rafmacalaba/datause-encoder-data huggingface dataset:
world. This changing nature of innovation implies that discovery activity would be more frequent at lower levels of development when countries are farther from the global technological frontier, but decline and eventually be overtaken by on-the-frontier innovation as incomes rise.

The second reason why expected levels of discovery activity would depend on the level of income is the recently-uncovered pattern of the stages of productive diversification across levels of development. Imbs and Wacziarg (2003) analyze how productive diversification behaves across income levels, and found that although there are theoretical arguments supporting both a positive and a negative monotonic relationship between diversification and growth, the evidence shows that neither view is correct. There is, in fact, a robust pattern whereby as countries develop, production is diversified until reaching a relatively high level of GDP per capita, between $13,000 and $14,600 1996 US dollars, after which point economies become increasingly specialized.

Although Imbs and Wacziarg (2003) used domestic production and labor data in their analysis of the stages of diversification, the same result can be found in export data. We construct a Herfindahl index ( _H_ ) of exports for each country in every year with reported exports between 1992 and 2003, and estimate the following equation using a fixed-effects estimator:<sup>4</sup> The results, summarized in Table 1, indicate that, similar to the pattern in domestic production data, a country’s export basket becomes more diversified as income rises until a relatively high level, at which point the process reverses itself and specialization occurs. This happens in the export basket at a higher transition point than that found by Imbs and Wacziarg (2003) in domestic production data ($22,500 in 2000 US dollars, PPP adjusted, compared to $14,600 in 1996 US dollars), but these results support the view that the pattern of economic diversification is related to patterns of international trade.

# _<Insert Table 1 here>_

This robust pattern in both domestic production and export data suggests a particular relationship between discovery and levels of development, given the relationship between diversification and new products.<sup>5</sup> We expect countries at relatively products.

> 5 The connections between Imbs and Wacziarg (2003) and the process of discovery were first suggested by Hausmann and Rodrik (2003b).

5
TABLE A14: Impact of leaders’ exposure on ECD outcomes, by leaders’ education levels

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|ITT Leader|-0,14<sup>_∗∗_</sup>|-0,01|-0,18<sup>_∗∗∗_</sup>|
||(0,05)|(0,09)|(0,06)|
|ITT Leader X Leaders medium educ|0,12|-0,03|0,18<sup>_∗_</sup>|
||(0,08)|(0,14)|(0,10)|
|ITT Leader X Leaders high educ|-0,03|-0,00|-0,06|
||(0,09)|(0,13)|(0,11)|
|Observations<br>|2051|624|1427|
|Adjusted_R_<sup>2</sup>|0,457|0,155|0,539|
|ITT Leader + ITT Leader X Leaders medium educ|0,79|0,69|0,98|
|ITT Leader + ITT Leader X Leaders high educ|0,02|0,89|0,01|

_Note:_ All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months and the first principal component of all 7 tests for children 36-83 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 to 35 months. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, memory and self-control) of children aged 36 to 83 months. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 62
areas (EA).<sup>26</sup> In the first stage, the EAs were selected from each of the 30 strata (formed by districts split into urban and rural) with probability proportional to size. In the second stage, 20 households were randomly selected from each EA, giving a total sample of 11,280 households.

MIHS collects data on household consumptions of a wide range of food and non-food items, detailed information about the socio-demographic composition of the interviewed households, labor status of the household members, health, educational achievements, various sources of household income including income in-kind, and individual wages. A section of the questionnaire asks the opinion of the household head about the household’s standard of living, including questions about satisfaction with life and the economic ladder questions, which were added to the survey for the purpose of this study.

Our measure of overall welfare is provided by answers to the satisfaction with life ( _SWL_ ) question: “Overall, how satisfied (content, happy) are you with your life? Are you (1) very unsatisfied; (2) unsatisfied; (3) neither unsatisfied nor satisfied; (4) satisfied; (5) very satisfied?” This was asked of the household head, but with explicit reference to the household’s standard of living. The majority of the sample were not satisfied; 62.53% answered either (1) or (2) (24.35% gave (1)). Slightly less than one quarter were satisfied; 18.33% gave (4) as their answer and 5.57% gave (5). (The remainder, 13.57%, reported that they were neither unsatisfied nor satisfied.) These numbers indicate a much lower level of satisfaction with life than found in Western Europe where 80-90% of the population report that they are satisfied with their lives when asked the same question in surveys, as compared to one quarter in Malawi (Delhey, 2004). While _SWL_ is the natural choice for identifying a measure of utility, how should we measure economic welfare ( _Y_ ), as one argument of utility? We will use alternative objective and subjective measures. The most widely-used objective measure of economic welfare in

> 26 For a more detailed description of the sample design see National Statistics Office (2005).

20
We focus on the developing countries which include all the low and middle income countries as defined by the World Bank and for which data are available for our main variables of interest. There are 106 such countries in our sample (listed in Table 1). Our main regression results are based on the Ordinary Least Squares (OLS) methodology. Instrumental Variables (IV) regression results are also reported in detail in order to increase our confidence against a possible endogeneity problem with our main estimation results. All regression results use Huber-White robust standard errors and have been checked for possible outliers that may have unduly large effects on the main regression results.

Table 2 formally defines all the variables used in the paper along with data sources. Table 3 provides descriptive statistics of the variables, and Table 4 shows the correlation between them.

# **2.1 Dependent variable**

The main dependent variable is a measure of trade facilitation defined as the number of documents required for exports and imports ( _Documents_ ). The data source for the variable is the World Bank’s Doing Business project. Average values of the variable for all years for which data are available (2005 to 2010) were first computed; log values of these averages were then used to arrive at _Documents_ . For the full sample, the values of _Documents_ range between 2.2 (St. Kitts and Nevis) and 3.2 (Central African Republic), with the mean value equal to 2.75 and standard deviation of 0.23.

Figure 1 pictures the correlation between _Documents_ and our measure of country size (total population of the country (log values)) that is formally defined below. It shows that the number of documents required for exports and imports (trade facilitation) tends to increase (decrease) when country size increases. This relationship is economically large and also statistically significant at less than the 1 percent level. Figure 2 shows the full distribution of _Documents_ in the sample. The Doing Business data also reports on the number of days it takes to clear shipments of exports and imports ( _Time_ ) and the monetary cost of complying with all the procedures involved in exporting and importing ( _Cost_ ). Like 5
the certificate was designed to make this expectation salient.<sup>12</sup> Table 4 presents the results. Those in the work arm who receive a certificate exhibit no additional improvements in psychosocial well-being.<sup>13</sup> While helpful, this is not conclusive: perhaps the certificate was not an effective way to raise the perceived probability of future work. We therefore additionally estimate the expected and actual likelihood of employment after the intervention. Appendix Table A5 presents the impacts of each treatment arm on post-intervention labor market experiences. Six weeks after the intervention, those who were formerly employed are no more likely to find work, receive a higher wage, nor expect work or a higher wage in the future than their cash counterparts.

**Experimenter demand effects** Are the effects of employment driven by a desire to please enumerators for the employment opportunity received? We offer three reasons why we view this as unlikely. First, our key margin of comparison is between those in the employment arm and the cash arm, both of whom receive an equivalently substantial sum of cash. As such, both groups are equally ‘indebted’ to the experimenter and therefore equally vulnerable to demand effects. Those in the cash arm may arguably feel more indebted, as they receive cash without completing daily work in return.

Second, we complement our psychosocial index with measures that are not vulnerable to experimenter demand. Demand effects are unlikely to alter one’s cognitive ability as measured through the arithmetic questions and memory tests of our cognitive index. Our risk and time preference games are incentivized with meaningful stakes (respondents gamble with a minimum of 1.20 USD in the risk preference game and trade off 3.50 USD today with higher amounts tomorrow in the time preference game), stake sizes that de Quidt, Haushofer, and Roth (2018) have found effectively eliminate demand effects.

Perhaps employed individuals feel a need to impress the enumerator, as their proximate employer, in a way cash recipients do not. This may lead to reporting better mental and physical health and investing greater effort in the cognitive tasks. However, we find that life satisfaction increases substantially for both groups, inconsistent with a differential desire to impress among the employed. We also observe patterns of treatment effects within our validated PHQ-9 module that are inconsistent with experimenter demand (Appendix Table

> 12The signaling value of the certificate may have been diminished if other employers learned about the nature of the certificate distribution. Our time in the field suggests this is unlikely: we randomized certificate distribution at the block level to limit spillovers, only five people in each block of<sup> ̃</sup> 200 adults was involved in the experiment, and job opportunities were scarce.

> 13The certificate read “I engaged with Pulse Bangladesh to do data collection”. It was written this way in order to be generic enough to apply to all the individuals in the experiment, all of whom were providing us data from the weekly surveys.

18
# VI. References

- Basu, Kaushik, 1998, "Child Labor: Cause, Consequence and Cure, with Remarks on International Labor Standards," Department of Economics, Cornell University, mimeograph.

- Canagarajah, R. Sudarshan and Harold Coulombe, 1998, "Child Labor and Schooling in Ghana," Working Paper Series, no. 1844, The World Bank, Washington DC.

- DeGraff, D. S., R. E. Billsborrow and A. N. Herrin, 1993, "The implications of high fertility for children's time use in the Philippines," in Lloyd (ed.).

- Durryea, Suzarne, 1999, "Children's Advancement through School: The Role of Transitory Shocks to Household Income," Working Paper #376, Inter-American Development Bank: Washington DC.

- Fafchamps, Marcel and Agnes R. Quisumbing, 1998, "Social Roles, Human Capital and the Intra household Division of Labor: Evidence from Pakistan," Department of Economics, Stanford University. Mimeograph.

- Greene, William, 1997, _Econometric Analysis,_ Prentice Hall: New Jersey. **3rd** Edition.

- Gronau, Reuben, 1977, "Leisure, Home Production and Work -- The Theory of the Allocation of Time Revisited," _Journal of Political Economy,_ 85, 1099-1123.

- Grootaert, Christian and Harry A. Patrinos, 1999, Policy Analysis of Child Labor: A Comparative Study, St. Martin's Press (forthcoming).

- Grootaert, Christian and Ravi Kanbur, 1995, "Child Labor," Policy Research Working Paper 1454, The World Bank, Washington DC.

- Ilahi, Nadeern and Saqib Jafarey, 1998, "Markets, Deforestation and Female Time Allocation in Rural Pakistan," Department of Economics, McGill University. Mimeograph.

- Jensen, Peter and Helena S. Nielsen, "Child labour or school attendance? Evidence from Zambia," _Journal of Population Economics, 10,_ 407-424.

- Jacoby, Hanan and Emmanuel Skoufias, 1997, "Risk, Financial Markets and Human Capital in a Developing Country," _Review of Economic Studies,_ 64, 311-335.

- Kevane, Michael and Bruce Wydick, 1998, "Social Norms and the Time allocation of Women's Labcr in Burkina Faso," Economics Department, University of Santa Clara, mimeograph.

- Kumar, Shubh K. and D. Hotchkiss., 1988, "Consequences of Deforestation for Women's Time Allocation, Agricultural Production, and Nutrition in Hill Areas of Nepal," Research Report 69, International Food Policy Research Institute: Washington DC.

- Mason, Andrew and Julian Lampietti, 1998, "Integrating Gender into Poverty Analysis: Concepts and Methods," Working Paper, The World Bank, Washington DC.

- Mason, Andrew D. and Shahid R. Khandker, 1997, "Household Schooling Decisions in Tanzania," The World Bank, Washington DC. mimeograph.

- Patrinos, Harry A., and George Psacharopoulos, "Family Size, schooling and child labor in Pent-An empirical analysis," _Journal of Population Economics,_ 10, 387-405.

- Pitt Mark M. and Mark R. Rosenzweig, 1990, "Estimating the Intrahousehold Incidence of Illness: Child Health and Gender-Inequality in the Allocation of Time," _International Economic Review,_ 31, 969-989.

24
rice price changes. Our empirical work uses household survey data from the Vietnam Living Standards Survey (VLSS) that spans the period of the rice market liberalization. The first round of the VLSS took place between September 1992 and October 1993, and the second round of the VLSS took place between December 1997 and December 1998 (World Bank 2000). Both rounds are designed to be nationally representative, cross-sectional household surveys. Our analysis focuses on the more than 3,500 households in rural communities that are visited in both rounds of the survey (so that we can examine price changes). This survey enables us to consider the labor supply (participation and time spent) in various categories of work. This includes wage work (i.e. work outside the household in either agriculture or non-agricultural activities), market work within the household (i.e. in either agriculture or non-farm business), and household production (i.e., collecting wood and water, chores, home repair and maintenance, etc.).<sup>5</sup> Focus on work within the household is important in policy analysis of a poor economy such as Vietnam because only a small share of individuals work for wages in the labor market (Rosenzweig 1988). In fact, Smith, Thomas, Frankenberg, Beegle, and Teruel (2002) and Thomas, Beegle, and Frankenberg (2000) convincingly show that studies that abstract from work within households miss an important channel through which families in poor countries adjust to economic shocks.

Our results suggest that rice price increases are associated with lower participation in wage work for children and young adults. For example, the 30 percent increase in rice prices, observed in Vietnam between 1998 and 1993 as measured by VLSS, lowers participation in wage work by 55 percent for girls age 6-15 over their 1993 base, by 28 percent for young women age 16-24, by 20 percent for boys age 6-15, and by 30 percent for young men age 16-24 (albeit

> 5 In the VLSS, household production includes cleaning, cooking, washing, shopping, collecting water or wood, caring for animals, and building or maintaining the house, its surroundings, or furniture.

4
**Table 14: Sensitivity Of Wage Subsidy Effect On Employment  To** **<u>Supporting Measure that Ease Skill Constraint or Promote Labor Intensive Activities</u>**

|<br>% change in employment<br>10% wage<br>subsidy<br>(reference<br>case)*<br>(i)|<br> <br>Policy<br>Intervention<br>(ii)|<br>10% wage<br>subsidy plus<br>policy<br>intervention<br>(iii)|<br>Marginal effect<br>of wage<br>subsidy,<br>given policy<br>intervention<br>(iv)=(iii)-(ii)|<br>Employment gains<br>of wage subsidy<br>plus policy over<br>wage subsidy alone<br>(v)=(iv)-(i)|
|---|---|---|---|---|
|I. Intervention: 5% Increase in the Supply of Skill<br>Set A. Low el<br> <br>|ed Labor<br>asticity case (0.5*<br>|Reference Elastic<br>|<br>ities)<br>||
|medium-skilled formal<br>3.086<br> <br>|1.146<br>|4.297<br>|3.151<br>|0.068<br>|
|low-skilled formal<br>3.464|3.173|6.508|3.335|-0.130|
|Set B. Slightly higher<br> <br>|low elasticity case<br>|(0.75*Reference<br>|Elasticities)<br>||
|medium-skilled formal<br>4.814|0.828|5.766|4.938|0.124|
|low-skilled formal<br>5.360|2.830|8.348|5.518|0.158|
|II. Intervention: Intervention I plus 5% Increase|in Capital||||
|Set A. Low el<br> <br>|asticity case (0.5*<br>|Reference Elastic<br>|ities)<br>||
|medium-skilled formal<br>3.086|4.929|8.277|3.348|0.265|
|low-skilled formal<br>3.464|5.530|9.099|3.569|0.104|
|Set B. Slightly higher<br>|low elasticity case|(0.75*Reference|Elasticities)||
|medium-skilled formal<br>4.814|4.767|9.920|5.183|0.339|
|low-skilled formal<br>5.360|6.154|11.897|5.743|0.383|
|III. Intervention: Intervention II plus 10% produ<br>Medium-Skilled Labor<br>Set A. Low el|ction subsidy to A<br>asticity case (0.5*|ctivities with High<br>Reference Elastic|Value Added Sha<br>ities)|res in Low-Skilled and|
|medium-skilled formal<br>3.086|6.644|9.915|3.271|0.188|
|low-skilled formal<br>3.464|7.927|11.498|3.571|0.106|
|<br> <br>Set B. Slightly higher|<br>low elasticity case|<br>(0.75*Reference|<br>Elasticities)||
|medium-skilled formal<br>4.814|6.998|12.175|5.177|0.363|
|low-skilled formal<br>5.360|9.023|14.870|5.847|0.487|

_Source:_ CGE model simulations.

- 28 -
**Table 9. Education Attainment: Output-Efficiency scores by regions across the world**

# **- Multiple Inputs,  Multiple Outputs**

||AFR|EAP|ECA|LA|MNA|SAS|
|---|---|---|---|---|---|---|
|2 inputs (public expenditure, teachers per pupil) – 2<br>outputs (gross primary and secondary enroll.)|.68|.83|.80|.85|.71|.79|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 2 outputs (gross primary and<br>secondary enroll.)|.82|.88|.89|.89|.91|.90|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 2 outputs (net primary and<br>secondary enroll.)|.79|.97|.96|.96|.92|1.0|
|2 inputs (public expenditure, literacy of adult)- 3<br>outputs (first complete, second level complete, avg<br>yrs ofschool)|.64|.87|.94|.80|.79|.83|
|3 inputs (public expenditure, literacy of adult,<br>teachers per pupil)- 3 outputs (first complete,<br>secondlevelcomplete, avg yrs ofschool)|.86|.94|.93|.86|.80|.89|
|3 inputs (public expenditure, teachers per pupil,<br>literacy of adult) – 3 outputs (literacy of youth, first<br>levelcomplete, secondlevelcomplete)|.98|1.0|1.0|.98|.99|.99|

Rows 4 and 5 of Table 8 allow comparing the impact of adding the variable teachers per pupil as an additional input. In Africa the change is dramatic, while in ECA and MNA there is no significant change. Further analysis is required to explain this differential response to the inclusion of this input.

In health there are multiple combinations of inputs (public expenditure, private expenditure, and literacy of adults) and outputs (life expectancy at birth, immunization DPT, immunization measles, and Disability Adjusted life expectancy (DALE)). The combinations we selected produce rankings that are more homogeneous. The rank correlation is in the range of .65 to .98 (Tables 10-12). Bangladesh appears as efficient, as well as Niger, mainly due to the inclusion as of the ( low ) levels of literacy of adults as an input, and hence, making these countries to appear as efficient.

**Table 10. Health Attainment: Multiple Inputs, Multiple Outputs**

||Input-Efficient|OutputEfficient|
|---|---|---|
|More efficient|Bangladesh, Malaysia, Costa<br>Rica, Kuwait, Morocco, Oman,<br>Mauritius, Niger|Bangladesh, Costa Rica,<br>Kuwait, Malaysia, Morocco,<br>Mauritius, Oman, Niger|
|Least efficient|Russia, Belarus, Namibia,<br>Romania, Estonia, Croatia,<br>Lituania,, Hungary, Jordan|Namibia, Togo, Ethiopia,<br>Mozambique, Cote d”Ivoire,<br>Cameroon, Congo, Central<br>African Republlic, Nigeria,<br>Uganda|

22
is about the same as incumbent exports both in OECD countries and elsewhere. Taken together, these findings firmly suggest a lack of convergence in export quality between rich and poor countries.

Our approach here is an entirely atheoretical one. That is to say, we conduct our analysis of the dynamics of unit values by neither imposing any structure derived from theory nor by looking for consistency with any particular theoretical models of trade, innovation and growth in the global economy. A impressive recent literature has begun to explore the determinants and implications of product quality in the domestic economy and in trade. For instance, Waugh (2008) develops a general equilibrium theory of product quality and international trade in which producers make choices regarding the quality of their intermediate inputs given the set of endowments to they have access, Hallak and Sivadasan (2007) study the export patterns when firms vary in their productivity as well as in their ability to produce quality, Verhoogen and Kugler (2010) propose a theory of endogenous input and output quality choices to explain a variety of empirical observations concerning the relationship between plant size and prices. We hope that the empirical findings presented in this paper will serve to inform and stimulate further contributions along these lines. The rest of this paper is organized as follows. Section II describes the data. Section III discusses our empirical framework and results. Section IV concludes.

# II. Data Sources and Description

The data used in this study come primarily from data compiled by Feenstra, Romalis and Schott (2002) using official customs records from the U.S. Census Bureau. This dataset records U.S. imports annually at the 10-digit HS level by source country. It contains import values and quantities as well as entries for tariffs and transportation costs. Several filters are applied to clean the data. First, all imports under 25,000 dollars are dropped as are 4