rafmacalaba/datause-encoder-data huggingface dataset:
and Bhatt (2015) and Azam (2016). Our sample includes not only the non-resident fathers but also other non-resident family members, and non-resident children of household heads in particular.

The data for Indonesia come from Indonesia Family Life Survey (IFLS) 2014 wave. IFLS's household roaster, nonresident parents module, and mother's marriage module allow us to construct father-children pairs whose education information is not subject to truncation bias. More details about the sample construction procedure, readers are referred to Ahsan, Emran, Shilpi (2021) and Mazumder et al. (2019).

The summary statistics for our main estimation samples are reported in Table 1. We rst report the average years of schooling for both father and children in our full sample born between 1950 and 1989 across three countries respectively. The average years of schooling for fathers is 4.24 in China, 3.63 in India, and 6.21 in Indonesia. The average years of schooling for children is 7.52 in China, 6.50 in India, and 9.52 in Indonesia. Therefore, Indonesia has the best education outcome for both fathers and children among the three countries while India has the lowest mean education for both generations.

We also report the summary statistics for our four main sub-samples: urban, rural, sons, and daughters in the following panels respectively. In each country, there is consistent ruralurban gap in education for both generations. Children in urban China and urban India have about 3 more years of schooling than children in rural areas, while the gap is smaller in Indonesia (2 years). All three countries exhibit dierent degree of gender gap in schooling among children: 1.3 years in China, 2.3 years in India, and 0.6 years in Indonesia. Gender gap in Indonesia is much smaller, consistent with a large literature showing that girls in Indonesia do not face any signicant disadvantages compared to the boys.

# (4) Evidence on Conditional Variance

Estimates of equations (1) (conditional mean) and (2) (conditional variance) for our full estimation samples (1950-1990 birth cohorts) are reported in Table 2. The estimates for the mobility equation (1) are in odd columns and those for the conditional variance equation (2) are in even columns. The evidence is consistent across the three countries: conditional variance of children's schooling is a negative function of father's education. Estimates from the mobility 10
Ideally, we would always have monthly data on revenues, expenses, and inventory levels which would allow for a more exact correction of the timing mismatches. But gathering data every month is expensive, both for the researcher and for the respondent. We experimented with two ways of asking more directly for the input purchases associated with the current month’s sales within the context of the quarterly SLMS survey. Both of these alternative methods increased the correlation between profits and revenuesexpenses, but not by as much as the markup method we have just discussed. The first alternative was to ask the rupee amount of inputs purchased in the reference month and used to produce goods sold in that same month, and then to ask for the rupee amount of inputs purchased in prior months which were used to produce goods sold in the reference month. For example, in the January 2007 survey, we asked for inputs purchased in December which were sold in December, and for inputs purchased prior to December which were used in goods sold in December. Added together, these represent the inputs associated with the reference month’s sales—that is, the cost of goods sold for the reference month. When we use these in place of the inputs purchased during the reference month, we find the correlation between profits and adjusted revenues minus expenses increases from around 0.26 to around 0.48 among manufacturers, and from 0.35 to 0.41 among retailers.<sup>7</sup> We also attempted to obtain estimates of the level of inventories held as raw materials or finished goods at the end of each calendar month. In the January, 2007 survey, we asked firms their level of inventories at the end of November and the end of December. We also asked firms what the level of inventories they expected to have at the end of January. We can calculate December’s cost of goods sold by subtracting the change in inventories during the month from the purchase of inputs in the month. That is, if the firm purchased 100 rupees of inputs in December, and inventories increased by 10 rupees between the end of November and the end of December, then the cost of goods sold in December is 90 rupees. We found that firms had a difficult time reporting on the level of inventories at

> 7 These data are from round 8 of the SLMS survey. We first asked these questions in the round 7 survey. As with the round 8 data, adjusting for the estimated cost of goods sold increases the correlations moderately, from 0.48 to 0.61 for manufacturers and from 0.51 to 0.65 for retailers.

13
larger than for non-exporters. Table 7 also provides some insight on the relation of foreign ownership with the probability of innovating. Foreign investment does not seem to increase the likelihood of innovation by exporters or non-exporters.<sup>26</sup> Table 7 has already revealed that a higher proportion of exporting firms than non-exporting firms say they have introduced technological innovations. We take this analysis further, looking at how the ratio of the percentage of innovating exporters to innovating non-exporters varies between manufacturing and services and across different levels of innovation.<sup>27</sup> The results are shown in Figure 7.

We can observe that in both manufacturing and services, exporters tend to have a much higher probability of innovating than non-exporters. Interestingly, the more “at-the-frontier” the innovation is, the larger the wedge between exporters and non-exporters. Exporters are 3 times more likely to produce an innovation at least new to the firm and this ratio increases to between 6 and 9 times for the likelihood to introduce an innovation new to the world.<sup>28</sup> Exporters seem to be more likely to be ‘cutting-edge’ innovators, while non-exporters seem relatively more likely to be adaptors of existing technology. This is consistent with the notion that exporters face tougher competition and need to innovate more aggressively to successfully compete on international markets.

Comparing these indicators between sectors, services firms looks similar to their manufacturing counterparts with a marginally higher ratio of innovations that are new to the world.

# **(v) Are services firms particularly intensive in ‘soft’ forms of innovation?**

The existing literature stresses the idea that services firms are more intensive in “non-technological innovations.” The Chilean innovation survey asks questions on non-technological improvements such as improvements in distribution, management, working environment and in relationships with other firms, which require binary subjective responses. Interestingly, running simple regressions on the probability of innovating in non-technological innovation shows no significant difference between services and 19
36 Appendix Table A2-3: Partial Correlation of Labor Force Growth with the Growth Rate of Output per Worker for Various Samples of Countries, 1970-1979

|Control variables:|None|YO<br>__-|YoBMP,LLY.XS|_N_|
|---|---|---|---|---|
|All|_0.05_<br>_________________|0.04<br>_<br>(.67**)**<br>(.76 .|0.08<br>(.48)|79|
|OECD|-.22<br>(.33)|-.08<br>(.72)|0.12<br>(.64)|22<br>_|
|Developing<br>___________|-0.06<br>~(.67)|0.02<br>(.90)|_M.M)_<br>(.99)|_-56_|
||.03<br>(.89)|0.01<br>_(.96)_|0.03<br>(.90)|19|
|Latn America<br>:_______________|0.13<br>__ (.60)|0.22<br>(.40)|0.08<br>(.81)|18|
|AsianPacific|0.17<br>(.59)|0.08<br>(.81)|0.47<br>(.24)|12|
|Low Iicome|-.03<br>(.90)|-.03<br>(.90)|0.04<br>(.87)|24|
|Middle Income|-.06<br>(.74)|-.02<br>(.93)|-.10<br>(.62)|32|
|Land Poor<br>W/l<br>vo SGP|0.07<br>(-67)|0.09<br>-<br>(.59)<br>*(.92)|0.02|38<br>I|
|Land Rich|0.04<br>(.83)|-.01<br>(.97)|0.20<br>(.24)|40<br>_|

Notes: 1) Number in parenthesis is the p-level of the test the correlation is zero.

2) Control variables: YO is inital income, BMP is the black market premium, LLY is the ratio of liqui liabilities to GDP. XS is the export to GDP ratio.
Table 12: Fixed Effects OLS Dep. Var. Sat. Index (0/1) - Unsatisfactory Starters

|log(GDP per capita growth)|-0.05|-0.05|-0.02|-0.01|-0.02|
|---|---|---|---|---|---|
||[0.06]|[0.06]|[0.08]|[0.08]|[0.07]|
|log(GDP per capita growth)*FCS|0.05|0.07|-0.02|-0.02|-0.02|
||[0.10]|[0.10]|[0.11]|[0.11]|[0.10]|
|CPIA Rating|1.12|0.99|1.40|1.07|0.51|
||[0.19]***|[0.20]***|[0.45]***|[0.45]**|[0.43]|
|(CPIA Rating)*FCS|-0.67|-0.46|-0.77|-0.56|-0.40|
||[0.33]**|[0.34]|[0.60]|[0.60]|[0.57]|
|TTL Quality||0.50|0.16|0.15|0.13|
|||[0.19]***|[0.25]|[0.24]|[0.21]|
|(TTL Quality)*FCS||-0.61|-0.21|-0.19|-0.11|
|||[0.29]**|[0.36]|[0.36]|[0.32]|
|log(Supervision Costs per Yr - $’000s)|||0.41|0.30|0.24|
||||[0.15]***|[0.15]**|[0.13]*|
|log(Supervision Costs per Yr- $’000s)*FCS|||-0.15|-0.06|-0.29|
||||[0.23]|[0.23]|[0.21]|
|log(Total Staff Weeks per Yr)|||-0.15|-0.12|-0.10|
||||[0.19]|[0.19]|[0.16]|
|log(Total Staff Weeks per Yr)*FCS|||-0.06|-0.07|0.20|
||||[0.27]|[0.26]|[0.23]|
|log(No. of Consultant Days per Yr)||||0.08|0.01|
|||||[0.03]**|[0.03]|
|log(No. of Consultant Days per Yr)*FCS||||-0.01|-0.00|
|||||[0.05]|[0.05]|
|log(Size of Country Offce)|||||0.48|
||||||[0.11]***|
|log(Size of Country Offce)*FCS|||||0.25|
||||||[0.19]|
|IRS Ratio of Country Offce|||||-0.18|
||||||[0.36]|
|(IRS Ratio of Country Offce)*FCS|||||0.71|
||||||[0.51]|
|Constant|-2.68|-2.63|-4.53|-3.62|-2.84|
||[0.50]***|[0.51]***|[1.13]***|[1.15]***|[1.06]***|
|_R_<sup>2</sup>|0.11|0.14|0.16|0.21|0.41|
|_N_|655|621|512|512|469|

43
of vocational school students versus score change for students in other tracks. The first row shows estimates of the relative performance change of vocational school students versus all students in other tracks. This is the most reliable comparison because of the highest possible sample size. As we noted above, the estimates show that the relative improvement in performance of vocational school students is higher than one standard deviation of international scores (100). Relative improvement in comparison to students in mixed general-vocational schools is slightly lower but still substantial.

Table 5: Relative score change (differences-in-differences) for students in vocational schools

|Relative score change|from PISA 2000 to PISA 2003<br>_Kernel matching_<br>_1-1 matching_|from PISA 2000 to PISA 2006<br>_Kernel matching_<br>_1-1 matching_|
|---|---|---|
|ISCED 3C versus ISCED 3A+3B|115.5<br>109.6|115.7<br>110.0|
|ISCED 3C versus ISCED 3A|127.1<br>121.5|127.2<br>113.3|
|ISCED 3C versus ISCED 3B|96.2<br>93.7|98.0<br>93.5|

Summing up, there is no doubt that students who were in vocational tracks in 2000 would score much lower without the reform. The results show that the reform improved the overall mean performance of 15-year-olds in Poland, mainly by boosting the performance of students in former vocational and mixed general-vocational tracks. There are two remaining question which are of policy relevance. One is whether this large positive impact of the reform was long lasting. More precisely, the question is whether 15-year-old students in lower secondary schools still have higher achievement one or two years later, after they were again separated into tracks at the upper secondary school level. Secondly, it seems interesting to find out what particular changes in curriculum or organization of the school system boosted student scores. These two issues are investigated below by utilizing data from the PISA 2006 national option in Poland which provides performance scores for 16- and 17-year-olds and by employing decomposition analysis.

# **7. Additional Analyses**

PISA gives an option to participating countries to conduct additional research using the framework and measurement tools of PISA. Poland utilized this option in 2006 for the first time to conduct additional surveys among 16- and 17-year-old students (see Federowicz 2007 for the report on PISA 2006 in Poland). These students were mostly in upper secondary school, which opens up a possibility for comparisons with students in lower secondary schools (from the international sample). After taking into account the difference in student age, one can compare the performance of 15-, 16- and 17-year-olds, also across educational tracks of upper secondary schools. In other words, knowing the achievement at the end of lower secondary schools we can compare how much students updated their skills during the time of education in different types of upper secondary schools.

21
that initially do not export to one or both markets, the results go weakly in the same direction. Hence: Analogously, it is straightforward to show that an increase in the Southern wage has the opposite effect on average output and input prices, since output and input prices are lower on the production line destined for South: To summarize, (A26)-(A29) indicate that an increase in the wage level in North (which can be interpreted as a real-exchange-rate appreciation in North) all else equal will lead to an increase in firm-average output and input prices among Home firms, and an increase in the wage level in South will lead to a decrease in both firm-average output and input prices. These are the testable implications that we take to the data in the main text.

# **References**

Arkolakis, C., S. Demidova, P. J. Klenow, and A. Rodriguez-Clare (2008): “Endogenous Variety and the Gains from Trade,” _American Economic Review_ , 98(2), 444 – 450.

Baldwin, R., and J. Harrigan (2011): “Zeros, Quality and Space: Trade Theory and Trade Evidence,” _American Economic Journal: Microeconomics_ , 3(2), 60–88.

Chaney, T. (2008): “Distorted Gravity: The Intensive and Extensive Margins of International Trade,” _American Economic Review_ , 98(4), 1707 – 1721.

Crozet, M., K. Head, and T. Mayer (2012): “Quality Sorting and Trade: Firm-Level Evidence for French Wine,” _Review of Economic Studies_ , 79, 609–644.

Eckel, C., L. Iacovone, B. Javorcik, and J. P. Neary (2011): “Multi-Product Firms at Home and Away: Cost versus Quality-Based Competence,” CEPR discussion paper no. 8186.

Gervais, A. (2013): “Product Quality and Firm Heterogeneity in International Trade,” Unpub. paper, Univ. of Notre Dame.

Ghironi, F., and M. J. Melitz (2005): “International Trade and Macroeconomic Dynamics with Heterogeneous Firms,” _Quarterly Journal of Economics_ , 120(3), pp. 865–915.

Hallak, J. C. (2006): “Product Quality and the Direction of Trade,” _Journal of International Economics_ , 68, 238–265.

Hallak, J. C., and P. Schott (2011): “Estimating Cross-Country Differences in Product Quality,” _Quarterly Journal of Economics_ , 126(1), 417–474.

Hallak, J. C., and J. Sivadasan (2013): “Product and Process Productivity: Implications for Quality Choice and Conditional Exporter Premia,” _Journal of International Economics_ , 91, 53–67.

Helpman, E., M. J. Melitz, and S. R. Yeaple (2004): “Export versus FDI with Heterogenous Firms,” _American Economic Review_ , 94(1), 300–316.

Hsieh, C.-T., and R. Ossa (2011): “A Global View of Productivity Growth in China,” NBER working paper no. 16778.

Johnson, R. C. (2012): “Trade and Prices with Heterogeneous Firms,” _Journal of International Economics_ , 86(1).

A8
mortality risks by 3.2 percentage points (Rukumnuaykit 2003). Cutting back on essential medical expenses during crises is an important factor leading to malnutrition and other long term health impacts (Klotz et al 2008). Given the clear links that have been shown to exist between economic shocks and worsening malnutrition rates (Alderman et al. 2006, Ferreira and Schady 2008), shocks of the order that was seen in 2008 and 2009 can clearly have serious human consequences.

Senauer and Sur (2001) assessed the impact of changes in global food prices on undernourishment (hunger). In this paper we build on their methodology to quantify the impact of the 2008 food price increases as well as modeling the impact of the 2009 growth slowdown on undernourishment. Like Senauer and Sur we use calorie insufficiency, based on cross-country data<sup>1</sup> compiled regularly by the Food and Agriculture Organization (FAO), as our measure of undernourishment. Calorie insufficiency is equated with hunger and food insecurity as it relates to the minimum food consumption required to attain required energy thresholds. We also recognize its limitations as an indicator. Calorie intake is an input-based indicator of undernourishment, while nutritional outcomes are clearly also affected by factors such as health and sanitation (Alderman 1993). Secondly a calorie-based measure ignores the other important micro-nutrients required for a healthy individual. However the advantage of this measure is that FAO is able to compile undernourishment numbers consistently for a large number of countries and the methodology which is used here to link changes in income and prices with calories, cannot be applied with other measures of malnutrition.

The rest of the paper is organized as follows. Section 2 discusses the methodology we use to assess the impact of these recent economic shocks in detail. In Section 3 we discuss the simulation results of the impact of higher food prices on undernourishment in 2008 and the

> 1 The FAO methodology relies on three key statistics: (i) an estimate of per capita daily energy (calorie) supply available in the country determined from food balance sheets developed from country level agricultural production and trade data; (ii) the coefficient of variation of the distribution of calorie consumption among the country’s population obtained from household surveys whenever they exist and regional averages when they don’t, and (iii) the minimum per capita daily energy requirement for the country’s average person that reflects the age and gender composition of the population.  Based on these statistics and assuming a log-normal distribution for calorie consumption, the probability that an average person is below the minimum energy requirement is determined and interpreted as the proportion of the population that is undernourished (FAO).
|Development Bank of Mauritius Project(06)||
|---|---|
|Development Bank of Mauritius Project(06)||
|Development Bank of Mauritius Project(06)||
|Water SupplyProject(01)||
|Water SupplyProject(01)||
|Water SupplyProject(01)<br>||
|Water SupplyProject(01)||
|Water SupplyProject(01)||
|Structural Adjustment Project(02)||
|HighwayProject(01)||
|HighwayProject(01)<br>||
|HighwayProject(01)||
|HighwayProject(01)||
|HighwayProject(01)||
|Technical Assistance Project||
|Technical Assistance Project||
|Technical Assistance Project||
|Industrial Sector Adjustment Loan Project||
|Industrial Sector Adjustment Loan Project||
|Industrial and Vocational TrainingProject|Joseph W. B. Bredie|
|Industrial and Vocational TrainingProject|Joseph W. B. Bredie|
|Industrial and Vocational TrainingProject<br>|Joseph W. B. Bredie<br>|
|Industrial and Vocational TrainingProject|Joseph W. B. Bredie|
|Industrial and Vocational TrainingProject|Joseph W. B. Bredie|
|Industrial and Vocational TrainingProject|Joseph W. B. Bredie|
|Industrial and Vocational TrainingProject|Joseph W. B. Bredie|
|Industrial and Vocational TrainingProject|Joseph W. B. Bredie|
|Industrial and Vocational TrainingProject|Joseph W. B. Bredie|
|Sugar IndustryProject||
|Sugar IndustryProject||
|Sugar IndustryProject||
|Sugar IndustryProject||
|Sugar IndustryProject||
|Sugar IndustryProject||
|Sugar IndustryProject||
|Agricultural Management and Services Project|Ousmane Seck|
|Agricultural Management and Services Project|Ousmane Seck|
|Agricultural Management and Services Project|Ousmane Seck|
|Agricultural Management and Services Project|Ousmane Seck|
|Agricultural Management and Services Project|Ousmane Seck|
|Agricultural Management and Services Project|Ousmane Seck|
|Agricultural Management and Services Project|Ousmane Seck|
|Agricultural Management and Services Project|Ousmane Seck|
|Agricultural Management and Services Project|Ousmane Seck|
|Industrial Finance Project||
|Industrial Finance Project||
|Industrial Finance Project||
specified in the survey. These problems call for prudence in the use of the econometric results, but it seems warranted to conclude that financial aid matters for university access.

# **6. Impact of Loans on Student Behavior**

The second channel through which financial support can contribute to human capital accumulation is by changing the behavior of students. According to the survey results, almost 60% of the students indicated that they increased their effort because of the loan, and 22% said that the loan affected their career choice. In this section we first discuss the proposed methodology to investigate the relationship between loans and student behavior, and then turn to a discussion of our findings for student performance, part-time employment decisions, and career choice.

# _6.1 Research strategy_

We want to investigate the effect of the SOFES loan on the behavior of students in terms of study choice, decision to work on the side, and student performance. To estimate the impact of SOFES on student behavior we need to have a control group. In case of the survey data the group of students that did not receive a credit from SOFES can be used as a control group. The general regression model is given by where _Y_ is the endogenous variable of interest, and the other symbols have the same meaning as above. The treatment effect, i.e. the impact of the intervention on the outcome variable, is measured by γ. Ideally, assignment to the treatment group and to the control group is random (a so-called controlled experiment; this is the standard research method in for instance pharmaceutical studies where the effects of new drugs are tested). In our case, assignment to the treatment group and the control group is not random, but linked to a set of observed and unobserved factors. Whereas we can correct for the observed factors (the most important being the student’s socio-economic background), the influence of unobserved factors (such as the student’s motivation) cannot be ruled out. When the incidence of treatment depends on unobserved factors (technically, cov(<sup>ε</sup> _i_ , _T_ ) ≠ 0 ), γ will be biased.

Let us further discuss some sources of selection bias. Treatment is not a random event, but the outcome of a deliberate application process. Before entering our analysis as a SOFES recipient, several decisions by students could create sample bias. Students applying for a SOFES loan are well-informed about the terms and repayment conditions, and this can create selection bias. In particular, unmotivated students are not likely to apply, as the consequences of default are severe. Whereas we control for student ability by including 17