rafmacalaba/datause-encoder-data huggingface dataset:
10 Economic Forum Survey on employers, who were asked to rate a country’s quality of education using a scale of 1 to 7 (see Figure 5). In both mathematics and science education as well as in management schools, educational quality in Thailand is well below regional counterpart institutions. The quality of education in Malaysia was perceived as comparable to Korea and Hong Kong SAR, China. There are drawbacks to these indicators, however, as they lack a standard for measurement, rely heavily on subjective judgments and are a small sample. Employer surveys provide some insights into how the supply of skills have been matched to the demand, but more reliable measurements are needed to provide an accurate understanding of the basic skills that an individual needs for success.

**Figure 5: Quality of Education as Perceived by Employers**

<!-- Start of picture text --> Quality of the Math & Science Education, 1-7 (best) Quality of Management Schools, 1-7 (best)<br>7 7<br>6 6<br>5 5<br>4<br>4<br>3<br>3<br>2005 2006 2007 2008 2009 2010 2011<br>2005 2006 2007 2008 2009 2010 2011<br>Korea, Rep. Malaysia Thailand Malaysia Thailand Hong Kong<br>Singapore Korea<br>Singapore Hong Kong<br><!-- End of picture text --> <!-- Start of picture text --> Source: World Economic Forum<br><!-- End of picture text -->

# _The economic benefits of human capital_

The economic benefits of human capital can be estimated from education produced in schools or elsewhere (the home, for example). There are both private – accruing to the individual in the form of better jobs, higher wages, for example – and social – meaning they affect not only the recipient of education but others as well – benefits. Better educated mothers have lower fertility rates while parental education raises children’s schooling level and scholastic achievement. Parental education also improves child health. More schooling in general improves one’s health and life expectancy. There are also a number of social benefits, such as technological change and diffusion, social cohesion and reduced criminal activity (Psacharopoulos and Patrinos 2008).

The market benefits of human capital can be captured in rate-of-return calculations estimated from wages reported by workers in nationally representative surveys. Thus, they are based on the wages more and less educated people command in the labor market. Such estimates also provide proxy indicators of shortages of quality education. Low-income countries tend to yield higher rates of return to schooling reflecting skilled labor shortages. The average rates of return to each additional year of secondary schooling are 20, 18 and 12 percent for low-income,
||**Table **<br>F<br>Cr|**6. Firms **<br>(1)<br>emale<br>ossover|**Characte**<br>Fem<br>cro|**ristics**<br>(2)<br>ale non-<br>ssover|<br>Male<br>domina|(3)<br>in male<br>ted sectors|t-test<br>(1)-(2)|t-test<br>(1)-(3)|
|---|---|---|---|---|---|---|---|---|
|Variable|N|Mean/SE|N|Mean/SE|N|Mean/SE|Difference|Difference|
|Number of hours work per week|1907|38.53<br>[0.610]|9934|36.09<br>[0.263]|7833|40.03<br>[0.314]|2.433***|-1.509**|
|Number of years work in this sector|2900|4.50<br>[0.067]|15448|5.27<br>[0.030]|10833|4.28<br>[0.034]|-0.768***|0.223***|
|Share of female workers|3237|74%<br>[0.006]|17264|87%<br>[0.002]|12362|61%<br>[0.004]|-0.127***|0.126***|
|Line of credit or loan from a bank or financial|2328|31%|12474|24%|8987|28%|0.064***|0.027**|
|institution||[0.010]||[0.004]||[0.005]|||
|_Ownership_|||||||||
|More than one owner|2831|63%<br>[0.009]|14507|43%<br>[0.004]|10285|38%<br>[0.005]|0.203***|-0.249***|
|Single owner|2831|37%|14507|57%|10285|62%|-0.203***|0.043***|
|||[0.009]||[0.004]||[0.005]|||
|_Challenges in your community_|||||||||
|A lack of opportunities for women and girls|1957|25%<br>[0.010]|10157|25%<br>[0.004]|7831|21%<br>[0.005]|-0.002|0.043***|
|Not enough job prospects|1957|51%<br>|10157|54%<br>|7831|51%<br>|-0.030**|0.002|
|||[0.011]||[0.005]||[0.006]|||
|_Where did most of the starting money orfunding for_|_this bus_|<br>_iness comef_|_rom?_||||||
|I'm not sure|2652|0.158|13891|0.132|18538|0.149|0.026***|0.008|
|||[0.007]||[0.003]||[0.004]|||
|Group savings|2652|0.054|13891|0.055|18538|0.047|-0.001|0.007|
|||[0.004]||[0.002]||[0.002]|||
|Loan from a bank or financial institution|2652|0.129|13891|0.133|18538|0.119|-0.003|0.011|
|||[0.007]||[0.003]||[0.003]|||
|Loan from relatives or friends|2652|0.063|13891|0.089|18538|0.079|-0.025***|-0.015***|
|||[0.005]||[0.002]||[0.003]|||
|Family inheritance|2652|0.092|13891|0.064|18538|0.087|0.028***|0.005|
|||[0.006]||[0.002]||[0.003]|||
|Spouse or partner's savings|2652|0.070|13891|0.080|18538|0.023|-0.010*|0.047***|
|||[0.005]||[0.002]||[0.001]|||
|Own savings|2652|0.400|13891|0.398|18538|0.466|0.001|-0.066***|
|||[0.010]||[0.004]||[0.005]|||
|Other|2652|0.034|13891|0.050|18538|0.030|-0.016***|0.003|
|||[0.003]||[0.002]||[0.002]|||

The value displayed for t-tests are the differences in the means across the groups.

18
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# **6 REFERENCES**

Abduh, M. (2020). Volatility of Malaysian conventional and Islamic indeces:does financial crisis matter? _Journal of Islamic Accounting and Business Research, 11_ (1), 1-11.

- Adeel, N., Patel, C., Martinov-Bennie, N., & Ying, S. X. (2022). Islamic Religiosity and Auditors’ Judgements: Evidence from Pakistan. _Journal of Business Ethics, 179_ (1), 551-572.

- Adhikari, B., & Agrawal, A. (2016). Does local religiosity matter for bank risk-taking? _Journal of Corporate Finance, 38_ (1), 272-293.

- Alam, N., Arshad, S., & Rizvi, S. A. R. (2016). Do Islamic stock indices perform better than conventional counterparts? An empirical investigation of sectoral efficiency. _Review of Financial Economics_ , _31_ , 108-114.

- Anglin, A. H., Milanov, H., & Short, J. C. (2022). Religious Expression and Crowdfunded Microfinance Success: Insights from Role Congruity Theory. _Journal of Business Ethics_ , 1-30.

- Barreda-Tarrazona, I., Matallín-Sáez, J., & Balaguer-Franch, R. (2010). Measuring Investors’ Socially Responsible Preferences in Mutual Funds. _Journal of Business Ethics, 103_ (1), 305-330.

- Benson, K. L., & Humphrey, J. E. (2008). Socially responsible investment funds: Investor reaction to current and past returns. _Journal of Banking & Finance, 32_ (9), 1850-1859.

- Besma, H., Hammoudeh, S., Aloui, C., & Yarovaya, L. (2017). Are Islamic indexes a safe heaven for investors? An analysis of total, directional and net volatility spillovers between conventional and Islamic indexes and importance of crisis periods. _Pacific-Basin Finance Journal, 43_ (1), 124-150.

- Blau, B. M. (2017). Religiosity and the Volatility of Stock Prices: A Cross-Country Analysis. _Journal of Business Ethics, 144_ (1), 609-621.

- Bollen, N. P. (2007). Mutual Funds Attributes and Investors Behaviour. _Journal of Financial and Quantitative Analysis, 42_ (3), 683-708.

- Bushra, N., Rizvi, S., Nawazish, M., & Reddy, K. (2018). Religion based investing and illusion of Islamic Alpha and Beta. _Pacific Basin Finance Journal, 52_ (1), 82-106.

- Callen, J. L., & Fang, X. (2015). Religion and Stock Price Crash Risk. _Religion and Stock Price Crash Risk, 50_ (1), 169-195.

- Charness, G., Gneezy, U., & Imas, A. (2013). Experimental methods: Eliciting risk preferences. _Journal of Economic Behaviour & Organization, 87_ (C), 43-51.

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- Chircop, J., Johan, S., & Tarsalewska, M. (2020). Does religiosity influence venture capital investment decisions? _Journal of Corporate Finance, 62_ (1), 1-14.

- Choi, J. J., & Robertson, A. Z. (2020). What Matters to Individual Investors? Evidence from the Horse’s Mouth. _Forthcoming Journal of Finance_ .

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emerges from the restricted sample. Together, the IHS4-based estimates suggest that bequest rights have either no effect or negative effect on investment, indicating systematic misreporting of bequest rights.

# **5. Conclusion and policy implications**

We use the example of Malawi to show that, even in customary settings where formal documentation is absent, the nature and extent to which women and men have rights to land vary widely. Variation in bequest and to some extent sale but not in other rights systematically affects land-based investment. Yet, standard household surveys may fail to capture such variation due to measurement error arising from reliance on proxy respondents rather than interviewing land owners themselves. We conclude by drawing out implications from our findings for research and policy.

An important area for follow-up research is to assess the extent to which the findings presented here can be generalized beyond Malawi. Doing so would allow exploring if a failure to either include information on bequest rights or to collect such information directly from (co-)owners can explain the lack of clear results on the impact of individual land rights in the literature. Also, if results similar to what was presented here are found in other countries, getting information on the right to bequeath or sell land from concerned individuals at least for a subsample of national household surveys will be important. Beyond identifying the impacts of specific land rights on socio-economic outcomes, this would provide a basis for understanding of changes in individuals’ rights in response to exogenous factors.

Our findings are relevant for Malawi in two respects. First, as most of our data were collected before the 2016 Land Act was assented to by the President in January 2017 and awareness of the specific provisions of the Act remains low, subsequent IHPS (or IHS) waves can help ascertain if passage of the Land Act, in particular its far-reaching modifications of inheritance patterns, affected individuals’ land rights and investment in agricultural and residential land. Second, defining customary estates to implement the Land Act will require adjudication of rights. Our analysis suggests that strategic motives can bias responses even in household surveys where stakes are low. To prevent such activities systematically disempowering weaker groups, information used to adjudicate rights in potential demarcation exercises will thus have to be carefully validated and cross-checked.

Beyond Malawi, the fact that investment impacts associated with bequest and transfer rights under informal regimes are of a magnitude that is comparable to what is often ascribed to formal title in the literature implies that greater attention ought to be devoted to designing and evaluating such schemes, including changes in laws or regulations for inheritance. This does not negate the importance of formal registries to facilitate impersonal transactions and use of land as collateral but recognizes that, as centralized registries 14
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choice of policy objectives. The results of table 4, also depicted in Graphs 3 and 4, show that, to the extent that the World Bank growth and inflation projections are consistent, the stabilization objective can be achieved during 1988-1991 with a lower rate for both inflation and growth. This is true both on average, and on a year-to-year basis. In other words, if taken literally, the Bank projections suggest that the performance of the Kenyan economy in the next four years will result in a substantial decline of its debt/GDP ratio. From the methodological point of view, the "warranted' growth and inflation rates of table 4 set the benchmarks against which any behaviorally consistent combination of those variables can be weighed.

# 3. GHANA

# a. Background

Both adverse external developments and prolonged inappropriate domestic policies caused an extended deterioration in the Ghanaian economy during the late 70s and early 80s. The effects of a worsening in the external terms of trade, a succession of severe droughts and the influx of emigrant workers returning from Nigeria were compounded by a highly overvalued exchange rate and misaligned domestic prices. By 1982, real per capita income was lower than in 1970 by 30 percent, real export earnings by 52 percent, and import volumes by 30 percent.
# **Table 12. Effect of preschool availability on female’s work participation by birth order**

||||**Work Par**<br>|**ticipation**<br>|||
|---|---|---|---|---|---|---|
|Child’s birth order:|(1)<br>First|(2)<br>Second|(3)<br>Third|(4)<br>Fourth|(5)<br>Fifth|(6)<br>Sixth|
|**Panel A: Public**|||||||
|Public * Eligible|0.075***|0.090***|0.058**|0.026|-0.023|-0.052|
||(0.020)|(0.024)|(0.026)|(0.036)|(0.043)|(0.071)|
|Public Preschools|-0.003|-0.010|-0.010|-0.029|-0.019|-0.054|
||(0.012)|(0.014)|(0.014)|(0.022)|(0.032)|(0.044)|
|Eligible Child|-0.046***|-0.023***|-0.007|0.011|0.005|0.014|
||(0.006)|(0.006)|(0.007)|(0.010)|(0.012)|(0.018)|
|FDR q-value|0.001|0.001|0.095|0.587|0.587|0.587|
|**Panel B: Private**|||||||
|Private * Eligible|0.002|0.004**|-0.002|-0.002|-0.005|-0.004|
||(0.002)|(0.002)|(0.002)|(0.003)|(0.005)|(0.007)|
|Private Preschools|-0.001|-0.001|-0.001|-0.000|0.004|0.006|
||(0.002)|(0.002)|(0.002)|(0.003)|(0.005)|(0.006)|
|Eligible Child|-0.046***|-0.026***|0.010|0.022|0.017|0.019|
||(0.009)|(0.009)|(0.011)|(0.015)|(0.019)|(0.025)|
|FDR q-value|0.548|0.135|0.581|0.581|0.581|0.581|
|Observations|226,400|187,376|110,371|57,258|28,857|15,460|
|Mean|0.520|0.526|0.538|0.550|0.555|0.558|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data inbetween PODES years. Each column regresses the work participation on a dummy that first child (or second, third, and so on) is preschool-aged eligible, the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 5 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. Number of observations fall with higher child’s birth order because sample is further restricted to mothers with at least as many children as the indicated birth order.

57
**Box 3: Governance Indicators: Togo and Benchmark Countries**

<!-- Start of picture text --> 0.75 Voice & Accountability 0.35 Government Effectiveness<br>Ghana<br>0.25 Benin 0.00<br>Senegal<br>‐0.35<br>‐0.25 MIC<br>Burkina Faso Nigeria ‐0.70<br>‐0.75 SSA SSA<br>‐1.05 Nigeria<br>‐1.25 Togo<br>‐1.40 Togo<br>‐1.75 ‐1.75<br>2000 2002 2003 2004 2005 2006 2007 2008 2000 2002 2003 2004 2005 2006 2007 2008<br>Political Stability Regulatory Quality<br>0.35<br>0.75<br>Benin<br>Ghana<br>0.00<br>0.25<br>Ghana Senegal MIC<br>Burkina Faso<br>‐0.35<br>‐0.25 MIC Burkina Faso<br>Senegal Benin<br>‐0.75 ‐0.70 SSA<br>SSA<br>‐1.25 Togo ‐1.05 Togo<br>Nigeria<br>‐1.75 ‐1.40<br>‐2.25 ‐1.75<br>2000 2002 2003 2004 2005 2006 2007 2008 2000 2002 2003 2004 2005 2006 2007 2008<br>Rule of Law<br>0.35 Control of Corruption<br>0.35<br>Burkina Faso<br>0.00 0.00 Ghana<br>Senegal Senegal<br>MIC<br>‐0.35 Benin ‐0.35<br>Benin<br>‐0.70 SSA ‐0.70<br>‐1.05 Togo ‐1.05 Togo<br>‐1.40 ‐1.40 Nigeria<br>Nigeria<br>‐1.75 ‐1.75<br>2000 2002 2003 2004 2005 2006 2007 2008 2000 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source_ : Governance Database, World Bank.

**The ownership structure and management of state owned enterprises (SOEs) have also been a reflection of the poor quality of governance.** The phosphate sector is operating at less than a quarter of its capacity and IMF (2009a) argues that GDP would increase by as much as 2% if full capacity is reached. The phosphate society OTP/IFG has been replaced by the Societe Nouvelle des Phosphates du Togo (SNPT), but the authorities have so far resisted proposals to identify a new strategic partner which will be needed to bring in the necessary capital and technical expertise. The main cotton company (SOTOCO) was highly criticized and has now been replaced by a new company in which the producers hold 40% of the capital. However, here too the government has delayed plans to

45
# **Appendix Table 2: Hedonic Price Regression Results, Urban Kigali, Spatial Lag Model**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Log of parcel area in sqm|0.622***<br>(0.009)|0.610***<br>(0.009)|0.524***<br>(0.009)|0.514***<br>(0.009)|
|Log distance from the village to the CBD in km|-0.108***<br>(0.014)|-0.037**<br>(0.016)|-0.203***<br>(0.065)|-0.108***<br>(0.015)|
|Log number of workers at the cell level in 2014|0.128***<br>(0.009)|0.095***<br>(0.009)|0.070***<br>(0.019)|0.049***<br>(0.009)|
|Log of residential land at the cell level|-0.101***|-0.049|0.010|0.040***|
||(0.010)|(0.011)|(0.029)|(0.027)|
|Log distance to tarmac road in meters||-0.119***<br>(0.006)||-0.096***<br>(0.006)|
|Log distance to a primary school in meters||-0.052***<br>(0.010)||-0.023<br>(0.016)|
|Log volume of buildings in m3|||0.175***<br>(0.005)|0.171***<br>(0.005)|
|Density of built up area of the village|||1.391***<br>(0.100)|1.142***<br>(0.100)|
|Log average height of buildings at the village in meters|||0.721***<br>(0.106)|0.825***<br>(0.117)|
|Log SD of building height at the village in meters|||-0.076*<br>(0.039)|-0.150***<br>(0.045)|
|Constant|-0.802***|0.523***|-1.551***|-0.589*|
||(0.151)|(0.189)|(0.234)|(0.386)|
|Number of observations|12.592|12,592|12,592|12,592|
|Log Likelihood|-14,852.420|-14,645.330|-14,056.620|-13,923.010|
|Wald Test (df = 1)|5,324.495***|2,989.083***|1,661.369***|1,025.168***|
|Rho|0.770***|0.673***|0.576***|0.507***|

_Note:_ Dependent variable is the log of the sales price per parcel for residential properties in Kigali in the 2013-16 period. The spatial weights are based on threshold distance of 435m at which each property has at least one neighbor. Alternative distance band weights (750m, 1,000m, 1,500m and 5,000m) were used, but the one of 500m produced better goodness of fit using the likelihood ratio and AIC criteria. Standard errors in parenthesis: *** significant at 1%; ** significant at 5%; * significant at 10%. The estimation is done using ‘spdep’ package of R.

28
Garrote Sanchez, Gomez Parra, Ozden, and Rijkers (2020) apply DN2020’s occupational measures in combination with indicators of essential sectors in European regions as determined and mandated by governments. They find that the possibility to work from home is lower for poorer workers and in less developed regions, but both studies fail to consider practical constraints to working from home.

Several papers have followed an approach different from DN2020 to identify the jobs that can be done at home. For instance, Hensvik, Le Barbanchon, and Rathelot (2020) use data from the American Time Use Survey (ATUS) and estimate the prevalence of home-based-work between 2011 and 2018. Alipour, Falck, and Schüller (2020) use data from a 2018 employment survey for Germany that includes a question on whether the worker would accept an offer from his or her employer to work from home temporarily. Adams-Prassl, Boneva, Golin, and Rauh (2020) collected new data from late March to early April 2020 in the United States and the United Kingdom, including a question about the share of tasks that they could do at home in their current (or last) job. They demonstrate that workers who are least able to work from home are most likely to lose their jobs. Bonacini, Gallo, and Scicchitano (2020) use data from the Italian Survey of Professions (ICP), which is the Italian equivalent of the O*NET, to construct an indicator of attitudes toward working from home. In general, all these articles find that working from home amenability increases with workers’ earnings and education. Their indices are positively correlated with those from DN2020, but since they are based on developed countries the extent to which their findings generalize to developing countries remains an open-ended question, given that the task content of jobs may vary with development (LoBello et al., 2019).

Two articles use skills surveys from different countries to test if US-based measures such as O*NET lead to biased results. Saltiel (2020) uses skills surveys from developing countries at varying levels of economic development to identify the share of jobs that can be done at home. He uses an approach similar to DN2020 by assuming that a job cannot be done from home if at least one of several conditions related to the job such as physical intensity, not using a computer, frequent contact with people, and so forth holds. Hatayama, Viollaz, and Winkler (2020) also use skills surveys for an expanded set of 53 countries at varying levels of development. In addition to the type of tasks considered by Saltiel (2020), they incorporate a measure of internet connectivity at home as a determinant of working from home amenability. Both papers find that more educated 5
# **Figure 7a:  Institutional dependence of sectors**

(measured by concentration of upstream and downstream transactions) <!-- Start of picture text --> Gini coefficient (%)<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Textile products Leather Minerals Basic metals Transport parts Metal products Textiles Wood Food Beverages Paper Telecom Banking Chemicalssiness & dwellings Tansport services Trade& Hotels<br>Bu<br><!-- End of picture text -->

# **Figure 7b: Per capita services output and institutional quality in Indian states**

(measured by transmission and distribution losses of public electricity undertakings) <!-- Start of picture text --> T&D Loss and Services per capita: averages over 1980-00<br><!-- End of picture text --> <!-- Start of picture text --> 6000<br>5000 MH<br>PJ GJ<br>4000<br>TN KR HY<br>3000 KN AP<br>WB RJ<br>2000 MP OR<br>UP<br>BH R 2 = 0.45<br>1000<br>0<br>10 15 20 25 30<br> T&D Losses (%)<br>Services per capita<br><!-- End of picture text --> Source: Amin and Mattoo, 2006.

# **4.2 Designing domestic services reforms**

A new World Bank database provides a first view of the state of services policy across a range of countries. We find that many developing countries have moved away from public monopolies in sectors such as communications, financial, and transport services, but still restrict new foreign entry. Asian countries, in particular, are unwilling to allow foreigners to acquire a majority share of ownership and full control of firms in these sectors, and Latin American countries are unwilling to give up discretion in licensing. Do these restrictions matter? And if they do, how might countries sequence their removal?

24