rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10198

# **Abstract**

The COVID-19 pandemic has had catastrophic economic and human consequences worldwide. This paper tries to quantify the consequences of the pandemic on global inequality and poverty in 2020. Since face-to-face household survey data collection largely came to a halt during the pandemic, a combination of data sources is used to estimate the impacts on poverty and inequality. This includes actual household survey data, where available, high-frequency phone surveys, and country-level estimates from the literature on the impact of the pandemic on poverty and inequality. The results suggest that the world in 2020 witnessed the largest increase to global inequality and poverty since at least 1990. This paper estimates that COVID-19 increased the global Gini index by 0.7 point and global extreme poverty (using a poverty line of $2.15 per day) by 90 million people compared to counterfactual without the pandemic. These findings are primarily driven by country-level shocks to average incomes and an increase in inequality between countries. Changes to inequality within countries were mixed and relatively modest.

This paper is a product of the Development Data Group, Development Economics. 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 dmahler@worldbank.org, nyonzan@worldbank.org, and clakner@worldbank.org.

_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
Rodrik, Dani, Arvind Subramanian and Francesco Trebbi (2004), “Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development;” _Journal of Economic Growth_ , June Vol. 9 (2), 131‐65 Sachs, J. D. and A. Warner. 1995, Economic Reform and the Process of Global Integration, in: _Brookings Papers on Economic Activity_ , W. C. Brainard and G. L. Perry, eds.,(Washington, D.C.) 1-117.

Rutherford, Thomas, David Tarr, and Oleksandr Shepotylo. 2005. The Impact on Russia of WTO Accession and The Doha Agenda: the importance of liberalization of barriers against foreign direct investment in services for growth and poverty reduction. In _Putting Development Back into the Doha Agenda: Poverty Impacts of a WTO Agreement,_ eds. Thomas Hertel and L. Alan Winters. New York: Palgrave McMillan and the World Bank.

Rutherford, Thomas, and David Tarr. 2002. “Trade Liberalization, product variety and growth in a small open economy: a quantitative assessment,” _Journal of International Economics_ , Vol. 56, Issue 2.

Rutherford, Thomas, and David Tarr. 2008. Poverty Effects of Russia’s WTO Accession: modeling real households and endogenous productivity effects. _Journal of International Economics_ . Vol 75 (1), 2008, pp 131-150.

Rutherford, Thomas, and David Tarr. 2010. Regional Impacts of Russia’s Accession to the WTO. _Review of International Economics,_ Vol. 18(1), 30-46.

Rutherford, Thomas, and David Tarr. 2010, forthcoming. Services Liberalization in the Regions of Russia under WTO Accession: Regional Household and Poverty Effects, _International Journal of Services and Economic Management_ .

Shepotylo, Oleksandr, and David Tarr. 2008. Specific tariffs, tariff simplification and the structure of import tariffs in Russia: 2001-2005. _Eastern European Economics,_ Vol. 46 (5), September –October, 49-58.

Tarr, David G. 1993. _How Moving to World Prices Affects the Terms of Trade in 15 Countries of the Former Soviet Union_ . World Bank Policy Research Working Papers, WPS 1074.

Tarr, David G. 1994. The Terms-of-Trade effects of moving to world prices on countries in the former Soviet Union. _Journal of Comparative Economics_ , Vol.18, pp.1-24 Tarr, David G. 1999. Design of Tariff Policy for Russia. In _Russian Trade Policy Reform for WTO Accession_ , eds. H.G. Broadman. World Bank Discussion Paper No.401, World Bank,Washington DC.

Tarr, David G. 2002. On the Design of Tariff Policy: Arguments for and Against Uniform Tariffs. In _Development, Trade and the WTO: A Handbook_ , eds. B. Hoekman, A. Mattoo and P. English. Washington: World Bank.

24
# **UNEMPLOYMENT AND WORKER-FIRM MATCHING: THEORY AND EVIDENCE FROM EAST AND WEST EUROPE**

## **1. Introduction**

During the two decades after the fall of the Berlin Wall, unemployment has been a major problem in the post-communist economies of the former Soviet bloc, including the new members of the European Union (EU). High unemployment has also been a serious issue in many western European countries. The question therefore arises as to whether similar or different factors bring about the high unemployment outcome in the two sets of economies and to what extent their labor markets converge to similar patterns.

In policy discussions in Central-East Europe, three hypotheses have emerged as leading explanations for this phenomenon, namely that high unemployment is the result of (1) problems related to the economic structures (mismatch) in these countries, (2) macroeconomic policies or major external shocks, or (3) ongoing (unfinished) transition from plan to market in the presence of globalization.<sup>1</sup> The discussion complements that in Western Europe, where the focus in explaining unemployment has been on the relative importance of (a) structural (mismatch) shocks, (b) aggregate demand shocks, and (c) hysteresis (e.g., Jackman, Pissarides and Savouri, 1990, and Jackman and Layard, 2004). The nature of appropriate policies for tackling unemployment obviously depends on identifying the nature of the problem.

In this paper, we use new data to address this issue, while advancing the theoretical and applied econometric literature on matching functions. Our strategy is to compare the evolution of unemployment dynamics and analyze the efficiency of matching of workers with job vacancies in four different transition economies and one geographically close West European market economy. In particular, we use

> 1 A fundamental systemic feature of the Soviet-type economies was the nonexistence of open unemployment.  An equally distinguishing feature of the transition during the early-to-mid 1990s was the emergence of double digit unemployment rates in all the rapidly transforming economies except for the Czech Republic.

− 2 −
Table 2. Summary Statistics: Migration for Marriage Purposes

|State|No. of migrants|Percentage|Out of district, same state|Percentage|Out of state|Percentage|
|---|---|---|---|---|---|---|
|Gujarat|1,937,395|4|1,560,660|81|376,735|19|
|Neighbors of Gujarat|10,416,132|24|8,062,937|77|2,353,195|23|
|Rest of India|31,629,202|72|23,066,868|73|8,562,334|27|
|Total|43,982,728|100|32,690,404|74|11,292,264|26|

_Note:_ This table reports the number and percentages of women migrating out of state and out of the district (same state) in Gujarat, its neighboring states, and the rest of India. Data source is the 2001 Indian Census.

Table 3. Women’s Marriage Outcomes Post the Gujarat Riots of 2002

||(1)<br>Age at<br>Marriage|(2)<br>Married Before<br>Age 18|(3)<br>Age at<br>Cohabitation|(4)<br>Age at<br>First Sex|(5)<br>Age at<br>First Birth|
|---|---|---|---|---|---|
|Post X Riot|-0.330<sup>_∗∗∗_</sup>|0.044<sup>_∗∗∗_</sup>|-0.271<sup>_∗∗∗_</sup>|-0.398<sup>_∗∗∗_</sup>|-0.030|
||(0.085)|(0.016)|(0.074)|(0.062)|(0.083)|
|Observations|415441|425254|405697|418219|389744|
|R-squared|0.248|0.164|0.216|0.228|0.128|
|Control Average|19.57|0.312|19.68|19.52|21.34|
|District Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Year Fixed Efects|Yes|Yes|Yes|Yes|Yes|
|Controls|Yes|Yes|Yes|Yes|Yes|

_Note_ : This table reports coefficients from specification 1. Standard errors are clustered at the state level. Control Variables include indicators for religion, sc/sct/obc, rural residence, wealth quintiles, and male household head.

* Significant at 10%; ** Significant at 5%; *** Significant at 1%.

27
# **_C. Regression results: effect of transportation on women’s labor force participation_**

||**No in**<br>|**come restric**<br>|**tion**<br>|**Lowe**<br>|**r-income gr**<br>|**oup**<br>|
|---|---|---|---|---|---|---|
||(1a)|(2a)<br>|(3a)|(1b)|(2b)<br>|(3b)|
|**Sample restriction**|All|Birth +<br>Marriage|Birth|All|Birth +<br>Marriage|Birth|
|Amman|-0.55|-0.611|-0.773|-0.467|-0.618|-0.422|
||(0.47)|(0.51)|(0.92)|(0.54)|(0.61)|(1.13)|
|Beirut|<br>-0.468|<br>-0.42|<br>0.035|<br>-0.285|<br>-0.343|<br>0.469|
||(0.47)|(0.51)|(0.93)|(0.56)|(0.62)|(1.15)|
|Accessibility|0.627|0.973*|1.755**|1.244**|1.558**|2.740**|
||(0.42)|(0.50)|(0.85)|(0.53)|(0.61)|(1.17)|
|Amman*Accessibility|<br>-0.531|<br>-0.832|<br>-1.5|<br>-0.874|<br>-1.113|<br>-1.516|
||(0.43)|(0.54)|(0.91)|(0.55)|(0.68)|(1.31)|
|Beirut*Accessibility|-0.642|-0.899*|-1.615*|-1.355**|-1.470**|-2.003|
||(0.44)|(0.54)|(0.90)|(0.57)|(0.67)|(1.23)|
|Availability|<br>0.006|<br>0.018|<br>0.149*|<br>0.038|<br>0.038|<br>0.205*|
||(0.05)|(0.06)|(0.09)|(0.05)|(0.06)|(0.12)|
|Amman*Availability|<br>0.031|<br>-0.035|<br>-0.322**|<br>0.079|<br>0.026|<br>-0.266|
||(0.06)|(0.10)|(0.16)|(0.08)|(0.12)|(0.22)|
|Beirut*Availability|0.059|0.021|-0.001|-0.046|-0.071|0.058|
||(0.07)|(0.09)|(0.16)|(0.10)|(0.12)|(0.22)|
|Safety|-0.761|-0.634|-0.448|-0.473|-0.544|-0.161|
||(0.69)|(0.73)|(1.25)|(0.81)|(0.86)|(1.54)|
|Amman*Safety|<br>0.715|<br>0.788|<br>1.392|<br>0.449|<br>0.623|<br>0.985|
||(0.71)|(0.77)|(1.33)|(0.84)|(0.92)|(1.68)|
|Beirut*Safety|0.274|0.267|-0.159|-0.318|-0.191|-1.379|
||(0.74)|(0.82)|(1.41)|(0.90)|(1.00)|(1.82)|
|Age|0|-0.001|0.009***|-0.001|-0.001|0.003|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Education (years of)|<br>0.028***|<br>0.025***|<br>0.036***|<br>0.020***|<br>0.017***|<br>0.024**|
||(0.00)|(0.00)|(0.01)|(0.00)|(0.01)|(0.01)|
|Single (vs. Married)|<br>0.153***|<br>0.152***|<br>0.032|<br>0.151***|<br>0.159***|<br>0.106|
||(0.02)|(0.03)|(0.05)|(0.03)|(0.04)|(0.07)|
|Has children under 10y|0.021|0.014|-0.204**|-0.014|-0.012|-0.217|
||(0.02)|(0.03)|(0.09)|(0.03)|(0.04)|(0.13)|
|People over 60y in household|<br>0.064***|<br>0.065**|<br>0.058|<br>0.031|<br>0.046|<br>0.048|
||(0.02)|(0.03)|(0.04)|(0.03)|(0.05)|(0.07)|
|Home ownership|<br>-0.083***|<br>-0.035|<br>0.036|<br>-0.089***|<br>-0.032|<br>0.103|
||(0.02)|(0.03)|(0.06)|(0.03)|(0.04)|(0.08)|
|Household size|-0.015**|-0.026***|-0.013|-0.008|-0.012|0.002|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.02)|
|_Constant_|0.714|0.649|0.006|0.703|0.74|-0.037|
||(0.46)|(0.49)|(0.89)|(0.52)|(0.56)|(1.08)|
|N|2961|1732|670|1701|999|345|
|R2|0.174|0.182|0.214|0.201|0.22|0.28|
|Adj. R2|0.148|0.139|0.102|0.159|0.153|0.096|
|Fixed-Effects|District|District|District|District|District|District|

Notes: *, **, *** denote significance at the 10 percent, 5 percent and 1 percent levels respectively. Robust standard errors in parentheses. N: number of observations. District-level fixed effects, their coefficients are not presented in the table. Baseline category for city=Cairo.

48
-40 -

- Psacharopoulos, George. 1987. "Earnings and Education in Brazil: Evidence from the 1980 Census." Education and Training Department, Research Division, The World Bank.

- Psacharopoulos, George. 1988. "Education and Development: A Review." The World Bank Research Observer 3(l):99-116.

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- Psacharopoulos, George and Francis Steier. 1988. "Education and the Labor Market in Venezuela, 1975-84." Economics of Education Review 7(3):321-32.

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- Psacharopoulos, George and Zafiris Tzannatos. 1992a. Women's Employment and Pay in Latin America: Overview and Methodology. Washington, DC: The World Bank.

- Psacharopoulos, George and Zafiris Tzannatos, eds. 1992b. Case Studies on Women's Employment and Pay in Latin America. Washington, DC: The World Bank.

- Psacharopoulos, George and Eduardo Velez. 1992. "Education and the Labor Market in Uruguay." Views from LATHR, I-atin America and the Caribbean Technical Department, The World Bank.

- Riveros, Luis A. 1990. "The Economic Return to Schooling in Chile. An Analysis of its Long*

- Term Fluctuations." Economics of Education Review 9(2): 111-21.

Schultz, T. W. 1961. "Investment in Human Capital." American Economic Review. March.

Tinbergen, Jan. 1975. Income Distribution: Analysis and Policies. Amsterdam: North-Holland.

Velez, Eduardo and George Psacharopoulos. 1987. "The External Efficiency of Diversified Secondary Schools in Colombia." Economics of Education Review 6(2):99-110.
# List of acronyms and abbreviations

AfDB African Development Bank AICD Africa Infrastructure Country Diagnosis CFAA Country Financial Accountability Assessment CG central government COFOG classification of functions of government DB development budget GDP gross domestic product GFS global financial system GFSM Government Finance Statistics Manual HR human resources IFAC International Auditing Practices Committee of the International Federation of Accountants IFRS International Financial Reporting Standards IMF International Monetary Fund INTOSAI International Organization of Supreme Audit Institutions IT information technology LCU local currency unit LG local government Ministry of Finance ministry of finance MTEF Medium-Term Expenditure Framework NEPAD New Partnership for Africa’s Development NGO nongovernmental organization ODA official development assistance OECD Organisation for Economic Co-operation and Development OHADA Organization for the Harmonization of Business Law in Africa PFM public financial management PPA Power Purchase Agreement PPP public-private partnership PRSP Poverty Reduction Strategy Paper SF special fund SOE state-owned enterprise SPI spending on public infrastructure VAT value added tax WB World Bank vi
**TABLE 2:** Observations with gender breakdown in the 1983-1999 October Inquiry Computer Files

**A.** SAMPLE SIZE

|**Maximum Conceivable Observations**|**227,171**|
|---|---|
|observations missing because country did not report in given year<br>observations missing because occupation missing in year country reported|80,500<br>133,651|
|**Actual year/country/occupation observation**|**13,020**|
|observations with multiple figures|5,860|
|multiple figures|5,911|
|**Total, including all multiple observations**|**18,931**|

- **B.** COUNTRIES AND OCCUPATIONS WITH AT LEAST ONE REPORTED GENDER BREAKDOWN **Countries With Reported Gender Breakdown for different numbers of occupations**

#s of occupation # of countries (total: 83) <5 17 5-9 13 10-24 16 25-49 17 50-74 5 75-99 7 100+ 8

|**Occupations with one Reported**|**Gender Breakdown for different numbers of countries**|
|---|---|
|#s of countries<br>||
|reporting on occupation<br>|# of occupations (total: 161)<br>|
|<10|23|
|10-19<br>|72<br>|
|20-29|48|
|30-39|16|
|40-49|2|
|**Pay concept**<br>|**C.**ACTUAL OBSERVATIONS<br>|
|Wages (70 countries)|9,532|
|Earnings (50 countries)|9,399|
|**Averaging concept**||
|Mean|17,407|
|Minimum|634|
|Maximum|138|
|Prevailing|173|
|Median|567|
|Other|12|
|**Period concept**||
|Monthly<br>|12345|
|Hourly<sup>34</sup>|2822|
|Weekly|3140|
|Daily|499|
|Annual<br>|115|
|Fortnight|10|

34 The hourly figures include small number for whom the data tell us that they relate to hours paid for and another small number for whom the wages relate to hours worked.

36
Table 7: Logit coefficient estimates of shock variables

||l_________|**women**||**me**|**n**|
|---|---|---|---|---|---|
||Wife|single<br>mother|unmarried<br>no kids|husbands|unmarried<br>no kids|
|**Model**I:||||||
|unemployment rate|0.35**<br>(0.047)|0.26*<br>(0.12)|-0.17<br>(0.42)|0.15<br>(0.12)|0.14<br>(0.33)|
|others involuntarily<br>OLF|0.02<br>(0.025)|0.057<br>(0.05)|0.1<br>(0.19)|0.03<br>_(0.5)_|0.043<br>_(0.15)_|
|spouse involuntarily<br>OLF<br>|0.41**<br>(0.13)|||0.6<br>(0.58)||
|**ModelII:**||||||
|unemployment rate|0.36**<br>(0.047)|0.27*<br>(0.12)|-0.18<br>(0.42)|0.15<br>0.12)|0.15<br>(0.33)|
|fall household<br>income|9.84x10-**<br>(4.07 x10-7)|1.0x 10**<br> (2.22 x 10-6|7.32x 10-0<br>) (1.15 x 10-)|4.58 x 10<br>(1.33 x 10-6)|7.83 xio-<br>(8.0 x 10-6)|
|**Model III:**||||||
|unemployment rate|0.36**<br>(0.048)|0.26*<br>(0.12)|-0.18<br>(0.42)|0.16<br>(0.12)|0.12<br>(0.33)|
|fall household<br>income by:||||||
|1-9%|0.032<br>(0.056)|0.03<br>(0.2)|0.49<br>(1.02)|-0.59**<br>(0.18)|-0.19<br>(0.69)|
|10-24%|0.027<br>(0.05)|0.092<br>(0.17)|-0.029<br>(0.77)|-0.031<br>(0.15)|0.85<br>(0.61)|
|25-49%|0.16**<br>(0.046)|-0.04<br>(0.18)|0.048<br>(0.69)|-0.1<br>(0.15)|0.37<br>(0.57)|
|50-74%<br>l________________|0.35**<br>l(0.058)|0.57**<br>(0.22)|-0.23<br>(1.28)|0.17<br>(0.19)|0.79<br>(0.64)|
|75-100%|0.28**<br>(0.056)|0.59**<br>_(0.15)_|0.11<br>(0.86)|0.32*<br>(0.13)|0.3<br>(0.63)|
|sample size|53463|6373|448|4193|542|

** significant at the 1% level

* significant at the 5% level

--- variable is not, by definition, included in the sample

39
_Figure 11. Poverty status by demographic household composition_ Note: the figure shows the demographic classification in a more aggregate fashion; households are classified by the number of adults present (regardless of their marital situation), and the presence or not of children. “Sole‐responsible adult” refers to households where the one adult is likely to be a single parent (see Box 1). Total sample 89 countries Source: WB Staff’s calculations based on GMD.

# **_Box 1. One adult‐households: Untangling who the “sole‐responsible” adult is._**

One adult (female or male) households with children, or single‐parent households, deserve special attention. This is especially true when thinking of female‐headed households, which tend to be equated with single mothers and their children.

According to the demographic classification, the sample contains over 7 million poor households composed of one adult female and children. This is, equivalent to 6.1 percent of the total number of poor households (the share is 1.6 percent in the case of one‐adult households where the adult is a male). By construction, one‐adult‐female households include the following possible combinations:

- 1.The female adult is the head of the household, she has no spouse ‐she is either never married, divorced/separated, or a widow, and she lives with children, or with children and seniors.

- 2.The female adult is the head of the household, is married, and she lives only with children in the household; the husband or partner is not present in the household.

- 3.The female adult is the head of the household, is married, and she lives with children and seniors; her husband may be one of the senior (65 or older) living in the household or might not be living in the household

- 4.The female adult is the spouse of the head, she lives with children or with children and seniors; the head (i.e. her spouse) is either absent or a senior living in the household,

- 5.The female adult is neither the reported head of the household, or the household head spouse; she lives with children only.

24
Table 7. Correlation in buildup between neighboring cells

||197|5|199|0|200|0|201|4|
|---|---|---|---|---|---|---|---|---|
||Coef.|Std.err.|Coef.|Std.err.|Coef.|Std.err.|Coef.|Std.err.|
|y(n1)|1.3102|0.0008|1.3761|0.0007|1.3953|0.0007|1.3708|0.0007|
|y(n2)|-0.3237|0.0008|-0.3888|0.0008|-0.4060|0.0007|-0.3807|0.0008|
|Intercept|0.0000|0.0000|0.0001|0.0000|0.0001|0.0000|0.0002|0.0000|
|R-square|0.7952||0.8402||0.8666||0.8507||
|N.obs.|3,853,228||3,853,228||3,853,228||3,853,228||

Note: the dependent variable is y. There are no controls included.

Table 8. Correlation in buildup between neighboring cells over time

||199|0|200|0|201|4|
|---|---|---|---|---|---|---|
|Regressors in|1975||||||
|y|0.8691|0.0008|0.7273|0.0013|0.6123|0.0018|
|y(n1)|0.3420|=©0.0016|0.4390|0.0025|0.5221|0.0036|
|y(n2)|0.1605|0.0013|0.4304|0.00271|0.5568|0.0030|
|intercept|0.0022|0.0000|0.0051|0.0000|0.0113|0.0000|
||0.7453||0.5945||0.4352||
||||200|0|201|4|
|Regressors in|1990||||||
|y|||0.8961|0.0007|0.7575|0.0012|
|y(n1)|||0.1839|0.0013|0.3688|0.0024|
|y(n2)|||0.1025|0.0011|0.1477|0.0019|
|intercept|||0.0024|0.0000|0.0082|0.0000|
||||0.812||0.6029||
||||||201|4|
|Regressors in|2000||||||
|y|||||0.8395|0.0008|
|y(n1)|||||0.2505|0.0016|
|y(n2)|||||0.0252|0.0012|
|intercept|||||0.0052|0.0000|
||||||0.7758||

Notes: each panel is a separate regression. The total number of observations is 3,853,228 in each regression.
# **Table OA3: Descriptive statistics of VVC members**

||Baseline<br>|Baseline HH||VVC memb|<br>er|P-value of t-<br>|P-value of t-<br>|P-value of t-<br> <br>|<br> <br>P-value of t-<br>|
|---|---|---|---|---|---|---|---|---|---|
||villager survey<br>respondents|survey<br>respondents|All|Lottery<br>Villages|Non-lottery<br>Villages|test that<br>(1) = (2)|test that<br>(1) = (3)|test that<br>(2) = (3)|<br>test that<br>(4) = (5)|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Household head is male (0/1)|0.826|0.613|0.710|0.719|0.700|0.000|0.024|0.141|0.873|
|Household head is married (0/1)|0.845|0.813|0.871|0.844|0.900|0.154|0.582|0.266|0.517|
|House has concrete floor (0/1)|0.580|0.459|0.613|0.688|0.533|0.000|0.619|0.022|0.220|
|Has cultivable land smaller than 1 Ha (0/1)|0.503|0.748|0.613|0.656|0.567|0.000|0.096|0.024|0.477|
|Number of observations|734|460|62|32|30|||||
|P-value ofjoint F-test that all coefficients are 0||||||_0.000_|_0.003_|_0.014_|_0.471_|

Note: This table reports means of characteristics of baseline villager survey respondents (Column 1), baseline household survey respondents (Column 2) and VVC members (Columns 3-5, data collected at baseline). Columns 6-9 show p-values of the equality t-tests. In the villager and eligible surveys, questions were asked about the household head. In order to appropriate compare the data in Columns 1 and 2, Columns 3-5 restrict the data to VVC member who were head of their households (around 70% of respondents). (0/1) indicates dummy variables, equal to one if the response is yes. See Appendix Table 1 for definition of variables.