rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 4000<br>3<br>£ 3500<br>S<br>2 3000 +——<br>3<br>2 2500<br>82<br>+ £2000<br>ow<br>i)<br>c 1500<br>2<br>S 1000<br>7 —CSTB data<br>5 500 . .<br>6 — Construction costs in the model<br>0<br>12 3 4 5 6 7 8 9 10 14 12<br>Number of floors<br><!-- End of picture text -->
citations. The distribution of citations is skewed like the distribution of downloads and is entered on zero and low numbers. Of the 147 policy reports that were cited, only 93 were cited between 1 and 5 times. Those policy reports with more than 5 (the “knee of the curve”) citations consist of 54 policy reports, or 3 percent of the data sample.

**Figure 7: Citation Distribution**

<!-- Start of picture text --> Citation Distribution of Policy Reports: 2008 to 2012<br>All Reports with Downloads<br>0 20 40 60<br>Downloads per Report<br>Source: Google Scholar and World Bank Documents & Reports.<br>30<br>20<br>10<br>Number of Reports<br>0<br><!-- End of picture text -->

**Similar to downloads, older policy reports have been cited more often.** Over the whole period policy reports have an average of 1 citation per document, with documents published in 2009 having the highest citation average of 1.7 citations per document.  The citations were collected in the first quarter of FY13, and have increased since this time.  The most cited policy report in our dataset was cited 59 times when the dataset was collected and assembled.  As of the third quarter of FY14, the document has been cited 91 times.<sup>24</sup> Table 4 shows the summary statistics for the cumulative citations for policy reports.

**Table 4: Citation Statistics for policy reports**

|**Fiscal Year**<br>**Published**|**Total in Google**<br>**Scholar**|**Total**<br>**Cited**|**Total Not**<br>**Cited**|**%**<br>**Cited**|**Average**<br>**Cites**|**Standard**<br>**Deviation of Cites**|**Max**<br>**Cites**|
|---|---|---|---|---|---|---|---|
|**2008**|223|36|187|16%|1.32|4.74|41|
|**2009**|242|40|202|17%|1.71|6.94|59|
|**2010**|258|32|226|12%|0.74|2.82|25|
|**2011**|268|26|242|10%|0.77|4.35|56|
|**2012**|210|13|197|6%|0.21|1.50|20|
|**2008–2012**|1201|147|1054|12%|0.96|4.51|59|

Source: Google Scholar and World Bank Documents & Reports.

> 24 This policy report is cited more since it was published in the peer-reviewed journal _Agricultural Economics_ .

15
||Prod|uctivity|<br>**T**|**Table 3**<br>**rainin**|**g**<br>Wages||||
|---|---|---|---|---|---|---|---|---|
||<br>Coef.|<br>Std. Err.|<br>t|P>ItI|Coef.|Std. Err.|<br>t|P>jtj|
|dtrain|-0.136|0.230|-0.593|0.554|0.420|0.112|3.747|0.000|
|dtraiou|0.332|0.103|3.208|0.001|0.075|0.050|1.490|0.137|
|metatrai|-0.024|0.076|-0.313|0.754|-0.120|0.037|-3.245|0.001|
|texttrai|0.118|0.299|0.395|0.693|-0.484|0.146|-3.324|0.001|
|woodtrai|-0.083|0.469|-0.178|0.859|-0.454|0.229|-1.987|0.047|
|nexptrao|-0.115|0.094|-1.218|0.223|-0.168|0.046|-3.664|0.000|

Training obtained outside an enterprise is revealed in productivity. The employees receiving training from external suppliers are statistically significantly more productive than non-trained co-workers, but trained employees are not compensated equally. This is revealed by the wage differential not being significantly different from zero. Another explanation is that the impact of training is not instantaneously reflected in wages due to a delay from the time the productivity increase from the training is realized until this productivity increase is rewarded as higher wages.

Trade unions are often associated with training. This seems also to hold in Zimbabwe. Trade union bargaining at the firm level may increase social welfare by counterbalancing a firm's monopsonistic power in wage determination."1 Long term contracts are socially optimal when workers are being trained because workers' incentive to quit and leave with the skills obtained is reduced. Alternatively, firms and local unions wage bargaining may ensure that post-training wage is set sufficiently high to deler inefficient quits, and thus to ensure that the number of trainees the firm takes on is near the social optimal number. Hence, we would expect to see higher post-training wages in our findings. In fact, we did so for training supplied by outside providers.

This is shown theoretically in Brooth and Chatteri (1997).

16
- Didier, T., I. Love and M. Martinez-Peria (2012) “ What explains comovement in stock market returns during the 2007-2008 crisis.” _International Journal of Finance and Economics_ , 17, 182–202.

- Elhorst, P., (2014) “Spatial Econometrics: From Cross-sectional Data to Spatial Panels.” _Springer, Berlin_ .

- Ertur, C. and Musolesi, A., (2017) “Weak and strong cross-sectional dependence: A panel data analysis of international technology defusion.” _Journal of Applied Econometrics_ , 32, 477–503.

- Ertur C. and Koch W., (2007) “Growth, technological interdependence and spatial externalities: theory and evidence.” _Journal of Applied Econometrics_ , 22, 1033–1062.

- Fama, E (1990) “Stock returns, expected returns and real activity.” _Journal of Finance_ , 45, 1089–1108.

- Forbes, K. and M. Chinn (2004) “ A decomposition of global linkages in financial markets over time.” _Review of Economics and Statistics_ , 86, 705–722.

- Forbes, K. and F. Warnock (2012) “ Capital flow waves: surges, stops, flight and retrenchment.” _Journal of International Economics_ , 88, 235–251.

- Frankel, J. A. and Rose, A. K. (1998) “The endogeneity of the optimum currency area criteria.” _Economic Journal_ ,108, 1009–1025.

- Ghosh, A., J., Ostry, T. Kapan and M. Qureshi (2015) “Exchange Rate Management and Crisis Susceptibility: A Reassessment.” _IMF Economic Review_ , 63, 238–276.

- Hale, G., T. Kapan and C. Minoiu (2016) “ Crisis Transmission through the Global Banking Network.” _IMF Working Paper 16/91_ .

- Hirata, H., Kose, M.A., Otrok, C., Terrones, M.E. (2013). “Global house price fluctuations: synchronization and determinants.” _IMF Working Paper_ , WP/13/38.

- Jensen, G. and R. Johnson (1995). “Discount rate changes and security returns in the U.S., 1962-1991.” _Journal of Banking and Finance_ , 19, 79–95.

- Jorda, O., M. Schularick, A. Taylor and F. Ward (2017). “Global financial cycles and risk premiums.” _Unpublished manuscript_ .

- Kose, M.A., C. Otrok and C. Whiteman (2003). “International Business Cycles: World, Region, and Country-Specific Factors.” _American Economic Review_ , 93, 1216–1239.

- Kuersteiner, G. and I.R. Prucha (2015). “Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity.” _Unpublished manuscript_ .

- LeSage, J. and Pace, R.. (2009). “Introduction to Spatial Econometrics.” _Boca Raton: Taylor and Francis_ .

31
in countries with better contract enforcement: The _Doing Business_ Getting Electricity indicator has a more positive association with firm growth, and the _Doing Business_ Construction Permit indicator has a more positive association with productivity. Provision of electricity and granting of construction permits both require strong contract enforcement. Thus, it is not surprising that these two Doing Business aspects tend to hinge more critically on Contract Enforcement. The _Doing Business_ Getting Credit indicator does not significantly interact with the Contract Enforcement indicator. Overall, there is support for the notion that the de jure indicators of _Doing Business_ are more effective in improving firm performance in countries with better contract enforcement.

# **5. Effect of the business environment on firm performance, foreign versus domestic firms**

Do domestic firms and foreign-owned firms respond differently to the business environment? If so, how? There are reasons why they might respond differently. Foreign firms, for instance, have better access to finance (from foreign owners) and to more advanced technology. They may also have stronger bargaining power with respect to the government: their ability to relocate affects the enforcement of government policies. On the other hand, they may have worse access to information about local markets and demands and preferences of local consumers. We thus assume that domestic and foreign firms respond differently to the domestic BE. In our empirical implementation, we interact the foreign ownership dummy with the BE indicators, while allowing the foreign dummy to have an independent effect on firm performance. Given that interacting all three sets of BE indicators would result in too many coefficients in a single specification (causing multicollinearity), we interact indicators separately from each set of BE indicators. The results are presented in table 7.

Foreign-owned and domestic firms differ not only in their dynamic responses (firm growth) to the BE but in their static response (firm productivity). We conducted an F test that all the interaction terms of the BE indicators with the foreign dummy are jointly zero.The interaction terms are jointly significant only in the regressions of productivity on Enterprise Survey indicators and Global Competitiveness indicators (the F statistic in the _Doing Business_ regression is close to being statistically significant).

_Government Protection_ . Foreign-owned firms benefit more from physical safety. This is shown in the more pronounced negative effect on productivity of the Security Costs indicator from the Enterprise Survey, and the more pronounced and positive effect on productivity of the 18
Table D.1: Program impacts on antisocial behaviors, 12–13 month survey only

|||||ITT regre|ssion (N=9|47)||
|---|---|---|---|---|---|---|---|
||Control|Thera|py only|Cas|h only||Both|
|Outcome|mean|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Summary index of antisocial behaviors, z-score|0.032|-0.083|[.093]|0.132|[.097]|-0.247|[.088]***|
|Usually sells drugs|0.135|-0.034|[.029]|0.035|[.030]|-0.059|[.029]**|
|# of thefts/robberies in past two weeks|1.839|0.073|[.395]|0.352|[.388]|-0.728|[.363]**|
|“Took something behind someone not for|0.275|-0.006|[.067]|0.106|[.072]|-0.063|[.069]|
|you” (stole)||||||||
|“Corrected someone’s mistake” (stole|0.338|-0.067|[.077]|0.091|[.078]|-0.087|[.084]|
|unwatched items)||||||||
|“Scraped from others” (Cheating)|0.299|-0.037|[.071]|0.023|[.074]|-0.104|[.077]|
|Pick-pocketed someone|0.094|-0.039|[.039]|0.041|[.041]|-0.074|[.035]**|
|“Scammed someone” (Sold false goods or<br>conned)|0.118|-0.005|[.038]|-0.044|[.039]|-0.093|[.036]**|
|“Black deed business” (Con artistry)|0.598|0.104|[.141]|0.016|[.128]|-0.196|[.137]|
|Mugged someone|0.086|0.075|[.077]|0.033|[.057]|-0.078|[.050]|
|Armed robbery|0.032|0.001|[.035]|0.021|[.036]|-0.026|[.026]|
|Disputes and fghts in past two weeks, z-score|-0.060|-0.026|[.091]|0.100|[.090]|-0.100|[.077]|
|Small palava (dispute) with a neighbor|0.152|-0.005|[.062]|0.062|[.059]|0.047|[.065]|
|Small palava (dispute) with a leader|0.059|0.008|[.049]|0.005|[.041]|-0.008|[.035]|
|Small palava (dispute) with the police|0.152|0.033|[.064]|0.039|[.053]|-0.050|[.052]|
|Large fght with a neighbor|0.076|0.021|[.043]|0.023|[.033]|-0.036|[.032]|
|Large fght with leader|0.100|-0.027|[.047]|0.027|[.045]|-0.084|[.037]**|
|Large fght with police|0.027|0.021|[.027]|0.045|[.026]*|0.007|[.036]|
|Physical fght|0.115|-0.004|[.055]|0.087|[.065]|-0.079|[.045]*|
|Engaged in a fght with a weapon|0.083|-0.028|[.041]|0.023|[.042]|-0.048|[.037]|
|Fined for a fght|0.051|-0.081|[.062]|0.020|[.069]|-0.040|[.059]|
|Carries a weapon (typically knife)<sup>_†_</sup>|0.148|-0.059|[.031]*|0.043|[.035]|-0.066|[.033]**|
|Arrested in past two weeks|0.118|-0.006|[.024]|0.007|[.025]|-0.033|[.024]|
|Aggressive behaviors, z-score|0.188|-0.153|[.110]|-0.043|[.107]|-0.339|[.109]***|
|In the last 4 weeks, have you been quick|0.611|-0.026|[.075]|0.047|[.078]|-0.117|[.078]|
|to react against others?||||||||
|In the last 4 weeks, have you refused to<br>take advice?|0.596|-0.113|[.081]|-0.042|[.082]|-0.183|[.082]**|
|Do you sometimes make hard jokes about<br>people?|1.236|-0.013|[.093]|-0.115|[.094]|-0.156|[.092]*|
|In the last 4 weeks, have you intentionally<br>destroyed property?|0.365|-0.026|[.073]|-0.057|[.072]|-0.110|[.074]|
|Do you sometimes cheat or scrape from|0.547|-0.047|[.084]|0.027|[.081]|-0.073|[.081]|
|people?||||||||
|In the last 4 weeks, have you ever had|0.606|-0.052|[.076]|0.016|[.079]|-0.123|[.082]|
|confusion with people about things?||||||||

Continued on following page.

xxii
of counterevidence against the significant selection bias is that the baseline characteristics and perceptions of women who attended the health clinic under Clinic condition and under Vaccine condition are not significantly different from each other (Appendix S6). Also, over 95 percent vaccine take-up among respondents under Clinic condition cannot be explained solely by selfselection anyway.

## **_Observational Data_**

Our baseline survey collected data on self-reported reasons for non-vaccination among women who had never received any vaccination before, as well as reasons why respondents who had children had never taken their children for any vaccination, if they had not previously done so, as commonly done in observational studies (Appendix S7). Although lack of awareness is the most common reason for women’s non-vaccination (36.9 percent), psychic costs, such as fear of side effects and fear of injection, are the second main reason (17.4 percent). Similarly, psychic costs are the main reason for the non-vaccination of children together with supply-side problems, such as insufficient supply of vaccines. These patterns certainly can mislead us to conclude that psychic costs of vaccination are significant barriers to vaccination. However, among those women who listed the psychic costs as the main reasons for non-vaccination either for themselves or for their children, more than 77 percent ended up receiving the vaccine in our study. Behavioral experiments are crucial to evaluate the causal effect of psychic costs.<sup>53</sup>

# **Priming about Disease Severity**

This sub-section examines whether the priming about disease severity increases the vaccine takeup. We find that, although the priming intervention increases the perceived severity of disease, it does not increase the vaccine take-up.

To identify the effect of priming about disease severity on vaccine take-up in a regression 18
Our results suggest that while certain structural parameters can favor simultaneously domestic human capital accumulation and skilled migration, this should nevertheless not prompt to conclude that higher migration necessarily encourage the constitution of higher stocks of human capital in source countries. Indeed, we cannot find, within the realms of likelihood, any conditions within which a change in migration possibilities alone results in higher net accumulation of human capital. Rather, the combination of high migration and high human capital accumulation patterns are due to third factors, and the conclusions obtained from cross-country analysis<sup>9</sup> should thus be interpreted with caution when used for policy making.

The rest of this paper is organized as follows. Section II describes the model and Section III assesses the impact of various parameters on the likelihood of brain gain. Section IV concludes.

# **II. Modeling Migration and Education Decisions**

To frame the migration-education nexus, we consider a small open economy in which agents make simultaneous decisions on education and migration based on contemporary information and assets. Our variable of interest is the domestic accumulation of human capital per capita, a major – if not the most important - driver of development. As embodied in persons, human capital is assumed to be imperfectly mobile across countries, as is physical capital. The latter is internationally mobile in the form of investment flows, but becomes immobile once installed.

Education investment decisions here refer to those allowing the acquisition of sufficient skills to migrate or fulfill domestic skilled jobs. In our framework, we assume that such decisions concern tertiary education expenditures, which are privately financed by households<sup>10</sup> .

In logarithmic approximation, the per capita domestic accumulation of human capital _h_ can be written as:

> 9 See Beine et al. (2008) for instance on the empirical impact of migration on human capital accumulation. 10 Alternatively, such expenditures could be financed by public authorities to respond to citizens needs. Given the fact that our model comprises only one representative household financing the government, this would not make any difference as long as the government reacts to the same signals to make its investment decisions. This is obviously not always or everywhere true, but this political economy dimension goes beyond the scope of this paper and would only complicate the discussion. See Docquier et al. (2008) for a discussion on the interaction between public expenditures in education and migration.

4
_________________________________________________________________________________

|Country|Year of<br>start|Labor produ<br>of export-sta|ctivity premia<br>rters|No. of<br>observ.|No. of<br>starters|
|---|---|---|---|---|---|
|______________|______________|(percent)<br>____________|[p-value]<br>________________|____________|_____________|
|Spain|1993|13.119|[0.170]|536|43|
||1994|25.713|[0.029]|529|42|
||1995|26.407|[0.009]|477|36|
||1996|30.361|[0.004]|471|41|
||1997|25.976|[0.024]|439|49|
||1998|21.451|[0.111]|369|30|
||1999|41.481|[0.032]|349|21|
|Sweden|2000|-5.918|[0.072]|6,857|126|
||2001|-7.133|[0.059]|7,171|110|
||2002|-11.308|[0.001]|6,945|107|
||2003|-9.787|[0.005]|7,097|120|
||2004|-8.881|[0.014]|6,827|106|
|UK|1998|8.255|[0.467]|652|32|
||1999|7.296|[0.571]|733|24|
||2000|13.591|[0.371]|784|24|
||2001|-9.412|[0.559]|851|49|
||2002|10.749|[0.452]|917|34|
||2003|25.458|[0.027]|932|36|
||2004|5.646|[0.667]|767|28|

_________________________________________________________________________________ Note: Results are for firms from ISIC industries 15 – 36 with at least 10 employees at the median over the years covered in the panel. The firms with the bottom / top one percent of labour productivity (defined as total sales per employee) in a year are excluded from all computations. The labour productivity premia are estimated in an OLS-regression of log (labor productivity) on a dummy variable for export starters controlling for the log of the number of employees and its squared value, the log of wages and salaries per employee, and dummy variables for 4-digit-industries, all measured three years before the start. To facilitate interpretation the estimated coefficients for the exporter dummy variable have been transformed by 100(exp(ß)-1).

79
Since identification critically depends on changes affecting only our eligible population, it is important to understand and account for simultaneous policies or other changes that could confound our analysis. The joint nature of our eligibility criteria is particularly helpful as identification is based on overlap between several demographic categories (caste/age/income/gender/sibling composition), reducing the likelihood that our estimates are confounded with the impacts of other programs targeted at one of these broad criteria (e.g. welfare assistance schemes for the poor). We also control for these criteria individually in our specifications. However, two female child welfare programs similar to _ABAD,_ should be explicitly addressed.

First, in August 2006, Haryana also launched the _Ladli_ ("Dearest") program. All families with a second female child born after August 2006 receive Rs. 5000 per year for a period of up to five years as long as both girls survive. This award is made regardless of caste, income or other restrictions. The money is invested in a government fixed deposit at 8.25% and released only when the younger sister turns 18. We do not anticipate any confounding effects from this program since our final survey measures are taken several months prior to August 2006.

Secondly, and more significantly, in August 1997, the Government of India introduced a centrally-sponsored national scheme called the _Balika Samridhi Yojna (BSY)_ . Under this scheme, a post-birth grant of Rs. 500 is invested in a savings account to be redeemed at age 18, with additional cash deposit bonuses for completing different education standards (levels). The education awards start from Rs 300 for Standards 1-3 to Rs 1000 for Standard 10. All female children belonging to families below the poverty line born on or after August 15<sup>th</sup> , 1997 were eligible, subject to a maximum of two girls per household. Like _ABAD_ , this scheme was also to be implemented via local health workers.

While _BSY_ might have been expected to complement _ABAD_ , reported distribution has been surprisingly low, perhaps due to high variance in the release of central funding. The annual number of recipients in Haryana since 1997 has been relatively insignificant, with a maximum of 9166 beneficiaries reported in 2001-2<sup>16</sup> . _BSY_ is thus unlikely to have had an economically meaningful impact.

# **6. Conclusions**

Using household survey data on fertility and child health from three rounds of India’s National Family Health Survey, we estimate the impact of a state-run program in Haryana that offers parents an immediate financial grant upon the birth of a daughter, coupled with a long-term savings bond redeemable by the unmarried daughter at the age of 18. We estimate program impacts on the sex composition of women’s living children and fertility preferences. We also estimate impacts on children’s health (mortality,

> 16 See <u>http://wcdhry.gov.in/balika_samridhi_yojana.htm</u> for details

17
4 modified with serious consequences for the number of persons enumerated in unpaid family work.<sup>5</sup> The need to adjust labor force statistics across countries and over time to reflect a common concept of gE.inful activity is widely recognized. This difficult task is not attempted here. Because of this measurement problem, separate analyses of the data for men ar.d women are reported, and since the definition of male and female wage earners is subject to less ambiguity than other job types, comparisons of male and female wage earners across populations are also undertaken. Because more than 95 percent of men age 25 to 55 tend to be counted as being in the 'Labor force, ambiguity is relativ_ly minor for measuring male participation in these ages. But even for men when participation is averaged for all persons over age i5, the extent of schooling and differences in age composition can affect substantially aggregate participation, in a manner that has not yet been sufficiently studied by economists.

The distinction is also drawn between the "informal" and "formal" sector, but this means many things to different analysts and no satisfactory scheme of measurement has been proposed.6 Some workers in small unregulated firms using traditional production technologies, and not benefiting from a formal contract, are said to belong to the 'informal" sector, but they are unavoidably classified here as wage earners, or in the 'modern sector," for lack of information on firm size, production technology, or type of labor contract. Most family and selfemployed workers are plausibly assigned to the "informal" sector. The firmfamily job type distinction analyzed here may therefore approximate the formalinformal sector dichotomy. The match, however, is anything exact.

# Descriptive Statistics of Labor Force by Sector

Tabulations of the labor force by job type, sex, and eight industrial