rafmacalaba/datause-encoder-data huggingface dataset:
|**Discrete dependent variable regressions of crises in potentially affected countries on the lagged uncertainty there**<br>Step 2, Effect of uncertainty in potentially affected countries, B, on the occurrence of crises there.<br>A dummy of stock market crises is the dependent variable. Countries A are excluded from the group of other countries, B.<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>pooled probit,<br>all crises, all<br>countries<br>pooled probit,<br>all crises,<br>emerging<br>markets<br>pooled logit,<br>all crises, all<br>countries<br>pooled logit,<br>all crises,<br>emerging<br>markets<br>linear<br>probability,<br>all crises, all<br>countries<br>linear<br>probability,<br>all crises,<br>emerging<br>markets<br>0.085*<br>0.104**<br>0.126<br>0.166*<br>0.045***<br>0.026***<br>(0.046)<br>(0.048)<br>(0.086)<br>(0.093)<br>(0.005)<br>(0.007)<br>12.868***<br>13.762***<br>26.069***<br>27.410***<br>0.193***<br>3.116***<br>(2.809)<br>(3.124)<br>(5.570)<br>(5.892)<br>(0.063)<br>(0.484)<br>6.561***<br>6.446***<br>13.032***<br>12.903***<br>1.430***<br>1.113***<br>(2.270)<br>(2.323)<br>(4.521)<br>(4.592)<br>(0.209)<br>(0.316)<br>-0.000<br>-0.000<br>-0.000<br>-0.000*<br>0.000<br>-0.000**<br>(0.000)<br>(0.000)<br>(0.000)<br>(0.000)<br>(0.000)<br>(0.000)<br>-0.010<br>-0.016<br>-0.015<br>-0.024<br>0.001<br>-0.001<br>(0.030)<br>(0.031)<br>(0.059)<br>(0.062)<br>(0.003)<br>(0.004)<br>0.611<br>0.488<br>1.137<br>0.918<br>0.057<br>0.040<br>(0.823)<br>(0.884)<br>(1.420)<br>(1.542)<br>(0.090)<br>(0.117)<br>34.737***<br>38.092***<br>67.306***<br>69.358***<br>1.500***<br>3.050***<br>(7.272)<br>(8.220)<br>(14.390)<br>(15.654)<br>(0.437)<br>(0.820)<br>country effects<br>yes<br>yes<br>yes<br>yes<br>yes<br>yes<br>Constant<br>-4.466***<br>-4.599***<br>-8.788***<br>-8.785***<br>-0.077**<br>-0.170***<br>(0.546)<br>(0.590)<br>(1.125)<br>(1.177)<br>(0.031)<br>(0.051)<br>Observations<br>1983<br>1280<br>1983<br>1280<br>2989<br>1474<br>R-squared<br>0.20<br>0.23<br>Standard errors in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%<br>lag of mean growth expectation<br>lag of stockmarket volatiltiy<br>lag of 10 year US gov bond rate<br>lag of interaction of uncert. in B &<br>all initial crises<br>lag of interact. com creditors &<br>crisis in A<br>lag of interact. tradeshare & crisis<br>in A<br>lag of interact. market size & crisis<br>in A|
|---|

Table 2: Step 2: Effect of uncertainty on the probability of crisis, all initial crises countries 21
variable for the endogenous dummy variable in the outcome equation, it adds an estimate of the omitted variable in the equation. It turns out that PSM can act as an effective adjuvant to all these methods.

With respect to the choice among various evaluation methods, this comparison suggests situations that are best suited to each method. Hence, no single method can be considered ideal in all circumstances, and one should consider a flexible application of available methods. In the end, the plausibility of an evaluation method hinges critically on the correctness of the socioeconomic model underlying program design and implementation. It also depends on the quality and quantity of data available. The specification of the underlying socioeconomic model must be grounded on a sound understanding of political and socioeconomic determinants of participation, and all relevant factors that influence outcome besides participation.

# **_3.  Numerical Implementation_**

This section illustrates how some of the algorithms and estimators described above might be implemented in EViews. We first describe the data used. Next, we explain the estimation of the propensity score. We also discuss the computer code and output for nearest-neighbor and kernel matching respectively. Finally, in the true spirit of impact evaluation, we present matched estimates along results from alternative methods.

# **_Data_**

Our numerical implementation is based on two data sets from Dehejia and Wahba (1999)<sup>12</sup> . These authors explain that the available data sets are constructed from the data underlying Lalonde (1986). The data for the treated is contained in the file NSWRE74_TREATED.TXT. This is the male sub-sample of a 185 observations from the National Supported Work (NSW) Demonstration. This was a temporary employment program seeking to help disadvantaged workers<sup>13</sup> acquire basic job skills through work

> 12 The actual data sets were downloaded from http://www.columbia.edu/%7Erd247/nswdata.html 13 The program targeted women receiving aid for dependent children (AFDC), ex-drug addicts, ex-criminal offenders and high school drop outs of both sexes.  It was run by the Manpower Demonstration Research

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# **5. Consolidation of GVCs: Theory and Evidence**

Through the 1980s and 1990s more and more countries entered into global export markets, typically producing intermediate inputs or performing assembly in global value chains. Different GVCs expanded at different rates, with apparel and automobiles expanding in the 1960s and 1970s in terms of the dispersion and complexity of the supply chain, and with the services sector and business services of other sectors being among the more recent parts of growing and expanding GVCs. But with the onset of the crisis, there is broader evidence of consolidation of some types of supply chains.

## **5.1 Historical Trends in GVC Structure**

Our measure of GVC structure is a modified version of the Herfindahl-Hirschman index (HHI) calculated for each product category by taking the total sum of the squared market shares of all countries exporting that product and multiplying the sum by 10,000: 2 _HHI j_   ( _Sij_ )  10,000, _i_ where _Sij_ is the share of country _i_ expressed as a percentage of total world exports of product _j_ .<sup>27</sup> The HHI can range between 1/ _n_ *10,000, i.e. all countries have the same share, and 10,000, i.e. one country exports all, where _n_ designates the total number of countries exporting this product. A decline reflects a decrease in “concentration” or, more accurately, a greater degree of spatial dispersion of export sourcing in that sector. The US Antitrust Division of the Department of Justice considers HHIs between 1,000 and 1,800 points to be moderately concentrated, and those exceeding 1,800 points to be concentrated.<sup>28</sup> Although this rule of thumb refers to the original HHI, i.e. to firms’ market shares in a particular market rather than to the market shares of exporting countries, it provides a convenient benchmark.

_Figure 10_ shows the graph of the index of industrial concentration for a selection of three-digit sectors for selected years from 1970-2008. Note that recent data should be interpreted with caution as export data might not be published yet for all individual countries which – at a given level of world trade – would overestimate the share of countries for which data are available and thus yield a higher HHI. Most of the product areas experience a dispersion of trade (a decline in the HHI) although there are a number of exceptions. This is most clearly the case in the subsectors of Textiles, Iron and Steel, Machinery and Transportation. In some sectors, however, consolidation already began in the 1990s, including handbags, clothing and footwear. These are sectors in which China made enormous gains in world market share, pushing out competitors especially from Africa and Latin America, but also from smaller East and South Asian countries.<sup>29</sup>

> 27 This measure was used by Mayer et al. (2002) and Milberg (2004).

> 28 See <u>http://www.justice.gov/atr/public/testimony/hhi.htm.</u>

> 29  See Gallgher et al. (2008), Kaplinky and Morris (2008), Wood and Mayer (2009).

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# **2.2 Data**

The main data needed for the study is a social accounting matrix (SAM) for Egypt. We used the SAM for 2019, jointly prepared by Egypt’s national statistical office - the Central Agency for Public Mobilization and Statistics (CAPMAS), and the International Food Policy Research Institute (IFPRI). The SAM and detailed documentation of how the SAM was prepared is publicly available (Serag et al. 2021). We have further worked on the same and mapped the sectors and commodities to those needed for our analysis (see Table 1). For example, detailed agriculture commodities are unnecessary in our analysis as the agriculture sector is relatively less energy and emission intensive. Therefore, we aggregated all crops, fruits and vegetables, fishery, and forestry in one sector: agriculture and forestry. It helps us present our results clearly and focus on the sectors that are energy- and/or emission-intensive. The SAM also provides data by household income group, which allows us to conduct the distributional analysis. The SAM is the latest available for Egypt.

The SAM indicates that commercial services, including tourism services (hotels, restaurants), financial services, real estate, and business services, accounted for highest share of gross output (17%) in 2019 followed by the energy sector (crude oil and natural gas production, petroleum refinery and electricity sector), which accounts for 15% of the gross output. The agriculture sector that includes forestry and fishery accounted for 10% of the gross output. Commercial services also accounted for the highest share (27%) in total export of goods and services in 2019, followed by the transportation goods and services (17%) and chemicals and fertilizers (10%). The rich households (HH Quintile 5) received more than 40% of the total household income in 2019, whereas poor households (HH Quintile 1) received only 4% of the total household income. Taxes accounted for 99% of the total government revenues in 2019. There are five types of taxes – sales tax, corporate income tax, personal income tax, import duties and others, which accounted for, respectively 48%, 31%, 13%, 6% and 1% of the total government revenues. The fuel subsidy was equivalent to 31% of the total government revenue. Fossil fuel combustion accounts for 80% of the total GHG emissions in the country (World Bank, 2022b). The carbon tax policy analyzed in this study reduces CO2 emissions from fossil fuel combustion.
Table 1 lists the approaches to development economics that we have discussed. The table makes it easy to see one of the themes of this essay: modern development economics has circled back to insights made in the 18<sup>th</sup> and 19<sup>th</sup> centuries about the limits on rationality and the impact of institutions and culture on preferences. With the obvious exception of ‘early economics,’ the approaches listed in Table 1 are alive and well today. Thus, development economics is fragmented, as it has always been. Even the individual approaches listed in Table 1 are fragmented. There is no agreement on what constitutes an institution nor, as we discuss later, on the degree to which culture (under various definitions) is a part of behavioral economics.

Today, the central focus of research and teaching in development economics is on microeconomics—market failures and RCT-based mechanism evaluations. Graduate students may read everything from the foundational works in neoclassical economics to research on the psychology of poverty. Many are steeped in instruction on RCTs. Within development economics, _behavioral_ development economics is gaining momentum. It is producing a coherent set of principles to improve the design of development projects and policies. The first journal symposium on behavioral development economics was published in 2014 (in the _Review of Income and Wealth)_ . In 2015, Harvard University introduced a PhD-level course in behavioral development economics, and the World Bank presented the first book-length synthesis of work in this field (World Development Report 2015: _Mind, Society, and Behavior_ ). Kremer and Rao (in progress) are writing the first _Handbook_ survey, entitled “Behavioral Economics and Development.” Development practitioners have applied behavioral insights in many low- and middle-income countries and in poor communities of rich countries. Extended discussions of the successes of these interventions are Datta and Mullainathan (2012), Demeritt and Hoff (2015), 9
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|**Table 1. OLS Regressions: Correlations between**|**women’s involvement in d**|**ecision-making and**|**empowerment**|
|---|---|---|---|
||(1)|(2)|(3)|
||||<br>Wife and husband|
||Wife says main<br>decision-maker<br>is herself|Wife says<br>decision-making<br>is herself or joint|<br>agree she is the<br>decision-maker /<br>diiki i|
||||ecson-mang s<br>joint|
|_Women's Status & Empowerment proxies_||||
|Difference in year of education (Husband-Wife)|-0.002***|-0.002***|-0.001|
||(0.00)|(0.00)|(0.00)|
|Wife not working|<br>-0.032***|<br>-0.077***|<br>-0.029***|
||(0.01)|(0.00)|(0.00)|
|Wife works off farm|0.038***|0.028***|0.013***|
||(0.01)|(0.00)|(0.00)|
|Wife says she earns more than her husband|<br>0.270***|<br>0.109***|<br>0.004|
||(0.01)|(0.01)|(0.01)|
|Wife says she owns land|0.149***|0.056***|-0.010|
||(0.01)|(0.01)|(0.01)|
|Wife is aged 15-19|-0.048***|-0.112***|-0.074***|
||(0.01)|(0.01)|(0.01)|
|Wife is aged 20-34|<br>-0.029***|<br>-0.048***|<br>-0.036***|
||(0.01)|(0.00)|(0.00)|
|Age difference: husband- wife|-0.001***|-0.002***|-0.000|
||(0.00)|(0.00)|(0.00)|
|Wife was married before age 20|<br>-0.001|<br>0.012***|<br>0.015***|
||(0.00)|(0.00)|(0.00)|
|Years of marriage|0.002***|0.001***|0.000|
||(0.00)|(0.00)|(0.00)|
|Total number of children|<br>-0.004***|<br>-0.007***|<br>-0.004***|
||(0.00)|(0.00)|(0.00)|
|Couple in a polygamous marriage|-0.006|-0.095***|-0.073***|
||(000)|(000)|(000)|
|_Individual/Household-level characteristics_|.|.|.|
|Husband works in agriculture|-0.001|0.004|0.004|
||(0.01)|(0.00)|(0.00)|
|Rural Area|<br>-0.045***|<br>-0.014***|<br>0.022***|
||(0.01)|(0.00)|(0.00)|
|Log time (mins) to the nearest water source|<br>-0.001**|<br>0.000|<br>-0.001***|
||(0.00)|(0.00)|(0.00)|
|Household has electricity|0.010|0.015***|0.011**|
||(0.01)|(0.01)|(0.01)|
|Wealth quantile: bottom 20%|<br>-0.003|<br>-0.053***|<br>-0.050***|
||(0.01)|(0.01)|(0.00)|
|Wealth quantile: next-to-bottom 20%|<br>0.000|<br>-0.028***|<br>-0.023***|
||(0.01)|(0.01)|(0.00)|
|Wealth quantile: second highest 20%|<br>0.012**|<br>0.024***|<br>0.027***|
||(0.01)|(0.01)|(0.01)|
|Wealth quantile: top 20%|<br>0.038***|<br>0.072***|<br>0.073***|
|<br>Constant|(0.01)<br>0196***|(0.01)<br>0634***|(0.01)<br>0163***|
||.<br>(0.01)|.<br>(0.01)|.<br>(0.01)|
|Observations|<br>37,590|<br>71,274|<br>71,274|
|R-suared|0204|0207|0155|
|q<br>Adjusted R-squared<br>(1) For columns (1)-(2), the dependent variable takes the value zero if th<br>the dependent variable takes the value zero if the husband and wife disa<br>agree the wife is not the main decisionmaker.<br>(2) Country fixed effects included; robust standard errors in parentheses|.<br>0.20<br>e respondent says her husband, no<br>gree (she claims more responsibili<br>. *** p<0.01, ** p<0.05, * p<0.1|.<br>0.21<br>t herself, is the main deci<br>ty in decision-making, or|.<br>0.15<br>sionmaker. For column (3),<br>vice-versa), or if they|

15
Aydemir A., and M. G. Kırdar. 2013. “Estimates of the Return to Schooling in a Developing Country: Evidence from a Major Policy Reform in Turkey.” MPRA Working Paper 51938. Germany: University Library of Munich.

- Angrist, J. D., and A. B. Krueger. 1991. “Does Compulsory Schooling Attendance Affect Schooling and Earnings?” _Quarterly Journal of Economics_ 106 (4): 979–1014.

- Becker, G. S. 1975. _Human Capital_ . New York: National Bureau of Economic Research.

- Black, S., Devereux, P., and K. Salvanes. 2008. “Staying in the Classroom and Out of the Maternity Ward? The Effect of Compulsory Schooling Laws on Teenage Births.” _Economic Journal_ 118: 1025–54.

- Brunello G., Fort M., and G. Weber. 2009. “Changes in Compulsory Schooling, Education and the Distribution of Wages in Europe.” _Economic Journal_ 119: 516–39.

- Chou S., Liu J., Grossman M., and T. Joyce. 2010. “Parental Education and Child Health: Evidence from a Natural Experiment in Taiwan.” _American Economic Journal: Applied Economics_ 2(1): 33–61.

- Dincer M.A, Kaushal N., and M. Grossman. 2014. “Women's Education: Harbinger of Another Spring? Evidence from a Natural Experiment in Turkey.” _World Development_ 64: 243– 58.

- Dominitz J., Fischhoff B., and C. F. Manski. 2001. “Who Are Youth At-risk? Expectations Evidence in the NLSY-97.” In R. T. Michael, ed., _Social Awakenings: Adolescent’ Behavior as Adulthood Approaches_ . New York: Russell Sage Publications.

- Duflo, E. 2001. “Schooling and Labor Market Consequences of School Construction in Indonesia.” _American Economic Review_ 91 (4): 795–813.

- Eckstein, Z., and I. Zilcha. 2002. “The Effects of Compulsory Schooling on Growth, Income Distribution and Welfare.” _Journal of Public Economics_ 54 (3): 339–59.

38
educational expenditures. The percentages of correct answers across these three tests were averaged to create an overall percentage of correct answers and standardized to serve as our primary outcome.

We supplement these objective measures of behavior with self-reported data from the household survey. We use the subjective likelihood of HIV infection at baseline to examine the heterogeneity of HTC impacts by prior beliefs.<sup>16</sup> Finally, we examine impacts on the following self-reported outcomes: subjective probability of HIV infection, perceived life expectancy, sexual behaviors, and school enrollment at follow-up. Given the caveats regarding the potential of bias in impact estimates on self-reported behaviors, we use these secondary outcomes to complement our findings of HTC effects on STIs and test scores.

In a randomized experiment with multiple rounds of data collection, the internal validity of the findings depends crucially on whether the randomization was conducted properly and whether attrition from the study sample was independent of treatment status. Table 1 presents a summary of baseline demographic characteristics, and measures of sexual behavior and education. Overall, the samples in the two arms are similar, with no difference between them that is statistically significant at the 5% level out of 16 comparisons, and two differences (female-headed household and never had sex) significant at the 10% level. The average age at baseline is just under 17 and more than three-quarters of the sample reported being enrolled in school – with the mean highest grade attended being Standard 8, i.e. the last year of primary school in Malawi. Less than a quarter of the sample reported being sexually active during the 12 months prior to baseline data collection, and thus, not surprisingly, less than 10% report any chance of being infected with HIV. Appendix Table B1 examines whether there is any differential attrition between the two study arms. The analysis indicates that the attrition in the control group is around 15% and treatment status is orthogonal to attrition from the sample.

> 16 Self-reports at baseline may still suffer from social desirability bias, but such misreporting should be orthogonal to treatment status as HTC was randomly assigned and both groups were untreated at baseline.

9
e¤ects using employment data (obtained applying group-speci...c employment-population ratios) relative to the baseline case calculated with population data. The new estimates are hardly distinguishable from the baseline case. This is because while the employment rate is somewhat di¤erent between highly and less educated, they are very similar for native and immigrants for the same education levels in most countries. In some countries immigrants have in fact slightly higher employment rates than natives. In most countries less educated immigrants have a somewhat smaller employment rate than corresponding natives. This is true in particular for Spain and Sweden where the low employment rate of highly skilled immigrants (relative to natives) reduces the positive wage e¤ects of immigration but only slightly.

# 5.5 Accounting for physical capital sluggishness (short-run e¤ects)

While the model has, so far, evaluated the long-run e¤ects of immigrants and emigrants, by assuming full capital adjustment, we can also calculate the short-run e¤ects by accounting for imperfect capital adjustment. From the production function in (3) we can obtain the compensation to the composite factor Q, wt<sup>Q;whichcorrespondstotheaveragewage,as:</sup> where �t = Kt=Qt approximates the capital-labor ratio. In balanced growth path (or in an <u>1</u> e <u>1</u> open economy) the capital-labor ratio is given by the following expression �<sup>�</sup> t<sup>=</sup> � <u>1R�</u><sup>�</sup> <u>�</u> � � At�<sup>:</sup> Hence the average wage in the balanced growth path (aggregating immigrants and natives) depends only on total factor productivity, A<sup>e</sup> and on the rate of return of capital and not on the total labor supply Qt: In the short run, however, the percentage change in average wages may depend on the percentage response of �t to immigration. Taking the di¤erential of the logarithm of (12) relative to immigration, and approximating logarithmic with percentage changes, we have: where (��t=�t)immigration is the percentage deviation of the capital-labor ratio from �<sup>�</sup> t due to immigration, while �A<sup>e</sup> t=A<sup>e</sup> t<sup>theproductivitye¤ect(ifany)fromim-</sup> � �immigration<sup>is</sup> migrants. With full capital adjustment and the economy on the balanced growth path, (��t=�t)immigration equals 0 and the only average wage e¤ect of immigrants is through A<sup>e</sup> t<sup>15</sup> . At the opposite extreme, with ...xed total capital, Kt = K; then (��t=�t)immigration equals the negative percentage change of labor supply due to immigration: �<sup><u>�</u></sup> Q<sup><u>I</u></sup> t<sup><u>t</u>,where�Itisthe</sup> net change in foreign-born workers and Qt is, approximately, the aggregate labor supply at the beginning of the period. While for short periods or for sudden shocks the short run

> 15Obviously immigration has also an e¤ect on relative wages of native-immigrants and highly-less educated as shown above. Physical capital, however, only a¤ects the average wage and hence we can simply add this e¤ect to the group-speci...c wage e¤ect.

20