rafmacalaba/datause-encoder-data huggingface dataset:
|Registered<br>deaths|-0.128**|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Registered<br>||-0115*||||||||||
|injury||.||||||||||
|Displaced|||-0.033|||||||||
|Income<br>||||-0220***||||||||
|dropped<br>||||.||||||||
|Lost<br>ownership|||||-0.125|||||||
|Lost job||||||-1.602***||||||
|Lost farm|||||||-0.516|||||
|Lost<br>livestock||||||||-0.682***||||
|Lost assets|||||||||-0.200|||
|Affected by<br>the war||||||||||-0.058||
|Experienced<br>violence|||||||||||-0.240***|
|Control<br>variables||||||||||||
|Birth fixed||||||||||||
|effects<br>(department)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Birth fixed||||||||||||
|effects (Age<br>Cohort)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Household<br>controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|6.152***|6.076***|6.185***|6.213***|6.181***|6.115***|6.192***|6.168***|6.190***|6.235***|6.172***|
|Observations|10,552|10,331|10,492|10,625|10,625|10,625|10,625|10,625|10,625|10,625|10,625|
|R squared|0.455|0.457|0.456|0.457|0.456|0.457|0.456|0.456|0.456|0.456|0.456|

Notes: The household level controls include log per capita consumption expenditure, gender, gender of household head, average years of education in the household, ethnic groups and religious groups; *** implies significant at 1%, ** implies significant at 5% and * implies significant at 10%.

The identification strategies used so far assume that the war victims (as identified above) and control groups are exchangeable, such that they have identical distributions of variables. This can be confirmed by data using a randomized controlled trial; however, drawing causal inference using survey data requires a more careful analysis because selection biases and confounding invalidates the exchangeability assumption. In such cases the estimated causal effects are likely to be biased. Here we discuss a number of events that could be potential sources of bias in the estimated causal effect. First, education could be seen as a catalyzer of war in Côte d‟Ivoire. The distribution of educational facilities was unequal between the rebel-held North and the government-controlled South. This might produce an upward bias in the causal effect of war on education. Second, there were reasons other than the conflict that had a negative impact on 15
26 .2000. _Irrational Exuberance._ Broadway Books. New York.

_.1993. Macro Markets: Creating Institutions for Managing Society's Largest Economic Risks._ Oxford University Press, Oxford.

Taylor, M. P., and D. A. Peel. 1998. "Periodically Collapsing Stock Price Bubbles: A Robust Test." _Economic Letters._ 61:221-28.

Vogelsang, T. J. 1998. "Additional Tests for a Unit Root Allowing for a Break in the Trend Function at an Unknown Time." _International Economic Review,_ 39: 1073-1100.

, and P. Perron. 1992. "Nonstationarity and Level Shifts with an Application to Purchasing Power Parity." _Journal of Business & Economic Statistics,_ 10: 301-321.

Worzala E., and K. Vandel. 1993. "International Direct Real Estate Investments as Alternative Portfolio Assets for Institutional Investors: An Evaluation." Paper presented at the AREUEA meeting.
- United Nations. 1983. _Indirect Techniques for Demographic Estimation._ New York, United Nations.

- Wagstaff, A. 2000. "Socioeconomic inequalities in child mortality: comparisons across nine developing countries." _Bulletin of the World Health Organization_ 78(1): 1929.

- Wagstaff, A., P. Paci and E. van Doorslaer. 1991. "On the measurement of inequalities in health." Social Science and Medicine 33: 545-57.

- World Bank, SIDA, AusAID, Royal Netherlands Embassy and Ministry of Health of Vietnam. 2001. _Vietnam. Growing Healthy: A Review of Vietnam 's Health Sector._ Hanoi: The World Bank.

35
# **Figure 3**

**The ‘Cotton Problem’ is more than Trade and Subsidies**

<!-- Start of picture text --> The ‘cotton problem’<br><!-- End of picture text --> <!-- Start of picture text --> Trade<br>Issues<br><!-- End of picture text --> <!-- Start of picture text --> Development<br>Issues<br><!-- End of picture text --> <!-- Start of picture text --> Non-disputed<br>at the WTO<br><!-- End of picture text --> <!-- Start of picture text --> Disputed<br>at the WTO<br><!-- End of picture text --> <!-- Start of picture text --> Low world<br>cotton prices<br><!-- End of picture text --> <!-- Start of picture text --> Structural<br>inefficiencies<br><!-- End of picture text --> <!-- Start of picture text --> Exchange rate<br>appreciation<br><!-- End of picture text --> <!-- Start of picture text --> All cotton<br>producing<br>countries<br><!-- End of picture text --> <!-- Start of picture text --> All WCA<br>countries<br><!-- End of picture text --> <!-- Start of picture text --> Most cotton<br>producing<br>countries<br><!-- End of picture text --> <!-- Start of picture text --> Benin, Burkina<br>Faso, Chad, Mali<br>(C-4 initiative)<br><!-- End of picture text --> <!-- Start of picture text --> Brazil (Brazil-<br>United States<br>WTO dispute)<br><!-- End of picture text --> <!-- Start of picture text --> Most WCA (and<br>other) countries<br><!-- End of picture text --> <!-- Start of picture text --> 3<br><!-- End of picture text -->

**Figure 4 Agriculture and Cotton Price Indices (Real, MUV-deflated, 2000=100)**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>Cotton<br>50<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Source: World Bank<br>4<br><!-- End of picture text --> 19
# Contents

|**1**Intr<br>|oduction<br>|1|
|---|---|---|
|1.1<br>|General Framework ..<br>.<br>..<br>|2|
|1.2|Organizatiou of the Paper .......<br>. . . . . . . . . . . . . . . .|. . .<br>..<br>6|
|2 Mo<br>|del<br>|6<br>|
|2.1<br>|Consumption<br>. ..........<br>. . . . . ............<br>|...<br>6|
|2.2<br>|Investment ........................<br>.<br>..<br>|...<br>8<br>|
|2.3<br>|The Intra-period CGE .i.............................<br>|10|
|2.4<br>|Equilibrium Conditions .....<br>.<br>.....................<br>|11|
|2.5|Solution Strategy ...............................|12|
|**3**Ext<br>|ernalShocks<br>|14|
|3.1<br>|The Impact of an Import Price Increase .......<br>.............<br>|.<br>16|
|3.2|Features in the Dynamic Behavior ......<br>.<br>.<br>.<br> <br>|19<br>|
||3.2.1 The Simultaneity of InvestmentandSaviz&gs<br>.1.9..**.**. . . <br>|. . . .<br>.<br>19|
||3.2.2 The Intertemporal behavior ......<br>. . . . . . . . . . . . <br> <br>|. . . .<br>.<br>20|
||3.2.3 The Forward-Looking<br>behavior ......<br>. . . . . . . . . .|. . . .<br>.<br>21|
|4Adj<br>|ustment Policies<br> <br>|23|
|4.1<br>|Tariff<br>Reform<br>...............................<br>|...<br>.<br>24|
|4.2|Policies During an Import Price Shock ......<br>. . . . . . . . . .|. . . .<br>.<br>26|
|SCon|clusions|28|
|Figu|res|32|
|Refe|rences|38|
|App|endices<br>|41|
||ListofEquations..........<br>Glossary<br>.........|41<br>.<br>46|

The analysis of this paper is based on the author's Ph.D. dissrtation, which received an individual grant from Ford Foundation, Philippines. Special thanks re due to Shantaynan Devarajan for his valuable comments and to Hollis Chenery and Dwight Perkins, the other membewr of the Ph.D. Committee. This paper also benefited from various comments in the Bank, among others, from Martha de Mdo, Norma Hicks, Emmanuel Jimenez, Javad Khali[zadeh-Shirasi, Johannes Linn, Pradeep Mitra, and Zmarak ShalizL
the job offer, 68 percent found that the job was no longer available upon moving to New Zealand, due in large part to the time elapsing between filing their residence application, having it approved, and actually moving to New Zealand.<sup>11</sup> As a result of this, a potential explanation for the low income expectations is that potential emigrants may have heard from friends and relatives that job offers would disappear by the time of immigration, leading new immigrants to have to search for new jobs and to take up jobs which are lower paid. Alternatively, even if information is available from both those taking up their initial jobs and those not doing so, the psychological literature suggests individuals may place disproportionate weight on negative outcomes compared to positive.<sup>12</sup> The second row of Table 3 shows that although the income earned by immigrants not taking up their initial job offer is lower than the average immigrant income, it is still much higher than the expected income. The mean and median work income of immigrants who did not start work in the job for which they had their initial job offer is $520. The mean of the mean expected income ($337) is still only 65 percent of this.<sup>13</sup> We can test for possible information flow from PAC migrants to ballot losers using GPS coordinates collected on the locations in Tonga of houses of ballot losers and of the houses where migrants lived before migrating (and where some family member of the migrant is likely to remain). We use this to measure the distance of PAC ballot losers from the closest emigrant who didn’t take up their initial job offer, and the proportion of PAC emigrants who didn’t take up their initial job offer within a 6km radius of the house of the ballot loser. The first two columns of Table 7 show no significant effect of distance to PAC emigrants on conditional income expectations, with the signs of the coefficients

> 11 Gibson and McKenzie (2006) provide more details of how this job offer functions in practice, and show that the proportion of immigrants taking up their job offer increased after a policy change in October 2004. 12 Montgomery (1998) discusses the psychological evidence for this, and suggests in a developing country setting that negative outcomes could play a disproportionate role in expectations about child mortality.

> 13 Similarly, if we relate the expected income to a hypothetical migrant income based on the _offered_ wages (even when these offers were not taken up), the underestimation is just as large. The mean and median offered wages were $528 and $480.

16
# **Appendix Table I. 7: Organized Sector Log (Employment) & Log (Output) and Unorganized Non-Household Sector Spillovers**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Hitl Elt Sill f|Emplo<br>_Other_|yment<br>_-sector Employ_|Real O<br>_ment/Output Spi_|utput<br>_llovers_|
|orzona mpoymen povers rom|||||
|NHH Unorganized Sector|0.1270***|0.0595***|0.1177***|0.0444***|
||(0.0163)|(0.0144)|(0.0177)|(0.0167)|
|Forward Employment Spillovers from|||||
|NHH Unorganized Sector|-0.2163|2.4785***|0.1555|1.1772*|
||(0.1754)|(0.8735)|(0.2543)|(0.7039)|
|Backward Employment Spillovers from|||||
|NHH Unorganized Sector|0.3219***|1.0150**|-0.0136|0.1350|
||(0.1038)|(0.3950)|(0.2030)|(0.8035)|
||_O_|_ther-sector Pro_|_ductivity Spillov_|_ers_|
|Average TFP of Horizontal-linked NHH|||||
|Unorganized Plants|0.0182|-0.0051|-0.0288|0.0060|
||(0.0530)|(0.0498)|(0.1531)|(0.1457)|
|Average TFP of Forward-linked NHH|||||
|Unorganized Plants|0.2101+|0.0318|0.3660|0.8341|
||(0.1384)|(0.3344)|(0.4527)|(0.9486)|
|Average TFP of Backward-linked NHH|||||
|Unorganized Plants|0.2241**|0.0250|1.1084***|-0.4945|
||(0.1011)|(0.2016)|(0.2882)|(0.9982)|
|Forward Employment Spillovers from|_Own-_|_sector Employ_|_ment/Output Spil_|_lovers_|
|Organized Sector|1.3281***|-3.4446***|2.8177***|12.3932***|
||(0.2781)|(1.2338)|(0.8268)|(3.0808)|
|Backward Employment Spillovers from|||||
|Organized Sector|1.2409***|0.6009***|1.7069***|0.5187|
||(0.1575)|(0.2262)|(0.4811)|(2.1252)|
|Constant|-25.4503***|-0.4314|-101.5250***|-321.5567***|
||(4.5183)|(8.1644)|(20.6930)|(43.0879)|
|Observations|5888|5888|5888|5888|
|R-squared|0.1385|0.3383|0.1047|0.2856|

_Note: All specifications contain panel (state-industry) fixed effects as well as year fixed effects. . Columns 2 and 4 include state-year and industry-year fixed effects while Columns 1 and 3 do not. Standard errors are clustered by the state-industry level. Significance at the 20% level is indicated by ++, at the 15% level by +, at the 10% level by *, at the 5% level by ** and at the 1% level by ***. NHH stands for the sub-sample of non-household unorganized plants_ 60
indicators underly the aggregate WBL index, each of which covers a different area of the law that may affect a woman’s working life. The _Mobility_ indicator examines laws that constrain a woman’s freedom of movement. The _Workplace_ indicator evaluates laws that may constrain a woman’s ability to work. The _Pay_ indicator assesses legislation that may affect a woman’s pay. The _Marriage_ indicator assesses legal constraints related to marriage and divorce. The _Parenthood_ indicator assesses the legislation that may impact a woman’s ability to partake in the workforce after having a child. The _Entrepreneurship_ indicator examines how legislation may impact a woman’s ability to start and run a business. The _Assets_ indicator considers how the law may constrain a woman’s ability to own and manage assets. Finally, the _Pension_ indicator examines how the law may affect the size of a woman’s pension upon her retirement. The data cover 190 countries from 1970 until 2021 (at the time of writing) and are updated annually.<sup>3</sup> The data examined in this paper were collected as part of the development of the _Parenthood_ indicator. There are five binary data points underlying the indicator: (1) Is paid leave of at least 14 weeks available to mothers? (2) Does the government administer 100% of maternity leave benefits? (3) Is there paid leave available to fathers? (4) Is there paid parental leave? (5) Is dismissal of pregnant workers prohibited? The main data presented here and used in the analysis are the detailed data underlying questions (1), (3) and (4) above. These data give the precise length (in days) of leave available to mothers, to fathers, or to both parents as granted under each country’s maternity leave, paternity leave or parental leave policies.

Under the WBL methodology, maternity leave refers to leave available to the mother for the birth of a child, consisting of days before, during, and immediately after childbirth. Similarly, paternity leave refers to leave available only to the father to be taken immediately for the birth of a child. While maternity and paternity leave refer to leave explicitly granted to mothers and fathers respectively, parental leave is designed for childcare that follows or replaces maternity or paternity leave and can be shared or specifically allocated to mothers or fathers. Some countries reserve a specific portion of parental leave for either parent. For example, according to WBL data for 2021, in Germany, there are 300 parental leave days available for parents to share, in addition to which there are 60 days reserved for the specific use of each parent.<sup>4</sup> Standardized assumptions are used by WBL to ensure data comparability. For example, the WBL methodology assumes women are lawful citizens living in the main business city of the country in question. They are healthy adults without any criminal records and are assumed to be employed as

> 3 The full data set can be accessed at https://wbl.worldbank.org/en/wbl-data.

> 4 Parental Benefits and Leave Act; Sections 4, 15, and 16.
# Educational Attainment of Formal and Informal Employees In European Countries, by Region. 2004-2009

<!-- Start of picture text --> HU PL BG LV RO EE SI SK CZ UA RU DK FI SE NO PT ES IT GR IL CY AT NL FR IE UK DE CH BE<br>East North South West<br>HU PL BG LV RO EE SI SK CZ UA RU DK FI SE NO PT ES IT GR IL CY AT NL FR IE UK DE CH BE<br>East North South West<br>Basic Secondary Higher<br>100<br>80<br>60<br>40<br>20<br>0<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Work status refers to the survey week Formal: Employee with a contract; Informal: Employee without a contract. Self-employed excluded Source: Calculation with ESS data

## **Figure 8 Formal and informal employees by educational attainment**

The contrast between skill composition of formal and informal employees is especially sharp in Poland, Bulgaria, Latvia, Denmark, Spain, Italy, Ireland and the Netherlands. However, it would be wrong to claim that generally informal employees in Europe are predominantly low-educated. In all countries except Poland, Hungary, Portugal, Spain, Italy, Austria, the Netherlands and France, three-fifths to nine tenths of those working without contracts have at least secondary Moreover, in Ukraine, Russia, Norway, Sweden, Israel, France, the UK and Belgium, 20 to 30% of informal employees hold university degrees, and in seven other countries this proportion is between 15% and 20%.

**_Occupations._** The generally lower educational attainment among informal employees results of course in a substantially different occupational composition than that found in the formal sector. Generally speaking, among informal employees one finds a much larger share of manual jobs and a much smaller share of highly-skilled non-manual jobs than among formal employees (Figure 9).

28
10 On the other hand, the goal of this study is to disaggregate the sources of income inequality as finely as possible. For this reason, it seems essential to clistinguish in rural Egypt between agricultural and livestock income, because these two income sources have very different effects on inequality. According to the data, the simple correlation between agricultural income and total income in Egypt is the highest of all five income sources: 0.844. By contrast, the simple correlation between livestock income and total income is one of the lowest: 0.232."6 It is unfortunate that data for livestock income in Jordan are lacking, in order to compare these results with those of Jordan.

Table 1 presents summary data for the various sources of income. The table shows quite clearly the importance of rural income other than agricultural income. In both countries, nonfarm income represents the single most important source of income, accounting for 42.2 and 50.6 percent of total rural household income in Egypt and Jordan, respectively. Although definitions of nonfarm income vary widely," these percentage figures for nonfarm income are comparable to those recorded in other studies. For example, a recent review of rural household budget surveys in 13 African, Asian and Latin American countries found that nonfarm income accounts for between 13 and 72 percent of total rural household income." The Gini coefficients of per capita rural income for Egypt and Jordan are 0.532 and 0.408, respectively. On the one hand, it is a bit surprising that the Gini coefficient for rural Egypt is much higher than that for rural Jordan. This large difference may reflect the absence of data on crop income in Jordan. In many countries crop income -- since it is dependent on land access -- is unequally distributed, and so the inclusion of this source of income would probably increase the Gini coefficient for rural Jordan.'<sup>9</sup> However, it should be noted that the Gini coefficients for Egypt and Jordan are well
[e-mail: IRRI@cav.pworld.net.ph)] Kerr, John Narayan, Deepa (Ed.) Paul, Samuel Rietbergen-McCracken, Jennifer and Deepa Narayan (Eds.) Spencer, Laura J.

Van Zyl, Johan, et al, 1995 Van Zyl, Johann; Sonn, Loretta; and Costa, Alberto World Bank World Bank World Bank World Bank Watershed Development, Environmental services and Poverty Alleviation in India World Development Vol 30 No. 8., 2002. EmRowerment and Poverty Reduction: A Sourcebook, The World Bank, Washington D.C. 2002.

Holding the State to Account: Citizen Monitoring in Action, Books for Change, Bangalore, India, 2002

[e-mail: bfc@actionaidindia.org)]. Participation and Social Assessment: Tools and Techniques, The World Bank, Washington D.C., 1998 Winning through Participation: The Group Facilitation Methods of the Institute of Cultural Affairs, Kendall/Hunt Publishing, Dubuque, Iowa, 1989.

Decentralized Rural Development and Enhanced Communitv Participation : a Case Study From Northeast Brazil, Policy Research working Paper 1498, The World Bank, Washmngton, D.C.

"Decentralized Rural Development, Enhanced Community Participation, and Local Government Performance: Evidence from Northeast Brazil". Draft Paper. July 2000.

Community Dnven Development: a Vision of Poverty Reduction through Empowerment. Washington, D.C., Dec. 2000. The World Bank Participation Sourcebook, Washington D.C., 1996 Village Level Participatory Approaches (VLPA). Washington, D.C., 2002 Project Appraisal Document on a Proposed Grant to the Repubhc of Niger for the Community Action Program. Jan 21, 2003.

43
across socio-demographic groups, except for pensioners, the elderly and the rural population which show a smaller negative impact of formalization, mostly because they are exempt from taxation (pensioners) or are less likely to be formal (rural population).

**Figure 8: Reform-1: Formalization of 50 percent of informal employees, no changes in taxation. Percentage change in consumable income w.r.t. no-reform scenario by decile, age group, household and location types.**

<!-- Start of picture text --> 1<br>0<br>-1<br>-2<br>-3<br>0<br>-1<br>-2<br>-3<br>Scenario A Scenario B Scenario C<br>Sour: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.<br><!-- End of picture text --> Table 6 summarizes the changes in the distributional and budgetary outcomes for reform 2 scenarios (i.e., the ‘tax maneuver 22/22’). First, if the informality rate remains unchanged (nobody formalizes), the tax maneuver would have a negative effect on the budget, resulting in a budget deficit increase equal to 0.9 percent of the GDP under scenarios A and C, and to 0.6 percent of the GDP under scenario B. As far as the population is concerned, the proposed tax changes are beneficial if we assume that the tax burden is fully shifted to workers (scenario B) and negative if the burden of labor taxes is partially or not borne by employees (scenarios C and A). In the latter case households lose due to increases in VAT, while under scenario B this loss is being offset by the reduced SIC rate. Second, Table 6 shows that formalization could indeed compensate for the fiscal losses due to the tax maneuver. However, under all scenarios, the budget starts to gain when at least 50 percent of all informal employees are moved to formal economy, which is relatively a high response to the tax maneuver (approximately 4.5 million people). However, a 50 percent formalization scenario seems to have little negative income effect only if we assume that all taxes are shifted to employees (scenario 23