rafmacalaba/datause-encoder-data huggingface dataset:
the condition to attend school regularly seems most effective in keeping attendance rates high when households need cash the most.<sup>34</sup> _Test Scores_ In Table VI, we present the results of the tests of cognitive ability, mathematics, and English reading comprehension, which were administered to all study participants at their homes.<sup>35</sup> We see across the board improvements in test scores in the CCT arm, while no significant improvement can be detected in the UCT arm. The 0.14 SD improvement (p-value=0.01) in English reading comprehension in the CCT arm is significantly higher than the program impact in the UCT arm at the 90% confidence level (p-value=0.07). The CCT arm also has a 0.12 SD advantage over the UCT arm in the TIMMS math score, but this difference is not statistically significant.<sup>36</sup> Finally, in terms of cognitive ability, measured by Raven’s colored progressive matrices, we see improvements of 0.17 and 0.14 SD in the CCT and UCT arms, respectively. However, while the improvement in the CCT arm is statistically significant at the 99% confidence level, the impact estimate for the UCT arm is noisy and insignificant.

Summarizing the program impacts on schooling outcomes in the two treatment arms, we find that the CCT arm had significant gains in enrollment on the extensive margin, in attendance on the intensive margin, and consequently in achievement in tests of English, mathematics, and

> 34 CCT households may also have taken additional measures to minimize school absence by having the girls sleep under bed nets: the share of girls who reported sleeping under a bed net the previous night was 10 pp higher (p-value=0.049) in the CCT arm than the UCT arm in Round 3.

> 35 As we presented evidence in Table I that there is no differential attrition between the CCT and UCT arms, and in Appendix Table B.1 that the baseline characteristics of those who did not take the tests do not differ between the control group and the two treatment arms, we proceed to assess program impacts on test scores using single difference OLS regressions. Moreover, in the sample used for analysis in this paper, i.e. those who were successfully interviewed in all three rounds, only 30 girls have missing test scores (22 in the control group, 5 in the CCT arm, and 3 in the UCT arm). Even if all the controls were assigned the highest score for the English test and all treatment girls were assigned the lowest score, the results would not change.

> 36 TIMMS stands for Trends in Mathematics and Science Study, which is a cycle of internationally comparative assessments in mathematics and science carried out at the fourth and eighth grades every four years. We borrowed five mathematics questions from the 2007 TIMMS (four fourth-grade and one eighth-grade question) and incorporated them into our independently developed mathematics test.

22
**Table 5:** Agricultural/ Non-Agricultural employment

## **Non-Agricultural**

||**1997-2000**<br>**(Asian Cris**|<br>**is)**|**2001-200**|**7**|**2008-201**<br>**(Global C**|**1**<br>**risis)**|
|---|---|---|---|---|---|---|
||**OLC**|**S.E.**|**OLC**|**S.E.**|**OLC**|**S.E.**|
|Korea,Rep.|53.50<sup>***</sup>|3.42|34.40<sup>***</sup>|12.78|10.97<sup>***</sup>|2.05|
|Philippines|79.38<sup>***</sup>|16.27|-0.11|23.03|-1.97|16.27|
|Taiwan,China|0.18|26.44|67.09<sup>**</sup>|28.40|30.52<sup>***</sup>|7.19|
|**Agricultural**|||||||
|Korea,Rep.|-6.35<sup>***</sup>|1.75|-0.90|2.94|-3.83<sup>***</sup>|1.37|
|Philippines|-60.57<sup>**</sup>|24.58|12.66|22.42|-14.66<sup>*</sup>|8.26|
|Taiwan,China|0.90|1.24|-27.17|20.84|-3.03<sup>**</sup>|1.35|

*** p<0.01; ** p<0.05; * p<0.1 Note: Based on separate SUR’s using data on non-agricultural and agricultural employment. The statistical model is the short-run model plus the first lag of the independent variables.

# **Conclusion**

Was growth in East Asia jobless? The evidence suggests that it was not. All countries included in the analysis for this paper grew under the period of study, with minor – though pronounced – exceptions during the Asian and global financial crises. Looking at Okun’s Law as the net effect of growth on employment suggests that, at least in the short run, growth has created jobs. However, the extent to which this relationship holds differs across countries. The analysis revealed that, especially where hiring and firing rules are more flexible, growth translates into job gains. In addition, the analysis suggests that Okun’s Law holds both during economic expansions and contractions. It does thus not have a discernible bias toward creating or destroying jobs in the aggregate. Finally, the analysis has documented a countercyclical relationship between growth and jobs in agriculture, in other words a ‘reverse Okun’s Law’, which is particularly pronounced during times of economic crisis. This suggests that agriculture may serve as a shock-absorber for employees who are laid off in an economic downturn.

The results from the analysis raise a number of interesting questions for future research. For one, in terms of statistical estimation, the analysis has been inconclusive on systematic differences between the OLC during economic expansions and contractions. Other research (e.g. Balakrishnan _et al._ , 2009) finds the OLC coefficient tends to be stronger in times of crisis, which does not come out clearly in the analysis in this paper – which may be due to the small sample size or something specific to East Asia. More research should be devoted to testing the robustness of this finding. In addition, the analysis in this paper is restricted to estimating short-run and medium-run effects of growth on employment. The medium-run in this paper encompasses four quarters only. As more data and longer time series become available, more long-term effects, stretching beyond one year, should be examined.

11
Table A1: Early Life Exposure to Coffee Mills and Test Scores: Sample excluding Kigali

|||All Subjects|||STEM|||Non STEM||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|CatchmentArea X Post|0.0831***|0.0974***|0.0833***|0.0636***|0.0651***|0.0537***|0.0897***|0.1117***|0.0967***|
||(0.0268)|(0.0226)|(0.0216)|(0.0239)|(0.0209)|(0.0205)|(0.0279)|(0.0234)|(0.0223)|
||[ 0.003 ]|[ 0.001 ]|[ 0.001 ]|[ 0.01 ]|[ 0.003 ]|[ 0.004 ]|[ 0.003 ]|[ 0.001 ]|[ 0.001 ]|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|
|R-squared|0.3614|0.4033|0.4033|0.3542|0.3918|0.3918|0.3375|0.3820|0.3821|
|Observations|1410066|1409774|1409774|1410069|1409777|1409777|1410065|1409773|1409773|

Notes: Dependent variable is the standardized aggregate test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. CatchmentArea X Post is an indicator variable equal to 1 if a student was born within the vicinity ( _≤_ 2.5km) of an operational coffee mill and 0 if otherwise. Controls include gender of the student, average annual temperature and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at school level in parenthesis. False discovery rate (FDR) adjusted p-values for CatchmentArea X Post are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level Table A2: Coffee Mills and School Construction

||# N|ew School|s Construc|ted||Tot|al Schools||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Coffee Mills (0/1)|-0.0140|-0.0206|||-0.1159*|-0.0934|||
||(0.0119)|(0.0126)|||(0.0674)|(0.0669)|||
|# Coffee Mills Operating|||-0.0034<br>(0.0058)|-0.0043<br>(0.0061)|||-0.0610*<br>(0.0349)|-0.0471<br>(0.0341)|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|No|Yes|No|Yes|No|Yes|No|
|Province-Year FE|No|Yes|No|Yes|No|Yes|No|Yes|
|R-squared|0.0898|0.1022|0.0897|0.1020|0.9455|0.9484|0.9456|0.9485|
|Observations|7218|7218|7218|7218|7218|7218|7218|7218|

Notes:

> _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level

41
share with poor food access between the first expenditure decile and all other expenditure deciles. Column (1) estimates a specification using an indicator for the household being severely food insecure according to the FIES as the dependent variable, and illustrates that there is little difference between the share that is severely food insecure in the lowest expenditure decile and the share in nearly every other expenditure decile.

The estimate of the constant in Column (1) of Table 4 illustrates that 26.4 percent of — — households in the first expenditure decile the very poorest Nigerians are severely food insecure according to the FIES, which is nearly identical to the average share reported for the country in Table 1. Additionally, aside from the indicator for the tenth — — decile the very richest Nigerian households the estimates are small in magnitude, imprecisely estimated, vary in sign, and are statistically indistinguishable from zero at standard significance levels. Only for the richest decile is the share that is severely food insecure lower by approximately 8.1 percentage points, with the poorest Nigerians having only 1.4 times the share of individuals that are severely food insecure than the richest Nigerians.

The FIES is similarly poorly aligned with expenditure using different thresholds to denote poor food access. Column (2) re-estimates Specification (2) using an indicator for the household being moderately food insecure or worse according to the FIES. Similar to Column (1), the share of the population that is moderately food insecure or worse is similar for nearly all expenditure deciles. Until the ninth expenditure decile, the estimates are again small in magnitude, imprecisely estimated, and are statistically indistinguishable from zero at standard significance levels. Only for the ninth and the tenth deciles is the share with poor access significantly lower than in the first decile, with the share declining by 8.7 and 20.5 percentage points respectively. However, even with the decline, the share of the very poorest Nigerians that were moderately food insecure or worse was only 1.5 times more than the share of the very richest Nigerians.

Again, these patterns are in stark contrast to the FCS. Column (3) re-estimates Specification (2) using an indicator for the household having a poor or borderline FCS as the dependent variable. The estimates illustrate that poor food access according to the FCS 20
18 estimates of program leakage to non-target beneficiaries. This allows a potential link between cost per beneficiary (as discussed in section II), and cost per child removed from severe malnutrition (as presented in table 9), although additional information or assumptions are required to actually make that linkage. Anderson (1977) finds that **to** convert cost per beneficiary to cost per malnourished child fed requires multiplying by a factor of between 1.6 and 10.5. (he actual figures, for five different CARE projects, were: Tamil Nadu, 1.6; Pakistan, 1.9; Costa Rica, 3.1; Dominican Republic, 4.9; and 10.5, Colombia). Anderson however does not present information such that it is possible to calculate cost per child removed from malnutrition.

Obviously program data on cost-effectiveness of nutrition interventions are severely deficient.

# V. Financing.

As in the area of costs, the financing of nutrition programs has received far less attention than the financing of other social sectors such as education or health. Recent World Bank work on social sector expenditures in Latin America for example does not include disaggregated information on nutrition expenditures. Although some of the nutrition expenditures may be included in health expenditures, those which go through institutions other than the Ministry of Health are likely to be excluded. Detailed information on country expenditures on nutrition could be obtained only for India (Subbarao, 1989) and Brazil (Musgrove, 1989, and unpublished World Bank documents). For India information is available on state and central government expenditures, and for Brazil for federal government expenditures (state level expenditures are estimated to be about 10% of federal ones for Brazil: Saxenian, personal communication). In addition, Mateus (1989) painstakingly compiles information on expenditures on MCH and school feeding programs **iti** 16 Latin American countries. (These data obviously form only part of country expenditure on nutrition interventions, although possibly the lion's share). Finally, Huffman and Steel (1990) reproduce data from two other
higher _Doing Business_ score is associated with a higher number of articles mentioning _Doing Business_ .

When using a fixed-effect regression model to account for within country and within year variation as well as to eliminate the impact of time-invariant characteristics, the results come out statistically significant at a 1% level when using media articles as an independent variable and at a 10% level with media articles as percent of media outlets (see Table B6: Model 4 in Annex B). The results hold with and without controls. This supports the findings of the previously tested models. With the fixed effects model the intraclass correlation is about 30% (variance due to difference across panels).

# **VII. Conclusion**

Based on the observed results, local media, if used prudently, can serve as a powerful reform impetus. Every year _Doing Business_ prepares regional and country specific media briefings as well as gives numerous interviews to local press across the globe. When regulators receive well-informed press releases on the status of _Doing Business_ indicators in their spheres of influence, they are likely to act on this information. Therefore, the annual upward media coverage trajectory of _Doing Business_ is expected to continue, inspiring positive change and enticing rule makers to further improve the regulatory environment for local businesses. These data findings also allow us to cautiously draw a more general conclusion, presuming that media does have the power to impact political actions.

To further substantiate our claims, more similar studies and research are warranted. Continuing to use _Doing Business_ as a proxy, it would be interesting to investigate the quality and relevance of the implemented reforms. One could also explore whether media influences the actual process of reform selection and identification. For instance, in cases when media gives more prominence to reforms tackling bureaucratic processes and red tape, do regulators tend to focus reform efforts primarily in this area or do they go beyond what is discussed by the press? Another viable supposition is that countries care about their performance on _Doing Business,_ or any other indicators, vis a vis their neighbors. There is commonly a race to be the best among the peers. Hence, further analysis could be carried out to measure the magnitude of neighbors’ influence on local reform agendas.

Given the data at hand, we used a somewhat narrow scope ( _Doing Business_ data) to test a broader hypothesis. However, it would be beneficial to test the same assumptions in other important areas such as health, education, environment and social services. An example of a similar question to ask could be – does media coverage/criticism of poorly functioning education systems lead to reforms aimed at improving the quality of schools? Alternatively, a similar question to pose is - does media attention to environmental degradation spur regulators to adopt more environmentally friendly policies? Researchers, policy makers and media outlets would all greatly benefit from finding robust answers to these and similar questions.

22
<mark>ANNEX 1. REFERENCES</mark> Åslund, Anders. 2015. _Ukraine: What Went Wrong and How to Fix It_ . Peterson Institute for International Economics. http://EconPapers.repec.org/RePEc:iie:ppress:7014 Balabushko, Oleksii and Beer, Sebastian and Loeprick, Jan and Vallada, Felipe Pinto, 2017, Direct and Indirect Costs of Tax Treaty Policy – Evidence from Ukraine, WU International Taxation Research Paper Series No. 2017-01. Available at SSRN: <u>https://ssrn.com/abstract=2922156</u> Betliy, Oleksandra, and Kravchuk, Vitaliy. 2012. “ _Reforming state aid in Ukraine”_ , Policy paper No.2, March 2012, Institute for Economic Research and Policy Consulting Diwan, Ishac, Philip Keefer, and Marc Schiffbauer. 2015. “Pyramid Capitalism: Political Connections, Regulation, and Firm Productivity in Egypt.” Policy Research Working Paper Series 7354. The World Bank Fritz, Verena. 2007. “ _State-building: a comparative study of Ukraine, Lithuania, Belarus, and Russia”_ , Central European University Press, 2007 IER/GAG ‘Towards Higher Standards of Living: An Economic Agenda for Ukraine’, December 2004, <u>http://www.ier.kiev.ua/English/books_eng.cgi</u> UKRAINE: Technical Assistance Report — Reforming The State Fiscal Service, IMF Country Report No.16/48, February 2016, <u>https://www.imf.org/external/pubs/ft/scr/2016/cr1648.pdf</u> Hellman, Joel S., Geraint Jones, and Daniel Kaufmann. 2000. “‘Seize the State, Seize the Day’: State Capture, Corruption, and Influence in Transition.” Policy Research Working Paper Series 2444. The World Bank. http://EconPapers.repec.org/RePEc:wbk:wbrwps:2444 Kang, David. 2002. Crony Capitalism: Corruption and Development in South Korea and the Phillipines, Cambridge studies in comparative politics, Cambridge University Press, 2002 Kaufmann, Daniel and Vicente, Pedro C., Legal Corruption (November 24, 2005). Available at SSRN: https://ssrn.com/abstract=829844 or <u>http://dx.doi.org/10.2139/ssrn.829844</u> Khatri N. 2016. Definitions of Cronyism, Corruption, and Crony Capitalism. In: Khatri N., Ojha A.K. (eds) Crony Capitalism in India. Palgrave Studies in Indian Management. Palgrave Macmillan, London Legeida, Nina. 2001. “Implicit Subsidies in Ukraine: Estimation, Developments and Policy Implications”, Working Paper No.10, December 2001, Institute for Economic Research and Policy Consulting.

Lunina I. & Vincentz F. 1998. “Subsidies to companies in Ukraine” in Ukraine at the Crossroads: Economic Reforms in International Perspective edited by Siedenberg A. & Hoffmann L VAT Refund Arrears in Ukraine. Analysis and Recommendations on How to Solve the Problem, With a Special Focus on Agriculture. 2010 BE Berlin Economics 13
48

# International Financial Statistics.

Given the likely presence of heteroskedasticity in cross-country regressions of this type, equation (10) was estimated using a weighted regression procedure, where each country's income per capita was used as the weight. The results obtained are reported in Table 5. As can be seen from equations (14.1) and (14.2), the coefficients of the investment ratio and of the rate of growth of the labor force have the expected signs and are significant in every regression. More importantly for our purposes, the coefficients of the intervention index were always negative and highly significant. These results provide evidence supporting the hypothesis that higher trade intervention affects negatively economic performance. In equationa (15.3) and (15.4) we have included the product of the intervcntion index and the technological gap as an independent variable. This was done because equation (15) suggested a nonlinear relation between growth and trade intervention is nonlinear. As before the results support our hypothesis.

An interesting question refers to whether the relation between growth and trade intervention is strictly linear or if we can detect some nonlinearities. When a r2 term was incorporated the following result was obtained using weighted regressions: g- -1.742 + 0.286 (I/Y)j + 1.489 n - 0 039 G (-0.972) (6.353) (3.556) J (-1641) J - 3.758 r + 0.520 x2 Period: 1970-82 (-1.982) J (0.536) J _N:_ 28 R2 0.706 As can be seen, the coefficient of the intervention index is negative and that of its square is positive. This last coefficient, however, is not significant.
_5_ value of the difference between the individual's wage and the wage for the rawest of raw labor. There are a number of elements of human capital in addition to the kinds of general education captured by formal schooling enrollment statistics. There are the additions to productivity from better health and physical strength (Fogel 1994). There are formal and informal occupational (but not firm) specific training programs (such as apprenticeships. There are firm specific training programs for employees<sup>7</sup> . In addition there are wage increments to seniority, perhaps from on the job learning not specifically associated with training. Finally there are rents to special acquired skills that can be called human capital<sup>8</sup> . Moreover, since wage regressions in the US (and elsewhere) with every conceivable individual specific observable characteristic (age, education, sex, race, location of residence) typically only explain only about 40 percent or less of wage differences, if "human capital" is invoked to explain wage difference there are clearly large amounts of human capital left unmneasured. Henceforth, to call spades, I shall refer to only educational capital, not human capital.

In addition to the measures of educational capital I use two measures of physical capital. These are both created by a perpetual inventory method from investment rates and an initial assumption about the capital stock (based on a guess of the initial capital-output ratio). The major difference is that the capital stock series created by King and Levine (1994) (K-L) uses the Penn World Tables, Mark 5 (Summers and Heston, 1991) investrnent data while Nehru and Dhareshewar (1993) (N-D) use World Bank investmnent data. The two capital stock series are highly correlated and give very similar results. In most of the subsequent regressions, I use K-L physical capital data in regressions with B-L human capital and N-D physical capital data with N-S-D human capital data, but this pairing of the different physical and 7 Total training expenditures by firms in the US is estirmated to be around 40 billion, roughly a tenth of total (1992) expenditures of educational institutions of $390,000.

8 Steve Young, the quarterback of the San Francisco 49ers, for instance, could be said to have extraordinarily large human capital, although it has very little to do with his formal schooig. After all, I attended the same educational institution at the same time and, I would guess, had better academic performance, and yet my human capital is a (vanishingly) small fraction of his.