexts. Figure 6 shows a comparison of three common cost components of education programs in different countries. We consider the monthly teacher salary, the transportation cost per school or facilitation visit, and the parental daily wage, which represents the opportunity cost of parents’ time for involvement in the education program. We compare each cost across three different countries, although owing to data limitations (not all the programs for which we have cost data include the same cost components) the sample of countries is not the same for each cost. For this example, we restrict ourselves to J-PAL (2014a) data and World Bank (2014) data on teacher salaries to maximize comparability. Unsurprisingly, we observe large differences in costs across countries, despite restricting our comparisons to developing countries. For example, the daily parental wage in Indonesia is five times that in Madagascar, the primary teacher salary in Nepal is 74 percent larger than that in Liberia, and the transportation cost per school or facilitation visit, which varies the most across countries, is 27 times larger in Kenya than in India. Any comparison of cost-effectiveness across countries must take this variation into account.

> 8 See, for example, Vivalt (2014); Pritchett and Sandefur (2013); Hotz, Imbens, and Mortimer (2005); and Flores and Mitnik (2013).

7