rafmacalaba/datause-encoder-data huggingface dataset:
Table 5.4b: Unemployment and mortality by accidents (IV with controls)

|VARIABLES|Other Acc.|Falls|Drowning|Fire|Poisoning|
|---|---|---|---|---|---|
|Total Job Losses|0.754*|0.172|-0.858|2.289|1.402*|
||(0.429)|(0.755)|(2.116)|(2.511)|(0.772)|
|Constant|0.203|0.634**|-0.894|2.279|-0.0983|
||(0.214)|(0.321)|(0.882)|(1.624)|(0.512)|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Observations|1,742|978|183|116|1,119|
|R-squared|0.132|0.103|0.155|0.240|0.101|

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

# **6. CONCLUSION**

This paper examines the impacts of unemployment on mortality during the Great Recession. We employ a Bartik (1991) type of identification strategy to identify exogenous job losses, namely tradable job losses of a county driven by declines in aggregate demand. We find that generally, unemployment has little impact on mortality during the Great Recession.

Our results are mostly consistent with findings in Ruhm (2015a): total mortality is weakly pro-cyclical, and so selected cause-specific death rates, except for poisoning and other diseases (specifically Alzheimer’s). Additionally, we found homicides to be strongly counter-cyclical. Our results therefore reconfirm that of Ruhm (2015a), although the contexts are different: while Ruhm’s studies are based on state-level time series, our study is based on a cross-section of counties during the Great Recession.

25
math, reading and writing skills. About 178 mentor/trainer teachers were trained for four days by well qualied national trainers, and then they conducted an average of 2.26 visits per school to mentor fellow teachers. The training of fellow teachers lasted for three days. This cascading of training enabled the delivery of teacher training in a more cost eective manner than other teacher training projects. The training cost was $ 3.14 per day per teacher under READ, relative to $ 7.62 for other similar training schemes in the country (World Bank, 2013).

To evaluate the impact of teacher training or books alone as well as teacher training complemented by books, a national-scale randomization was carried out. The initial design of the evaluation strategy was such that schools in the 21 provinces/aimags would be randomly assigned to Treatment One ( _T_ 1 ), Treatment Two ( _T_ 2 ) or a control group ( _C_ ) (see Figure 1). The control group was later divided into two: Control One ( _C_ 1 ) and Control Two ( _C_ 2 ).<sup>1</sup><sup>_,_2</sup> _T_ 1 includes primary schools randomly selected in ve provinces (Arkhangai, Bulgan, Zavkhan, Sukhbaatar and Tov), and these schools received classroom libraries and in-service teacher training in May 2007. _T_ 2 , schools in Ovurkhangai and Govi-Altai provinces, were provided classroom libraries, but not teacher training, in May 2007. _C_ 1 , which includes schools in Dornogovi, Omnogovi, Uvs, Khovd, Khovsgol, Khentii and Govisumber provinces, was originally to receive classroom libraries and teacher training at the end of the experiment (in May 2008), but the plan was changed later and it received books in October 2007 and training between October 2007 and March 2008. _C_ 2 encompasses schools in Bayan-Olgii, Bayankhongor, Dornod, Dondgovi, Selenge, Darkhan-Uul and Orkhon provinces, and these schools received treatment at the end of the experiment (books in May 2008 and training between May and September 2008). Figure A.1 (see annex) shows aimags in which the four groups of schools are located, and Table 1 presents the timeframe of the survey and interventions, and the number of schools and students surveyed.

> 1 _C_ 1 received treatment halfway through the study period. Therefore, direct comparison of _T_ 1 and _T_ 2 with _C_ will not be feasible. In addition, the `pure' control group ( _C_ 2 ) has smaller sample size. Hence, the follow up survey included additional schools in the sample.

> 2Administratively, Mongolia is divided into 21 aimags and the capital city, Ulaanbaatar. These aimags are further divided into soums, and then into bags (NSO, 2006).

5
# **Figure 2.  Predicted probability of war onset**

<!-- Start of picture text --> Sub-Saharan Africa<br>0 .1 .2 .3<br>Predicted probability of civil war onset<br>Other countries in the developing world<br>0 .1 .2 .3<br>Predicted probability of civil war onset<br>30<br>20<br>Density<br>10<br>0<br>40<br>30<br>20<br>Density<br>10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Sub-Saharan Africa – The 1990s<br>0 .1 .2 .3<br>Predicted probability of civil war onset<br>Other countries in the developing world – The 1990s<br>0 .02 .04 .06 .08 .1<br>Predicted probability of civil war onset<br>20<br>15<br>Density10<br>5<br>0<br>50<br>40<br>30<br>Density<br>20<br>10<br>0<br><!-- End of picture text --> 33
# **Figure 3: China’s poverty rate over time using 1993 and 2005 PPPs with and without correction for sampling bias in the ICP**

<!-- Start of picture text --> Headcount index ( % living under international poverty line)<br>100.0<br>90.0<br>80.0<br>$1.25/day in 2005 PPP without rural/urban<br>prices adjustment<br>70.0<br>60.0<br>50.0<br>$1.25/day in 2005 PPP with rural/urban<br>prices adjustment<br>40.0<br>30.0<br>$1.08/day in 1993PPP<br>20.0<br>10.0<br>0.0<br>1980 1985 1990 1995 2000 2005<br>Year<br><!-- End of picture text --> 14
-5-

the observavion time required, fcr example), to limit itself to a small subset of the (possible) options imbedded in his training.

Informational asymmetry, then, is particularly pronounced and intractable for the option values of training. Hence, the informational asymmetry between the training firm and other firms will tend to be an increasing function of the options component in general training. Even if the options component is small, however, there will still be a major information gap between the firm that provides the training and other firms.

# 3. THE VALUE OF A RECRUITED WORKER

This section examines the information-based costs incurred by a recruiting firm. Given these costs, the net present value of a generally trained worker to a recruiting firm, VN, is smaller than his value, VT, to his training firm. Let i denote the number of periods since a worker has been recruited. Define ViT and _ViN_ as the value in period i a worker with general training, to the training and recruiting firms, respectively. In the event of full discovery over time, ZiN would increase with i. Nonetheless, given the intractability of discovering full information about OVN, ViN will fall short of ViT, no matter how large i becomes.

The matrix in Table 1 illustrates the loss in a worker's value due to the information asymmetry, for the simple case of one type of non-option general training and one type of job requiring training. The value of a worker to the recruiting firm in a time unit during the discovery period,<sup>21</sup> depends on the job in which he is placed (requiring or not requiring training), and on whether or not the worker has received general training.AQ/ In Table 1, K will be the largest payoff and Q the smallest. Q might well be negative: placing an untrained worker in a position requiring training may be directly wasteful and destructive. Also, if the values of workers are
27

# TABLE 6 : Tests for Non-Nested Regression Models of Replanting

# Dependent variable is RPBAHR2

Regressors for model MI: CONS, AREVPROD(-l), AREVPROD(-2), REALINT(-1), MAPRI, KAOLDPR Regressors for model M2: CONS, AREVPROD(-l). AREVPROD(-2), REALINT(-1), PRICOCO(-1). OLDPROD(-1) Teot Statistic Teot Statistic Ml agalnst M2 M2 against MI COX-PESARAN TEST -2.5758 -1.4636 ADJUSTED COX-PESARAN -1.8761 -1.0374 DAVIDSON-MACKINNON J-Test 1.6607 1.0080 ADJUSTED J-Test 1.6792 .9518 Model Ml: DV 1.9375 ;R-Bar-Squared .9459 88.4834 Model M2: DW 2.1109 ;R-Bar-Squared .9524 89.6325 Akaike's Information Criterion of Ml versus K2 - -1.1491 favours M2
**Table 4.1a: Coverage bias in HFPS**

|||**Ethiopia (**|**ESS)**||**Malawi (I**|**HPS)**|**N**|**igeria (GHS**|**-Panel)**||**Uganda (U**|**NPS)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**All**|**With C**|**ontact info**|**All**|**With C**|**ontact info**|**All**|**With C**|**ontact info**|**All**|**With C**|**ontact info**|
||||_Coverage_|||_Coverage_|||_Coverage_|||_Coverage_|
||_Mean_|_Mean_|<br>_bias_†<br>_(2) -(1)_|_Mean_|_Mean_|<br>_bias†_<br>_(5) -(4)_|_Mean_|_Mean_|<br>_bias†_<br>_(8) -(7)_|_Mean_|_Mean_|<br>_bias†_<br>_(11) -(10)_|
|**Characteristic**|_(1)_|_(2)_|_(3)_|_(4)_|_(5)_|_(6)_|_(7)_|_(8)_|_(9)_|_(10)_|_(11)_|_(12)_|
|**Sample size(number of households)**|_6,788_|_5,372_||_3,181_|_2,337_||_4,976_|_4,934_||_3,308_|_2,421_||
|Rural|0.675|0.588|-0.088**|0.826|0.765|-0.060|0.686|0.683|-0.002|0.684|0.659|-0.026|
|Average household size|4.483|4.484|0.000|4.904|4.990|0.086|5.528|5.515|-0.013|4.515|4.613|0.098|
|Dependencyratio|0.955|0.915|-0.040|1.055|0.975|-0.079**|1.016|1.014|-0.002|1.064|1.062|-0.002|
|Female head(%)|0.263|0.262|-0.001|0.296|0.262|-0.034|0.186|0.187|0.001|0.342|0.332|-0.010|
|Age of head|42.912|41.962|-0.950|45.855|44.911|-0.945|48.797|48.775|-0.022|45.311|45.133|-0.178|
|**_Religion of head_**|||||||||||||
|Orthodox|0.478|0.485|0.007||||||||||
|Muslim|0.293|0.284|-0.010|0.808|0.828|0.020|0.425|0.422|-0.003||||
|Christian|0.211|0.217|0.006|0.145|0.133|-0.012|0.557|0.560|0.003||||
|**_Marital status of head_**|||||||||||||
|Married|0.739|0.740|0.001|0.716|0.744|0.028|0.750|0.749|-0.001|0.713|0.738|0.024|
|Formerlymarried|0.189|0.175|-0.014|0.265|0.232|-0.033|0.193|0.194|0.001|0.246|0.227|-0.019|
|Never married|0.072|0.085|0.013|0.018|0.023|0.005|0.057|0.057|0.000|0.041|0.036|-0.005|
|**_Education level of head_**|||||||||||||
|Head Literate|0.520|0.586|0.067***|0.921|0.944|0.023**|0.744|0.745|0.002|0.772|0.809|0.037**|
|None|0.523|0.456|-0.067***|0.675|0.591|-0.084**|0.375|0.371|-0.003|0.087|0.076|-0.011|
|Primary|0.296|0.310|0.015|0.207|0.251|0.043|0.241|0.242|0.000|0.584|0.555|-0.030|
|Secondary|0.102|0.129|0.027**|0.083|0.111|0.028*|0.225|0.226|0.001|0.290|0.307|0.017|
|Tertiary|0.079|0.105|0.025**|0.034|0.047|0.013|0.160|0.161|0.001|0.120|0.132|0.012|
|**_Working status of head_**|||||||||||||
|Any|0.735|0.724|-0.011|0.819|0.829|0.010|0.827|0.828|0.001|0.855|0.865|0.009|
|Wage|0.133|0.165|0.032*|0.158|0.198|0.04*|0.177|0.178|0.001|0.324|0.326|0.002|
|Agriculture|0.551|0.492|-0.059*|0.469|0.445|-0.025|0.442|0.442|0.000|0.479|0.474|-0.005|
|Nonfarm enterprise|0.115|0.136|0.020|0.193|0.214|0.021|0.367|0.367|0.000|0.266|0.288|0.023|
|Ganyu||||0.248|0.205|-0.043*|||||||

†The results of an adjusted Wald test comparing weighted means are presented (*** p<0.01, ** p<0.05, * p<0.10).

14
Figure 2. Global Gini inequality extraction ratio 1820-2002 (in percent) <!-- Start of picture text --> BFLZ<br>1800 1850 1900 1950 2000<br>year<br>Note: Bars based on the data from Table 3. Dots based on global inequality calculations of Baten,<br>Foldvari, van Leeuwen, and van Zanden (2009). [From summary_utrecht.dta. ]<br>Figure 3. Global Theil inequality extraction<br>ratio 1820-2002 (in percent)<br>1800 1850 1900 1950 2000<br>year<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>global extraction ratio -- theil<br>20<br>10<br>0<br><!-- End of picture text --> Note: Bars based on the data from Table 3. Dots based on global inequality calculations of Baten, Foldvari, van Leeuwen, and van Zanden (2009). [From summary_utrecht.dta. ] . Source: Table 3.

20
The role of gender is also of interest in this context. We know very little about interpersonal inequality in living standards, including between men and women, given that survey data on consumption are almost invariably collected at the household level. Since adult women are generally married, it is difficult to separate their own welfare from that of their husbands on the basis of household data. However, gender dimensions of inequality are considered important. Male control over land and its inheritance has long been a prominent gender issue in development studies.<sup>4</sup> Maternal education and work experience might also be expected to play a role. It has often been argued that maternal education has an important influence on children‘s health, nutritional status and schooling.<sup>5</sup> There might also be implications for adult welfare and economic activity, though there has been less research on this intergenerational linkage.

In an effort to yield some new knowledge on these issues, this paper takes advantage of an unusual dataset that measures consumption at a relatively disaggregated level within the household, so that we can build an ―individualized‖ consumption-based welfare measure. This allows us to distinguish inheritance and its economic welfare impacts by gender. The data were collected in Senegal—one of many developing countries where these issues have salience.

We document new evidence on the extent of inequality in living standards and its origins, paying special attention to the role of inheritance. Our results lead us to question the traditional model of a developing economy with imperfect credit markets in which privately-owned land is a marketable asset generating an income stream exclusively for the designated individual owner. But our results are easier to understand in the light of richer models of underdeveloped rural economies. We already know from the literature (in anthropology as well as economics) that market failures and non-market allocation processes play an important role in how land is used. As anthropologists have emphasized, agricultural land inheritance in much of Africa is typically filtered through customary land allocation processes involving kinship or community groups.<sup>6</sup> Inheritance signals a change in responsibility, such that the recipient of inherited land inherits obligations as well as an asset, and it is an asset that is not easily monetized to support other productive investments.

> 4 In the context of land rights in Africa see Gray and Kevane (1999) for an overview of the issues.

> 5 See, for example, Haveman and Wolfe (1995), and Hill and King (1995). The causal interpretation of these correlations can be questioned given the possibility of inter-generationally correlated latent factors; see Behrman and Rosenzweig (2002). Dumas and Lambert (2011) find that maternal education plays a weaker role once properly instrumented.

> 6 For a useful overview of these issues see Shipton and Goheen (1992). Also see the discussion in Platteau (2000).

3
a policy-guided near halving of investment growth, which tends to be resource-intensive, to ease growth to a more sustainable level.<sup>15</sup> In the United States, the boost to private consumption from lower oil prices was partly offset in the short run by a sharper-than-expected contraction in capital spending in the energy sector (Baumeister and Kilian 2016a). This investment is highly price elastic (Bjørnland, Nordvik, and Rohrer 2017; Cakir Melek 2018; Newell and Prest 2019): mining investment halved in the two years that followed the mid-2014 oil price plunge, lowering growth by 0.2 percentage point in both 2015 and 2016.

Additionally, the impact also reflects EMDE energy importers’ lower sensitivity to oil price shocks. Activity in energy-importing EMDEs is less responsive to oil price shocks than that in major advanced economies (Aastveit, Bjørnland, and Thorsrud 2014; Caldara, Cavallo, and Iacoviello 2019). This reflects less oil-intensive energy mixes, less energy-intensive consumption, and energy price controls that limit the pass-through of world prices to domestic retail prices. In addition, many countries seized the opportunity to lower energy subsidies (Section 6). While this improved fiscal and external positions, it dampened the benefit to activity in energy-importing EMDEs.

A number of non-oil commodity exporters and commodity importers raised monetary policy rates during 2015–16 to stem currency depreciation. Others reacted to above-target inflation. In some cases, fiscal deteriorations amid slow growth reduced government revenues and required spending cuts.

# **5.2 The 2020 oil price plunge**

Low oil prices are likely to provide, at best, temporary initial support to growth once restrictions to economic activity are lifted and until excess inventories are unwound. In the very short term, restrictions to stem the pandemic are likely to close off the main channel for low oil prices to benefit growth, by limiting transport and other energy-intensive activities. However, even once these restrictions are lifted and energy demand recovers, the current demand-driven oil price plunge is likely to be associated with deep and lasting output losses.

> 15 See Huidrom, Kose, and Ohnsorge (2017); Kang and Liao (2016); and World Bank (2016a).

16
# **INTRODUCTION**

It is widely recognized that the Brazilian economy is facing considerable competitiveness challenges.<sup>2</sup> After several years of strong expansion, the recent slowdown seems related to supply-side difficulties stemming from a wide range of inefficiencies and rising costs, instead of insufficient aggregate demand. Such inefficiencies and costs have increasingly burdened economic activity and have shown signs of aggravation in recent years.

This note examines in detail Brazilian export performance over the last 15 years, focusing not only on growth and composition, but also on different performance dimensions, including diversification, sophistication, and firm dynamics. The analysis uses international comparisons to better situate the Brazilian performance, and explores different databases, including firm-level data recently published by the World Bank. This note uses a recent diagnostic toolkit developed by the World Bank (Reis and Farole 2012). The objective is to generate hypotheses to identify factors that have been inhibiting exports and industrial production expansion.

# **EXPORT PERFORMANCE: 1998–2011**

# **Growth, composition, and destination**

The aggregate performance of Brazilian foreign trade can be considered favorable in the last 10 years. In the wake of strong global economic growth, the expansion of international trade, as well as favorable commodity prices, Brazilian exports of goods and services grew 262 percent between 2000 and 2010, almost twice the global average, 135 percent. As a result, exports have also grown as a proportion of gross domestic product (GDP), from 10 percent of GDP in 2000 to a peak of 16.4 percent in 2004, falling back to 11.2 percent in 2010.

Brazil’s export performance is less stellar when compared with other emerging countries. In fact, the Brazilian export expansion was significantly below the 439 percent growth in the other BRICS countries (a group that includes Brazil, the Russian Federation, India, China, and South Africa). Among BRICS, the most successful case was India, which increased the ratio of exports to GDP from 13.2 to 21.5 percent between 2000 and 2010, followed by China, from 23.3 to 29.6 percent during the same period. In this sense, Brazil is among the countries that have least exploited the potential of international trade (figure 1), and this picture has not changed in more recent years.

> 2 There is controversy over how to define competitiveness. Following Krugman (1996), this note identifies competitiveness as mainly stemming from total factor productivity of the economy.

2
terms than their better-off counterparts, especially after 1992. Moreover, geographic targeting has clearly improved over time. By 1996, households living in the wealthiest quartile of districts were receiving virtually no investments in school facilities from FONCODES at all.

Figure 5.5 presents comparable figures for expenditures made by FONCODES in 1995 in three sectors: education, electrification, and water and sanitation. The corresponding curves for FONCODES projects in other sectors- community centers, health, agriculture, and transportation-are not included to avoid cluttering the pictures, but the results are very similar for other years and other sectors. Figure 5.5 suggests that education projects may be somewhat better targeted to the poorest districts than some of the alternatives in the FONCODES portfolio, although these differences do not appear to be very large.'<sup>0</sup> Figure 5.6, finally, compares district-level expenditures made by FONCODES, the educational infrastructure program INFES, and the feeding program PRONAA in 1995. T-he results show that the geographic distribution of expenditures made by FONCODES was clearly more pro-poor than that of INFES or PRONAA. In sum, the geographic targeting of FONCODES projects in the education sector is progressive, has improved over time, and is noticeably more pro-poor than that of two other large public sector programs in the sector.

(ii) Household-level targeting: In Peru, there is considerable heterogeneity in the intradistrict distribution of welfare: for example, a simple decomposition of the variance in per capita income into inter- and intra-district components suggests that only 24% of the variance is explained by differences across districts. Reaching poor districts is therefore only a weak proxy for reaching poor households. This section looks at the targeting record of FONCODES investments in education at the household level.

The basic approach we use calculates the household-level incidence of FONCODES benefits with information from the 1996 INEI survey on per capita income, access to educ;.tional infrastructure, and access to school breakfast programs financed by FONCODES. Two (pc ssible) concerns with this approach are measurement error, and the impact of participation in FONCODES programs on household income.

In rural areas in Peru, households are likely to have little choice of primary school. I n the absence of measurement error, one would therefore expect a high degree of consistency in I he answers given by households within a given rural community about the presence of FONCGI)DES-

> 10 Note that it is important to break the analysis down by year, because of changes over time in both targeting performance and the composition of expenditures. For example, most FONCODES projec s in education were funded in the earlier years, whereas most FONCODES projects in water and sanitatic n were funded in the later years. Graphs of the distribution of expenditures by sector for the entire 19921998 period would therefore (misleadingly) suggest that it was easier to target water and sanitation tlan education projects.

9