rafmacalaba/datause-encoder-data huggingface dataset:
negotiation.<sup>6</sup> Appendix Table A3 provides a definition for each of the 10 skills under study as well as example items aggregated to construct each skill measure. The complete list of items combined to construct a given skill measure is available on request. In order to create an index for each skill, we first reversed all socio-emotional skills measures such that higher numbers indicate "higher" socio-emotional skills levels. Second, we standardized each item such that the mean is zero and the standard deviation is 1 for women in each study. We then took an average of the items for a given skill based on the categorization exercise. Finally, we standardized the index to the women’s sample in each study. This allows us to later interpret regression coefficients on gender differences relative to the variation between women.

Additionally, we created three aggregated skills measures: _Intra_ that averages all intrapersonal skill indices available in a given study (positive self-concept, emotional regulation, self-control, perseverance, personal initiative and problem-solving and decision-making (PSDM)), _Inter_ that averages all interpersonal skill indices available in a given study (empathy, expressiveness, interpersonal relatedness and teamwork), and _All_ that averages all socio-emotional skill indices available in a given study. Our analysis thus considers 13 measures of socio-emotional skills (10 skill-specific and 3 aggregated measures).

# **3.2 Sample description**

Our pooled sample comprises 41,873 individual-level observations from surveys (see Appendix Table A2 for sampling criteria) conducted in 8 Sub-Saharan African countries (Benin, the Republic of Congo, Côte d’Ivoire, Ghana, Kenya, Mozambique, Nigeria, and Togo) or under the Future of Business Survey (15 SubSaharan African countries). While all studies target both men and women, and more specifically couples in Côte d’Ivoire and Mozambique, only 42.7% of the respondents are women. Most studies’ respondents are adults and young adults, in both urban and rural areas, with an average age of 36 years old (23 in the Republic of Congo skills project to 49 in the Future of Business project). Very few studies limit sample selection by education level and the average respondent has completed 9.6 years of education (a minimum average of 2.6 years in Mozambique and a maximum of 13.3 years in Nigeria). Among the respondents, 57.3% are married. Of the respondents, 74.5% report earning a positive income, with some variation across studies (43% in Mozambique up to 98% in Togo’s Private Sector Development Project). Respondents earn on average $138 (USD) monthly with the average income varying between $2 in

> 6 _Self Awareness_ in the framework was renamed _Positive Self Concept_ and _Collaboration_ from the framework was renamed _Teamwork_ , as the latter terms are similar, but better reflected the specific data that was available.

13
- 22 -

**Figure 2. Total Private Capital Flows (net)**

<!-- Start of picture text --> 25000 14%<br>20000 12%<br>15000 k0<br>8%<br>10000 6%<br>5000 4<br>2%<br>0 0<br>-5000 ~~~~~~~~~~~~~~~~-2%<br>o 0)0) Co -N CO CD 0) C a7 0 CO co ) In CO 00) ) co CO co 0)0 XO co co CO co CO 0) 0 0) m) C) o N ) 0) C 0) 0) 0) L Q) O a) CD 0)<br>-.--+$ Million -% of GDP<br>Source: Bank of Thailand.<br><!-- End of picture text --> Private capital flows were already significant in the early 1980s, but declined in 1985 and 1986 as a result of the uncertainties surrounding the macro adjustment. Again, the data seem to suggest a structural break in 1988, when flows increased rapidly until 1990 to about 13 percent of GDP, a local maxima. After stabilizing at about 8 percent of GDP in 1992-94, there was a second local maxima in 1995 when flows again surpassed 12 percent of GDP.

_Main Components of Capital Inflows._ The Bank of Thailand classifies capital flows into nonbank and bank flows. The latter are resident banks borrowing from overseas sources (either from financial institutions or by issuing debt instruments), and, starting in 1993, a separate category for borrowing by BIBF banks (see Box 1). The nonbank categories are the following:

- Foreign Direct Investment: including both net FDI inflows and outflows (Thai direct investment overseas);

- Portfolio Capital: distinguishing between fixed income and equity flows, and including direct investrments by foreign residents in domestic instruments and Thai sovereign and corporate issues overseas (e.g., eurobonds, ADRs);

- Nonresident Baht Accounts: capital inflows deposited by nonresidents in domestic currency accounts in local banks mainly for investing in domestic securities;

- Trade credits: a minor component; and

- Other Borrowing: presumably mostly composed of syndicated borrowing by domestic corporates from overseas financial institutions.

_Bank Intermediation._ Banks and finance companies played a key role in intermediating capital inflows in Thailand as shown in Figure 3. Their net foreign liabilities rose from 6 percent of domestic deposit liabilities in 1990, to one third by 1996. During the full inflow period 1988-96, bank borrowing accounted for 37 percent of total inflows. But this average number hides a large difference between the initial phase of the
and decrease product diversity and competition. In addition, they could undercut the demand for formal credit and affect firms’ productivity.

Moreover, because interest rate caps distort the market and generate adverse selection, financial entities tend to lend to clients with higher collateral, thereby creating inefficiencies in financial intermediation. Consequently, financial institutions curtail their lending to those who need it most and have little access to alternative sources of credit (Miller 2013). Even though financial institutions can remain profitable in the presence of interest rate caps, such restrictions may reduce investments in new markets. In extreme cases where ceilings are set at unprofitable levels, banks and microfinance institutions may withdraw from certain locales such as rural areas or from expensive market segments because they cannot cover their costs.<sup>1</sup> In some cases, then, low-income borrowers with few options for borrowing in the formal market could turn to unlicensed moneylenders, probably at a higher interest rate. Finally, according to some evidence, interest rate caps on loans discourage microfinance nongovernmental organizations (NGOs) and other sources of finance for the poor from converting into licensed financial institutions (Helms and Reille 2004).

Caps that are set too low and caps that are set too high are both problematic. In countries where the caps do not cover fees and commissions and when the definition of _interest rate_ is not clear, for example, financial institutions may give the impression of compliance with the ceiling but charge fees and commissions that are not considered part of the cost of the loan. Some also argue that in the opposite case, when caps are not set too low, interest rates will still tend to rise toward the caps (Helms and Reille 2004; Miller 2013; and OFT 2010).

The introduction of interest rate ceilings causes credit suppliers to withdraw from the market and competition to decrease. Less market competition leads to a narrower range of products and less incentive to innovate. On the other hand, some expected benefits of financial liberalization are related to potential gains in terms of efficiency in the allocation of investment resources. In this context, Galindo, Schiantarelli, and Weiss’s study (2007) of firms in 12 developing countries concluded that financial liberalization – such as reduction of credit controls – had improved the efficiency of investment in the majority of cases.

In light of all the arguments pro and con on interest rate caps, this paper examines the characteristics and effectiveness of interest rate regimes around the world and, when possible, provides policy recommendations.

> 1 Evidence of market contraction was seen in Nicaragua and West Africa.

4
increased, idiosyncratic heterogeneity decreases and choices become increasingly sensitive to monetary and time cost differences between the modes, making tolls more effective in reducing congestion and emissions. For example, when  1.25 or 1.50, the optimal toll reduces emissions by 81%. At even higher  , idiosyncratic variation diminishes and choices are concentrated on a single mode. When  10 the lowest three income groups all walk, and the highest three all choose car. No one chooses public transit. To induce efficient changes in this extreme situation, the toll begins to rise with  .

**TABLE 6: Effect of taste heterogeneity changes on CO2 reductions**

|**T**|**No C**|**ongestion T**|**olling**|**Optimum T**|**olling**|**Tolling Delay**|**Externality**|
|---|---|---|---|---|---|---|---|
|**aste**<br>**heterogeneity**<br> |**CO2**<br>(<br>.<br>)<br>_mil tons_|**Speed**<br>(km/hr)|**Auto Trips**<br>6<br>( 10 )<br>|**% CO2 **<br>**Reduction**|**Toll**<br>(BRL/trip)|**% CO2 **<br>**Reduction**|**Toll**<br>(BRL/trip)|
|0.001|7.6549|20.18|6.4348|1.62|12.21|0.56|4.17|
|0.01|7.4727|20.51|6.3600|14.35|11.89|5.27|4.18|
|0.15|6.1206|23.41|5.7731|68.66|10.21|45.86|5.16|
|0.25|5.8830|24.10|5.6744|75.72|10.07|57.99|5.96|
|0.50|5.8369|24.49|5.6994|79.48|9.83|67.93|6.67|
|0.75|5.9411|24.45|5.7950|80.83|9.79|71.14|6.85|
|1.00|6.0441|24.38|5.8821|81.29|9.82|72.79|6.93|
|1.25|6.1191|24.34|5.9473|81.44|9.86|73.72|6.97|
|1.50|6.1631|24.35|5.9921|81.47|9.90|74.21|6.99|
|5.00|5.9513|25.40|5.9756|81.40|10.30|73.56|7.07|
|10.00|5.9204|25.54|5.9691|81.61|10.41|73.40|7.0988|

**6.3 The value of time (** _bf Gm_<sup></sup> 1 **)**

Table 7 shows that as _bf_ increases, car trips increase initially, decreasing after _bf_ = 0.75 _wf_ .

Correspondingly, speed decreases as car trips increase and then begins to increase. Because the speed reduction is dominated by the initial increase in car trips, emissions roughly follow the car trips, rising initially and then falling. Why do car trips first rise and then fall, as the value of time increases? Since public transit is the slowest mode, increasing the value of time from zero induces marginal transit users to switch to car, and more so the higher their wage. At the same 21
**Figure 10(c):Thailand: Return to skill by sector over time (Tertiary and above)**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5 Agriculture/Mining<br>1 Manufacturing<br>Trade/Services<br>0.5<br>0<br>1990 1994 2000 2004<br>Return<br><!-- End of picture text -->

**Year**

**Figure 10(d): Thailand: Proportion of skilled workers by sector over time (Tertiary and above)**

<!-- Start of picture text --> 25<br>20<br>15 Agriculture/Mining<br>Manufacturing<br>10<br>Trade/Services<br>5<br>0<br>1990 1994 2000 2004<br>% skilled<br><!-- End of picture text -->

**Year**

50
**Table B2: Parameter Estimates of Equation (1) and Stationarity Properties of Equation (2) between Tanzanian Markets and U.S. Gulf**

||**--------------**|**---------- Regr**|**ession results**|**, eq. (1) --------**|**----------------**|**--- Price sp**|**read, eq. (2)---**|
|---|---|---|---|---|---|---|---|
||μ|pt<br>W|_R-square_|ADF|PP|ADF|PP|
|_Arusha_|2.72***<br>(7.28)|0.76***<br>(20.55)|0.68|-2.90**|-2.91**|-2.53|-2.67*|
|_Bukoba_|2.79***<br>(9.60)|0.76***<br>(26.00)|0.75|-3.00**|-3.21**|-2.85*|-2.99**|
|_Dar es Salaam_|2.44***<br>(7.91)|0.79***<br>(25.56)|0.73|-2.64*|-2.85*|-2.61*|-2.92**|
|_Dodoma_|2.03***<br>(4.81)|0.83***<br>(19.84)|0.67|-3.07**|-2.76*|-2.95**|-2.72*|
|_Iringa_|2.38***<br>(6.02)|0.77***<br>(19.60)|0.67|-3.03**|-3.20**|-2.86*|-3.03**|
|_Lindi_|2.80***<br>(5.94)|0.76***<br>(16.22)|0.62|-3.27**|-3.96***|-3.26**|-3.46**|
|_Mbeya_|1.38***<br>(4.32)|0.87***<br>(27.39)|0.78|-2.96**|-2.99**|-2.07|-3.05**|
|_Morogoro_|2.86***<br>(7.09)|0.75***<br>(18.69)|0.65|-3.18**|-2.97**|-2.58*|-2.83*|
|_Moshi_|2.71***<br>(6.64)|0.76***<br>(18.94)|0.65|-2.70*|-2.51|-2.53|-2.49|
|_Mtwara_|3.30***<br>(6.67)|0.70***<br>(14.43)|0.59|-2.92**|-4.42***|-2.65*|-3.99**|
|_Musoma_|2.93***<br>(8.40)|0.75***<br>(21.66)|0.73|-2.93**|-3.28**|-2.78*|-2.95**|
|_Mwanza_|2.14***<br>(5.72)|0.83***<br>(22.52)|0.77|-3.17**|-3.24**|-2.96**|-3.09**|
|_Shinyanga_|2.51***<br>(5.63)|0.78***<br>(17.84)|0.70|-2.86*|-3.59***|-2.61*|-3.24**|
|_Singida_|2.03***<br>(4.49)|0.83***<br>(18.65)|0.71|-3.27**|-3.24**|-2.99**|-3.08**|
|_Songea_|2.80***<br>(6.56)|0.72***<br>(16.74)|0.60|-3.27**|-3.40**|-2.92**|-3.19**|
|_Sumbawanga_|2.49***<br>(6.23)|0.75***<br>(18.62)|0.69|-2.90**|-3.23**|-2.73*|-3.12**|
|_Tabora_|2.51***<br>(6.32)|0.78***<br>(19.85)|0.73|-3.57***|-3.43**|-2.90**|-3.16**|
|_Tanga_|2.48***<br>(5.60)|0.78***<br>(17.85)|0.63|-3.01**|-3.13**|-2.74*|-2.96**|

**Notes** : ADF and PP denote the Augmented Dickey-Fuller and Phillips-Perron statistic for unit roots, respectively. The numbers in parentheses are absolute t-statistics. Significance levels: * = 10 percent, ** = 5 percent, *** = 1 percent .

45
impact studies. Without solving for endogeneity, impact results are biased and in some cases spurious.

The literature review showed that results vary substantially depending on the countries and/or the types of sectors/industries analyzed; other categories of R&D interventions are inconclusive due to the nature of the weak designs created to evaluate these interventions. Overall, the meta-analysis revealed that there are some studies that produce biases in metaestimates: such studies tend to use perception surveys and subjective methods to evaluate R&D programs. The meta-analysis bias correction assigns weights to be computed in metaregressions (WLS), where the papers producing bias receive a weight of zero. The final regressions with the bias-correction weights show positive and significant impacts of R&D subsidies on a firm’s innovation activities with a mean of 19 percent (compared to different types of non-recipient/counterfactual firms) controlling for precision, methodology, and paper attributes. Although the results are robust from a meta-analysis standpoint, the weakness of the original methodologies makes it hard to build a case for causality and calls for increasing randomized designs in R&D interventions.

The design of a public program of subsidies to business R&D projects requires defining a selection and ranking system in order to decide which projects should be supported. The decision criteria of the agency should be part of the evaluation of a public program because, as the structural models on this subject show (David et al., 2000), such decision criteria in the selection of projects has as major impact on the results of the program as well as on the effect of the subsidies.

Government-wide evaluations of efficiency are often based on complex composite indicators. These indicators are useful to get a broad overview of efficiency gains achieved. However, in order to arrive at concrete policy recommendations, it is more promising to investigate the efficiency of public expenditure in individual spending areas. Growth enhancing expenditures, such as R&D, education and to some extent infrastructure, as well as expenditures, affected by the ageing of population (such as health care), are first candidates for such investigations.

30
relative to the 17.7 percent decline observed for non-exporting firms on average. The decline in sales among exporting firms was substantial enough to eliminate their sales-volume premium over nonexporting firms. After the invasion, the average sales among firms exporting to Russia were 39 percent lower than the average sales of firms not exporting to Russia—while they were more than three times higher before the crisis.

The estimation results show a significant negative impact on employment for firms that exported to Russia compared with firms that did not export to Russia before the invasion. The estimated effect indicates about 120 log points (or around 85 percentage points) larger drop in employment for firms exporting to Russia. This finding could imply that firms not exporting to Russia may have experienced growth in employment during the same period and potentially absorbed a substantial portion of the labor released by firms negatively affected by the decline in the export market to Russia. The firms not exporting to Russia, which serve as a counterfactual in this analysis, could have continued to grow their employment based on the pre-invasion common trend. Additionally, the employment growth may have been supported by hiring immigrants from Russia.

**Table 5: Effect of Russia’s Invasion of Ukraine on Firms’ Sales and Employment if a Firm Exports to Russia**

||(1)<br>**Sales**|(2)<br>**Employment**|
|---|---|---|
|Effect of the invasion|-0.195|0.200|
||(0.229)|(0.150)|
|Exported to Russia|1.446***|0.044|
||(0.318)|(0.170)|
|Invasion x Exported to Russia|-1.807***|-1.193**|
||(0.491)|(0.534)|
|Manufacturing|0.572**|0.084|
||(0.267)|(0.086)|
|Retail|0.679***|-0.100|
||(0.259)|(0.087)|
|Other Services|-0.153|0.005|
||(0.235)|(0.086)|
|Small (5-19)|1.403***|1.107***|
||(0.145)|(0.036)|
|Medium (20-99)|2.044***<br>(0.161)|2.306***<br>(0.049)|
|Large (100+)|4.572***|4.067***|
||(0.650)|(0.301)|
|Constant|11.707***<br>(0.263)|1.297***<br>(0.078)|
|Observations|1149|1767|
|R-squared|0.789|0.728|
|_Robust standard errors are in parentheses._|||
|_*** p<.01, ** p<.05, * p<.1_|||

# **Could digitization drive varying responses of firms’ performance to the invasion shock?**

The use of digital business solutions can lead to significant benefits for firms, including reduced production costs, upgrading capabilities, and expanded market opportunities. Goldfarb and Tucker (2019) provide a 10
International Monetary Fund. 2006. Burundi: Selected Issues and Statistical Appendix, IMF Country Report No. 06/307, August 2006.

Jefferson, P.N. and S. A. O’Connell, 2004. _Rainfall shocks and economic performance in four African countries._ In prep. (http://acadweb.swarthmore.edu/acad/rain-econ/Framesets/Data.htm) Joint IMF-World Bank. 2013. _Burundi Debt Sustainability Analysis_ . Prepared by the Staff of IMF and the IDA. January 2013.

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Lim, Christian and Rugwabiza, Leonard. 2009. _A shock Analysis of Burundi’s Economy_ . Africa Development Bank, East Africa Department.

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Painchaud, F. and T. Stučka, 2011. _Stress testing in the Debt Sustainability Framework (DSF) for Low-Income Countries_ , Knowledge Brief, Economic Policy and Debt Department, World Bank.

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29
# 1. INTRODUCTION

It is well known that women are generally less schooled than men. There is also scattered evidence that people invest less in girls' health, though this is generally reflected not in lower life expectancy but in the higher incidence of illness among women. Explanations of lower investment in human capital of women assume that demand somehow exists, and focus on determinants of supply of human capital by the household.1 We argue here that this neglect of demand side factors results in serious gaps in our understanding of the process of accumulation of human capital, and hence of economic development. We attempt to remedy this neglect by including "exogenous technological dimensions to the development process" (Schultz 1988) that differ over gender, time, and countries. Specifically, the structure of production, represented by shares of agriculture, industry and services in total employment or in gross domestic product (GDP), is included as an identifying variable for demand for human capital.

We first develop an analytical framework within which some of the determinants of differences in the human capital of males and females in developing countries can be comprehensively examined. Then, some of the implications of the theory are tested for about 90 countries in 1965 and<sup>1987.</sup> In future work, we plan to put the implications of this and competing models See Schultz (1989) for a survey. Empirical a lyses of gender differences in human capital confound the effects of (1) gender biases inherent in the utility function of parents, (2) gender differences in appropriability of returns to investments in children by their parents, and (3) gender differences in market returns to human capital.

1
a test was implemented in a large population in Niger. The test displayed appropriate reliability and validity in both the baseline and follow-up sample. The baseline cognitive score is correlated with both nutritional status and stimulation practices, suggesting sensitivity to factors known to be associated with cognitive development.<sup>27</sup> We report results with the scores normalized based on an international norm.<sup>28</sup> Cognitive scores are measured as z scores for children aged 6-42 months at the time of the follow-up survey.

_Socio-emotional development (Final outcome domain #3)_ . We build a z score for an index of socioemotional development based on the strength-and-difficulties questionnaires for children aged 24-59 months at the time of the follow-up survey. The overall score aggregates sub-scales on emotional problems, conduct problems, hyperactivity, peer problems, and pro-sociality. The overall scale is scored so that a higher score indicates that a child has less socio-emotional difficulties.

Finally, we measure a range of _secondary outcomes_ at the household level to analyze additional mechanisms (Section 6). First, to complement the analysis of dietary diversity among children, we document results on food security at the household level based on the food consumption score. We also consider the brachial perimeter for breastfeeding women. Second, we document changes in household consumption (separately for total, food and non-food consumption), as well as the share of food in total consumption. Third, we document impacts on assets (separately for the number of household durable goods, household productive assets, as well as a livestock index expressed in tropical livestock units) and savings (whether households participated in a rotating savings groups, and the amount contributed over the last week). Lastly, we measure impacts on human capital variables for older children and household members, including school enrollment, attendance, illness and health service utilization for older children and adults, as well as use of contraception and recent pregnancies among women aged 15-49.

> score can be transformed into a normed score based on norms established for the original version of the test.  The normed score is calculated by each age and is standardized to have a mean of 10 at each age.

> 27 At the same time, it should be noted that the correlation with such environmental factors does not appear as strong as in some other settings.

> 28 Results are robust when using the raw score, or a score based on locally constructed norms.

19