rafmacalaba/datause-encoder-data huggingface dataset:
important as nonagriculture starts to drive economic growth and countries start to progress through their occupational and spatial transformation. From columns 3 and 4 in Table 5 it can be seen that at the margin (and controlling for growth and its sectoral sources), a 1 percent increase in the population share in the missing middle yields on average quite a bit more national poverty reduction than a 1 percentage point increase in the amount of aggregated (share weighted) per capita GDP growth originating in agriculture. This is very much like the detailed country case findings reported by Suryahadi, Suryadarma, and Sumarto (2009) for Indonesia, a transforming country.<sup>23</sup> Using 4 period provincial panel data spanning 1984-2002, they estimate that 1 percentage point aggregate GDP growth originating in rural services yielded about the same decline in rural poverty as 1 percentage point aggregate GDP growth originating in agriculture, highlighting the importance of the rural nonfarm economy in this transforming economy. In addition, they report a similar size effect on rural poverty from a 1 percentage point aggregate GDP growth originating in urban services.<sup>24</sup> Similar to Ravallion and Datt’s (1996) analysis of Indian data, they also relate the effect of the latter to the more labor-intensive, low capital, lower skilled part of the urban service sector. Clearly, the effect of diversifying into rural nonfarm and secondary town activities on poverty reduction can be substantial.

Turning to the second robustness test (Table 5, columns 5 and 6), the estimated coefficients remain also virtually unchanged when initial poverty is included. This provides confidence that it is not poverty induced migration that is behind the observed empirical regularities. Together these robustness tests for different metrics, specifications and competing hypotheses provide support for the notion that, controlling for growth, rural diversification and secondary town development are on average associated with more inclusive growth

> 23 During 1984-2002, the period of their study, overall GDP growth in Indonesia was no longer driven by agriculture. It was on average only 15 percent of the economy. People exited agriculture into the missing middle at 1.08 per cent per year, while poverty remained predominantly a rural phenomenon, with more than 80 percent of the poor living in rural areas and about two thirds of them deriving the bulk of their income from agriculture. 24 Accounting for their respective shares in the economy, 10 percent GDP growth in rural services was found to reduce rural poverty by 0.8 percentage points, similar as a 10 percent agricultural GDP increase, which reduced rural poverty by 0.7 percentage points. The effect of 10 percent growth in urban services was twice as large (1.5 percentage points) (reflecting its size in the economy which was on average 36.35 percent during the study period, compared with 15.4 percent for agriculture and 14.7 percent for rural services).

19
their income upon moving to the United States. Matching on a broader range of characteristics, Gibson and McKenzie (2012, 2014b) also show large immediate income gains for both highskilled migrants and seasonal migrants.

An alternative approach to dealing with the selection issue has been to use evidence from migration lotteries. In the first such study, McKenzie et al. (2010) examine the short-term impacts of migrating to New Zealand through the Pacific Access Category and find that Tongan migrants had large wage gains in the first year equivalent to a 263 percent gain in income. While large, this is only half of the gap in per-capita incomes, with their evidence suggesting that even the best-performing non-experimental methods would overstate these income gains by at least 20 percent. Stillman et al. (2015) use data from the same migrant population four years after migrating and find their income gain is approximately 20 percent higher than it was at one year. Clemens (2010) provides a second example.<sup>2</sup> He finds Indian IT workers getting six-fold increases in income when working for the same company with the same technology and getting to migrate to the U.S. as a result of being selected by lottery to have their H1-B visa processed. While large, US per capita income was more than 30 times that of India’s during the same period, so the gain from migration is only one-fifth of the per-capita income gap.

This existing literature therefore shows that migrants gain some, but not all, of the income gap between countries upon migration. A key question, for which there is little evidence to date, is whether they are then able to gain more of this gap over time? One hypothesis is that the income gains increase over time as migrants assimilate and gain new skills. But a competing hypothesis is that the gains may weaken over time if migrants have given up occupations at home that had growing income trajectories to work in occupations abroad that offer higher immediate incomes but less prospects for career growth. For example, migrants working as

> 2 Two other migration lotteries have been used to examine the impact on those left behind, but not the gains to migrants themselves: Mergo (2011) does this for Ethiopian households with a family member migrating through the U.S. Diversity Visa lottery, while Gibson et al. (2013) examine outcomes for Samoan households with a family member migrating through the Samoan Quota lottery.

3
fulfilling the necessary data requirements for our analysis do not seem to bias the representation of the final sample, which covers 36 host countries. The data set used in the analysis also includes stock market prices for all other banks and firms in the home and host countries that are used to construct default risk control variables in the regressions.

We use daily stock market information from Compustat Global for international banks and firms and stock market information from CRSP for U.S. banks and firms. Bank level variables are constructed from Bankscope, a comprehensive commercial database of banks' financial statements produced by Bureau Van Dijk. Since we are interested in how bank characteristics affect the correlation between parents’ and their foreign subsidiaries’ default risks, and since there have been significant changes to bank balance sheets during the crisis, we construct and use bank-level variables measured prior to the crisis (as of December 2006). For each bank, we calculate relative bank _size_ (bank assets to total system assets), _capital ratio_ (regulatory capital to risk weighted assets), _equity ratio_ (equity to total assets), _provisions_ (loan loss provisions divided by total loans), _deposit funding_ (deposits divided by total funding), _profitability_ (net income divided by total assets), and _liquidity_ (liquid assets divided by total assets).<sup>5</sup> We winsorize all financial variables at the 1<sup>st</sup> and 99<sup>th</sup> percentile level of their distributions to reduce the influence of outliers and potential data errors.

Host country-level variables are collected from a number of sources. We use data from the World Bank 2007 Bank Regulation and Supervision survey to construct different indexes of bank regulation and supervision, following the methodology proposed by Barth et al. (2001,

> strategic partners, and they often have indirect control in the subsidiaries. In unreported results, we verify that the degree of ownership is not a significant factor explaining the relative strength of transmission of default risk from parents to affiliates.

> 5 Note that in the ratios mentioned here assets and equity are measured as book values. We also tried running regressions using the market value of assets and equity computed from the Merton model discussed above and found similar results.

6
for a weighted average of cluster-specific conditional means and that the outcome variance only depends on ( _d, t_ ), the variation in treatment assignment that is controlled by the experimental design. Assumption 2 can be interpreted as a requirement that the assignment ( _Dig, Tg_ ) contains all the relevant variation in the outcome moments, so that the spillovers model is “correctly specified”. To further justify this assumption, in Section 3.6 we show that this condition is guaranteed when peers are assumed to be exchangeable, so that potential outcomes only depend on the proportion of treated peers and not on their identities. This exchangeability assumption is very common in the spillovers literature. This requirement may be violated, for example, in networks where units have different degrees of network centrality, and thus both the proportion of and the identities of the treated units matter.

Finally, we restrict cluster heterogeneity in the following way.

# **Assumption 3 (Cluster heterogeneity and bounded moments)**

2 _/r (i) For some_ 2 _≤ r < ∞, as n →∞,_ max _g n_<sup>2</sup> _g_<sup>_/n →_0</sup><sup>_and_��</sup> _g_<sup>_nr_</sup> _g_ � _/n ≤ C < ∞. (ii) For some l> r,_ sup _i,g,d,t_ E[ _|Yig|_<sup>_l_</sup> _|Dig_ = _d, Tg_ = _t_ ] _≤ C_<sup> ̃</sup> _< ∞._

Condition (i) is taken from Hansen and Lee (2019). The first part ensures that the largest cluster is small relative to the total sample size, so no cluster dominates the sample. The second part of condition (i) is a regularity condition that rules out unbounded _r_ -th moments in the distribution of cluster sizes. As an example, setting _r_ = 4 restricts the fourth moment of the cluster size distribution, which rules out heavy tails.<sup>5</sup> Condition (ii) is a standard regularity condition that ensures that the _l_ -th conditional moment of the outcome is bounded.

In what follows, we use “ _→_ P” to denote convergence in probability “plim _n→∞_ ” to denote probability limits, “ _→D_ ” to denote convergence in distribution and _∥·∥_ to denote the Euclidean norm. We define any generic (2 _M_ + 1)-dimensional vector **v** as: Consider the vector of estimators **_μ_ ˆ** _n_ = (ˆ _μ_ ( _d, t_ ))<sup>_′_</sup> ( _d,t_ )<sup>from (5) and define the vector:</sup> Define the (2 _M_ + 1) _×_ (2 _M_ + 1) covariance matrix Ω _n_ with elements: 5Notice that condition (i) holds automatically when group sizes are seen as fixed or bounded in the asymptotic analysis.

9
Table 8

|Dependent variable: Fisca|l adjustmen|t when nee|ded based|on debt/rev|enue||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|-0.009*|-0.012**|-0.017***|-0.020***|-0.021**|-0.017|-0.017|-0.046**|
|structural as % GDP|(0.005)|(0.006)|(0.006)|(0.006)|(0.010)|(0.023)|(0.019)|(0.020)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-0.277||-0.219|-0.229|-0.218|-0.225|-0.236|
|||(0.327)||(0.527)|(0.533)|(0.528)|(0.535)|(0.528)|
|Inflation (t-1)||0.041||0.083|0.081|0.084|0.082|0.053|
|||(0.085)||(0.167)|(0.166)|(0.167)|(0.166)|(0.167)|
|U.S. Interest rate||-0.026**||-0.031**|-0.030**|-0.031**|-0.030**|-0.031**|
|||(0.010)||(0.013)|(0.014)|(0.013)|(0.015)|(0.014)|
|**Political factors**|||||||||
|Gini|||0.004|0.004|0.004|0.005|0.004|0.005|
||||(0.005)|(0.004)|(0.004)|(0.005)|(0.004)|(0.004)|
|Rule of law|||-0.054|-0.028|-0.029|-0.028|-0.023|-0.032|
||||(0.039)|(0.045)|(0.045)|(0.045)|(0.053)|(0.045)|
|Margin of Majority|||0.314**|0.312**|0.311**|0.311**|0.311**|0.033|
||||(0.142)|(0.138)|(0.138)|(0.139)|(0.138)|(0.186)|
|Election's year|||-0.045|-0.051|-0.050|-0.051|-0.051|-0.050|
||||(0.032)|(0.032)|(0.032)|(0.032)|(0.032)|(0.032)|
|**Interactions**|||||||||
|EMEs * Deviation|||||0.003<br>(0.013)||||
|Gini * Deviation||||||-0.000<br>(0.001)|||
|Rule of law * Deviation|||||||-0.001<br>(0.005)||
|Majority * Deviation||||||||0.044<br>(0.030)|
|Constant|0.310***|0.438***|0.264|0.315|0.314|0.299|0.295|0.487*|
||(0.041)|(0.065)|(0.288)|(0.283)|(0.282)|(0.291)|(0.302)|(0.293)|
|Observations|1,015|1,003|713|709|709|709|709|709|
|R-squared|0.003|0.013|0.022|0.033|0.034|0.034|0.034|0.037|
|Number of ifscode|137|137|98|98|98|98|98|98|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard errors cl<br>***p<0.01,**p<0.05,*|ustered by c<br>p<0.1|ountry in p|arentheses||||||

- 26 -
12

**_C. Financial Needs:_ 0.4 percent of GDP on average per year.** The financing gap for MDG2 is the fourth in order of magnitude, after poverty reduction (MDG1), maternal mortality (MDG5) and child mortality (MDG4). The cost of achieving this target for the period 2008-2015 amounts to 3.8 percent of GDP on average per year, corresponding to US$ 679.71 million in total. Once the allocation of government expenditure is taken into account, the country needs an additional total financing of 0.4 percent of GDP on average per year (Figure 5), or US$ 66.4 million in total (Table 5). For a detailed, yearly cost breakdown, see Annex 1.

**Table 5. MDG 2: Cost, Budget and Financing Gap (2008-2015)**

<!-- Start of picture text --> Figure 5. MDG 2: Financing gap<br>Financing Gap (2008-2015)<br>-5 -0.10<br>2008 2009 2010 2011 2012 2013 2014 2015<br>-6.73 -6.47<br>-0.20<br>-10 -0.22 -11.03<br>-0.24 -12.19 US$ 679.71 m<br>Total Cost<br>-0.30 MRO 187,221.60 m<br>-15 -0.35 -15.23 -0.40 Budget US$ 613.29 m<br>-0.37 -0.40 Allocation MRO 168,863.47 m<br>-0.43 -19.43 -17.53 US$ 66.43 m<br>-20 -20.85 -0.50 Financing MRO 18,358 m<br>Millions of US$ -0.51 -0.51 gap 0.38% of non-oil GDP<br>% of non-oil GDP (secondary axis) US 4.08 p.c.<br>-25 -0.60<br>Source: Authors’ calculations. Source: Authors’ calculations.<br><!-- End of picture text -->

# **MDG 3: Promote Gender Equality and Empower Women**

_Target 4: Eliminate gender disparity in primary and secondary education, preferably by 2005, and in all levels of education no later than 2015._

**_A. Current Situation:_ gender parity in enrollment in primary education** . At the primary level, gender parity has already been reached. In 2007, girls’ GER exceeded that of boys for the fourth consecutive year. Also, in 2007, 68.1 percent of girls registered in the first grade reached Grade 6, against 58 percent for boys. In secondary school, progress on gender parity has been slower due to, _inter alia_ , the household workload that girls are asked to perform, early marriage or pregnancy. Evidence from recent labor market research suggests that nearly half or women in the 15-24 age-group do not participate in the labor market because of domestic duties, while only 20 percent cite being a student as a non-participating reason (Rajadel et al., 2008).

**_B. Trends and Likelihood:_ a high probability of achieving MDG3** . At the primary level, between 1990 and 2004, the ratio of girls to boys rose from 0.72 to 0.99. Between 1990 and 2007, girls’ participation as a percent of total enrollment increased from 42 to 50 percent. At the secondary level, the ratio of girls to boys rose from 0.79 in 2000 to 0.85 in 2004 (Figure 6). Reaching gender parity at the secondary level is possible but crucially depends on easing girl’s ability to avoid the household workload (Figure 6).
# **Do Phoenix Miracles Exist?**

**Firm-level evidence from financial crises**

## **Meghana Ayyagari**

**Asli Demirgüç-Kunt**

**Vojislav Maksimovic**<sup>**1**</sup>

**Keywords:** Output collapse, systemic crises, sudden stop, credit crunch **JEL Classification:** F31, F32, F34, F41

> 1 Ayyagari: School of Business, George Washington University, ayyagari@gwu.edu, Ph: 202-994-1292; DemirgüçKunt: World Bank, ademirguckunt@worldbank.org, Ph: 202-473-7479; Maksimovic: Robert H. Smith School of Business at the University of Maryland, vmaksimovic@rhsmith.umd.edu, Ph:301-405-2125. We would like to thank organizers and participants at the July 2009 NBER Project on Market Institutions and Financial Market Risk for helpful comments and suggestions. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.

1
Figure 3: Italy 2002–2006–2010: Within-Types Growth Incidence Curve <!-- Start of picture text --> 0 20 40 60 80 100 0 20 40 60 80 100<br>% type population % type population<br>within poorest types gic ’02−’06 within poorest types gic ’06−’10<br>within richet types gic ’02−’06 within richet types gic ’06−’10<br>avg. growth poorest types ’02−’06 avg. growth poorest types ’06−’10<br>avg. growth richest types ’02−’06 avg. growth richest types ’06−’10<br>5 2<br>4<br>0<br>3<br>% yearly growth2 % yearly growth−2<br>1<br>−4<br>0<br><!-- End of picture text --> _Source: Authors’ calculation from SHIW (Bank of Italy)_ period, in the first period, the variation of outcome inequality and the variation of opportunity inequality are in the opposite direction. This result reveals that a conflict may arise in the evaluation of growth when these two different perspectives are adopted for the assessment of the same growth process.

It is interesting to examine why such a conflict arises. If inequality between types increases while overall outcome inequality declines, the within-type share of total inequality must necessarily decline.<sup>27</sup> From this perspective, it may be helpful to look at Figure 3, which reports the GICs within types for the nine poorest and the nine richest types in each process. As expected, growth is progressive in both the poorest and richest types, with an higher average growth in the richest type.<sup>28</sup> This within-type dynamic explains the divergence between the income- and opportunitybased distributional assessments.

Turning the focus to the type-specific growth, the picture changes dramatically. The type

> 27Note that in these empirical applications, the inequality measure used is additively decomposable for within and between groups.

> 28We aggregate types to have sufficient observations in each quantile of the within-type GIC.

23
the war.<sup>20</sup> We use more than 30 books that have been published over the past two decades with various publishers; these books are listed in detail in Appendix 2.

# **IV. Long-run Impacts of School Policies**

# **IV.1. Estimation Results**

A key assumption underlying the DD model is the parallel trend assumption, whereby the difference in the outcome variable between the treatment group and the control group remains essentially the same over time (in the absence of the treatment). Without this assumption, the change for the treatment group may not be attributed to the treatment, but it can result from other time-varying unobservable factors. We check this assumption in Table 1, which shows the balance tests for the differences across different cohorts from the pre-treatment period 1924-35 to the posttreatment period 1950-61 between DRV-occupied provinces and French-occupied provinces (in North Vietnam).

The average years of education in French-occupied provinces were 1.1 and 0.6 smaller than those in DRV-occupied provinces respectively for the cohorts of 1924-35 and those of 1950-61, and are highly statistically significant. However, these two numbers are not statistically significantly different from each other. This suggests that the gap in the years of education between French-controlled provinces and DRV-controlled provinces before and after the treatment years remains similar. Table 1 also reports the balance tests for a number of other variables, including the percentage of the population who have reading and math literacy, who have completed at least

> 20 As an example, the history book for Bac Kan Province records that the French attacked and occupied Phu Thong town of Bach Thong District (in Bac Kan Province) on October 15, 1947. A battle took place in Phu Thong town in March 1948, and the French were defeated in July 1948. The French attacked and occupied Cho Don District on October 7-8, 1947; there was also a battle between the French and the Vietnamese in late October 1947. In 1949, Bac Kan province was completely liberalized from French rule. We list all the battles occurring in each district within each province, and then count the total number of battles at the province level for each province in northern Vietnam. The number of battles per province ranges from 4 to 137 and averages 37.9.

19
38 Table 7: Commodity Welfare Reduction Index, African regional market of 19 focus countries, 31 covered products, 1961–64 to 2000–04 <u>(percent)</u>

||1961-64|1965-69|1970-74|1975-79|1980-84|1985-89|1990-94|1995-99|2000-04|
|---|---|---|---|---|---|---|---|---|---|
|**Grains**|**59**|**50**|**44**|**34**|**28**|**33**|**26**|**20**|**18**|
|Cassava|0|0|1|1|3|1|1|4|3|
|Maize|114|73|63|71|54|67|40|38|35|
|Millet|18|18|11|5|10|13|16|18|8|
|Rice|31|30|40|36|48|60|38|16|18|
|Sorghum|153|144|118|95|83|95|80|52|49|
|Wheat|17|37|40|30|14|16|35|16|16|
|**Oilseeds**|**28**|**42**|**54**|**49**|**47**|**40**|**72**|**43**|**36**|
|Cashew|na|na|na|80|80|85|61|13|11|
|Groundnut|27|43|54|51|50|35|60|41|47|
|Oilseed|na|na|na|na|47|52|61|56|42|
|Palmoil|25|31|45|26|28|44|132|50|13|
|Sesame|50|60|62|65|56|44|47|45|38|
|Soybean|na|14|34|44|45|44|56|52|64|
|Sunflower|0|0|0|0|0|0|0|0|0|
|**Tropical crops**|**36**|**41**|**45**|**61**|**54**|**49**|**53**|**44**|**51**|
|Cocoa|31|51|46|62|54|41|37|37|38|
|Coffee|39|41|46|64|56|48|47|35|21|
|Cotton|42|35|44|57|59|59|71|59|64|
|Sugar|22|35|47|49|43|38|45|45|87|
|Tea|12|8|24|56|52|47|51|50|49|
|Tobacco|39|38|48|56|50|50|40|39|58|
|**Fruit & vegetables**|**0**|**0**|**0**|**4**|**5**|**5**|**2**|**5**|**5**|
|Banana|2|4|0|2|2|1|5|5|2|
|Bean|7|10|3|48|62|73|35|42|40|
|Roots & tubers|0|0|0|0|0|0|0|0|0|
|Pepper|na|42|9|39|47|80|30|62|27|
|Plantain|0|0|0|0|0|0|0|0|0|
|Potato|na|na|na|0|0|0|0|0|0|
|Sweet potato|0|0|0|0|0|0|0|0|0|
|Yam|0|0|0|1|2|1|1|4|4|
|**Livestock**|**30**|**36**|**52**|**35**|**33**|**68**|**66**|**40**|**38**|
|Beef|34|42|58|29|29|60|73|43|42|
|Camel|38|60|34|38|34|68|84|49|99|
|Milk|19|16|41|36|29|79|40|30|29|
|Sheepmeat|42|48|61|46|38|59|70|54|33|

Source: Authors’ calculations based on NRA and CTE data in Anderson and Valenzuela (2008).
# **ASSESSING THE ECONOMIC IMPACTS OF CLIMATE CHANGE ON AGRICULTURE IN EGYPT: A RICARDIAN APPROACH**<sup>**1**</sup>

**Helmy M Eid, Samia M El-Marsafawy and Samiha A Ouda**<sup>**2**</sup>

> 1 An earlier version of this Working Paper was published as CEEPA Discussion Paper number 16.

> 2 Soil, Water & Environment Research Institute (SWERI), Agricultural Research Center, Egypt. E-mails: <u>samiaelmarsafawy797@hotmail.com samihaouda@yahoo.com.</u> This paper was funded by the GEF and the World Bank. It is part of a larger study on the effect of climate change on agriculture in Africa, managed by the World Bank and  coordinated by the Centre for Environmental Economics and Policy in Africa (CEEPA), University of Pretoria, South Africa.
**Table** **<u>7: Determinants of assets received at the time of marriage by generation II</u>**

||**Marriage at**|**less than 30**|**Marriage at**|**less than 20**|
|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|
|Father died after 1994 or alive|0.093|0.268|0.095|0.232|
||(0.214)|(0.233)|(0.168)|(0.224)|
|Young|1.741***|3.482***|2.005**|3.739**|
||(0.097)|(0.038)|(0.270)|(0.839)|
|Young*Father died after 1994 or alive|-0.026|-0.128|-0.129|-0.001|
||(0.105)|(0.202)|(0.100)|(1.046)|
|Reform*Father died after 1994 or alive|-0.174|0.246|-0.025|0.139|
||(0.214)|(0.233)|(0.168)|(0.224)|
|Reform*Young|1.684***|0.254**|0.143|1.211|
||(0.097)|(0.038)|(0.270)|(0.839)|
|Reform*Young*Father died after 1994 or alive|0.584**|-0.787*|0.484**|-1.308|
||(0.105)|(0.202)|(0.100)|(1.046)|
|Observations|1,200|1,200|1,200|1,200|
|R-squared|0.538|0.573|0.537|0.572|

_Note:_ As survey data do not include the year of marriage by generation II, age is used as a proxy with two cut-offs corresponding to a marriage age of 30 (in col. 1 and 2) or 20 (in col. 3 and 4), respectively. All regressions include state specific year of birth fixed effects and village fixed effects. Figures in brackets are robust standard errors clustered by state. * significant at 10%; ** significant at 5%; *** significant at 1%.

22