rafmacalaba/datause-encoder-data huggingface dataset:
is not as internationally comparable and available than data on trade (Egger, Nigai, and Strecker, 2019). Further, to our knowledge, the literature has not identified a credible instrumental variable for capital openness (Magud, Reinhart, and Rogoff, 2011; Alfaro, Kalemli-Ozcan, and Volosovych, 2005).39 Notwithstanding, we provide here some evidence on the impact of capital liberalization based on an event-study design.

We rely on Chari, Henry, and Sasson (2012) who identified capital liberalization events in 25 developing countries corresponding to the date when foreign investment in the domestic stock market was first allowed.40 The paper shows that these seemingly narrow events actually greatly expand foreign capital flows into the country, including foreign direct investment (FDI), and raise the import of capital goods.41 Compared to other reforms aimed at lifting restrictions on FDI, opening the domestic stock market internationally occurs at a precise point in time (other policies are often less precise and staggered); is not marked by policy-reversal or by net capital outflow; and is unambiguously related to capital liberalization (Henry, 2007; Eichengreen, 2001).

We employ the same empirical design as in in Section 6, and create a synthetic control for each treated country and outcome of interest (see Appendix C.3 for details). We measure capital openness as the sum of foreign assets and liabilities, as a share of GDP (Lane and Milesi-Ferreti, 2017). Figure 8 reports the results.42 Starting from a stable pre-trend, we observe a rise in capital openness, precisely at the time of the event, which keeps on building over the post-event decade. The _ETRK_ also increases, but with a few years lag relative to the event: in the medium-run, the effect is precisely estimated and significant

> 39Trade and capital flows may also exhibit strong co-movement. Per example, as the cost of importing intermediate goods decreases, a firm can decide to outsource the production of intermediate goods abroad – which may result in an outflow of capital if the firm decides to purchase the production process abroad. This co-movement makes it challenging to estimate separate effects of capital and trade flows with precision.

> 40Removing restrictions on the stock market constitute a liberalization of the capital account in relation to domestic financial markets. Lane and Milesi-Ferretti (2009) find that capital account liberalization and domestic financial development are strongly correlated with financial integration across countries

41FDI includes both green field investments (building of plants from scratch) and cross-border mergers and acquisitions; the latter is directly impacted by stock market liberalization and makes up 40-60% of FDI in recent times in developing countries. It is likely that the increased foreign ownership on the stock market subsequently triggers an increase in green field investments.

42In Appendix Figure A14, we show that the results are robust to creating synthetic controls that are based on simultaneously matching all outcomes for each treated country.

32
# **Impacts of Improved Biomass Cookstoves on Child and Adult Health: Experimental Evidence from Rural Ethiopia**

Daniel LaFave,<sup>*</sup> Abebe Damte Beyene, Randall Bluffstone, Sahan T. M. Dissanayake, Zenebe Gebreegziabher, Alemu Mekonnen, and Michael Toman Keywords: Child Growth, Household Air Pollution, Cookstoves, Ethiopia JEL Codes: I15, O13, Q55, Q23, Q42

> * Corresponding author: LaFave: Colby College, Waterville, ME, USA. drlafave@colby.edu Dissanayake: Portland State University, Portland, OR, USA Beyene: Environmental Economics Policy Forum for Ethiopia, Addis Ababa, Ethiopia Bluffstone: Portland State University, Portland, OR, USA

> Gebreegziabher: Environmental Economics Policy Forum for Ethiopia and Mekelle University, Addis Ababa, Ethiopia Mekonnen: Environmental Economics Policy Forum for Ethiopia and Addis Ababa University, Addis Ababa, Ethiopia Toman: The World Bank, Washington DC, USA. Financial support from the World Bank and the Knowledge for Change Trust Fund is gratefully acknowledged, as are helpful comments from Erick Gong, Nicole Klingen, Aart Kraay, John Maluccio, Lindsey Novak, and Andrew Simons.
We also find that cognitive skills and the non-cognitive skills of openness to experience are positively associated with non-automation-prone occupations. As an initial, empiricallygrounded benchmark, this is instructive. Vocational programs that are seen as an important piece of developing countries’ industrialization deemphasize these types of skills for the youth that lag furthest behind in them. In the labor force survey data, they rarely provide an advantage to general secondary education in avoiding automation-prone occupations.

Our model suggests countries will be negatively affected by automation unless their education systems are of high enough quality to allow even those with relatively low innate ability to acquire higher skills. For developing countries, this poses a significant challenge. Increasing educational attainment further, especially to higher education, is quite costly. While we see educational attainment associated with avoiding non-automation prone occupations, if they are of low quality and unable to increase the skills of lower innate ability students, then increasing attainment may not be effective.

As for non-cognitive skills, currently very little is known about how to promote them, especially social intelligence and creativity which the automation literature believes is crucial to future employment. For social intelligence, the evidence is limited but emerging. Social and behavioral skills tend to be most malleable in adolescence (Almlund et al. 2011). Several programs target social skills including youth programs in Latin America that tie socio-emotional learning with technical skills (Sanchez Puerta, Valerio and Bernal 2016). Creativity, on the other hand, is a much more amorphous concept. Much of the psychology literature on creativity focuses on definitions and theory; there is little consensus on whether creativity should be considered as a general skill or a skill specific to a field or domain (see Baer and Kaufman 2005). Several instruments have been developed to measure creativity, especially in terms of divergent thinking (Plucker and Makel 2010). Only a small body of empirical literature so far exists on how to stimulate or improve creativity (Grigorenko et al. 2008); the only consensus is that education systems, as they are designed now, do not integrate or promote much creativity and how to do so is also not well understood (Beghetto and Kaufman 2014).

39
**Policy, Res"rch, and Externl Affalis**

<!-- Start of picture text --> 'i'L* :i i l leU I±s :i U::<br><!-- End of picture text -->

# **Wellaro and Human Roeourcos**

This papcr-- a product of thc Welfare and Human Resources Division, Population and Human Resources Department i.. part of a largcr effort in PRE to examine the causes and consequences of poverty in developing countries. Copics are available free from the World Bank, 1818 H Strcet NW, Washington DC 2(433. Please contact Angela Murphy, room S9-137, extension 33750 (36 pages with tables) Thic starting point for reducing world poverty was to provide accurate, up-to-date data on poverty in developing countries. The sparse, outdated data previously available were often of dubious accuracy and usually unavailable in a form uscful for policy analysis.

One o lthe most ambitious attempts to improve the quality of data collected at the househol(d level from developirng countries is thC Living Standards Mcasurement Study (LSMS) program, which Lhe World Bank launched in 1 9X(.

hleiC main objective of LS.IS surveys is to proN ide household-level data for evaluatinig tle eflect ol various governient policies on the populition's living coniditiois -in studics, for cxaniple. of the impact of education on nutrition, thc clTect of hcalth on employment, and the relationship hcbween incoine and fertility.

Alter describing how 01c LSMS bcgan and how data are collected, CGlevw\kc presents selectcd results. Sonme gencral trenlds have cmerge(d in studics Of five of thc six countries for "Ihichi LSMS data arc available: Most of **the** poor are in rural areas; the fraction of the poor population in rural areas is always substantially higher than the fraction of the total population in rural arcas.

Most of the poor are in households in which the hcad works in agriculture. The heads of poor households arc most likely to be selfemployed, especially in Africa. (Very few heads of poor households work for the govemment, so freezes on govemment wages are unlikely to hurt many of the poor.) The thcads of poor households have low lcvels of education - most of them elementary education or less, and in some (particularly African) countries no education at all.

Glewwe also prescnits selected results of studies on the persistence of poverty, and of studies of the effeccs on the poor of structural adjustment, of food stamns and food subsidies, and of raising user fees for health care and education.

1Ih. I'RI. Wvorking Piaper Series dissetninltes thc iendings of work under vav in the Rank's Policv, Rcserch, and ExternaI Affairs Complex. An objective of the series is to get thcsc findings out quickly, even if presentations arc less than fully k)ihshod. Thc fFindings. int.rpretationF andl conclusions in **thcse** papers do not necessarily represent ofricial Bank policy.

Produced at the l'RE Dissemination Center
# Figure 3: Financial fragility

<!-- Start of picture text --> By region/income group By firm-size<br>100 100<br>80 80<br>66 70 68<br>66<br>60<br>60 52<br>52<br>43 46 40 43 43 40<br>40 31<br>30<br>20<br>20<br>0<br>0 Micro (0-4) Small (5-19) Medium (20-99) Large (100+)<br>High income Non-high income EAP SSA SAR SAR Rest of the world<br>(excl. SAR)<br>Predicted probability of falling in arrears (%)<br>Probability of falling in arrears (%)<br><!-- End of picture text --> Panel (a): Probability of falling into arrears <!-- Start of picture text --> 80<br>60<br>45<br>40<br>20<br>8 9.6<br>1.9<br>0<br>Size Sector Exporter status Shock suffered<br>Relative Shapley Value (%)<br>Pseudo R2 explained by group<br><!-- End of picture text --> Panel (b): Decomposing the factors that explain financial fragility country variation can be attributed to the type of shock firms suffered rather than firm size, sector or exporter status (Figure 3, panel b). Size and sector explain merely 18% of this variation in financial fragility relative to the shock that firms observe, which explains about 45%. This observation is consistent with the established literature on the heterogeneity in the performance of firms facing a crisis even within the same country, size and sector (World Bank, 2020c).

# **2.3 Expectation and uncertainty**

Firms’ expectation and uncertainty matter for their future investments and strategic decisions. To quantify firms’ expectations generated by COVID-19 outbreak, the World Bank’s BPS collects information on firms’ expected changes in sales for the next 6 months relative to the same period in the previous year. South Asian firms have a very pessimistic future outlook characterized by expectation of significantly larger average decline in sales compared to firms in other regions and income groups (Figure 4, left panel a). On the average, South Asian firms expect a 19% decline in sales, which is significantly higher than that predicted in EAP (-12%) and in SSA (-1%).

8
- Armand, Alex et al. (2017). Do public health interventions crowd out private health investments? Malaria control policies in Eritrea. Labour Economics 45, pp. 107115.

- Balcan, Duygu et al. (2009). Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences 106.51, pp. 21484 21489.

- Barofsky, Jeremy, Tobenna D Anekwe, and Claire Chase (2015). Malaria eradication and economic outcomes in sub-Saharan Africa: evidence from Uganda. Journal of health economics 44, pp. 118136.

- Bjorkegren, D and Darrell Grissen (2018). Behavior revealed in mobile phone usage predicts credit repayment. World Bank Economic Review. Accepted.

- Bleakley, Hoyt (2010). Malaria eradication in the Americas: A retrospective analysis of childhood exposure. American Economic Journal: Applied Economics 2.2, pp. 145.

- Blumenstock, Joshua (2016). Fighting poverty with data. Science 353.6301, pp. 753754. (2018). Don't forget people in the use of big data for development.

- Blumenstock, Joshua, Gabriel Cadamuro, and Robert On (2015). Predicting poverty and wealth from mobile phone metadata. Science 350.6264, pp. 10731076.

- Blumenstock, Joshua, Nathan Eagle, and Marcel Fafchamps (2016). Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters. Journal of Development Economics 120, pp. 157181.

- Bogoch, Isaac I et al. (2016). Anticipating the international spread of Zika virus from Brazil. Lancet (London, England) 387.10016, pp. 335336.

- Buckee, Caroline O, Andrew J Tatem, and Jessica Metcalf (2017). Seasonal population movements and the surveillance and control of infectious diseases. Trends in parasitology 33.1, pp. 1020.

- Casey, Nicholas (2016). Hard Times in Venezuela Breed Malaria as Desperate Flock to Mines. The New York Times August 15, 2016.

40
**Table A5. Comparison of bilateral and multilateral food price index**

||GL<br>|SS7 market price sur<br>|vey<br>|CPI (item)<br>|CPI (variety)<br>|
|---|---|---|---|---|---|
||Fisher|GEKS-Fisher|WCPD|WCPD|WCPD|
||(1)|(2)|(3)|(4)|(5)|
|Western|98.4|99.5|100.1|98.7|100.6|
|Central|106.3|105.5|104.1|99.3|97.9|
|Greater Accra|106.6|107.9|116.9|114.4|109.6|
|Volta|98.9|98.9|99.4|104.9|105.6|
|Eastern|102.1|99.9|103.6|99.0|100.0|
|Ashanti|108.5|106.8|106.9|104.0|104.4|
|Brong Ahafo|91.0|91.1|96.6|97.9|98.0|
|Northern|85.8|86.4|84.2|83.3|85.1|
|Upper East|108.9|111.5|95.9|96.0|96.1|
|Upper West|93.6|92.6|92.4|102.5|102.8|
|Ghana|100.0|100.0|100.0|100.0|100.0|

_Source:_ Authors’ calculations based on the GLSS7 market price survey data and the CPI price data.

**Table A6. Summary of food and non-food price indexes**

||Food+ <br>|non-food<br>||Food+non-foo<br>|d+housing<br>||
|---|---|---|---|---|---|---|
||CPI (item)|CPI (variety)|CP<br>|I (variety) and GL<br>|SS7 housing<br>|data<br>|
||WCPD|WCPD|WCPD +<br>median<br>paid rent|WCPD +<br>median<br>imputed rent|WCPD +<br>hedonic<br>paid rent|WCPD +<br>hedonic<br>imputed rent|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Western|105.7|100.9|101.1|100.3|101.4|100.8|
|Central|97.5|96.7|95.9|96.5|96.3|96.7|
|Greater Accra|111.1|106.3|113.8|112.1|111.5|110.7|
|Volta|88.2|94.8|92.7|94.9|93.0|94.1|
|Eastern|103.9|98.9|98.2|97.8|97.9|98.1|
|Ashanti|97.7|103.9|103.3|102.6|103.4|103.6|
|Brong Ahafo|101.9|98.6|97.2|96.9|97.3|97.4|
|Northern|84.7|89.3|85.9|90.3|88.6|88.2|
|Upper East|98.0|97.8|94.0|96.3|95.3|95.9|
|Upper West|110.6|109.5|100.6|105.6|102.6|104.7|
|Ghana|100.0|100.0|100.0|100.0|100.0|100.0|

_Source:_ Authors’ calculations based on the GLSS7 data, the GLSS7 market price survey data, and the CPI price data.

**Table A7. Housing** **<u>price indexes</u>**

||Median<br>paid rent<br>(1)|Median<br>imputed rent<br>(2)|Hedonic<br>paid rent<br>(3)|Hedonic<br>imputed rent<br>(4)|
|---|---|---|---|---|
|Western|44.5|45.4|57.0|54.8|
|Central|36.6|47.9|48.3|56.5|
|Greater Accra|100.0|100.0|100.0|100.0|
|Volta|31.4|50.8|39.1|50.1|
|Eastern|39.3|40.2|43.0|48.3|
|Ashanti|40.1|39.9|50.0|55.6|
|Brong Ahafo|34.0|36.2|41.3|46.0|
|Northern|25.0|50.5|43.8|45.3|
|Upper East|30.5|44.3|41.9|46.7|
|Upper West|25.2|44.0|37.2|46.1|

_Source:_ Authors’ calculations based on the GLSS7 data.

30
**Table 10** <u>: Chow test of structural changes in parameter space, from 1990/91 to 1995/96</u>

||**Rural**|**Urban**|
|---|---|---|
|**Explanatory Variables**|Change in coefficient<br>(t-value)|Change in coefficient<br>(t-value)|
|Year (1995)|-0.57<br>(4.6)***|-0.78<br>(2.0)*|
|Income diversification (S)|0.44<br>(3.6)***|0.92<br>(4.9)***|
|Head sex (male)|0.03<br>(1.50)|-0.05<br>(1.7)*|
|Head education, secondary or higher|-0.20<br>(5.0)***|-0.29<br>(4.7)***|
|Household size|-0.03<br>(4.9)***|-0.03<br>(2.2)**|
|# Boys|0.03|0.01|
||(2.8)***|(0.8)|
|# Girls|0.06|0.02|
||(5.1)***|(1.1)|
|# Male adult with sec./ higher education|0.01<br>(1.0)|-0.04<br>(3.1)***|
|# Female adult with sec./ higher education|-0.03|0.01|
||(1.3)|(0.7)|
|Per capita asset holding|-0.09<br>(5.8)***|-0.10<br>(3.7)***|
|Access to credit|0.23<br>(9.0)***|0.38<br>(4.4)***|
|Observations|19252|11738|

Absolute value of t statistics in parentheses

* significant at 10%; **significant at 5%; *** significant at 1%

25
# **Appendix F. Controlling for Real Shocks**

Table F.9: Financial Premium (Tax) of Bureaucrats in Business - Robustness

||OLS|OLS|OLS|OLS|OLS|OLS|IV|
|---|---|---|---|---|---|---|---|
|SOE|-0.0004***|-0.0004***|-0.0004***|-0.0006***|-0.0008***|-0.0008***|-0.0001**|
||(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0000)|(0.0000)|(0.0001)|
|(Publicly Listed=1) x SOE||-0.0004|-0.0004|-0.0002|-0.0008**|-0.0008**|0.0014**|
|||(0.0006)|(0.0006)|(0.0007)|(0.0004)|(0.0004)|(0.0007)|
|Age|||-0.0378***|-0.0594***|0.0128***|0.0129***|-0.0207**|
||||(0.0090)|(0.0099)|(0.0035)|(0.0035)|(0.0098)|
|Log(Total Assets)||||-0.3284***|-0.5358***|-0.5359***|-0.1575***|
|||||(0.0009)|(0.0003)|(0.0003)|(0.0011)|
|Log(TFPQ)|||||0.4019***<br>(0.0001)|0.4018***<br>(0.0001)|0.0640***<br>(0.0005)|
|Real Shock||||||0.0107***<br>(0.0006)|-0.0432***<br>(0.0019)|
|Observations|5896499|5896499|5896499|5896499|5896499|5896499|3917199|
|Country fxed efects|Y|Y|Y|Y|Y|Y|Y|
|Firm fxed efects|Y|Y|Y|Y|Y|Y|Y|
|Industry-time fxed efects|Y|Y|Y|Y|Y|Y|Y|

Standard errors in parentheses. * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Note: The real shock variable is measured as a dummy that indicates whether a firm’s value-added level is an outlier for that year. A value-added level is considered an outlier if it exceeds 2.5 times the interquartile range of that firm’s value-added observations within the panel.

41
The next step is to solve for the price equation (1) from 1981 to 2015. Figure II.A shows the theoretical price that results from the simulation and compares it against observed prices. The ...gure suggests two key observations: First, di¤erences between the model-based and observed price are much larger before 1996 than after. That year, a more market-friendly US agriculture policy bill was passed, which reduced the budget for government purchases (hence the scope for intervention) and lowered price ‡oor targets below market equilibria. It is clear that after such an event, the distance between both time series reduces heavily. Regarding the 2000s spike in prices, the hypothesis of a non-fundamental driver seems pretty weak, since prices moved as one would expect them given demand and supply shocks. Second, theoretical volatility reduces signi...cantly after the year 1996.

Table I.A

|Description|Parameter|Value|
|---|---|---|
|Demand Elasticity|!|0:18<sup>���</sup>|
|Supply Elasticity|�|0:15<sup>���</sup>|

Table I. *,**, and *** indicate p-values inferior to 0.1, 0.05, and 0.01, respectively: Table I.B

|||Standard Deviation|
|---|---|---|
|Variable|All|Prior to 1996<br>After 1996|
|Observed Price (in logs)|0:39|0:36<br>0:32|
|Fundamental Price (in logs)|0:59|0:74<br>0:36|
|Observed to Fundamental Ratio|0:41|0:59<br>0:12|
|||Correlation|
|Observed vs Fundamental Prices|0:82|0:72<br>0:96|

Table I.B.The ...rst two rows show standard deviations of both observed and fundamental prices for each time interval. The row "Observed to Fundamental Ratio" indicates the standard deviation of the ratio of observed prices divided by fundamental ones. The ...nal row "Observed vs Fundamental Prices" shows the correlation between observed and fundamental prices for each time interval. A traditional statistical F test 12
**Figures 4 and 5. Deposits and Loans per Population against Branch Networks**

<!-- Start of picture text --> 2,000 600<br>UAE 500 Lebanon<br>1,500<br>400 Oman<br>Lebanon<br>1,000 Oman 300<br>Jordan<br>Algeria Tunisia 200 Tunisia Jordan<br>500<br>Syria EgyptMorocco 100 Syria Egypt<br>0 Yemen 0 Yemen<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Branches per 100,000 Adults Branches per 100,000 Adults<br>Deposit accounts per 1,000 Adults Loan accounts per 1,000 Adults<br><!-- End of picture text -->

**Figure 6. Deposits/population vs. Per Capita Income.  Figure 7. Deposits/population vs. State Bank Networks**

<!-- Start of picture text --> 2,000 2,000<br>UAE UAE<br>1,500 1,500<br>Lebanon Lebanon<br>Tunisia<br>1,000 Oman 1,000 Oman<br>Jordan Jordan<br>Algeria<br>Tunisia<br>500 500<br>Egypt Egypt<br>Morocco Morocco<br>YemenSyria YemenSyria<br>0 0<br>0 10,000 20,000 30,000 40,000 50,000 0.0 0.2 0.4 0.6 0.8 1.0<br>Per Capita Income Percentage of state branches<br>Deposit accounts per 1,000 Adults Deposit accounts per 1,000 Adults<br><!-- End of picture text --> Source: CGAP and World Bank Financial Access 2010

**While branches facilitate a range of financial services, and can enable a personalized**

**service to clients, ‘branchless’ outlets such as ATMs and point-of-sale devices also be effective delivery mechanisms** not only for withdrawals but also for payments and deposits, at a considerably lower cost than branches.  While MENA is typically at or below the averages for ATM access for other developing regions, access through point-of sale (POS) devices is relatively more widespread in MENA. Population densities, income levels, telecommunications networks, banking sector development, and Government policy all influence the density of touch points in the region, as elsewhere.  ATM networks in the GCC countries are relatively dense and well developed (tables A3.1 and A3.2 have more ATM and POS detail).

**The MENA region reported the second highest increase (24 percent) in ATM networks in 2009 globally** , after South Asia (64 percent).  MENA‟s relative density of branches, ATMs and points of sale devices are illustrated in table 5.  Iran, UAE, Oman and Lebanon have the highest levels of branch density per population, while Lebanon has (by far) the highest density per geographic area (Annex 1.2).

15