rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Legal regulations and the welfare state

|_Dependent Variable:_||Migratio|n Flows||
|---|---|---|---|---|
||WGI CC <br>(1)|WGI GE<br>(2)|CPI<br>(3)|ICRG<br>(4)|
|WGI CC in _o_|0.22***<br>(0.01)||||
|WGI CC in _d_|–0.34***<br>(0.01)||||
|WGI GE in _o_||0.33***<br>(0.02)|||
|WGI GE in _d_||–0.53***<br>(0.02)|||
|CPI in _o_|||0.21***<br>(0.02)||
|CPI in _d_|||–0.33***<br>(0.01)||
|ICRG in _o_||||0.18***<br>(0.01)|
|ICRG in _d_||||–0.27***<br>(0.01)|
|Social protection exp. (_%_ GDP) in _o_|–0.01*<br>|–0.01<br>|–0.01*<br>|–0.00<br>|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Social protection exp. (_%_ GDP) in _d_|0.01*<br>|0.01<br>|0.01*<br>|0.01**<br>|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Product market regulation in _o_|–0.65*** <br>(0.05)|–0.58*** <br>(0.05)|–0.64*** <br>(0.05)|–0.64***<br>(0.05)|
|Product market regulation in _d_|–0.63*** <br>|–0.79*** <br>|–0.64*** <br>|–0.58***<br>|
||(0.06)|(0.06)|(0.06)|(0.07)|
|EPL: Individual dismissals in _o_|–0.06<br>(0.04)|–0.02<br>(0.04)|–0.06<br>(0.04)|–0.04<br>(0.04)|
|EPL: Individual dismissals in _d_|–0.20*** <br>(0.05)|–0.30*** <br>(0.04)|–0.20*** <br>(0.05)|–0.18***<br>(0.05)|
|EPL: Temporary contracts in _o_|–0.02<br>|–0.05*<br>|–0.03<br>|–0.01<br>|
||(0.03)|(0.03)|(0.03)|(0.03)|
|EPL: Temporary contracts in _d_|0.15***|0.20***|0.18***|0.13***|
||(0.03)|(0.03)|(0.03)|(0.03)|
|Constant|0.86**|1.31***|0.82**|0.80*|
||(0.41)|(0.41)|(0.42)|(0.46)|
|Gravity controls|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Adj. R-Square|0.58|0.59|0.58|0.57|
|N|4136|4136|4136|3328|

The additional gravity controls are those included in Table 3. Robust standard errors clustered by county in parenthesis. ***, **, and * indicate statistical significance at the 1 _%_ , 5 _%_ , and 10 _%_ levels, respectively.

19
**Table 2: Number of trade measures by status of implementation**

||**_Meas_**|**_ures still in effec_**|**_t as of mid-July_**|**_2020_**|**_Measur_**|**_es no longer in eff_**|**_ect as of mid-Ju_**|**_ly 2020_**|
|---|---|---|---|---|---|---|---|---|
||<br>(1)|<br>(2)|<br>(3)|<br>(4)|<br>(5)|<br>(6)|<br>(7)|<br>(8)|
|**Variables**|**lib_m**|**res_m**|**lib_x**|**res_x**|**lib_m**|**res_m**|**lib_x**|**res_x**|
|Ln(Total_stepsj)|3.86***|3.72|20.61***|2.75*|-0.12|274.81***|2.06|-1.52|
||(1.31)|(4.45)|(6.56)|(1.60)|(3.11)|(3.72)|(2.33)|(1.88)|
|Ln(Total_timej)|0.31|1.71*|-13.33***|2.04***|1.57|-47.89***|0.73|1.10|
||(0.32)|(1.04)|(3.24)|(0.79)|(2.05)|(1.37)|(1.56)|(0.75)|
|Eprocj|-0.06|-1.02**|-0.18|0.31|0.19|-0.61***|1.06***|0.08|
||(0.10)|(0.49)|(0.25)|(0.23)|(0.26)|(0.10)|(0.30)|(0.17)|
|Ln(Num_DPAj)|<br>-0.51***|<br>-2.50***|<br>4.35***|<br>-0.06|<br>-0.16|||<br>0.11|
||(0.15)|(0.71)|(1.35)|(0.20)|(0.25)|||(0.21)|
|GPAj|<br>1.14***|<br>1.16||<br>0.71|<br>0.09|||<br>0.75*|
||(0.32)|(1.10)||(0.53)|(0.63)|||(0.44)|
|Ln(POPj)|<br>0.13*|<br>0.22|0.26|<br>0.40**|<br>-0.19|-0.20|-0.29|<br>0.16|
||(0.07)|(0.24)|(0.19)|(0.16)|(0.16)|(0.16)|(0.25)|(0.15)|
|Ln(MPj)|<br>-0.13*|<br>0.25|<br>-0.11|<br>0.31**|<br>0.21|<br>11.23***|<br>-0.21|<br>0.16|
||(0.07)|(0.33)|(0.39)|(0.13)|(0.24)|(0.18)|(0.27)|(0.11)|
|GEj|<br>0.35**|<br>3.20***|<br>-3.03***|<br>1.08***|<br>0.63|<br>8.37***|<br>-0.31|<br>0.21|
||(0.16)|(0.52)|(0.97)|(0.38)|(0.54)|(0.67)|(0.83)|(0.43)|
|Mshj|4.72|-5.45|||-0.77|-299.18***|||
||(3.37)|(13.71)|||(10.44)|(8.57)|||
|RCAj|||-0.75|-0.26|||1.01*|-0.28|
||||(0.87)|(0.63)|||(0.61)|(0.44)|
|Ln(1+Tarj)|0.31|0.03|2.25**|-0.22|1.10**|29.81***|-1.61**|-0.42|
||(0.21)|(0.41)|(0.93)|(0.29)|(0.52)|(0.54)|(0.67)|(0.40)|
|Ln(Covid_casesj)|-0.03|0.15|-0.34|0.01|0.10|8.20***|-1.48***|0.08|
||(0.12)|(0.23)|(0.24)|(0.17)|(0.31)|(0.29)|(0.46)|(0.26)|
|Ln(Covid_deathsj)|<br>0.20|<br>0.47|<br>-0.52|<br>0.01|<br>0.04|<br>-7.06***|<br>1.20***|<br>-0.15|
||(0.13)|(0.32)|(0.35)|(0.15)|(0.26)|(0.23)|(0.34)|(0.20)|
|Constant|<br>-13.92***|<br>-26.98***|<br>20.79*|<br>-24.84***|<br>-14.54|<br>-642.77***|<br>-5.16|<br>-3.85|
||(4.09)|(9.28)|(11.41)|(7.86)|(9.93)|(8.66)|(13.02)|(6.68)|
|Observations|97|97|75|97|50|33|43|50|
|R-squared|0.66|0.86|0.35|0.45|0.30|1.00|0.63|0.48|

**_Note_** _:_ Robust standard errors, clustered by country, included in parentheses. Levels of significance: *10%, **5%, ***1%.

13
**Table 9:** Life Expectancy at Birth Effects on Lifetime Earnings with Correction for Selection including Cohort Size in Labor Force

||**Pooledsample**|**Males**|**Females**|**Rural**|**Urban**|
|---|---|---|---|---|---|
|**Life**e**xpectancy at**b**irth**|**_0.013***_**<br>**_[0.003]_**|**_0.015***_**<br>**_[0.004]_**|**_0.013***_**<br>**_[0.003]_**|**_0.013***_**<br>**_[0.004]_**|**_0.010***_**<br>**_[0.003]_**|
|Age|0.077***<br>[0.011]|0.090***<br>[0.015]|0.060***<br>[0.009]|0.062***<br>[0.012]|0.083***<br>[0.012]|
|Age-squared*(1/100)|-0.10***<br>[0.000]|-0.10***<br>[0.000]|-0.10***<br>[0.000]|-0.10***<br>[0.000]|-0.10***<br>[0.000]|
|Rural|-0.367***<br>[0.066]|-0.360***<br>[0.073]|-0.357***<br>[0.050]|||
|Male|0.431***<br>[0.017]|||0.486***<br>[0.016]|0.403***<br>[0.018]|
|Marital status|-0.103***|-0.240***|0.076***|-0.114***|-0.096***|
||[0.021]|[0.046]|[0.029]|[0.034]|[0.019]|
|Cohort size|-2.564|-0.009|-1.548|-3.384|-1.228|
||[3.161]|[4.284]|[2.835]|[4.292]|[3.315]|
|Parents’ life expectancy|0.005***<br>[0.002]|0.008***<br>[0.003]|0.002<br>[0.001]|0.006***<br>[0.003]|0.006***<br>[0.002]|
|Correction term for|0.202|-4.038|0.984|2.349|-1.619|
|selection|[4.499]|[6.217]|[4.005]|[6.324]|[4.074]|
|(Proportion of birth-year<br>cohort in labor force)||||||
|Constant|4.473***|4.467***|4.898***|4.552***|4.398***|
||[0.475]|[0.698]|[0.301]|[0.593]|[0.425]|
|N|2478294|1434525|1043769|822189|1656105|
|Adjusted R-square|0.899|0.918|0.869|0.888|0.906|

Note: Standard errors in brackets. Significance levels: *p<0.05, ** p<0.01, *** p<0.001.

47
Table 5 CROSS-SECTION SCHOOLING REGRESSIONS: PERU, 1985-86

|Dependent||Ind|ependent V|ariables||
|---|---|---|---|---|---|
|Variable|CONSTANT|PER CAP.<br>GDP|INDUSTRY<br>SHARE|SERVICES DEGREE OF<br>SHARE<br>URBANIZN.|UNADJ.<br>R-SQR.|
|||All|Ages|||
|l.Female Total<br>Illiteracy Rate|73.713<br>|-0.019<br>(0.02)|9.196<br>(17.61)|-13.378<br>-0.647<br>(31.28)<br>(0.15)|.817|
|2.Male Total|33.479|-0.013|-0.584|-13.979<br>-0.239|.714|
|Illiteracy Rate||(0.01)|(10.42)|(18.49)<br>(0.09)||
|3.Female Urban|50.189|-0.040|-8.892|-60.113|.545|
|Illiteracy Rate||(0.02)|(13.72)|(17.42)||
|4.Male Urban|15.010|-0.012|-4.410|-16.739|.587|
|Illiteracy Rate||(0.00)|(3.76)|(4.77)||
|5.Female Rural|86.408|-0.049|-3.506|-80.723|.354|
|Illiteracy Rate||(0.03)|(25.73)|(32.67)||
|6.Nale Rural|42.407|-0.030|-4.380|-44.040|.362|
|Illiteracy Rate||(0.02)|(14.61)|(18.56)||

Note: Sample consists of 23 departments in Peru Significant at 10 percent, at 5 percent level, at 1 percent level. Standard errors in parentheses.

47
# Where hqq a1l the ediieation ganen'

The claim that expanding education is good for economic growth seems intuitively obvious, receives apparent empirical support from both individual and aggregate data, and has become a fundamental tenet of development strategy. However, like many beliefs the empirical basis for this claim is substantially weaker than is often supposed. Two recently created data sets on the education attainment of the labor force show that the growth of educational capital per worker has had no (or even perhaps a mildly negative) impact on the rate of growth of output per worker. Put another way, the growth of education has a strong negative association with conventionally constructed growth-accounting measures of TFP.

The first section demonstrates the strength of this surprising and striking finding in several ways. First, the result is robust to choice of sample and estimation technique. Second, while acknowledging the potential weaknesses of the data, I show the results are not caused by pure measurement error of educational attainment. Third, in order to build credibility for the fact that schooling has not always paid off in economic growth, I show the intuition behind this result with regional and country examples. Finally, many believe the previous cross country growth regressions have established the importance of human capital accumulation. However, I show that previous empirical work which relies on enrollment rates as a proxy for human capital growth, while not invalid statistically, is irrelevant to the discussion of human capital because in the available data both primary or secondary enrollment rates are _negatively_ correlated with the rate of human capital growth.

t Without implicating any I would like to thank mnany. I amn very grateful for discussions with and conmnents from Harold Aldernan. Jere Behrman, Deon Filmner, Mark Gersowitz, Paul Glewwe, Peter Lanjouw, David Lindauer, Mead Over, Harry Patrinos, Martin Ravallion, Dani Rodrik. and Michael Walton and the participants at the Johns Hopkins development seminar.
**Figure 3: Correlation Water, Sanitation and Children’s Health**

<!-- Start of picture text --> 0.20<br>0.18<br>0.16<br>0.14<br>0.12<br>0.10<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>DIARRHEA CHILD MORTALITY<br>open latrine flush surface well/pump piped open latrine flush surface well/pump piped<br><!-- End of picture text -->

# **4. Empirical Specification and Results**

# **4.1 Empirical Specification**

The general equation we estimate is given by where _CV is_ the child variable of interest. _Latrine_ is the basic sanitation technology indicator (household having access to a private or public latrine), _Flush_ is the indicator for access to a flush toilet, _Pump_ and _Piped_ is households’ access to pump/well/spring or piped water, respectively, and _X_ is a matrix of additional control variables (see the following paragraph). _i_ is the household index, _c_ is the cluster index<sup>9</sup> _, j_ is the (sub-national) regional index, and _t_ the index of the survey year.

Since health preferences and budget constraints are likely to be correlated with both child health as well as with sanitation and water infrastructure, we include a wide set of control

> 9 In general within the DHS surveys, in rural areas clusters are villages and districts in urban areas.

16/36
Winters, P., B. Davis, G. Carletto, K. Covarrubias, E. Quinones, A. Zezza, K. Stamoulis, G. Bonomi, and S. DiGiuseppe. 2009. “A Cross Country Comparison of Rural Income Generating Activities”, _World Development_ , 37(9): 1435–1452.

Winters, P., Essam, T., Davis, B., Zezza, A., Carletto, G. and Stamoulis, K. 2010. “Patterns of Rural Development: A Cross-Country Comparison Using Microeconomic Data”, _Journal of Agricultural Economics_ , 61(3); 628-651.

31
Worldwide Governance Indicators). Equation 2-3 includes only these three regressors, and is estimated for the same 74-country sample included in equations 2-1 and 2-2. Collectively the three RST regressors explain 63% of the variation in income, approximately the same as the three WVS regressors in equation 2-2. Geography and institutions but not trade are strongly significant, and in the expected direction. These results are very similar to those reported in Table 2 of RST, despite a much different sample of countries, suggesting there is nothing peculiar about this 74-country sample for which WVS data are available. As shown in Table 3, there are fairly strong correlations among some of these six regressors (three from the WVS and three from RST). Ability (human capital), morality and the morality-ability correlation are all significantly higher in countries located further from the equator and with a stronger “rule of law.” Unsurprisingly, the strong relationships of some of these variables with income in equations 2-2 and 2-3 weaken somewhat when all six are included together in equation 2-4. Relative to the previous results, in equation 2-4 coefficients for morality and distance from equator are cut roughly in half, but morality remains significant at the .10 level and distance remains significant at the .05 level. Coefficients for ability, rule of law and the morality-ability correlation are reduced by roughly one-third, but all remain significant at the .01 level.<sup>14</sup> Collectively the six regressors explain 77% of the variation in income.

Based on equation 2-4, an increase of .1 (equal to about one quarter of the range exhibited in the sample) in the correlation between morality and ability is associated with a 36% increase in per capita income. Each percentage-point increase in morality is associated with a 1.7% increase in income. Each percentage-point increase in the labor force share with no secondary schooling

> 14 Although structural modeling of these interrelationships is beyond the scope of this paper, these results are consistent with the interpretation that geography and institutions as measured by RST affect income in part through their effects on schooling, morality and on the correlation between them. Alternatively, “institutions” (i.e. the rule of law index) may be measuring, in part, morality and its correlation with ability.

20
compensation, other informal experiences did not, and the new system contains no provision that explicitly links pay to performance.

# **5.3 Procurement**

All reports on the state of public hospitals by the end of the war in Lebanon point to severe mis-matches between inputs required and inputs available (asset non-complementarities) that precluded the hospitals from responding to demand in health service delivery (Jabbour 1994). This situation was due to delays in central administration financing and procurement, exceedingly complicated processes for the delivery of inputs to public hospitals (as described in Section **4** above), inadequate information processing, etc. Table 5.3 summarizes HDB's decision rights allocation in the area of procurement. HDB's creation of decision rights in procurement complemented its HRM decision rights in allowing the hospital to behave like a private sector hospital. If it ran out of certain types of drugs, needed syringes, sutures, or maintenance services, HDB was able to make the decision to purchase them from the market immediately, instead of going through the process of requesting them from the MOH central stock. As such, HDB slowly developed a reliable and loyal supplier base in the market for hospital inputs. HDB suppliers were so pleased at the timeliness with which accounts payable were settled, that they often made donations to HDB functions and provided discounts or inputs at no charge.

**Table 5.3a. HDB's Decision Rights Equilibrium in Procurement** Rights Created Implementation of local competitive bidding Rights Appropriated Medical consumables Other consumables Major medical equipment ______________________ Other fixed equipment

Source: Author's construction based on results from the Decision Rights Analysis Framework (Appendix A).

Table 5.3b presents a comparison of HDB procurement financed by the ALSM versus HDB procurement financed by the public sector, for a six-month period at the height of the HDB experiment in 1994. The figures show that 50% of the cost of HDB procurement was covered by the ALSM, while 48% came from the MOH.<sup>4</sup> 1 41 Because some of the items procured for HDB by the public sector are sent to the hospital without information about their cost, Table 5.3b required extensive efforts to compile, especially in gathering cost information for centrally procured items and services delivered by the MOH and the Ministry of Public Works. Both the former and current director of HDB have estimated that the share of non-ALSM expenditures in HDB procurement has continued to decline over time, and that the hospital was virtually completely financially independent by the time it was legally corporatized in July, 1999 (Interviews with Edouard Abboud and Edouard Chalouhi, former Directors of HDB).

By 1995, the ALSM's capacity to cover HDB's expenses became so well recognized and relied upon by the MOH that the ALSM was asked to settle a bill for laundry services on behalf of the MOH! Given the collaborative relationship between the MOH and the ALSM members at the time, the request was 29
**Figure 2. Effect of the 2017 tax-benefit system, by types of households, age groups and locations, change in consumable income, %**

**Figure** 3 **. Effect of the 2017 tax-benefit system, by deciles of original income** , change in consumable income, %

<!-- Start of picture text --> 100<br>50<br>30<br>10 50<br>-10<br>-30<br>-50 0<br>-50<br>1 2 3 4 5 6 7 8 9 10 Total<br>direct taxes and SIC pensions<br>direct taxes and SIC pensions other transfers indirect taxes<br>other transfers indirect taxes total<br>total<br>Source: Authors’ calculations using RUSMOD for 2017<br>Source: Authors’ calculations using RUSMOD for 2017<br>policy year and 2016 RLMS-HSE data.<br><!-- End of picture text --> Source: Authors’ calculations using RUSMOD for 2017 policy year and 2016 RLMS-HSE data.

The fiscal system redistributes income from the upper to the lower part of the income distribution. When ranked by the original income, the bottom five deciles are net beneficiaries, while the top five deciles are net contributors to the system (Figure 3). At the bottom, government transfers (pensions in particular) are the main sources of income. The flat rate PIT limits the redistribution beyond a certain point: the richer seventh to tenth deciles appear to pay a similar share of income in direct taxes and social insurance contributions. The indirect taxation (VAT and excises) is slightly regressive, particularly beyond the fourth decile.

Finally, a preliminary analysis reveals that people in rural areas are net beneficiaries of the tax-benefit system in Russia, although the effect is small. One of the standard objectives of a fiscal policy is to help equalize living standards across the country. In principle, RUSMOD could be used to investigate this issue, except that the RLMS-HSE data are not available for all regions and the survey is not representative at the regional level. However, it is possible to divide population into broader groups by the level of urbanization (Figure 3). Under this breakdown the difference between how the fiscal system affects various types of localities appears not to be large—all groups benefit from direct transfers and pay relatively similar shares of their income in taxes. Overall, rural areas emerge with a slight net gain. More research into this topic on a representative sample is needed.

# _International comparison_

Results above notwithstanding, the Russian system of direct taxes and benefits is less redistributive than any system in the European Union. The redistributive power of the tax-benefit system in Russia is moderate, for international standards. The overall effect of direct taxes and transfers is close only to the least redistributive countries in the EU, such as Latvia and Bulgaria (Figure 4). Moreover, the Russian system is almost entirely dominated by pensions with other components having little effect on the reduction of the Gini index, while in many EU countries the direct taxes (mostly the progressive PIT) and means-tested benefits have a significant impact on reduction in income inequality.

11
# **Figure 11. Impact of Increased Investment**

<!-- Start of picture text --> 45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Capital expenditure increase Baseline<br>2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026<br>Debt as a % of GSDP<br><!-- End of picture text --> Source: Authors’ simulation based on equation (4). Investment increases to 3 percent of GSDP from 2008/09 onwards, while all other assumptions remain as in baseline. Note: Tamil Nadu’s debt service payments increase to only 15.9 percent of revenue over the same period – an indicator that is below the debt stress ratio of 20 percent.

# **_4.4.1 Interest rate risk_**

In 2002/03 interest expenditure reached the debt stress level of 20 percent of revenue. Rising interest expenditure contributed to the increase of the fiscal deficit (accounting for over 60 percent of the fiscal deficit in some years), which itself increased almost seven times in absolute terms over the period. The result was an increase in borrowing and debt accumulation at an average borrowing cost<sup>37</sup> that escalated from 9.7 percent in 1991/92 to 12.5 percent in 2000/01, due to high interest rates on some forms of borrowing.

Interest rates have declined since 2000/01 as a result of changes in monetary and fiscal policy and the decline in international interest rates. These have led to deliberate reductions in bank and risk-free interest rates.<sup>38</sup> The cuts in nominal interest rates have reduced the average borrowing cost of the Government of Tamil Nadu to 10.7 percent in 2003/04 and stabilized interest expenditure at 20 percent of revenue. Together with other measures of expenditure restructuring, decline in interest expenditure has helped reduce the fiscal deficit and the borrowing requirement.

> 37 Average borrowing cost is defined as the share of interest expenditure in the outstanding debt stock. 38 The risk free interest rate on tax saving instruments such as the national small savings scheme has been gradually lowered from 9.5 percent in 2001 to 8 percent in 2003. The bank rate, which is the lending rate of the Reserve Bank of India, has been gradually reduced from 8 percent in 2000 to 6 percent in 2003. The cash reserve ratio for commercial banks has also declined from 8.25 percent in 2000 to 5 percent in 2004. This has had the effect of increasing the liquidity in the banking system.

25
- Non-routine cognitive interpersonal tasks (interactive, communication and managerial skills).

- Routine cognitive tasks (adaptability to work requiring limits, tolerances or standards).

- Routine manual tasks (repetitive physical movements).

- Non-routine manual tasks (physical movements requiring adaptability and dexterity).

Given that the O*NET is based on variables that are specific for the US, and such classification has not been repeated in developing countries, matching it using STEP surveys is not straightforward. We select the STEP variables which provide a good approximation for each of the five skill groups. The list of variables chosen to mimic the O*NET structure are reported in Table 1.

Regarding non-routine analytical skills, we select three STEP variables to measure the task “Analyzing data/information”: 1) ”Number of types of document typically read”; 2) ”Length of longest document typically read” , and; 3) ”Number of math tasks performed”. We use one variable to measure the task “Thinking creatively”, namely “How often the job requires thinking for at least 30 minutes”.

With regards to non-routine interpersonal skills, the task “Guiding subordinates” is elicited by a dummy variable capturing whether the job involves supervising co-workers. “Establishing personal relationships” is proxied by using the variable “How important interaction with people other than co-workers is”.

To estimate the routine cognitive content of jobs, we use three STEP variables: 1) “How often your work involves learning new things“ (mapped to the O*NET task “Importance of repeating the same task” (inverse)); 2) “How much autonomy you have in your work“ (mapped to “Structured vs. unstructured work”), and; 3) “How repetitive your work is“ (mapped to “Importance of repeating the same task”).

The routine manual content of jobs is estimated using the following variables: 1) a binary outcome capturing whether the work involves operating machines (mapped to “Controlling machines and processes”, and; 2) a categorical variable measuring the how physically demanding the work is. Finally, the non-routine manual content of jobs is approximated using two dummy variables: 1) 7