rafmacalaba/datause-encoder-data huggingface dataset:
Table 3. Ordinary least squares – Mincer’s Equation. Year 2015

|Variables|Argentina|Uruguay|
|---|---|---|
|Non-routine cognitive|0.138261***<br>(0.006626)|0.0571613***<br>(0.003453)|
|Routine cognitive|0.0579983***<br>(0.004752)|0.0297189***<br>(0.003486)|
|Routine manual|0.0430737***<br>(0.012302)|-0.0182148***<br>(0.004679)|
|Non-routine manual|-0.0248547**<br>(0.0105)|-0.0263061***<br>(0.005292)|
|Experience|0.012084***<br>(0.000921)|0.0247491***<br>(0.000736)|
|Experiencia2|-0.000134***<br>(0.000002)|-0.0002611***<br>(0.000001)|
|Man|0.0722601***<br>(0.009096)|0.2277636***<br>(0.006925)|
|Years of education|0.0371603***<br>(0.001416)|0.0808401***<br>(0.001138)|
|Formal|0.4958803***<br>(0.00881)|0.3572354***<br>(0.008088)|
|Constant|2.571179***|3.188941***|
||(0.02658)|(0.01651)|
|No of observations|21707|49256|
|Statistic F|1053.0|2143.1|
|Prob > F|0.0000|0.0000|
|R2|0.30|0.28|
|R2 adjusted|0.30|0.28|

Source: Our own estimation based on household surveys and O*NET Note: Standard error shown in parenthesis. *** significant to 1%, ** to 5% and * to 10%.

# 5. Implications for public policy

The process of technological change could generate a reduction in the employment demand for those on middle incomes (in general associated with routine manual tasks), establishing a polarization of the labor market, creating two large groups of employment: one of poorly-paid activities, related to the performance of non-routine manual tasks, and the other of higher incomes related to non-routine cognitive tasks.

- 28 -
**Table 2: Participant characteristics**

|||Mex|ico|||Per|u||
|---|---|---|---|---|---|---|---|---|
||Mean|St. Dev|Min|Max|Mean|St. Dev|Min|Max|
|**_Demographics_**|||||||||
|Male (1 = yes)|0.50|0.50|0|1|0.45|0.50|0|1|
|Married (1 = yes)|0.55|0.50|0|1|0.63|0.48|0|1|
|Age|39.27|9.27|25|60|39.87|9.24|25|65|
|Monthly household income|$641|$424|$0.4|$3,516|$783|$590|$1|$4,734|
|Reports having no income|0.14|0.35|0|1|0.15|0.35|0|1|
|**_Socioeconomic level*: (1 = yes)_**|||||||||
|NSE: C|0.32|0.47|0|1|0.82|0.39|0|1|
|NSE: C-|0.31|0.46|0|1|0.06|0.24|0|1|
|NSE: D|0.37|0.48|0|1|0.12|0.33|0|1|
|**_Education: (1 = yes)_**|||||||||
|Secundary|0.48|0.50|0|1|0.56|0.50|0|1|
|Post Secundary|0.16|0.37|0|1|0.43|0.50|0|1|
|**_Occupation: (1 = yes)_**|||||||||
|Employed|0.38|0.49|0|1|0.32|0.47|0|1|
|Owns business|0.20|0.40|0|1|0.17|0.38|0|1|
|Unemployed or housewife|0.27|0.44|0|1|0.28|0.45|0|1|
|**_Internet: (1 = yes)_**|||||||||
|Has internet at home or office|0.69|0.46|0|1|0.84|0.36|0|1|
|Uses internet to compare prices and search for offers|0.16|0.37|0|1|0.79|0.41|0|1|
|**_Familiar with Types of Financial Insitutions_**|||||||||
|<br>Banks (1 = yes)|0.26|0.44|0|1|0.42|0.49|0|1|
|Number of types of financial institutions known out of 10|0.54|0.93|0|7|1.67|2.01|0|10|
|(banks, cajas, cooperatives, etc)|||||||||
|Knows at least one type of financial institution (1 = yes)|0.34|0.48|0|1|0.61|0.49|0|1|
|**_Product Usage: (1 = yes)_**|||||||||
|Has credit|0.33|0.47|0|1|0.33|0.47|0|1|
|Has savings account|0.36|0.48|0|1|0.67|0.47|0|1|
|Has credit card|0.19|0.39|0|1|0.53|0.50|0|1|
|**_Sources of information about financial products* (1 = yes)_**|||||||||
|Staff of financial institutions|0.37|0.48|0|1|0.27|0.45|0|1|
|Brochures and marketing materials|0.21|0.41|0|1|0.17|0.38|0|1|
|<br>Friends and family|0.21|0.40|0|1|0.10|0.30|0|1|
|<br>Media|0.17|0.38|0|1|0.16|0.37|0|1|
|**_Financial literacy (1 = yes)_**|||||||||
|Is familiar with Key Facts Statement|0.14|0.35|0|1|0.16|0.37|0|1|
|Knowledge of interest rate***|0.66|0.47|0|1|0.48|0.50|0|1|
|Number of observations||107|3|||90|0||

Data come from the initial survey. The types of financial institutions considered (out of 10) are: banks, cajas de ahorro, insurance companies, cooperatives, finance companies, edpymes, EEDE, AFP, pawn shop and moneylenders. *In Mexico, C-D socioeconomic groups identify low-to-middle income household. ** Only credit products for Mexico. *** Knowledge of interest rate is tested with the following multiple choice question: "If you deposit 100 soles/pesos in a bank account that charges you nothing and guarantees you a yield of 2% per year, how much would there be in the account by the end of the year, if no deposits or withdrawals are made?" Possible answers are: (a) Over 102. (b) Exactly 102. (c) Less than 102. (d) I don’t know. (e) I prefer not to answer.
Finally, we use a set of micro-level variables to control for the differences in firm characteristics across countries. First, larger firms may face reduced information asymmetries and less financial constraints. Therefore we control for firm size by using the natural logarithm of firms’ total assets. Second, we use the return on assets to capture profitability and liquidity of firms. Third, the ratio of fixed assets to total assets is included to reflect the maturity structure of firms’ assets. Firms with more fixed assets have higher collateral value but lower asset liquidity (Grudes and Opler, 1996) and tend to raise more long-term debt in order to match the maturity of their assets and liabilities (Stohs and Mauer, 1996). Fourth, we include the standard deviation of firms’ default probabilities in each country, based on Altman’s (1968) Z-score, to control for the higher dispersion in the credit quality of firms in certain markets and hence the banks’ increased need for screening their clients (as implied by our model in Section 4).

# **5.2. Econometric results**

We start with a brief review of bivariate correlations among the key variables that will enter our analysis. Figure 5 displays the average ratios of short-term debt to total debt for firms in our sample of 45 countries over the 1994-2004 period. China has the highest short-term debt ratio, whereas the United States have the lowest, at about one-third of China’s. There is a clustering of developing countries at the top of the range, such as Malaysia, Turkey and Hungary, indicating that firms in low-income countries use more short-term debt as a proportion of total debt. Additionally, we find the short-term debt ratio to be positively correlated with countries’ corruption levels, inflation rates, firms’ default probabilities and domestic banks’ non-performing loans (see Figures A1 and A2 in the Appendix). Figure 7 shows the key scatter-plots, which will be the focus of our empirical analysis. They suggest a negative correlation between short-term debt ratios and variables capturing the quality of credit information, namely the coverage of private credit bureaus and the quality of accounting standards.<sup>26</sup>

> 26 In Appendix Table A1, we report the average short-term debt ratio, the average private and public credit registries coverage, the average annual GDP growth and consumer price inflation rate for each country in our sample over the 1994-2004 period. We find that credit bureaus in countries with the lowest short-term debt ratios, namely US, Norway, New Zealand and Canada, have a higher than 80% coverage of the population, whereas the coverage in China is zero.

18
Groh, M., D. McKenzie, and T. Vishwanath, 2015. “Reducing Information Asymmetries in the Youth Labor Market of Jordan with Psychometrics and Skill Based Tests”, _World Bank Economic Revie_ _<mark>w</mark>_ <mark>29(suppl 1): S106-S117.</mark>

- Heckman J.J. and T. Kautz, 2012 “Hard Evidence on Soft Skills”, _Labour Economics_ , 19(4):451– 464.

Heckman, J. J. 2007. “The Economics, Technology and Neuroscience of Human Capital Formation.” _Proceedings of the National Academy of Sciences_ 104(33): 13250–255.

- Heckman, J. J., 1995. “Lessons from The Bell Curve”. _Journal of Political Economy,_ 103 (5), 1091-1120.

- Heckman, J. J., J. Stixrud, and S. Urzua, 2006. “The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior.” _Journal of Labor Economics_ , 24 (3), 411-482.

- Hicks, Loue E., 1970. “Some properties of ipsative, normative and forced-choice normative measures.” _Psychological Bulletin_ , 74, 167-184.

Hopkins, D., and G. King, 2010. Improving Anchoring Vignettes: Designing Surveys to Correct Interpersonal Incomparability. _Public Opinion Quarterly_ : 1-22.

<mark>Jack, B.K., 2011. “Market inefficiencies and the adoption of agricultural technologies in developing countries” White paper prepared for the Agricultural Technology Adoption Initiative, JPAL (MIT) / CEGA (Berkeley)</mark>

- John, O.P; L.P. Naumann, and C.J. Soto, 2008. “Paradigm Shift to the Integrative Big-Five Trait Taxonomy: History, Measurement, and Conceptual Issues. In O.P. John, R.W.robins & L.A. Pervin (Eds.), _Handbook of personality: Theory and research_ , pp114-158. New York, NY: Guilford Press.

- Krueger, A.B, and D.A Schkade, 2008. The Reliability of Subjective Wellbeing Measures. _Journal of Public Economics_ 92(89):1833-1845.

- Krutikova, S. and H.B. Lilleor, 2015. “Fetal Origins of Personality: Effects of early life circumstances on adult personality traits”, _CSAE working paper_ 2015-03.

- Kyllonen, P., 2016. “Designing Tests to Measure Personal Attributes and Noncognitive Skills”, in Lane, Suzanne; Raymond, Mark R.; Haladyna, Thomas M. (eds.) _Handbook of Test Development_ , Second Edition. New York: Routledge, p190-211”

36
|**Table 4: Using informal co**|**mpetition in**|**the service sec**|**tor as aproxy**||
|---|---|---|---|---|
|Dependent variable: Quality Certificate|**(1)**|**(2)**|**(3)**|**(4)**|
|Y:1 N:0|||||
|**Pane**|**l A: Log odd**|**s ratios**|||
|Informal Competition (service sectors,|2.515**|3.794**|3.574**|4.996***|
|CY avg.)|(1.273)|(1.707)|(1.801)|(1.873)|
|GDP per capita (logs)||0.994|1.181|0.235|
|||(1.227)|(1.240)|(1.164)|
|Labor Productivity (logs)||0.482***|0.432***|0.407***|
|||(0.121)|(0.131)|(0.135)|
|Number of workers (logs)||1.185***|1.139***|1.018***|
|||(0.188)|(0.189)|(0.188)|
|Age of firm (logs)||0.269*|0.291**|0.325**|
|||(0.139)|(0.146)|(0.141)|
|Manager experience (logs)||-0.269|-0.236|-0.235|
|||(0.177)|(0.185)|(0.186)|
|Herfindahl index||3.794**|-0.394|-0.258|
||||(0.984)|(0.970)|
|Exports (proportion of sales)|||0.904**|0.644|
||||(0.395)|(0.395)|
|Foreign ownership (%)|||0.012***|0.012***|
||||(0.004)|(0.004)|
|Women top manager Y:1 N:0|||-0.243|-0.277|
||||(0.325)|(0.301)|
|Annual employment growth (%)|||-0.008|-0.008|
||||(0.006)|(0.006)|
|New Product Y:1 N:0||||0.098<br>|
|||||(0.202)|
|Line of Credit Y:1 N:0||||0.001|
|||||(0.002)|
|Number of power outages (per month)||||<br>-0.014<br>(0.019)|
|Foreign Technology Y:1 N:0||||0.008***<br>(0.002)|
|Website Y:1 N:0||||0.006***|
|||||(0.002)|
|Total population (logs)||||10.715*<br>(6.163)|
|Industry fixed effects|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Country fixed effects|Yes|Yes|Yes|Yes|
|Number of observations|4,853|4,853|4,853|4,853|

45
|||**Increase in Import Value**|**Country with Largest Increase**<br>**Country with Largest Decrease in**|
|---|---|---|---|
|**Product**|<br>**Product Description**|**(US$, thousands)**|**in Import Value**<br>**Import Value**|
|848210|<br>Ball bearings|786|China<br>Russian Federation|
||||Change in import value<br>955<br>-527|
||||Import value in 2008<br>16,799<br>10,728|
||||% Change in import value<br>5.68<br>-4.91|
|901890|<br>Instruments & appliances used in medical/surgical/veterinary sciences, incl. other electro-medical apparatus|724|Germany<br>Russian Federation|
||& sight-testing instr., n.e.s. in 90.18||Change in import value<br>288<br>-110|
||||Import value in 2008<br>22,691<br>3,670|
||||% Change in import value<br>1.27<br>-3.00|
|830210|<br>Hinges of base metal|714|Germany<br>Russian Federation|
||||Change in import value<br>207<br>-68|
||||Import value in 2008<br>2,849<br>337|
||||% Change in import value<br>7.27<br>-20.15|
|300210|<br>Antisera & other blood fractions & modified immunological products, whether/not obt. by means of|695|United States<br>Russian Federation|
||biotechnological processes||Change in import value<br>282<br>-241|
||||Import value in 2008<br>8,705<br>4,004|
||||% Change in import value<br>3.24<br>-6.02|
|640399|<br>Other footwear without outer soles of leather, not covering the ankle.|661|China<br>Russian Federation / Ukraine|
||||Change in import value<br>708<br>-105 / -105|
||||Import value in 2008<br>16,888<br>1262.8 / 1269.2|
||||% Change in import value<br>4.19<br>-8.3 / -8.3|
|845229|<br>Sewing machines (excl. h-hold. type; excl. book-sewing machines of 84.40), other than auto. units|654|Japan<br>Russian Federation|
||||Change in import value<br>271<br>-4|
||||Import value in 2008<br>5,789<br>30|
||||% Change in import value<br>4.68<br>-13.47|
|732690|<br>Articles of iron/steel, n.e.s.|654|Germany<br>Russian Federation|
||||Change in import value<br>378<br>-534|
||||Import value in 2008<br>20,899<br>30,221|
||||% Change in import value<br>1.81<br>-1.77|
|850940|<br>Food grinders & mixers; fruit/vegetable juice extractors, domestic, with self-contained electric motor|605|China<br>Russian Federation|
||||Change in import value<br>284<br>-85|
||||Import value in 2008<br>5,624<br>672|
||||% Change in import value<br>5.05<br>-12.64|
|294190|<br>Antibiotics & their derivatives (excl. of 2941.10-2941.50); salts thereof|561|China<br>Russian Federation|
||||Change in import value<br>448<br>-3|
||||Import value in 2008<br>11,363<br>26|
||||% Change in import value<br>3.94<br>-11.63|

_Source:_ World Bank .

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 47
household is involved in planting any commercial crops<sup>9</sup> that would also be considered a household.

Table 1 displays the percentage of households involved in the different income generating activities, and how these activities are combined. There are four main activities which we consider: non-farm enterprises (NFE), non-farm wage labor, farm wage labor (outside one’s household’s farm), and commercial crop production. A number of preliminary findings are worth noting. First, the majority of Nigerian farming households diversify according to the aforementioned classification. On average, depending on the GHS-Panel survey round and location, 60-65 percent of households have a portfolio that is not solely based on food crop farming. Second, NFE diversification activities constitute the lion’s share for all non-farm diversification activities. Diversification by pursuing commercial crop production (which is more prominent in the South than in the North) follows after NFE activities. The other two options are quite marginal. Households also combine more than one activity. The most common combination is commercial crop production and NFE ownership.

Table 1 also illustrates a sharp variation in the number of diversifying households between the two survey rounds; Northern households in particular increased their participation in NFE activities by close to 10 percent. A plausible explanation for this increase is the climatic shock faced by Nigeria in 2011, (Figure 1). The country, in particualr in the Northern area, faced severe droughts that started just after the Post-Harvest 2011 survey data were collected and lasted until a few months before the new survey round (Post Planting 2012) started (Bjerge and Fisker, 2016). The increase in diversification observed can be considered a a response to these unexpected climatic events.

The welfare measure chosen for the analysis is expenditures per capita. We expect diversified households to have higher per capita expenditures. The reason for this is that these households, as discussed above, should have lower income volatility, and should be less susceptible to shocks. Table 3 presents summary statistics by survey round, for the whole country, and by region. Diversified households have significantly higher levels of per capita expenditures in the first survey round. Additionally, it is evident that average expenditures in the North are considerably lower than those in the South in both survey rounds. Non-diversifying households in the South appear to have on average higher expenditures than Northern households which diversify.

Education of the household head, is significantly greater (by more than a year) among diversifying households. Interestingly, due to lower levels of education in the North, during

> 9Cotton, sesame, ginger, Arabic gum, cocoa, kolanut, palm oil.

7
**PCR:** We report PCR at school level using EMIS data. We report PCR rather than PpCR, with no restrictions on the type of classroom.<sup>12</sup> We report PCR school-wide and at lower primary level. For overall PCR, we sum the total enrollment at each school and divide by the total number of classrooms available for use at the school. For lower primary PCR, we sum the total enrollment in Grades 1-4 and divide by the total number of classrooms at the school which are used entirely or partially for these grades.<sup>13</sup>

Our _ultimate_ outcomes of interest are student repetition rates and test scores.

**Repetition:** We report repetition rates at school level, both the overall rate across grades and the lower primary rate across grades 1-4. We calculate repetition rates from EMIS data. The rate is calculated using the number of students in each grade repeating a grade in the most recently completed school year, divided by the total number of students enrolled in that grade for that year; averaged across all grades offered in a school for overall repetition, or across Grades 1-4 for lower primary repetition.

**Student test scores:** We report student test scores at student level, using data from the MLSS learning assessments, which are norm referenced tests that cover competencies from Grade 1 through Grade 6. We employ data from a longitudinal sample of students who were in Grade 4 at baseline and progressed to upper primary grades during the course of MESIP (see Sample). We report student scores in English, Chichewa and Mathematics. Percentage scores in each subject are adjusted using Item Response Theory and converted to knowledge scores mean-centered at 500 points with a standard deviation of 100 points. For more on the MLSS learning assessments, see Appendix A.

- Mathematics Knowledge Score: This is measured by the knowledge score of each student in the mathematics test for each school.

- English Knowledge Score: This is measured the knowledge score of each student in the English test for each school.

- Total Knowledge Score: This is measured by the knowledge score of each student averaged across both subjects for each school.

in larger schools teachers may teach multiple grades. Our expansive definition of lower primary PTR includes all teachers teaching at least some of their time regularly in grades 1-4.

> 12 Although the learning shelters constructed via MESIP SIG are considered permanent classrooms, there is some evidence some were mis-classified as temporary during EMIS data collection; we use PCR to ensure all MESIP-supported and other construction is identified.

> 13 The majority of Malawi’s primary school teachers are class teachers teaching all subjects to one primary class, however, in larger schools teachers may teach multiple grades. Our expansive definition of lower primary PTR includes all teachers teaching at least some of their time regularly in Grades 1-4.

13
:17 As we can see from Figure 3, the static consequence of a development strategy oriented towards heavy industry is that the economy, based on the prices before distortion, suffers a loss of 'ea' in absolute magnitude or 'ea/eO' in relative measure." The income loss due to allocative inefficiency implies the reduction of surplus available for :investment. Assuming that a fixed portion of national income is used for investment, then the decline in investment will further diminish gross investment. Yet, assuming that the government's plan is to develop light and heavy industry in a fixed ratio of OX,IOY,, each production cycle will then repeatedly generate an income loss of 'ea/eO' in relative measure. All these will significantly dampen the growth of the whole economy. To maintain the growth rate, it is necessary to raise the accumulation rate. This will occur at the expense of consumption and lower living standards over an extended period of time)<sup>2</sup> Low technical efficiency Because profits ceased to be a measure of- efficiency and the planneo allocation system often failed to distribute materials on time, managers were forced to keep large reserves and had no incentive for utilizing resources economically. Over-staffing, under-utilization and overstocking of inventories are all characteristics of puppet-like state :The studies by Desai and Martin (1983) and by Whitesell and Barreto (1 988) estimate the misallocation of capital and labor among the sectors of the Soviet economy, which also adopted the heavy-industry-oriented development strategy. Desai and Martin find losses from misallocation in the range of. 3 to 10 percentpossibly up to 15 to 17 percent of the inputs employed in industry. Whitesell and Barreto find that in the early 1980s output gains equivalent to .4 to 6 percent could have been achieved by a reallocation of capital and labor among the sectors of Soviet industry.

t The average annual rate of accumulation was raised from 24-2 percent of national income in the first five year plan up to 33.0 percent and 33.2 percent in the fourth and fifth five year plans. The average annual growth rate for national income dropped from 8.9 percent in the first five-year plan down to 5.5 percent and 6.1 percent in the fourth and fifth five-year plans. As a result. wages for state employees were held almost constant between the years 1952-1978.