rafmacalaba/datause-encoder-data huggingface dataset:
affects the transition of non-essential entrepreneurs from the lockdown period to the next. Thereafter, however, we observe a notable weakening of the recovery. The unemployment rate is almost 3 percentage points higher than its _t_ = 2 value in the benchmark and decreases slowly. Investment, capital stock and output remain below their initial levels for a longer period of time.

As mentioned above, financial and labor market frictions are at the core of the weaker recovery. As older businesses exit and are replaced by newcomers, financial frictions impede the growth of those productive entrants who are not yet wealthy enough to overcome credit constraints. Furthermore, as workers leave their former employers and wait to be hired by newcomers, the labor market is congested by the matching frictions, so the unemployment rate remains high for many quarters after the combination of shocks disappear. The evidence of a strong rise in business formation in the US in Section 2 provides a measure of empirical support for this reallocation channel.

# **6 Concluding Remarks**

The Covid-19 pandemic triggered a burst of economic research seeking to understand its economic implications. Motivated by this episode, and also noting a scarcity of quantitative frameworks that can help us think about large temporary shocks more broadly, we propose one in this paper. Our model incorporates the main elements that can capture the first-order effects that operate on impact and during the recovery of an economy hit by such a shock: imperfect access to credit, costly reallocation of workers, recall unemployment (especially relevant to the temporary nature of the shock), and imperfect consumption insurance.

Disciplining the degree of the market frictions in the economy to the US data, we show that the deep but short-lived shutdown results in a deep but short-lived recession followed by a swift recovery. While the recovery is fast in the aggregate variables, it carries lasting scarring effects on young non-essential firms. Paying for capital costs while generating no revenue for one period, these firms sustain a significant damage to their balance sheets, which shows up as significantly lower growth over their entire life-cycle. Nevertheless, these firms do not account for a large share of the aggregate employment or output, and this channel has only a small macroeconomic effect and the aggregate variables bounce back quickly.

More protracted ripple effects are possible, as shown by alternative specifications of the shocks triggering the recession and by different government policies. Longer and bigger lockdowns generate persistently higher unemployment rates. The recovery is slower if a reallocation shock interacted with the lockdown and demand shocks. Without government policies supporting the firms and workers, the worsening balance sheets of non-essential 32
consistent patterns of lower socio-emotional skills levels for women but positive associations with earnings across studies, and with interpersonal skills being more strongly correlated with earnings for women than for men.

# **6. Conclusion**

This paper investigates gender differences in a wide range of socio-emotional skills, at different education levels, as well as how these skills correlate with earnings and whether this correlation differs for men and women. We exploit a rich and unique combination of datasets encompassing 41,873 individuals across 17 Sub-Saharan African countries. We use standardized measures of socio-emotional skills covering six intrapersonal and four interpersonal skills by aggregating self-reported items across studies.

While results may not be applicable to all settings, this study is the first to examine gender differences in a large variety of socio-emotional skills, separating out intra- and interpersonal skills, and for an extensive number of African countries. We find a significant gender gap disfavoring women by about 0.151 standard deviations, across most socio-emotional skills, equivalent to the gap induced by 5.6 years of education. The largest gap is observed in problem-solving and decision-making while the gap in empathy is the smallest. Self-control is the only skill for which women do not exhibit significantly lower levels than men. We hypothesize that gender norms play an important role in explaining gender differences in socioemotional skills. More work is needed to better understand and estimate this role.

The gender gap in socio-emotional skills is only partially explained by lower education levels among women. Closing the gender gap in education would close about 17% of the socio-emotional skills gender gap. Overall, women and men experience similar returns to education in terms of aggregate socioemotional skills levels. However, we observe differences at a more disaggregated level. Indeed, the gender gap in interpersonal skills, emotional regulation and self-control is wider at higher education levels. In general, these results are suggestive rather than definitive, as two important considerations arise in the measurement of each skill. First, self-reported measures may reflect gender-specific biases in assessments of skills. In complementary work, we are developing observation-based measures of skills, that will help address this issue. Second, this study is based on available data, and measures were not validated for this division of skills. In addition, larger efforts were spent on measuring intrapersonal skills (75% of socioemotional skills items) than interpersonal skills across studies. Thus, measures of interpersonal skills suffer 28
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- everybody? The evolution of global bilateral migration 1960–2000’, _The World Bank Economic Review_ , **25** (1), 12–56.

Parsons, C. R. (2012), ‘Do migrants really foster trade? The trade-migration nexus, a panel approach 1960-2000’, Policy Research Working Paper Series 6034, The World Bank.

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56
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Transport

# Roads

## **Achievements**

Angola is making extraordinary efforts to reconstruct its dilapidated road infrastructure. The level of public spending on roads increased dramatically in recent years, averaging a staggering $2.8 billion over the period 2005–09. This makes Angola one of the highest spenders on road infrastructure in Africa. This spending is about four times the estimated $423 million annual longer-term requirement for road-network preservation, reflecting Angola’s desire to reconstruct its road infrastructure on an accelerated timetable (figure 9). According to budget figures, as much as two-thirds of this total is classified as maintenance. This is somewhat surprising; however, the boundary between maintenance and rehabilitation activities can be a blurry one, and so this may simply reflect issues of expenditure classification.

**Figure 9. Angola’s spending on roads is more than sufficient to cover maintenance and rehabilitation needs**

<!-- Start of picture text --> 400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>-50<br>-100<br>Maintenance Rehabilitation<br>Spending as % of requirements<br><!-- End of picture text --> _Source:_ Gwilliam and others 2008.

## **Challenges**

_Angola’s road network is in very poor condition._ Angola’s road network covers 62,560 km. Of this total length, the classified network (primary, secondary, and tertiary) accounts for 58 percent or 36,399 km. The rest is the urban network of 11,057 km and the unclassified network of 15,104 km. Most of the traffic is concentrated in the area surrounding Luanda (figure 10), but overall traffic levels are comparatively low (table 8). The inadequate condition of the roads caused by years of destruction and undermaintenance is one of the factors contributing to the low traffic levels. As much as 58 percent of Angola’s classified network and 40 percent of its rural network is in poor condition, which is among the worst road condition statistics in Africa (figures 10 and 11). Only 17 percent of classified and urban roads are paved. Poor road quality in combination with very low road density and a lack of bridges—as many were destroyed or imbedded with mines during the war—makes some of the provincial capitals impossible to access by road. In addition, feeder roads are nonexistent in many parts of the country.

18
Overall, the quality of the evaluations is also affected by the fact that selection of the funded projects is not often granted based on quality and on specific goals. Despite that it is recognized that rigorously evaluating R&D interventions has high implementation and monitoring coststhere is a new set of evaluations that are introducing more robust statistical analysis to build valid counterfactuals. Most of the time, randomized methodologies are not to be applied to these sorts of interventions because of competition rules and the institutional regulations (Lentile and Mairesse, 2009). But more often, governments sponsor cutting-edge pilot programs that can be subject to prospectively-designed randomized evaluations. With these types of methods the researchers may even have more outcomes to evaluate, by building different comparison groups. More recently, researchers are intrigued by determining if a lower user cost entails higher R&D expenses, or the degree to which innovation outputs and productivity relate to one another in the presence of subsidies. Other important research questions deal with understanding at which point R&D produces desired effects. If marginal productivity of R&D is decreasing, additional units could generate less innovation. Further, it is important to include R&D evaluation programs in the innovation agenda by knowing the degree to which subsidies stimulate innovation that is valued by the market.

As the evaluation methods used are more sophisticated and statistically robust, the effects appear to be conclusive. Recent R&D evaluations that follow a logical framework and focus on addressing the issue of causality, through matching methods, find consistently positive and significant results. A tendency to overestimate impacts in the absence of statistically valid counterfactual, regardless the aspect of the firm influenced by R&D subsidies reflects the ambiguous results come from a design effect of the evaluation methods used. The metaanalysis revealed that there is indication of positive R&D impacts, but such effects are highly sensitive to the method used. In addition, evaluations of certain areas of R&D still have important knowledge gaps because of lack of data.

Finally, one of the main challenges to overcome the insufficient number of randomized evaluations has to do with the potential endogeneity of the subsidy, the assignment of which fails to satisfy the randomness property that should characterize pure social experiments. This is why most recent R&D IEs studies estimate a counterfactual through Propensity Score Matching. An evaluation of the expected innovative outcome, by both the firm, which has to 31
# **Implications of Higher Global Food Prices for Poverty in Low-Income Countries**

# **1 Introduction**

Since 2005, the world has experienced a dramatic surge in the price of many staple food commodities. The price of maize increased by 80 percent between 2005 and 2007, and has since risen further. Many other commodity prices also rose sharply over this period: milk powder by 90 percent, wheat by 70 percent and rice by about 25 percent. Annual average prices of key staple foods are shown in Figure 1. Clearly, such large increases in prices may have tremendous impacts on the real incomes of poor households in developing countries.

Despite widespread concern about the impacts of high food prices on poor people and on social stability (eg FAO 2007; World Bank 2008a), little hard information appears to be available on actual impacts on poor people. The overall impact on poverty rates in poor countries depends on whether the gains to poor net producers outweigh the adverse impacts on poor consumers. Whether higher food prices improve or worsen the situation of particular households depends importantly on the products involved; the patterns of household incomes and expenditures; and the policy responses of governments (World Bank 2008b).

Existing analyses tell us that the impacts of higher food prices on poverty are likely to be very diverse, depending upon the reasons for the price change and on the structure of the economy (Hertel and Winters 2006; Ravallion and Lokhsin 2005). A great deal depends on the distribution of net buyers and net sellers of food among low-income households (Aksoy and Isik-Dikmelik 2007). Only with careful examination of outcomes at the household level is it possible to tell whether changes in the prices of specific staple foods will help or hurt poor people.

A particular reason for concern about the impacts of high food prices on poor countries arises from the fact that the poorest people spend roughly three quarters of the
households to insure against shocks makes them often adopt coping strategies in bad times that can have harmful consequences in the long term, even for generations. In times of crisis, poor households are not able to feed anymore infants with the nutrients they need, which affects their long term development prospects; pull children out of school; and sell assets, such as their house or farm land, initiating a vicious spiral towards increased destitution.<sup>5</sup> Finally, macroeconomic volatility has also proven to have a negative and robust impact on economic growth, and thus on future consumption. Output growth volatility is negatively correlated with economic growth (Figure 4), and several studies have proven the existence of a robust causal link.6 The link between volatility and growth remains strong for East Asia as well: our estimates indicate that if the standard deviation of the growth rate of per capita GDP in East Asia would decrease from 1.4 percent (the volatility in the period 1996-2000) to 0.9 percent (the average level observed in OECD countries over the same period), the growth rate would increase by 0.48 percentage points.

**Figure 4: Volatility of Output Growth vs. Average Growth**

<!-- Start of picture text --> Output Growth Volatility Crisis Volatility<br>3 6<br>5<br>2.5 TMP LBR<br>4<br>2 KIR SLB<br>ZAR LBRVUT NERHTIMDGCAFBRNZMB1.50.51 SENVENNICCIVSLEGHABDIFSMMHLBOLBENTCDTGOCMRGMBSLVRWAMWIPERGUYZAFNGAMRTARGJAMHNDBHSSDNKENURYPNGBGDGTMNPLNZLUGAPHLGEOCHECOGDZABFAECUPRYPYFJORMNGWSMFJINCLCOLBRBMEXMARCRIAUSGBRUSAPANSWECANIRNDNKBRATONTURNLDGABTTOSYRSYCVCTBELCHLPAKMMRINDITAAUTISRDOMFINISLLKANORLUXEGYLAOLSOBLZTUNGRCESPLVAHUNPRTIDNPRIJPNMYSMACIRLPLWVNMTHAMLTHKGKHMSGPKOR OMNCHNBWA -4 ZAR-2 VUT NERHTIMDGCAFBRNZMB321 0 SEN0KIRVENZWENICCIVSLEBDIGHAFSMMHLBOLBENTCDTGOCMRSLBGMBSLVRWAMWIPERGUYZAFNGAMRTARGJAMHNDBHSSDNKENURYPNGBGDGTM N UGAPHLGEOCHECOGDZAPZ BFA ECUPRY L PYFJORMNGWSMFJI2NCLCOLBRBBRAMEXMARCRI AUSGBRUSA PAN SWECAN IRN DNKTON TUR NLD GABTTOSYRSYC FRA VCT BEL CHL PAK INDMMR ITAAUTISR DOMFINISLLKA NOR LUX EGY LAOLSO BLZTUN GRC ESP LVAHUNPRTIDN4PRI JPN MYSMAC IRP L VNMW THA MLT TMPHKG KHM SGPK6OR OMNCHN BWA 8<br>0 FRA<br>-4 -2 0 2 4 6 8 -1<br>Average Growth Rate of GDP pc Average Growth Rate of GDP pc<br>Crisis Volatility<br>Volatility of GDP pc<br><!-- End of picture text --> Sources: author’s own calculations using WDI data. For a definition of crisis volatility see Box 1.

> 5 See, among others, Ravallion, 2008, and Grosh _et al._ , 2008, for a review of coping strategies.

> 6 Ramey and Ramey’s (1995) were the first to document empirically the negative relationship between volatility and growth. This link was explored further in Fatàs (2002), Acemoglu _et al._ (2003), and Hnatkovska and Loayza (2005). The negative effect of macroeconomic volatility on long term economic growth acts for instance through increasing economic uncertainty, which reduces investment and tightens binding investment constraints. See Aizenman and Pinto (2005) and Wolf (2005) for a -review.

7
# 4. Additional robustness checks

This section provides the results of extensive robustness checks to ensure the reliability and consistency of the analysis. Various parameter configurations for nearest neighbor searches and training data are explored. Additionally, established alternative approaches, including Survey of Well-being via Instant and Frequent Tracking (i.e., SWIFT) and GDP-poverty elasticity methods, are employed. Moreover, Annex 3 presents results from a straightforward trend reconstruction using common items to define alternative poverty lines and consumption aggregates. These comprehensive checks consistently align with the core findings of this study.

## a. Same methodological approach under different options

To conduct additional robustness checks to the proposed methodology, additional simulations have been implemented in three distinct scenarios that relax the default setup. The first scenario eliminates the constraint of restricting the nearest neighbor search to the same decile. The second scenario involves using only the 2022 dataset for model training and then making predictions for both 2022 and 2016 (or 2010), analogous to the more traditional Lasso PMM approach. The final scenario combines elements of both the first and second scenarios.

Poverty and extreme poverty rates for 2016 show no significant differences across the scenarios (Figure 15.a). For 2010, where the removal of the decile-based constraint results in slightly larger differences, these remain statitically insignificant (Figure 15. b). This outcome is anticipated given the longer backcasting period and the associated challenges in satisfying the assumptions underlying the approach.

Figure 15. Poverty and extreme poverty rates under different scenarios <!-- Start of picture text --> a. 2016 b. 2010<br>29.0 13.0 39.0 16.0<br>27.0 26.46 26.39 25.94 26.24 12.0 37.0 37.08 37.08 15.0<br>25.0 11.0 35.0 35.40 35.4314.0<br>9.22 9.22 9.19 8.95 10.0 13.0<br>23.0<br>9.0 33.0 12.18 12.23 12.0<br>21.0 8.0 31.0 11.54 11.47<br>11.0<br>19.0 7.0<br>29.0 10.0<br>Poverty Extreme poverty (right axis)<br>Poverty Extreme poverty (right axis)<br>Poverty<br>Poverty<br>Extreme poverty<br>Extreme poverty<br>Sc. 2 + Sc. 3<br>Sc. 2 + Sc. 3<br>constraint<br>constraint<br>Current approach training stage<br>Sc.2: Remove decile Current approach training stage<br>Sc.3: Use only 2022 in Sc.2: Remove decile<br>Sc.3: Use only 2022 in<br><!-- End of picture text --> Source: Own estimations based on 2016 and 2022, and 2010 and 2022 HIES.

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Harmonized Micro data from Household Surveys that cover almost 100 developing countries to compute the HOI for another set of indicators: completing sixth grade on time, school attendance for children 10– 14 years old, and starting primary school on time for children between 6 and 7 years old. While these opportunities are not as attractive as PISA scores, since they cannot capture the quality of learning, they are reasonable proxies for educational opportunities. This is particularly true for timely completion of sixth grade (or six years of education), which correlates with variables like timely start to school and (absence of) grade repetition that are useful predictors for learning achievement. Taking advantage of the richer information available from household data, the circumstances considered here are: gender of the child, location, head of household education, per capita family income, number of children in the household and family structure (presence of both parents in the household).

Appendix 1 includes a description of the variables (with basic statistics included) for all the relevant measures used in the study, as well as a brief explanation of how these were constructed. Furthermore, Table A.2-2 in Appendix 2 shows correlations between all the opportunity measures computed for the study.

# **Identification strategy**

Below we present our structural equation model for identification.

Equation (1) denotes our outcome variable “Yi” indicating level of GDP, institutional quality, or infant mortality. These outcomes can be explained by different control variables ( _Xi_ ) as well as our main variable of interest ( _Ei_ ), which proxies for unequal opportunities.

The problem with estimating equation (1) is the possibility that the outcome variables also affect unequal opportunities, meaning that we have an endogenous variable. This would imply that if we replace Ei in Equation (1) we will end up with In order to be able to use an Ordinary Least Squares (OLS) approach, we need the classical assumptions to hold, which would require εi1 to be independent of εi2. Since we suspect that our variable is endogenous, we know the errors from Equations (1) and (2) will not be independent. As a result, OLS results would be biased. To address this problem we use Instrumental Variables (IV). In Equations (6) and (7) the instrument is represented by Zi .

8