rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table A.22. The district fixed-effect RDD-DD regressions of individual-level outcomes without control variables</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Self-<br>employed<br>farm work<br>(yes=1,<br>no=0)|Self-<br>employed<br>non-farm<br>work<br>(yes=1,<br>no=0)|Having<br>wage job<br>(yes=1,<br>no=0)|Log of<br>monthly<br>wage (all<br>workers)|The annual<br>number of<br>healthcare<br>visits|The annual<br>number of<br>outpatient<br>healthcare<br>visits|Currently<br>attending<br>school<br>(children<br>aged 6-17)|Log of<br>education<br>subsidy for<br>students|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program * Post-program period|-0.1087*|0.0571***|0.0401|0.6282**|0.3414**|0.3553**|0.0027|1.2549**|
||(0.0567)|(0.0171)|(0.0515)|(0.2876)|(0.1556)|(0.1419)|(0.0437)|(0.4825)|
|(Poverty rate – 50) * Post-program|0.0066|-0.0056***|-0.0045|-0.0407|-0.0554**|-0.0527**|0.0074|-0.0458|
|period|(0.0057)|(0.0020)|(0.0044)|(0.0275)|(0.0228)|(0.0218)|(0.0049)|(0.0529)|
|Program * (Poverty rate – 50) * Post-|-0.0024|0.0044**|0.0033|0.0066|0.0529**|0.0498**|-0.0104**|0.0492|
|program period|(0.0062)|(0.0021)|(0.0049)|(0.0306)|(0.0232)|(0.0221)|(0.0051)|(0.0558)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.7368***|0.0378***|0.0819***|1.3837***|0.6064***|0.5057***|0.8226***|1.0583***|
||(0.0059)|(0.0018)|(0.0045)|(0.0329)|(0.0073)|(0.0069)|(0.0035)|(0.0418)|
|Observations|128,449|128,449|128,449|128,449|86,983|86,983|50,645|50,645|
|R-squared|0.125|0.039|0.132|0.088|0.062|0.064|0.061|0.304|

Note: This table reports district fixed-effect RDD-DD regressions of individual-level outcomes. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

72
|PPR<br>Title|Working Paper Series<br>Author|Date|Contact|
|---|---|---|---|
|WPS19 Cote d'lIvoire's<br>Vocational<br>and<br>Technical Education|Christiaan<br>Grootaert|<br>June1988|R. Vartanlin<br>34678|
|WPS20 Imports and Growth In Africa|Ramon Lopez<br>Vinod Thomas|June1988|61679|
|WPS21Effects of European VERs on Japanese<br>Autos|Jaime de Melo<br>Patrick Messerlin|June1988|S.Fallon<br>61680|
|WPS22Methodological<br>Problems in Cross-<br>Country Analyses of Economic Growth|Jean-Paul Azam<br>Patrick Guillaumont<br>Sylviane Guillaumont|June1988|E. Zamora<br>33706|
|WPS23Cost-Effective<br>Integration<br>of<br>Immunization<br>and Basic Health Services<br>in Developing<br>Countries: The Problem<br>of Joint Costs|A. Mead Over, Jr.|July1988|N.Jose<br>33688|
|WPS24World Bank Investments<br>in Vocational<br>Education and Training|John Middleton<br>Terri Oemsky|July1988|W.Ketema<br>33651|
|WPS25AComparison<br>of Alternative Training<br>Modes for Youth in Israel: Results<br>from Longitudinal<br>Data|Adrian Ziderman|July1988|W.Ketema<br>33651|
|WPS26 Chdnging Patterns In Vocational<br>Education|John Middleton|July1988|W.Ketema<br>33651|
|WPS27Family Background and Student<br>Achievement|Marlalne E. Lockheed<br>Bruce Fuller<br>Ronald Nyirongo|July1988|R.Rinaldi<br>33278|
|WPS28Temporary Windfalls and Compensation<br>Arrangements|Bela Balassa|June1988|N.Campbell<br>33769|
|WPS29The Relative Effectiveness<br>of<br>Single-Sex<br>and Coeducational<br>Schools<br>In Thailand|Emmanuel Jimenez<br>Marlaine E. Lockheed|||
)) = f�β0 + β1Xmit + β2Zmi(t−1) + β3Tmi(t−1) � (1) mm mm mm mm cc logit (Pr(YY = 1|XX , ZZ , TT + δδ

The statistical analysis proceeds in several steps. We first start with educational aspirations (m=1) and estimate the probability of university or post-degree aspirations (Y1i) by country, and within countries in a set of logit regressions for exploring its correlation with (1) youth’s characteristics (X1it), i.e., sex, age group, highest level of education, age at which they started working, type of dream job; (2) household characteristics Z1i(t−1), i.e., age of the oldest child of the household, highest level of education and current occupation of the adult head of the household, and income quintile; and finally (3) community characteristics T1i(t−1), i.e., place of residence (urban and rural) and role models in the community. We observe the independent associations of each set of personal, family and community variables with educational aspirations. are fixed effects at the country level. cc δδ We extend this step analysis to explore career and migration aspirations. Regarding career aspirations (m=2), we estimate the probability of reporting a STEM-related occupation as dream job<sup>6</sup> (Y2i) by country, and within countries in a set of logit regressions using individual-level weights for exploring its correlations with (1) youth’s characteristics X2i(t−1), i.e., sex, age group, educational level, age at which started working, interest in the job and youth likelihood to reach the dream job (self-efficacy); (2) household characteristics Z2i(t−1), i.e., household size, adult head of the household engaged in STEM-related jobs, and income quintile; and finally (3) community characteristics T2i(t−1), i.e., place of residence (urban and rural), highly regarded job by friends and parents and role models in the community.

Lastly, we explore how personal characteristics, household characteristics and exposure to migration correlates with the desire to migrate (Y3i), in a set of multivariate logit regressions (m=3). In addition, we explore the correlation of youth’s characteristics X3i(t−1), household characteristics Z3i(t−1), and exposure to migration with the desired location selected by the youth that expressed their desire to migrate T3i(t).

For all set of logit regressions, household and community data are obtained from previous LSMSISA pre-COVID-19 surveys in Ethiopia, Malawi, and Nigeria. Standard errors are clustered at the enumeration-areas (EA) level.

# **4. Descriptive Results**

# **4.1. Educational levels and aspirations**

> 6 The STEM-related occupations are defined following the PISA25 definition that includes science and engineering professionals, health professionals, science technicians and associate professionals, and formation and communication technology (ICT) professionals (OECD, 2021).

8
# **Guide to the Services Trade Restrictions Database**

Ingo Borchert, Batshur Gootiiz and Aaditya Mattoo* _JEL Classification_ : F13, F14, L80 _Keywords_ : Services trade barriers, quantification, trade policy patterns.

*World Bank, 1818 H Street NW, Washington DC 20433.  Contact information: I.Borchert@sussex.ac.uk, <u>bgootiiz@worldbank.org, and amattoo@worldbank.org.  The authors would like to thank Rudolf Adlung, Nora Dihel,</u> Ana M. Fernandes, Shushanik Hakobyan, Bernard Hoekman, Aart Kraay, Hildegunn Nordås, Sebastian Saez and David Tarr and the World Bank‟s Council of Chief Economists, for many helpful suggestions.  We are grateful to Thelma Choi, Samantha De Martino, Yinuo Geng, Anna Rakhman, Nadia Rocha and, especially, Christine Lee, who contributed significantly to the creation of the database, and to Qinghua Zhao, who designed the website.  This paper is part of a World Bank research project on trade in services supported in part by the governments of Norway, Sweden, and the United Kingdom through the Multidonor Trust Fund for Trade and Development, and by the UK Department for International Development (DFID).  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, or any of the aforementioned individuals or institutions.
- 11 -

!2erational Categories There are two basic types of trading system-order driven and quote driven; three methods of determining price-continuous auction, call (or single) auction, and non-auction (or dealer market); and two possible physical arrangements-centralized or distributed (see Box 4).

While the different categories have historically been regarded as separate and, to some degree in competition, the trend now is towards integrated and hybrid systems. This trend means that market supervisors often have to consider IT policy questions concerning partial computerization and the interaction between different parallel and linked systems. The matrix in Figure 1 illustrates the permutations possible from combining the various operational categories. Many of the combinations indicated are in operation now. For example: the Korea Stock Exchange, like most continuous auction markets, opens with a call auction to clear the pre-session order build up; the NASDAQ system is an evolution from the traditional dealer market into a form of auction market because of the competition between market makers built into the NASDAQ system; and the New York Exchange switches to distributed network computer trading after the close of each day's floor trading session.

# Box Operational Categorieg

The quotes which drive the trading process may be the bid and offer prices specified in investors' orders or they may be the spread quoted by market makers. The first is called an order driven market, the second, a quote driven market.

Each may be conducted as either a continuous auction in which quotes are constantly adjusted in line with new orders coming to market, or a call auction in which the quotes collected in a trading period are executed all at once at a price calculated to clear the maximum number of orders. Another alternative is a quote driven non-auction (or dealer market) in which one market maker has exclusive right to quote a buy and sell spread for a stock and sets the quote without competition.

The physical arrangement of the market may be centralized, on a trading floor for example, or it may be distributed via a network of computer terminals. In either case, the objective is to bring together the buy and sell quotes by some means which will allow them to interplay as a price setting mechanism.
seasonality) are included in the analysis (see Beegle et al 2016). Table 1 provides a list of the microdata used in this study.

We obtain monthly national CPI data disaggregated into food and non-food components from the respective NSOs. In terms of geographical coverage, all countries, except Nigeria and Rwanda, only collect prices in urban areas. In other words, the national CPIs in these countries reflect the prices paid by urban residents. Conversely, the national CPIs in Nigeria and Rwanda reflect both rural and urban prices. In terms of the availability of cross-sectional CPIs, regional CPI data are available for only four countries – Ethiopia, Ghana, Mozambique, and Uganda. These data allow us to estimate the CPI-bias based on equation (4). The remaining countries only have national CPIs, and we are therefore required to apply equation (10) for the CPI-bias estimation. Table 2 lists the CPI data used in this study.

Detailed expenditure data are aggregated into two broad groups: food expenditure and total expenditure. Food expenditure is defined as expenditure for the consumption of food, nonalcoholic beverages, alcoholic beverages, and tobacco from own-production, market purchase, and gifts. Total household expenditure includes spending on food and non-food expenditure, such as clothing, housing, household furnishings and equipment, health and personal care, education, transport, communications, recreation, and miscellaneous goods and services. We then merge the consumption data for each household with the monthly CPI during the period when the household was interviewed. This allows for temporal variations in inflation patterns.

We use several criteria to select our analysis sample. First, to minimize the effect of extreme outliers, which may indicate data quality problems, households with a food budget share of less than 2 percent or greater than 95 percent are excluded. Second, to render household demographics more homogeneous, we exclude households in which the household head is younger than 21 years of age or older than 75 years of age. These restrictions are similar to those used by Costa (2001), Gibson, Stillman, and Le (2008) and Hamilton (2001). Finally, rural households in all the countries in this study, except Mauritius, Nigeria, and Rwanda, are dropped because the CPIs in these countries reflect prices faced by urban populations only.<sup>12</sup> Because of this, our estimates of CPI

> 12 While the national CPIs for Mauritius reflects urban prices only, the Household Budget Survey for Mauritius does not identify households’ location – either urban or rural areas. Therefore, all households must be included for the estimates of CPI bias in the case of Mauritius.

13
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1006 Preparing Multiyear Raiiway<br>Investment Plans: A Market-Oriente<br>Approach|Jorge M. Rebelo<br>d|October 1992|A. Turner<br>33933|
|WPS1007 Global Estimates and Projections<br>of Mortality by Cause, 1970-2015|Rodolfo A. Bulatao<br>Patience W. Stephens|October 1992|0. Nadora<br>31091|
|WPS1 008 Do the Poor Insure? A Synthesis of<br>the Literature on Risk and<br>Consumption in Developing Countrie|Harold Alderman<br>Christina H. Paxson<br>s|October 1992|C. Spooner<br>32116|
|WPS1 009 Labor and Women's Nutrition:<br>A Study of Energy Expenditure,<br>Fertility, and Nutritional Status in Gha|Paul A. Higgins<br>Harold Alderman<br>na|October 1992|C. Spooner<br>32116|
|WPS1010 Competition and Efficiency in<br>Hunqarian Banking|Dimitri Vittas<br>Craig Neal|October 1992|W. Pitayatonakarn<br>37664|
|WPS1 011 How Tax Incentives Affect Decisions<br>to Invest in Developing Countries|Robin Boadway<br>Anwar Shah|November 1992|C. Jones<br>37754|
|WPS1012 The Brady Plan, the 1989 Mexican<br>Debt Reduction Agreement, and Bank<br>Stock Returns in the United States<br>and Japan|Haluk Unal<br>Asli Demirgii-Kunt<br>Kwok-Wa. Leung|November 1992|W. Patrawimolpon<br>37664|
|WPS1013 The Impact of Mexico's Retraining<br>Program on Employment and Wages|Ana Revenga<br>Michelle Riboud<br>Hong Tan|November 1992|D. Young<br>30932|
|WPs1014 Ethnicity, Education, and Earnings<br>in Bolivia and Guatemala|George Psacharopoulos|November 1992|L. Longo<br>39244|
|WPS1015 Benefit Incidence Analysis in<br>Developing Countries|Thomas M. Selden<br>Michael J. Wasylenko|November 1992|C. Jones<br>37754|
|WPS1 016 Europe and Central Asia Region,<br>Middle East and North Africa Region<br>Population Projections, 1992-93<br>Edition|My T. Vu<br>Eduard Bos<br>Ann Levin|November 1992|0. Nadora<br>31091|
|WPS1017 Rural Poverty, Migration, and tha<br>Environment in Developing Countries<br>Three Case Studies|Richard E. Bilsborrow<br>:|November 1992|WDR<br>31091|
|WPS1018 Tariff and Tax Reform: Do W'orld<br>Bank Recommendations Integrate<br>Revenue and Protection Objectives?|Anand Rajaram|November 1992|P. Pender<br>37851|
to provide a signal to the market about the future prospects of the splitting firm, as some theoretical papers suggest. Taken together, these two findings would imply that the transaction costs channel is the main channel through which a stock split could affect the average holding period of households.

Consistent with the prior literature, we first verify that transaction costs decrease (stock liquidity increases) subsequent to stock splits in our sample time-period. Then we show that investors’ average holding period declines in response to the increase in liquidity following stock splits. Our analysis suggests that the holding period for a stock decreases by about 37 trading days, or nearly two trading months after a stock-split. This finding is economically significant as the mean holding period, defined as the number of trading days from the first purchase of a stock to the first sale, is 185 days.

The remainder of this paper is organized as follows. Section 2 presents the hypotheses tested in the paper. Section 3 describes the individual transaction data sets and the construction of the main liquidity variables used in this study. Section 4 reports our main findings using US trades that show the relationship between transaction costs and holding periods. Section 5 studies the implications of liquidity decisions of individual investors on their investment performance, and empirically documents that households who pay closer attention to transaction costs earn higher raw and characteristics-adjusted excess returns. Section 6 provides robustness tests to address concerns that holding periods are determined endogenously and also uses individual transactions from Finland as an out-of-sample test to verify US results. Section 7 concludes.

# **2. Hypotheses**

Amihud and Mendelson (1986) develop a model where risk neutral investors with different exogenous holding periods and limited capital trade securities are subject to fixed transaction costs.

7
**Table 1: Description and summary statistics of the main variables used in this paper, household level, 2010-2013, rural Malawi**

|Variables|Definition|Types of variables|Overall<br>Mean|SD|N|Estimation approach|
|---|---|---|---|---|---|---|
|**Treatment variables**|||||||
|Non-farm wage employment<br>(0/1)|A member of household is engaged in non-farm wage employment||15.11|35.82|5532|GEE CRE Probit|
|Non-farm self-employment|A member of household is engaged in non-farm self-employment||21.58|41.14|5532|GEE CRE Probit|
|(0/1)|||||||
|**Outcome variables**|||||||
|HHPCE|Household total real consumption expenditure per capita|Continuous|57,759.59|46,702.55|5532|FE, CRE, Quantile regression|
|Log of HHPCE|Log of Household total real consumption expenditure per capita|Continuous|10.75|0.64|5532|FE, CRE|
|Poverty incidence|1[HHPCE<=Poverty Line]|Binary (0/1)|37.20|48.34|5532|GEE CRE Probit, Linear FE|
|Poverty gap|Consumption shortfall relative to poverty line as a fraction of poverty line|Fractional [0, 1]|11.2|18.8|5532|GLM CRE FRM, Linear FE|
|Poverty severity|Squared poverty gap|Fractional [0, 1]|0.05|.11|5532|GLM CRE FRM, Linear FE|
|Food insecurity (0/1)|Did you worry that your household would not have enough food in the past<br>7 days|Binary (0/1)|32.57|46.87|5532|GEE CRE Probit, Linear FE|
|Food consumption adequacy|Which of the following describes best your food consumption over the past<br>month|Categorical ordered<br>{1,2,3}|||5532|ML CRE Ordered Probit,<br>Linear FE|
|1|1== less than adequate for household needs||41.45|49.27|||
|2|2== just adequate for household needs||51.93|49.97|||
|3|3== It was more than adequate for household needs||6.62|24.86|||
|Fertilizer purchase decision<br>|The household purchased fertilizer for agricultural production in the rainy|Binary (0/1)|38.85|48.75|5305|GEE CRE Probit, Linear FE,<br>|
|(0/1)|season|||||system ML|
|Inputs purchase decision (0/1)|The household purchased seeds, fertilizer or other chemicals for<br>agricultural production in the rainy season|Binary (0/1)|60.13|48.97|5305|GEE CRE Probit, Linear FE,<br>system ML|
|Fertilizer purchase per acre<br>(1000MKW)|Value of fertilizer purchased per acre of land cultivated|Corner solution [0, +)|3.59|9.79|5305|CRE Tobit, Linear FE|
|<br>Inputs purchase per acre<br>(1000MKW)|Total Value of seed, fertilizer, and chemicals purchased per acre of land<br>cultivated|Corner solution [0, +)|4.10|10.01|5305|CRE Tobit, Linear FE|
|Land cultivated (acres)|Number of acres of cultivated land by household|Corner solution [0, +)|4.43|52.31|5305|CRE Tobit, Linear FE|

Source: Generated by authors using LSMS data.

26
Table 3: Restricted DPR Specifications

|||mean|80th pctile|90th pctile|98th pctile|
|---|---|---|---|---|---|
|_β_1 :|_H ×_log_θ_|0.213***|0.176***|0.157***|0.185***|
|||(0.0516)|(0.0436)|(0.0402)|(0.0453)|
|_R_<sup>2</sup>||0.642|0.641|0.641|0.643|

_Notes:_ See notes to Table 2. Columns 1-4 correspond to, respectively, columns 1-4 of Table 2 with the restriction _βH_ = _−βT_ imposed.

– interacted with the head of channel dummy ( _Hpc_ ). These controls the total number of outlets on the channel, whether the channel is on the tail of its parent channel (versus at middle or head), whether the channel is a distributary (versus a minor or sub-minor), and a full set of 23 division dummies (see Appendix Table C.1) – are exogenous determinants of head-tail differences in DPR or land values. For example, these variables might capture differences in the extent of flow variability into the channel, as discussed earlier.

The main takeaways from Table 2 are as follows. First, all four forms of _G_ do about equally well in fitting both the canal discharge and the land values data, with a slight preference for the 98th percentile. Second, in all cases, the estimates of _βH_ and _βT_ have the predicted sign. For instance, when the top 2% of family landownership at the head (tail) is larger (smaller), head-tail inequity in canal water availability and in land values worsens, in the sense that head outlets become even more favored. Moreover, the hypothesis of symmetry ( _βH_ = _−βT_ ), established in Proposition 3, cannot be rejected in any specification. Finally, in the case of DPR, this symmetry test has considerable power, which is to say that nonrejection is noteworthy. In particular, we are able to strongly reject the joint null that _βH_ = _βT_ = 0. By this same metric, however, power is substantially lower in the case of land values, an issue revisited in the next subsection.

28
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