rafmacalaba/datause-encoder-data huggingface dataset:
That study began in 2009 when the authors identified about 2,000 junior secondary school graduates who had been admitted to senior secondary school but did not intend to go. Study participants lived in rural areas in southern parts of Ghana, where a government scholarship program already existed. The vast majority cited the cost as the main deterrent to continuing their studies. (Ghana still charged school fees for senior secondary schooling at that time.) The study offered a scholarship for three years of senior secondary schooling at a nearby public high school to a randomly selected group of those students and followed the participants for 12 years, up to about age 29 (and continues to follow them).

This experiment is about as close to the general question of the value of the quantity of public schooling as one can get: It estimates the impact of providing tuition-free secondary schooling. Still, as the authors note, the study population is a marginal one—those students who could not afford to attend school when faced with fees—rather than the average secondary school student.

The results are sobering. While the authors find significant impacts on senior secondary school graduation, university admissions, test scores, and delayed fertility, the impact on labor earnings is small—about 74 cedis ($36 in purchasing power parity dollars) per year in 2019— statistically insignificant, and dwarfed by the cost of three years of senior secondary schooling (the scholarship’s value), totaling about 12,000 cedis, even when the difference in labor earnings is accumulated and discounted over a working life. Viewed from the perspective of future labor earnings alone, then, this result casts doubt on the value for money provided by secondary schooling in Ghana, at least for this population.

# **b. Nonexperimental Evidence: Mincer Regressions**

# _Mincer regressions and their critics_

We take a straightforward approach to estimating the increase in labor earnings generated by attendance at public schools. We estimate a Mincer regression of (log) labor earnings on years of schooling and years of experience in a cross-section—the 2016/17 Ghana Living Standards Survey (2016/17 GLSS)—and use those estimates to calculate the increase in the present discounted value of lifetime earnings for each additional year of school. Such estimates are traditionally made with ordinary least squares (OLS): 13
recent short-term period 2009-15 saw income redistribution accounting for more than half of total mobility.<sup>15</sup> This result is consistent with our earlier findings that both indexes M<sup>�</sup> and M<sup>�</sup> remained relatively stable over time, and that it was income growth that was the driving factor behind mobility for the country. It is also interesting to note that income growth is higher for longer periods: the average income growth for the four shorter periods is 34 percent, which increased respectively, by almost twice and three times to an average growth rate of 66 percent and 95 percent for the two medium-term periods and the long-term period.

Table 6 provides estimation results for Shorrocks’ mobility index M<sup>�</sup> , short-term inequality, and long-term inequality. We estimate M<sup>�</sup> in two different ways, using the Gini index and the variance of log income, for robustness checks. Estimation results for both methods are, however, qualitatively similar and suggest a couple of emerging patterns that are consistent with our earlier findings for the other mobility indexes.<sup>16</sup> First, both short-term and long-term inequality has been steadily decreasing over time for Russia, over the four shorter periods. For example, the Gini index over the short term decreased by 30 percent, from 0.44 in the 1994-98 period to 0.31 in the 200915 period. The corresponding decrease for the variance of log income over the same time span is even larger at more than 50 percent (i.e., from 0.76 in the 1994-98 period to 0.35 in the 2009-2015 period). There is a similar decrease in inequality for the two medium-term periods, but this is unsurprisingly smaller since inequality measures are averaged over a longer time span for the medium-term periods compared to the shorter periods. Second, long-term inequality is less than

> 15 Increased minimum wages may have some moderate impacts on reducing poverty in these periods; see Calvo, Lopez-Calva, and Posadas (2015) and Kapelyuk (2015) for recent discussions on the role of higher minimum wage on poverty and inequality reduction. A recent study by Aristei and Perugini (2015) also suggests that the income growth component in the Fields-Ok index is relatively more important for income mobility for the period 2004-06 for most former centrally planned economies in Eastern Europe.

> 16 We use the balanced sample for each period for the estimates in Table 6, which varies from period to period due to attrition. Another approach is to use the fully balanced sample for the whole 1994-2015 period. We provide estimation results using this approach in Table 1.3 in Appendix 1, which are largely qualitative similar to the results in Table 6.

20
TABLE 7 Regression results for labor force participation rates, baseline

||**15-19 y**|**ears old**|**20-24 y**|**ears old**|**25-49 ye**|**ars old**|**50-64 ye**|**ars old**|**65+ ye**|**ars old**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|
|Fertility|0.734***||0.057*||0.000||||||
||(0.000)||(0.000)||(0.945)||||||
|Secondary enrollment|0.197***<br>(0.000)|0.127***<br>(0.000)|||||||||
|Tertiary enrollment|||-0.114***|-0.180***|||||||
||||(0.000)|(0.000)|||||||
|Completion of tertiary|||0.039|-0.023|0.235***|0.130***|0.406***|0.063|||
|education|||(0.249)|(0.394)|(0.000)|(0.000)|(0.000)|(0.221)|||
|Completion of tertiary|||0.158**|-0.099*|0.323***|0.313***|0.486**|0.426**|||
|education|||(0.002)|(0.045)|(0.000)|(0.000)|(0.003)|(0.002)|||
|Life expectancy|||||||0.569***|-2.679**|0.101***|0.227***|
||||||||(0.000)|(0.003)|(0.000)|(0.000)|
|Cycle|16.14***|21.43***|1.04|11.54***|1.504|-0.591**|0.590|-2.329**|1.435|21.76|
||(0.000)|(0.000)|(0.144)|(0.000)|(0.182)|(0.008)|(0.495)|(0.008)|(0.394)|(0.399)|
|Cycle * life expectancy|||||||||-0.031<br>(0.216)|-0.192<br>(0.584)|
|Fertility * EMDE|-0.669***||-0.066**||||||||
||(0.000)||(0.006)||||||||
|Secondary enrollment *|-0.337***||||||||||
|EMDE|(0.000)||||||||||
|Completion of secondary|||-0.027|-0.038|||||||
|education * EMDE|||(0.495)|(0.238)|||||||
|Completion of tertiary|||-0.127|0.153*|||||||
|education * EMDE|||(0.056)|(0.000)|||||||
|Life expectancy * EMDE|||||||||-0.143***<br>(0.000)|-0.608***<br>(0.000)|
|Secondary enrollment *|-0.337***||||||||||
|EMDE|(0.000)||||||||||
|Completion of secondary|||-0.027|-0.038|||||||
|education * EMDE|||(0.495)|(0.238)|||||||

51
# **Drivers of Gross Capital Inflows: Which Factors Are More Important for Sub-Saharan Africa?**<sup>*</sup>

César Calderón<sup>a</sup> , Punam Chuhan-Pole<sup>a</sup> , and Megumi Kubota<sup>a</sup> ,<sup>**</sup>

> a _The World Bank_

**Key Words:** Gross capital inflows, composition of capital flows, financial openness **JEL Codes:** E32, E51, F21, F32

> *Calderón: The World Bank, Office of the Chief Economist of the Africa Region (AFRCE). E-mail: <u>ccalderon@worldbank.org.</u> Chuhan-Pole: The World Bank, Development Finance (DFCII). E-mail: ** <u>pchuhan@worldbank.org.</u> Kubota (Corresponding author): The World Bank, AFRCE. Email: <u>mkubota@worldbank.org. The views expressed in this paper are those of the authors, and do not necessarily reflect</u> those of the World Bank or its Boards of Directors.
# **References**

Akbulut-Yuksel, M. (2009). Children of war: the long-run effects of large-scale physical destruction and warfare on children, IZA Working Paper No.4407.

Aguirre, D. & Pietropaoli, I. (2008). Gender Equality, Development and Transitional Justice: The case of Nepal. _The International Journal of Transitional Justice, 2_ , 356-377.

Annan, J., Blattman, C., Mazurama, D., & Carlson, K. (2009). Women and Girls at War: “Wives”, Mothers, and Fighters in the Lord’s Resistance Army, HICN Working paper 63.

Ariño, M. V. (2008). Nepal: A Gender View of the Armed Conflict and Peace Process. Quaderns de Construcció de Pau No.4, Escola de Cultura de Pau.

Akresh, Richard and Damien de Walque (forthcoming) **.** "Armed Conflict and Schooling: Evidence from the 1994 Rwandan Genocide," _Review of Economics and Statistics_ .

Bertrand, M., Duflo, E., & Mullainathan, S. (2004). How Much Should We Trust Differences-inDifferences Estimates? _Quarterly Journal of Economics, 119_ (1), 249-275.

- Blattman, C. & Annan, J. (2010). The Consequences of Child Soldiering. _Review of Economics and Statistics_ , 92(4), 882-898.

Blattman, C., & Miguel, E. (2010). Civil War. _Journal of Economic Literature, 48(1), 3-57_ .

Bohara, A., Mitchell, N., & Nepal, M. (2006). Opportunity, Democracy, and the Exchange of Political Violence: A Subnational Analysis of Conflict in Nepal. _Journal of Conflict Resolution, 50_ (1), 108-128.

Bohra-Mishra, P. & Massey, D. (forthcoming) Individual Decisions to Migrate During Civil Conflict. _Demography_ .

Brakman, S., Garretsen, H., Schramm, M. (2004). The Strategic Bombing of German Cities During World War II and its Impact on City Growth. _Journal of Economic Geography_ , 4(2), 201-218.

Caddell, Martha (2007). Private Schools and Political Conflict in Nepal. In: Srivastava, Prachi and Walford, Geoffrey eds. Private Schooling in Less Economically Developed 29
failures – i.e., limited or no updating of mistaken beliefs after receiving accurate information – that should inform the design and investment in interventions intended to mitigate misperceptions.

Third, while conventional wisdom suggests that information campaigns can ameliorate farmers’ mistaken beliefs, the empirical validity of this assumption remains largely untested. Because some behavioral phenomena that may generate NCME (e.g., inattention, confirmation, and self-esteem bias) might also obstruct learning and updating of mistaken beliefs, implying that the effectiveness of information interventions may be limited and might vary across observable and unobservable characteristics of farmers. Different farmers may pay attention to different technological features – or 'objects' – as the multi-object and selective attention learning literatures emphasize (Gabaix et al., 2006; Hanna et al., 2014; Schwartzstein, 2014; Ghosh, 2016; Wolitzky, 2018; Gabaix, 2019; Nourani, 2019; Kohlhas and Walther, 2021; Maertens et al., 2021).

Fourth, mistaken beliefs about one object may spill over to affect beliefs and decisions about other objects. As such, the provision of information that is meant to correct NCME in selfreported plot areas may subsequently impact a farmer’s reporting on other objects during the same household survey interview (as we demonstrate concerning fertilizer and labor inputs), and, given that some NCME reflects mistaken beliefs, prospectively the farmers' future decisions regarding these objects (which is beyond the scope of this paper). Both scenarios have implications for measurement, inference and policy. Understanding whether and how farmers respond to new information about the size and quality of one production input by adjusting their reporting of other agricultural inputs can help us understand survey data generating processes and farmers’ decisionmaking process.<sup>2</sup> Misinformation spillovers generate correlated NCME, which considerably complicates econometric correction for measurement error, because incomplete correction of measurement error in the presence of correlated NCME can aggravate rather than reduce bias in key parameter estimates (Abay et al., 2019).

We embedded an information experiment within an agricultural household survey in Malawi, providing an uncommon opportunity to study the nature of NCME and learning in response to corrective information to subjects' erroneous self-reports regarding cultivated land areas. Part of the appeal of this design is that most economics studies on inattention, learning, or

> 2 For example, if providing farmers with GPS measures of their plots' size affects self-reported input use, introducing GPS-based plot area measurement in follow-up longitudinal data collection (as part of national panel surveys or impact evaluation studies) may compromise the inter-temporal comparability of self-reported, non-land variables.

3
- I1 -

enrollment (i.e. 1970 and 1975 enrollment levels) appeared the most appropriate; the results reported belong to these years.

4.2 We estimated the equations with both female and male secondary enrollments so as to capture the effect of female enrollments, controlling for male enrollments. We recognize that male and female education tend to be correlated and therefore tried female education separately (reported in Appendix Table **_5)._** But we felt that relying only on female education might overstate its effect. This is, we recognize, a difficult judgement. We could see no strong theoretical case for simply relying on male education. We also estimated the equations with and without interaction between female education and family planning services. In each equation, non-linearity in the linkages between enro!!inents and fertility was tested with quadratic terms. The descriptive statistics are given in Appendix l able 3, and the results are presented in Table 1.

4.3 Irrespective of the model specification, female secondary school enrollment, lagged by either 10 or 15 years, is inversely related to fertility rate, and the coefficient is relatively large and highly significant. We evaluated the positive quadratic term and found that the turning point occurs at about 60 percent of gross secondary school enrollment in 1970 and 80 percent in 1975 - a level which none of the countries in our sample had reached in either year. Male enrollment is not statistically significant. Family planning services is statistically significant by itself odv in the equations wi1 the interaction terms. Once interacted with female education, it is no longer independentdy statistically significant. Equation 4 in Table 1 (using 1975 education data) suggests that family planning works in conjunction with education (the interaction term is significant). This implies that the effect of family planning can only be evaluated with reference to specific levels of female secondary enrollment, as it differs depending on the enrollment level. It is well known that education promotes more willingness to use contraception and
checks, etc.). And for digital marketing practices, this verification occurred by collecting and auditing digital footprints (e.g. Twitter handles, Facebook page addresses, Instagram pages, Website URLs, etc.).

These 41 business practice measures are then formed into six indices (see Appendix 4 for components): Finance and Accounting (10 practices); Marketing and Sales (9 practices); Operations and HR (11 practices); Verified Traditional Practices (10 of 30 practices); Verified Digital Marketing Practices (11 practices); Overall Index (41 practices). Each index takes the form of the proportion of business practices used by firms and therefore ranges from 0 to 1. The mean firm in the control group implemented 47.3% of the 41 practices at the time of the first follow-up, and 45.1% at the time of the second follow-up. This shows that firms had room to improve their business practices.

<u>Firm Growth. We used multiple measures of sales, profits and employees to proxy for firm growth.</u> Two measures of firm sales were included (total sales last month; total sales last year), with each winsorized at the 99<sup>th</sup> percentile then transformed using the inverse hyperbolic sine (IHS) function. Similarly, two measures of firm profits were used (total profits last month; total profits last year). Both were winsorized at the 99<sup>th</sup> percentile then IHS transformed. These 4 measures were also combined to construct a fifth measure: an overall Sales/Profit Index (computed by summing up the standardized z-score of each measure). As an additional proxy of growth, we also included two measures of firm employees. The first was total employment (sum of all wage/salary workers, casual/daily workers, apprentices/interns and unpaid workers) winsorized at the 99<sup>th</sup> percentile. The second was an IHS transformation of this total employment value. Overall, we have 7 variables as outcome measures of firm growth.

<u>Other Measures. In addition to the detailed measurement of business practices and firm growth,</u> our follow-up surveys included measures of: firm survivorship; ongoing use of a market for professional business services; and possible mechanisms related to the boundary of the entrepreneur (e.g., time allocation) and boundary of the firm (e.g., financial investment, product innovation, social media technology). Firms that closed (non survivors) are coded as earning zero sales and profits, having zero employees, and using no business practices.

16
of their 2018 labor forces. These were followed by the Kyrgyz Republic and Tajikistan which incurred negative net migration of 17 percent and 12 percent respectively of their 2018 labor forces. Apart from Turkmenistan, which incurred negative net migration equivalent to nearly 6 percent of its labor force, the other oil and gas producers experienced small positive net migration as a share of their labor forces, although the net flow was negligible for Uzbekistan. Not all migrants are members of the labor force, but it is a reasonable assumption that a large majority of them are. Hence, we can derive a rough estimate of the magnitude of the reduction in the labor force emanating from net migration from the data in table 2. These data suggest that Georgia may have incurred a reduction of around a third in its labor force since the beginning of the century and Armenia a reduction of close to a quarter, with the Kyrgyz Republic and Tajikistan suffering smaller but still substantial falls in their labor forces.

**Table 2 Net Migration 2000-18 as Percentage of 2018 Labor Force**

||Labour|Net Migration|Net migration|
|---|---|---|---|
||force|2000‐18|2000‐18|
||millions|millions|% of 2018|
||2018||Labour force|
|Armenia|1.4|‐0.37|‐26.9|
|Azerbaijan|5.1|0.09|1.8|
|Georgia|2.0|‐0.76|‐37.3|
|Kazakhstan|9.3|0.17|1.9|
|Kyrgyz Republic|2.6|‐0.45|‐16.9|
|Tajikistan|2.6|‐0.31|‐12.0|
|Turkmenistan|2.6|‐0.15|‐5.8|
|Uzbekistan|15.5|0.02|0.1|

Sources: labor force, WDI; Net Migration, Asian Development Bank Table 3 presents data on the stock of migrants from the eight FSU countries in the CASC by country of residence in 2017. One point to note about these data is that the stock of migrants from some of the FSU countries is much greater than the net flows from these countries since 2000. This difference mainly reflects very large out-migration flows which took place before 2000, especially around the time of the breakup of the Soviet Union. The Russian Federation is by far the most important destination for migrants from the FSU CASC countries, accounting for 54 percent and 56 percent of the migrant stock from Georgia and Armenia respectively and 78 percent and 81 percent respectively from the Kyrgyz Republic and Tajikistan in 2017.

The gross receipts of workers’ remittances as a percentage of GDP are shown in table 4. The Kyrgyz Republic and Tajikistan have become the largest recipients of remittances among the countries of CASC, with annual remittances currently amounting to around a third of their GDP, although remittances to Tajikistan have declined from the levels recorded during 2005-15 when they were closer to 40 percent of GDP. Remittances to Armenia are currently around 13 percent 4
Western Kenya. Marenya and Barrett (2007) report the average MPP at 22 in Western Kenya, although they find considerable heterogeneity.

In Table 6, we find that the MPP varies from 11 to 20 in Kenya and 21 to 25 in Uganda. Compared with the previous estimates, these estimates are within a reasonable range. The MPP is 14 for the purchased HYV during the first wave of the panel surveys in Kenya. The nitrogen-maize relative price is 13 during this period. The t-test indicates that the MPP is not different from the relative price, suggesting that the nitrogen application is roughly at the optimal level for the purchased maize during this period in Kenya. For the local/recycled maize, the MPP is lower than the relative price, suggesting a slightly over-application of the nitrogen. During the next survey period, the results are the opposite. We find an almost optimal application on the local/recycled HYV maize but somewhat an under-application for the purchased HYV maize. Because of unexpected events, both agro-ecologically and economically, it is not surprising that Kenyan farmers miss the optimal application levels occasionally. It is more important to point out that the MPPs move in the same direction as the relative-price over time. From the first to the second wave, the relative price increased from 13 to 16 and the MPPs of the purchased and local/recycled HYV maize also increased from 14 to 20 for the purchased HYV maize and from 11 to 16 for the local/recycled HYV maize. Thus, the results indicate that the Kenyan farmers are responding to the change in the relative price and successfully achieving the near optimal application level in one of the two years for both the purchased and local/recycled HYV maize.

The relative price is much higher in Uganda than in Kenya: it is 22 and 34 in the first and second wave, respectively. Because of the low use of the nitrogen fertilizer in Uganda, the MPPs are not precisely estimated. Despite the low precision, we find that 20
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