rafmacalaba/datause-encoder-data huggingface dataset:
5.6 Projected Job Losses by Educational Attainment For both Jordan and Georgia, our methodology produces a pronounced educational gradient, with projected job loss rates declining in the level of education.

# **5.6.1 Jordan**

For Jordan, the educational gradient of job losses is similarly steep as that observed in the CMMHH. Job losses for “PFPS” workers with at most a basic education were on the order of 30 percent, whereas for those with higher

|higher<br>education<br>they were<br>around 17-<br>19 percent<br>(Table|_Table 5-21 Jordan: Distributio_<br>_Attainment_<br>By education|_n of PFPS Job Losse_<br>CFUWBES1<br>Modeled|_s (Percentage o_<br>CFUWBES2<br>Model-<br>predicted|_f Jobs Lost) by E_<br>CFUWBES2<br>Survey<br>Measured|_ducational_<br>CMMHH<br>Survey<br>Measured|
|---|---|---|---|---|---|
|5-21),<br>|Less than basic|19.3|29.9|28.0|33.9|
|relative to|Basic Education|16.2|28.3|25.0|29.9|
|our|Secondary Education<br>|15.1|24.9|25.8|21.4|
|model’s|Higher education<br>|13.3|17.4|24.2|19.4|
|<br>|Total|15.8|25.2|25.5|23.3|
|prediction|Sources: CFUWBES1, LMPS 2016,|and CMMHH Jordan 202|1.|||
|of 29.9||||||

and 17.4 percent, respectively. The model-predicted estimates for wave 2 were once again more in line with the CMMHH data than were the survey-measured numbers.

As with gender, the reverse pattern occurs when considering non-PFPS jobs, according to the CMMHH Less than basic survey. In non-permanent or Basic Education Secondary Education informal private sector Higher Education positions, those with higher Total levels of education Source: CMMHH, 2021.

_Table 5-22 Jordan: Percent of Non-Permanent Private Sector Workers Employed Pre-COVID No Longer Employed, by Demographic Group_

||Unemployed,|2021<br>Out of labor force,2021|
|---|---|---|
|Less than basic|10.9|0.0|
|Basic Education|16.1|1.7|
|Secondary Education|19.5|9.3|
|Higher Education|24.9|4.8|
|Total|17.8|3.4|

# experienced a higher rate of

job loss (Table 5-22). Yet the highest rates of withdrawal from the labor force occurred for those with secondary education.

# **5.6.2 Georgia**

In Georgia, where a much higher share of the working age population has tertiary education, the rate of loss of PFPS jobs held by those with less than tertiary education in wave 1 was projected to have a modest gradient from 25.6 for primary schooled (or less) workers to 14.9 percent for university-educated ones. For wave 2 the gradient was similarly flat for those with less than a university education at 11-13 percent, and jobs held by those with more than a university degree were predicted to actually increase by the winter of 2020 compared to COVID-19 levels (Table 5-23).

40
# Table A10: Description of variables

|Variable|Description|
|---|---|
|**Panel (a): Outcome variables**||
|Total consumption expenditure|Annual household consumption expenditure per capita|
|Food consumption expenditure|Annual household food consumption expenditure per capita|
|Education expenditure|Annual household education expenditure per capita|
|Other non-food expenditure|Annual household other non-food expenditure per capita|
|Consumption poverty|=1 if a household is poor based on consumption expenditure per capita|
|Wealth|Household wealth score|
|Wealth poverty|=1 if a household is poor based on wealth score|
|Employed|=1 if an individual has had salaried employment, 12M|
|Employed permanently|=1 if an individual has had salaried permanent employment, 12M|
|Hours worked, permanent|Hours worked by an individual in salaried permanent employment, 12M|
|Employed temporarily|=1 if an individual has had salaried temporary employment, 12M|
|Days worked, temporary|Days worked by an individual in salaried temporary employment, 12M|
|Owns an NFE|=1 if a household owns a non-farm enterprise (NFE), 12M|
|Number of NFEs owned|# of NFEs a household owns, in the last 12 months (12M)|
|Plans to open a new NFE|=1 if a household plans to open a new NFE, in the coming 12 months|
|Worked for other HHs for free|=1 if an individual has supplied free labor for other households, 12M|
|# of other HHs worked for|# of other households an individual has supplied free labor for, 12M|
|A fall in the prices of food items|=1 if a household is negatively affected by a fall in food prices, 12M|
|A rise in the prices of food items|=1 if a household is negatively affected by a rise in food prices, 12M<br>|
|A rise in the prices of agri. inputs|=1 if a household is negatively affected by a rise in the prices of<br>agricultural inputs (seed and fertilizer), 12M|
|**Panel (b): Measures of refugee inte**|<br>**nsity**|
|Refugee intensity (RI)|RI based on the average # of refugees hosted in the last two years|
|Refugee intensity (RI) (Future)|RI based on the average # of refugees hosted in the future two years|
|**Panel (c): A measure of receptivity**||
|Receptivity|Receptivity based on the average # of refugees in the last two years by<br>country of origin|
|**Panel (d): Control variables**||
|Head is male|=1 if head of a household is male|
|Head's age|Age in years of head of a household|
|Head's education in years|Education in years of head of a household|
|Head is married|=1 if head of a household is married|
|Head is born in the residence region|=1 if head of a household is born in the region of current residence|
|Household size|# of household members|
|# of young dependents in the HH|# of household (HH) members below 15 years old|
|<br># of elderly dependents in the HH|<br># of household members above 64 years old|
|<br>Total annual rainfall (mm)|<br>Total annual rainfall (household level)|
|Rainfall in wettest quarter (mm)|Total rainfall in the wettest quarter (household level)|
|Change in greenness in_Meher_|Change in Enhanced Vegetation Index (EVI) in_Meher_(zone level)|
|Peakof EVIvaluein_Meher_|Peakof EVIvaluein_Meher _(zonelevel/averaged byround-zone)|

60
**Table A6: Crisis probability: Panel probit model with random effects**

|**Explanatory**<br>**variables**|**Debt crisis**|**Banking**<br>**crisis**|**Currency**<br>**crisis**|**Any crisis**|
|---|---|---|---|---|
|Inflation shock|0.052|-0.545|0.249|-0.252|
||[1.181]|[0.351]|[0.537]|[0.472]|
|Reaction shock|0.355|0.568|2.095***|1.436***|
||[1.377]|[0.459]|[0.612]|[0.534]|
|Real shock|-1.095**|-0.076|0.457**|-0.117|
||[0.489]|[0.138]|[0.215]|[0.174]|
|GDP growth (t-1)|-0.104**|-0.020|-0.068***|-0.031|
||[0.048]|[0.014]|[0.023]|[0.021]|
|Short-term debt (t-1)|-0.007|0.002|0.009|-0.008|
||[0.023]|[0.006]|[0.009]|[0.009]|
|Debt service (t-1)|-0.002|0.007**|0.001|0.000|
||[0.013]|[0.003]|[0.005]|[0.005]|
|Reserves cover (t-1)|-0.332**|-0.0358*|-0.076*|-0.033|
||[0.150]|[0.021]|[0.039]|[0.026]|
|Change in government debt (t-1)|0.004||0.022**|0.005|
||[0.011]||[0.009]|[0.007]|
|Change in private debt (t-1)||0.030**|0.009|0.041**|
|||[0.014]|[0.023]|[0.018]|
|Change in government debt (t-1)|||0.003*|-0.000|
|x Change in private debt (t-1)|||[0.002]|[0.001]|
|Concessional debt (t-1)|-0.055**|||-0.008**|
||[0.028]|||[0.004]|
|Funding ratio (t-1)||0.002**||0.001|
|||[0.001]||[0.001]|
|Currency overvaluation (t-1)|||0.000|0.075***|
||||[0.000]|[0.0119]|
|Currency mismatch (t-1)|||-0.000|-0.000|
||||[0.000]|[0.000]|
|FDI (t-1)|||-0.001|-0.013|
||||[0.016]|[0.016]|
|Constant|-1.710**|-2.119***|-1.764***|-1.503***|
||[0.746]|[0.158]|[0.295]|[0.290]|
|No. of observations|1,634|2,085|1,325|1,271|
|No. of countries<br>Estimated on annual data 1985-2018 e<br>*** p<0.01, ** p<0.05, * p<0.1|103<br>xcluding 2009. St|92<br>andard errors in b|88<br>rackets.|88|

48
28

# **Table 3.  Per Capita Household Expenditure Estimates (Selection Corrected) for Ghana**

|Variable|Receive inter<br>(from|nal remittances<br>Ghana)<br>Sli|Receive in<br>remittances (f<br>otherc|ternational<br>rom African or<br>ountries)<br>Sli|
|---|---|---|---|---|
||OLS|eecton<br>Corrected|OLS|eecton<br>Corrected|
|**Human Capital**<br>Number of members over age 15 with<br>secondary education|130,238.9<br>(1.51)|54,480.9<br>(0.44)|338,529.6<br>(1.90)|525,128.2<br>(2.64)**|
|Number of members over age 15 with<br>university education|853,179<br>(3.34)**|580,091.9<br>(1.51)|421,254.5<br>(1.24)|178,448.3<br>(0.22)|
|**Household Characteristics**<br>Age of household head|-10,846.8<br>(-8.49)**|-10,676.9<br>(-1.80)|-9,444.6<br>(-2.69)**|-16,314.5<br>(-1.43)|
|Household size|-173,367.6<br>(-14.25)**|-183,495.7<br>(-10.14)**|-239,621.1<br>(-7.37)**|-276,308.1<br>(-6.44)**|
|Number of males over age 15|96,527.5<br>(3.48)**|79,885.2<br>(0.62)|85,802.7<br>(1.16)|134,897.8<br>(1.56)|
|Number of children under age 5|-109,208.3<br>(-3.42)**|-143,904.6<br>(-4.24)**|-157,015.4<br>(-1.67)|-94,722.6<br>(-0.41)|
|**Migration Networks**<br>Head of household is of Asante ethnicity<br>(1 = yes)|361,000.7<br>(6.27)**|361,207.5<br>(5.36)**|443,127.0<br>(3.25)**|321,861.6<br>(0.71)|
|Lamda (Selection control)||203,026.9<br>(0.25)||-947,900.3<br>(-0.51)|
|Constant|3,048,656<br>(31.41)**|2,795,416<br>(2.35)*|3,780,232<br>(16.46)**|4,793,133<br>(1.59)|
|Adjusted R<sup>2</sup>|0.296|0.336|0.332|0.354|
|F-test|70.19|73.18|18.09|20.13|
|N|2139|2139|488|488|

Notes: Dependent variable is annual per capita household expenditure (excluding remittances). Six locational dummy variables are included in the model, but not reported in the table. All values are weighted. Figures in parentheses are t-values.

* Significant at the 0.05 level. ** Significant at the 0.01 level.
pounds the following year and to 25,000 pounds in 1989. Under the 1987 definition, some 20,000 facilities filed TRI reports. These were subsequently reduced to 18,846 as a result of the de-listing of six major chemicals (see below), and increased again to 19,762 facilities following the lowering of the reporting threshold.

The list of chemicals covered by the TRI is subject to an on-going review by the EPA. In the first year of reporting (1987) 328 individual chemicals and chemical categories were included, but this was adjusted to 322 the following year when the EPA determined that six chemicals were not sufficiently toxic to warrant reporting. The exclusion of three chemicals in particular - sodium sulfate, aluminum oxide and sodium hydroxide - had a dramatic impact on overall TRI totals, since they were respectively the first-, second-, and sixth-ranked chemicals. As a result, the total amount of releases and transfers reported was cut by two-thirds. The pollution intensities calculated in this paper do not include the chemicals de-listed up to 1989.

The TRI chemicals are drawn from lists developed independently by the states of Maryland and New Jersey, and vary widely in toxicity. No non-toxic substances or other environmental parameters, such as chemical or biological oxygen demand (COD/BOD), are recorded. TRI facilities must report annually all releases of TRI substances to air, water, or land, whether routine or accidental, and all transfers of TRI substances for off-site disposal. Although the identity of a particular substance may be claimed as a trade secret if 5
28

# Panel B

||_Pension_<br>_Fund_<br>_Assets_|_Mutual_<br>_Fund_<br>_Assets_|_Stock_<br>_Market_<br>_Turnover_|_Stock_<br>_Market_<br>_Capitaliza_<br>_tion_|_Domestic_<br>_Private_<br>_Debt_<br>_Securities_|_Domestic_<br>_Public_<br>_Debt_<br>_Securities_|_Foreign_<br>_Private_<br>_Debt_<br>_Securities_|_Foreign_<br>_Public Debt_<br>_Securities_|
|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|
|Log initial GDPPC|−0.0629|0.713***|0.593***|0.481***|1.173***|0.346***|1.006***|−0.224***|
|Log GDPPC minus|||||||||
|<br>Loginitial GDPPC|−2.166|−2.300***|1.268***|0.644*|5.861***|1.311**|−0.894|−1.431**|
|Interaction|0.285|0.476***|−0.0795|−0.000579|−0.487***|−0.224***|0.327***|0.133|
|Log population|−0.0525|0.287***|0.619***|0.155***|−0.0301|0.154***|0.123***|−0.467***|
|Log population<br>density|−0.142***|−0.119***|0.00520|0.0397**|−0.0740*|0.129***|−0.0179|−0.184***|
|Fuel dummy|0.182|−0.335**|−0.183**|0.0137|−0.380***|−0.00603|0.0845|−0.0817|
|Offshore dummy|0.121|1.230***|−0.334***|0.0959|−0.567***|−0.509***|−0.243*|−0.478***|
|Transition dummy|−3.052***|−1.387***|0.635***|−0.926***|−0.744**|0.359*|−0.673***|−0.460***|
|Private credit crash|−5.985***|−7.414***|−6.495***|−4.187***|4.262**|−2.988**|−0.742|8.200***|
|Strength of legal<br>rights index|0.189***|0.000925|0.00960|0.0372**|0.176***|−0.0530**|0.0421*|−0.0906***|
|Credit information<br>index|0.275***|−0.324***|−0.0881***|−0.132***|0.181***|0.108***|−0.111***|0.115***|
|Strength of investor<br> protection index|0.0476|0.0617|0.157***|0.121***|0.0480|0.0952***|−0.0470|−0.0532|
|Enforcement costs|−0.00971|−0.00576|−0.0127***|0.00268|0.00390|−0.00294|0.00623**|−0.00164|
|Observations|565|567|1,292|1,344|645|707|883|1,073|
|Pseudo R<sup>2</sup>|0.378|0.669|0.598|0.490|0.567|0.277|0.617|0.330|

Note: This table extends table 1 by adding the following additional policy variables: _Private credit crash_ (which assumes a value of 1 if private credit to GDP drops by over 20 percent for a particular country-year) and a set of variables taken from the World Bank Doing Business Database, including the _Strength of legal rights index_ (the extent to which creditors are legally protected), the _Credit information index_ (the quality of credit information), the _Investor protection index_ (the extent to which investors are protected by law), and _Enforcement costs_ (the cost to enforce a contract). The contract enforcement index is the first principal component of the following indicators (also from Doing Business): contract enforcement costs, number of days to enforce a contract (in logs), and number of procedures to enforce a contract. GDPPC stands for gross domestic product per capita. ***, **, and * indicate _p_ < 0.01, _p_ < 0.05, and _p_ < 0.1 Source: De la Torre, A., E. Feyen, and A. Ize (2013).

28
estimates thus suggest that rank correlation and IGC (Pearson correlation) are practically in a tie in coresident samples, but the most widely used measure IGRC is clearly the most biased by the truncation due to coresidency criteria.

# **(5.1.2) Estimates of Father-Sons and Mother-Daughters Intergenerational Persistence**

There is evidence that the intergenerational linkages between father-sons and motherdaughters may be stronger than the cross-gender effects. In this subsection, we discuss the truncation bias in the estimates of intergenerational persistence between schooling of fathers and sons, and between mothers and daughters. Table 4 presents the estimates.

Consistent with the evidence from the all children sample in Table 3, the estimates in table 4 show that the bias in _β_<sup>ˆ</sup> 1 (IGRC) is significantly higher compared to the bias in (ˆ _ρ_ 1) (IGC) and _δ_ ˆ1 (rank correlation). A comparison of the estimates of (ˆ _ρ_ 1) and _δ_ ˆ1 , how( ) ( ) ever, shows that the conclusion depends on the gender: for the father-sons intergenerational link, the estimated bias in rank correlation is somewhat smaller than that in Pearson correlation. In contrast, for the mother-daughters link, the bias in estimated rank correlation is larger in magnitude compared to the bias in IGC (Pearson correlation coefficient). The results in Table 4 also show that the estimates of mother-daughter schooling persistence in general suffer more severe downward bias compared to the estimates for father-sons. The larger bias in the daughters sample reflects the fact that coresidency rates are much lower for them both in Bangladesh and India. In the father-son sample the coresidency rate is 79 percent in India, while the corresponding rate is 52 percent in Bangladesh. In the mother-daughter samples, the coresidency rates are much lower: 39 percent in India and 26 percent in Bangladesh, reflecting the fact that women leave the natal family following marriage in both countries. The evidence thus suggests that an analysis of the gender gap in intergenerational mobility based on most of the available household surveys may be seriously misleading, because of significant gender differences in coresidency rates.

13
28 Maryland Working Paper No.89-3

- MaJd, S. and S.C. Myers (1987), "Tax Asymmetries and Corporate Income Tax Reform", in Feldstein, M., The Effects of Taxation on Capital Ac_cMulagtio, Chicago: The University of Chicago Press

- Majd, S. and S. Myers (1985), "Valuing the government's tax claim on risky corporate assets", NBER Working Paper no. 1553

- Mintz, J. (1989), "the Economic Impact of Corporate tax Losses: A Survey", mimeo, Faculty of Management, University of Toronto, December 4.

- Press, W., B. Flannery, S. Teukolsky and W. Vetterling (1986), Numerical Recipes: The Art of Scientific Computing, Cambridge University Press.

- Schnabel, J.A. and E. Roumi (1990), n A Contingent Claim Analysis of Partial Loss Offset Taxation and Risk-taking", Public Finance/Finances Publipues, No.2
Policy Research Working Paper 9455

# **Abstract**

This paper analyzes the relationship between poverty and conflict in the macro and regional data, including a detailed case study of Uganda. The paper relies on a large and growing literature that provides evidence on the devastating impact that conflict has on health and expectations. Based on this evidence, it develops a statistical framework to track the cumulative long-term impact that armed conflict has on poverty, which the paper calls conflict debt. The data confirm that contemporaneous conflict leads to a conflict debt which is only recovered slowly. The empirical model is not only a good description of the cross-country aggregate poverty time-series data, but also regional cross-sectional data. A new aspect in the model is that armed conflict can prevent poverty reduction and, once it is over, allow for strong catch‐up effects as they exist in the data. But in the most conflict-ridden countries, repeated cycles of violence prevent poverty from recovering. According to the most conservative estimates, these countries and regions would have 5–10 percentage points lower poverty rates without their conflict debt.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at h.mueller.uni@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
pledgeability. The demand for liquidity and the composition of investment in these industries are therefore predicted to be more sensitive to variations in the rate of inflation.

Second, we test these predictions with firm-level data. We use novel data from the WBES to overcome the small sample problems typically encountered in empirical models that aim to examine the consequences of aggregate macroeconomic conditions (inflation) for individual microeconomic units (firms). For the harmonized panel covering firms in 139 developing countries, we find that inflation shifts the composition but not the overall quantity of investment. The impact of inflation on firms’ technology choice is statistically and economically significant: a 1 pp increase in a given developing country’s rate of inflation reduces the probability that establishments in that country engage in product or process innovation by 4.3%. Inflation also reduces the share of working capital financed by internal funds/retained earnings. And the adverse effects of inflation increase with establishments’ dependence on retained earnings to finance their working capital, consistent with the view that innovation activities are hard to finance externally. Finally, exploiting variation in industry characteristics, our generalized difference-in-difference estimations validate the sector-specific predictions of our theoretical model.

In a further robustness exercise, we consider balance sheet data for U.S. firms which provide direct information about firms’ liquidity holdings. We find that corporate liquidity (cash) holdings are increasing with firms’ R&D investment but that inflation reduces their liquidity holdings. Our findings thus corroborate the key components of the transmission mechanism proposed in our theoretical model also in the context of an advanced economy with developed markets.

29
**Table F5: Relationship between food security measures and weight-for-age z-scores (Nepal): Limited Controls Version**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||weig|ht-for-age z-s|cores|||
|Per capita kcal|0.016<br>(0.022)|||||||
|1-SSR||0.185***<br>(0.026)||||||
|1 - SSEXR|||0.193***<br>(0.025)|||||
|FCS||||0.164***<br>(0.025)||||
|DDS|||||0.110***<br>(0.025)|||
|PC consumption||||||0.279***<br>(0.047)||
|Food share|||||||-0.215***<br>(0.027)|
|Rural|-0.518***|-0.373***|-0.386***|-0.402***|-0.443***|-0.258***|<br>-0.287***|
||(0.056)|(0.059)|(0.057)|(0.059)|(0.058)|(0.069)|(0.061)|
|Eastern|-0.011|0.034|0.034|0.058|0.033|0.063|0.085|
||(0.072)|(0.071)|(0.071)|(0.072)|(0.072)|(0.071)|(0.072)|
|Central|-0.007|0.003|-0.000|0.029|0.014|-0.046|-0.025|
||(0.068)|(0.067)|(0.067)|(0.068)|(0.068)|(0.067)|(0.067)|
|Midwestern|<br>-0.178**|<br>-0.079|<br>-0.097|<br>-0.082|<br>-0.118|<br>-0.110|<br>-0.121*|
||(0.073)|(0.075)|(0.074)|(0.074)|(0.074)|(0.074)|(0.073)|
|Far Western|-0.138|-0.051|-0.042|-0.098|-0.099|-0.029|-0.095|
||(0.084)|(0.085)|(0.085)|(0.083)|(0.084)|(0.084)|(0.084)|
|Constant|-1.037***|-1.185***|-1.172***|-1.168***|-1.121***|-1.256***|-1.236***|
||(0.068)|(0.071)|(0.069)|(0.070)|(0.070)|(0.074)|(0.070)|

59