rafmacalaba/data-use-mentions-extended huggingface dataset:
possibilities, we turn back to our main calculations to get a sense of the magnitude of this issue.

Again, we highlight that these cost estimates are underestimated in the sense that we only consider the lost productivity in the labor market, thus not estimating the costs incurred to women’s health, the possible implications for the child’s future productivity as indicated by studies that show that children of adolescent mothers have lower school attainment rates, the social costs of unwed adolescent mothers, and so forth. Also, due to data availability, we cannot account mothers under the age of 15 or those who have paid the ultimate price of adolescent pregnancy: the girls who die from its complications.

# **6. A better path for girls: Conclusions and policy implications**

Social inclusion of adolescent girls that keeps them on a path to achieving their maximum human potential will result in significant economic growth. This paper has presented simple non-parametric methodologies to roughly quantify the costs incurred by societies as a result of the social exclusion of adolescent girls. The estimates are limited to the opportunity costs, which measure the losses in terms of potential productivity gains and income young girls could have achieved if they were employed, if they had delayed pregnancy, or if they had attained higher educational levels. Using secondary data drawn from the International Labour Organization, World Bank, and World Health Organization, we estimate the costs in several African countries (Burundi, Ethiopia, Kenya, Malawi, Nigeria, Senegal, South Africa, Tanzania, and Uganda) and a few Latin American (Brazil and Paraguay), and South Asian (India and Bangladesh) and East Asian (China) countries.

The rough estimates showed that marginal investments in girls can have a substantial impact on GDP growth and well-being. If girls just completed one higher level of education, the total value of productivity generated over the work life of those girls is equivalent to nearly one year’s GDP (Burundi), equivalent to GDP growth that is 2 percentage points
**Figure 4. WFH amenability index across countries.**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.6 0.8<br>0.6<br>0.4<br>0.4<br>0.2<br>0.2<br>0 0<br>-0.2<br>-0.2<br>-0.4<br>-0.4<br>-0.6<br>-0.6 -0.8<br>-1<br>-0.8<br>-1.2<br>MEX PER ECU TUR CHL RUS GRC KAZ ITA IRL POL USA ESP NZL SVK SVN LTU KOR CZE GBR AUT SGP JPN FRA DEU EST ISR CAN HUN SWE DNK NOR FIN BEL NLD LKA LAO SLV GHA BOL COL KEN UKR ARM VNM PHL GEO KOS MKD SRB<br><!-- End of picture text --> Note: Each bar shows the number of standard deviations below/above the mean. A higher value indicates a greater amenability of jobs to working from home. The magnitude of the estimates is not comparable between the PIAAC and STEP datasets.

The difference between our results and those of Dingel & Neiman (2020a) seems to be driven by the fact that while the United States has a higher share of jobs in occupations that are more amenable to working from home than other countries, the tasks associated with these occupations are different across countries and tend to be less favorable to working from home in the United States. Figure A 2 in Appendix 2 illustrates this issue using Norway, the United States and Spain as examples. The United States has 61 percent of its jobs in the four occupational categories that are more amenable to WFH, a figure higher than for Norway (55 percent) and Spain (59 percent).
forward employment and pay reforms in Ghana, and is being financed by the recently approved Structural Adjustment Institutional Support Project. In addition, several national civil service commissions have carried out similar exercises on their own.

As the basis for public pay and employment reform, functional reviews provide technical guidance to policy makers as to where, if necessary, changes can and should be made in the employment structure. To the extent that they have such management decision uses, functional reviews thus constitute a more sophisticated tool than civil service censuses (which provide only raw numbers). They also supply a credible, technical rationale for the difficult, political decision of employment reduction. A few prominent consulting firms have developed functional review methodologies that borrow heavily from work carried out in advanced industrial countries. Concerns have been expressed that these reviews are expensive and time-consuming (in the Gambia a three-person team took six months to review a civil service of approximately 15,000). Given delicate political circumstances, personnel reduction reforms may be more successful if they are carried out swiftly, so some countries may be unable to wait for the completion of a detailed functional review before taking action. In addition, questions have been raised about the propriety and difficulty of expatriate consulting firms carrying out such delicate reviews. In Ghana, for example, government officials were reportedly uncomfortable with this prospect. In Senegal, the resistance of various ministries to
, we rely only on estimates from a partial equilibrium model, leaving a more comprehensive examination of GE effects for further research.

The rest of the paper is organized as follows. Section 2 describes the sources of data used in the paper, as well as the definition of digital goods. Section 3 documents basic facts about Africa’s trade over the period of analysis. Section 4 explains the methodology for estimating quality and quality-adjusted prices, and presents the corresponding estimates on average import prices, quality and quality adjusted prices. Section 5 examines the likely impacts of AfCFTA on the imports of digital goods of member countries. Section 6 compares this scenario with that of a full tariff liberalization on digital goods imports. Section 7 concludes the paper.

# **2 Data**

The empirical analysis in this paper exploits and combines several sources of data, including international trade flows, tariffs, a set of gravity-like variables and a definition of digital goods based on the Bureau of Economic Analysis’s (BEA’s) Digital Economy list of Digital Inputs. In 4
and subsequent volatility as we do in our main estimations. Thus, we present these results only as supplementary to our initial findings. Nonetheless, the results from this procedure confirm many of our initial _findings-PRIVATE_ is negatively related to the variability of GDP, consumption, and investment growth and _BANK_ is negatively related to GDP variability. Consistent with the mixed nature of results we have reported for investment variability, _BANK_ is positively associated with the variability of investment calculated in this manner.

Finally, an additional possibility that we consider here is that our finding of a negative association between financial development and reduced volatility is being driven by the developed countries in our sample which have both developed financial sectors and lower macroeconomic volatility. Although we do not report the results here, we confirmed that our major conclusions still hold even when we drop high income countries from our sample."<sup>4</sup> Our results are also robust to using a logarithmic specification for either of our three dependent variables or for the measure of financial development. Thus, the relationship we identify is not likely to be the result of a an omitted variable common to high income countries that also are likely to have more developed financial sectors.

# 3.3. Further Discussion

So far, our results indicate that the manner in which the financial sector develops could have important implications for the severity of the business cycle. Others have suggested that the financial system is in fact influenced by several important underlying characteristics of the economy (e.g., La Porta et al., 1997, 1998, Levine, 1998, 1999, and Demirguc-Kunt and Levine, 1999).

These characteristics may be linked to macroeconomic fluctuations and it would be desirable to separate them out from the country-specific fixed effect we estimate above. VVhile panel data is not available to allow us to do this in our main estimation, we are able to implement a cross-country regression for a subset of the countries in our panel.

The additional variables we add to the cross-country regressions are from
The second equation in the model is about the formation of perceptions of inequality. We assume that information about inequality is acquired by being exposed to a specific economic context (in concrete, unemployment, poverty, and inequality) and argue thus that the metric or the definition of inequality might differ between the researcher (who typically uses the Gini coefficient) and individuals in the society, who might relate inequalities also to economic uncertainty (unemployment) or to poverty. We also argue that perceptions of inequality might depend on the fairness of the process that has generated them and therefore we include intergenerational mobility in the set of variables describing the economic context. In concrete, we use the intergenerational elasticity of education.<sup>14</sup> Similarly, and to account for future mobility, we also postulate that individuals’ perceptions might be influenced by current government expenditures in education. A second key element of our model, as in Zeller’s, is the dependence of inequality perceptions on ideology, notably the different views on social values and norms (hard work, meritocracy, circumstances, luck) spanning from left-leaning individuals to right-leaning ones.

Finally, we assume that perceptions of inequality relate also to other personal characteristics, such as employment, age, or gender. We write the equation as: Where EC represents the economic context, ID is ideology, and X is the set of individual characteristics including income and education levels. We will discuss the functional form of this relationship in section 5.

Equation (2) differs from the model of ‘knowledge’ acquisition of Blinder and Krueger in an important respect. In their model, individuals have individual-specific exposures to information; in fact, for each individual, they have micro data about sources of information, quantity of information, and ‘desire’ to acquire information. In contrast, we assume that everyone is exposed to the same degree to the relevant economic context, but ideology plays a role in interpreting the elements of such context. That is, faced with a
44</sup> It accounts for approximately 20 percent of the total US trade deficit. Naughton (2000)

> 41 Tariff collection rates were lower than 3 percent between 1994 and 1998. In 1999 they jumped to slightly more than 4 percent as a result of the government’s anti-smuggling efforts and the policy of encouraging domestic demand through government spending.

> 42 According to China Statistical Yearbook (2002) the average government wage in 2001 was 12,142 Yuan.

> 43 Lardy (2002) reports China’s trade surplus with the United States to be around US$69 billion. 44 Source: WITS/COMTRADE.

30
possibility, real or imagined, of debt re-negotiation or default could limit the extent to which voters in Peru consider these future costs. This paper only considers FONCODES transfers.

Table 2 provides descriptive statistics of socio-economic characteristics, voting patterns and per capita expenditures made by FONCODES. It shows that provinces in Peru vary a great deal on all of these counts. The variation in per capita FONCODES expenditures is particularly noteworthy: some provinces received no transfers whatsoever, while others received more than six times the mean per capita transfer.

Table 2: Socio-economic characteristics, voting patterns, and FONCODES expenditures on

||co|mmunity-|based pro|jects||||
|---|---|---|---|---|---|---|---|
||Mean|St Dev.|Median|25th %|75th %|**Min**|Max|
|Population|97,165|118,630|<br>58,168|28,814|116,486|185|692,265|
|FONCODES index*|15.31|6.03|16.14|11.49|19.57|1.00|28.24|
|Proportion rural|44.92|30.49|49.88|14.40|72.29|0|91.00|
|Pro-Fujimori vote: 1990**|65.37|13.70|65.07|56.32|75.01|17.76|93.35|
|Pro-Fujimori vote: 1993**|50.60|12.05|51.52|45.78|58.77|12.17|80.05|
|Per capita expenditures***|31.18|28.97|24.25|13.59|41.71|0|191.83|
# WPS3490

## **Roads out of Poverty? Assessing the Links between Aid, Public Investment, Growth, and Poverty Reduction**

Pierre-Richard Agénor,* Nihal Bayraktar,** and Karim El Aynaoui***

### **Abstract**

This paper develops a macroeconomic framework that captures linkages between aid, public investment, growth, and poverty. Public investment is disaggregated into education, infrastructure, and health, and affects both aggregate supply and demand. Dutch disease effects are captured by accounting for changes in the relative price of domestic goods. The impact of policy shocks on poverty is assessed by linking the model to a household survey. The model is calibrated for Ethiopia and changes in the allocation of aid and public investment are simulated. The amount by which foreign aid should increase to reach the poverty targets of the Millennium Development Goals is also calculated.



> *Hallsworth Professor of International Macroeconomics and Development Economics, University of Manchester, and Centre for Growth and Business Cycle Research; **Penn State University and World Bank; and ***World Bank. This paper was prepared as a background study for the World Bank’s 2004 Country Economic Memorandum for Ethiopia. We are grateful to Ishac Diwan, Delfin Go, Patrick Guillaumont, Michael Grimm, Henning Jensen, Jeni Klugman, Brian Ngo, Emmanuel Pinto Moreira, Mathew Verghis, and participants at seminars at the World Bank and the Economic Development Research Institute in Addis Ababa for discussions and suggestions, and to Mesfin Girma Bezawagaw for research assistance. The views expressed in this paper are our own and do not necessarily represent those of the Bank.
100%<br>0.60 ● ● ● ● ●<br>0.55<br>$1 $3 $10 $30 $100<br>Monthly Phone Spending (log)<br>AUC<br>AUC<br><!-- End of picture text -->

**:**

_Note:_ AUC represents the area under the receiver operating characteristic curve. CDR (Call Detail Record) represents base random forest model, and CDR-W weekly ensemble model. Since logistic regression performed better with bureau data, we present that model here. Sparse CDR results derived from estimating and testing on synthetic datasets that subsample every _k_ th transaction transactions. Bars represent 1 standard deviation. In our sample, spending is measured per phone account. National spending levels obtained from a household survey; that survey reports total spending per household; plot assumes one phone account per household. (Bottom two figures reproduced from (Björkegren & Grissen, 2018).) Source: authors’ analysis from telecom data.

3
Policy Research Working Paper 9779

# **Abstract**

Estimates of average per capita consumption and income from national accounts differ substantially from corresponding measures of consumption and income from household surveys. Using a new compilation of more than 2,000 household surveys matched to national accounts data, this study finds that the gaps between the data sources are larger and more robust than previously established. Means of household consumption estimated from surveys are, on average, 20 percent lower than corresponding means from national accounts. The gap with gross domestic product per capita is nearly 50 percent. The gaps have increased in recent decades and are largest in middle-income countries, where annualized growth rates for consumption surveys are systematically lower than national accounts growth rates. The paper shows that the gaps in measures across these two sources have implications for assessments of economic growth, poverty, and inequality. The study finds that typical survey measures of consumption and income may exaggerate poverty reduction and underestimate inequality.

This paper is a product of the Development Data Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at userajuddin@worldbank.org, djolliffe@worldbank.org, or espen.prydz@gmail.com.

_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
household survey fielded as part of the evaluation of the current (PSNP IV) phase of the program includes a question asking why households prefer food (cash). Their responses are shown in Table 6.

Consistent with the results shown in Table 5, relative prices are the dominant reasons, 59.0 and 51.1 percent, why respondents prefer at least some of their payment in food (cash). Respondents who stated that they preferred cash payments also noted the ability to exercise choice that cash provides; 22.3 percent noted that cash allows them buy foods that are not part of the PSNP payment package and 10.1 percent stated that cash payments mean that they can buy non‐food items or save the money for future investments. Transactions costs (money, time) to collect payments are relatively unimportant. Consistent with the idea that food transfers are a form of commitment device, 21.8 percent of respondents who stated that they preferred food payments, did so because they were concerned that cash would be more easily misused.

# **6. Conclusions**

Economists often default to the assumption that cash is always preferable to an in‐kind transfer. Do beneficiaries feel the same way? This paper uses longitudinal household data from Ethiopia where a large‐scale social safety net intervention – the PSNP operates. Even though most PSNP payments are paid in cash, and even though the (temporal) transaction costs associated with food payments are higher than payments received as cash, the overwhelming majority of the beneficiary households stated that they prefer their payments only or partly in food. However, these preferences are neither homogeneous nor stable. Higher food prices induce shifts in stated preferences towards in‐kind transfers but more food secure households, 22
speci fi c testable predictions. At an extreme if the MFN tariff is zero no tariff preference can be offered and so no preferential agreement is possible in the absence of subsidies. Therefore, to the extent that the U.S. and E.U. value the cooperation with the side conditions in those agreements, reductions in MFN tariffs on products imported from the preferential partner are more costly than reductions in similar products.

Limão (2005) estimates the direct effect of the U.S.’s PTAs on its MFN tariffs by exploring the fact that this effect will be weaker for goods it imports only from non-PTA countries. Using tariff data for over 5000 products he fi nds that the U.S.’s PTAs generated a stumbling block effect in the Uruguay Round. After controlling for several determinants of U.S. tariff changes during the Uruguay Round he estimates that the U.S.’s average cut for goods exported by any of its PTAs was about half that of other goods. The effect is stronger for products that are exported under all PTAs or constitute relatively larger shares of a given PTA’s exports to the U.S. The average cut for GSP goods on their own was on average only 74% relative to non-PTA goods. Interestingly the estimates also allow a calculation of the direct effect on the U.S. MFN tariffs for all its goods: it would have been about 1 percentage point 9
(MDBs) may systematically prefer certain contractual forms or projects with larger required investments.

In this paper, the probability of receiving MLS at the project level is estimated to serve as a metric –a _means_ by which to compare PPP projects with and without MLS. To this end, project-level and aggregated country- and regional-level factors are taken into consideration to identify most-similar sets of PPPs with and without MLS for later comparison with respect to cancellation rates.

# **_Data_**

The statistics and probabilistic models in this study follow the definition of PPP adopted by the PPI Database. According this source, infrastructure projects with private participation (PPI) that have reached financial closure excluding divestitures, telecom projects, or merchant<sup>4</sup> projects are considered PPPs. Using this definition, of the full PPI data set of 7,042 projects, 5,054 are PPP projects. Of these, 637 involve MLS and 4,417 do not (Figure 1). This PPP project sample is used to estimate the likelihood of receiving multilateral support.

Figure 1. Project data set for econometric models: PPPs from 1990-2015 <!-- Start of picture text --> PPIs<br>(7,042 projects)<br>PPPs<br>5,054 projects (71%)<br>With MLS Without MLS<br>637 PPPs (12.6%) 4,417 PPPs (87.4%)<br><!-- End of picture text --> Source: PPI Database, Feb 2016 The project outcome analyzed in this paper –cancellation rate– is based only on PPP projects in that are currently classified as operational, concluded, or canceled; and have reached financial closure between 1990 and 2010 (i.e., PPPs that have survived five or more years). Of the 5,054 PPPs in the PPI Database, 3,552 projects have survived more than 5 years, of which 2,777 are
and risk aversion; de Palma and Prigent (2008, 2009). In addition, individuals change pension funds based on performance (not short term returns), thus the pension fund managers with superior performance tend to grow in comparison with those which underperform. Thus, the market is regulated and portfolio allocations move toward optimal allocations. This (surrealistic) framework assumes that pension contributors know their optimal investment strategy and that pension fund managers then only have to offer these investment strategies so that the contributors may then select the investment options.

12. Empirical evidence, however, has made it clear that these assumptions are not valid, and therefore it becomes necessary to design pension systems based on more realistic assumptions. This will require assuming that contributors do not have a solid foundation to select a pension fund; the investment options are limited; and pension fund managers have strong incentives to maximize short-term returns.

13. In mandatory pension fund plans, people typically choose pension funds strongly encouraged by the sales force. According to Marinovic and Valdes (2005), the main variable explaining the choice of a pension fund management company is a visit by a sales agent. Berstein and Cabrita (2007) also corroborate this finding, but they claim that returns, coupled with a visit from a sales agent, offer a strong fuller explanation. This means that short-term returns are a persuasive rationale to switch to a different pension fund management company. The work of Calderón et al. (2008), using data from Mexico, shows that people generally do not switch to their optimal alternatives.

14. The selection of a pension fund based on its attributes is not necessarily the most appropriate action. Although the cited literature finds clear evidence of price inelasticity, there is confusion by what is meant by the price for members. In particular, these studies take short-term past returns from pension funds to explain the decisions of individuals. However, theory indicates that these variables should not be the principal explanations for portfolio selection, since it is already well-known that