rafmacalaba/datause-encoder-data huggingface dataset:
In these studies, the variables that most influence attendance at private tutoring include household income (household wealth for Egypt, household expenditure in the case of Turkey and Vietnam), parental education, and whether the household lives in an urban area. The latter two variables arguably correspond to household tastes for education. All these variables predict higher student attendance at private tutoring, meaning that students from richer, more educated households living in urban areas will be more likely to attend and spend more on tutoring classes. These results are not unexpected, but they are surprisingly consistent across the countries and fairly robust to the different models being used.<sup>10</sup> Other factors that may matter across countries are the student’s grade level and household size. In Egypt, students in diploma-granting years spend more on private tutoring (Assaad and El-Badawy, 2004); in Vietnam, the closer students are to the last grade in their current school level, the more they spend on private tutoring (Dang, 2007b). And in Korea, Turkey, and Vietnam, the number of children in households is found to be negatively correlated with private tutoring expenditures (Kim and Lee, 2004; Tansel and Bircan, 2006; Dang, 2007b). Presumably, the grade-level pattern reflects the use of private tutoring to prepare for the school-leaving examinations, while the household-size effect hints at the much-studied quantity-quality tradeoff between number of children and average child educational achievement.<sup>11</sup> However, these variables are not used in all the studies, and the household size variable is likely to be endogenous (see the next section for more discussion) which may bias estimated results. Thus it is not possible to investigate whether these two patterns hold for all these five countries, or to draw firm conclusions from them.

Beyond this core group of factors, other variables that affect spending on private tutoring vary from country to country. This diversity of findings may simply reflect differences in the models or in the variables available in the datasets of the different countries; alternatively, it could reflect countrylevel differences in tutoring patterns, perhaps as a result of differing institutions, cultures, or relative prices.

Another micro question naturally arises: do students take private tutoring for remedial or enrichment purposes? In other words, are most tutored students those who are performing at levels below or above their (conditionally) expected levels? Analyzing data from the Third International Mathematics and Science Study (TIMSS) in 1995 for 41 countries, Baker et al. (2001) show that in three-fourths of these countries, private tutoring is used significantly more often by low math achievers than by high achievers, controlling for family income, student, and community characteristics. That study calculates that the probability of attending private tutoring increases by 3.5 percent for each point decrease in TIMSS mathematics scores in countries such as Denmark, Germany and the United States.

6
SCI with further lags in both levels and differences. The Hansen J- statistic shows a p-value of 1, and thus does not reject the null that the instruments are valid, however the high p-value raises concerns of instrument proliferation. We also cannot rule out the possibility that the instruments are weak.<sup>9</sup> The coefficient of the lagged log of GDP per capita is statistically significant at the 1 percent level. Its magnitude appears to be close to unity, implying that shocks to the level of (log) GDP per capita are highly persistent. The magnitude of the coefficient can also reveal information on the degree of conditional convergence. Using the cross-sectional OLS estimates including the standard controls (Table 1, column 2) and the System GMM estimates (Table 1, column 8), we obtain the convergence coefficient of -0.002 (annualized) and -0.01, respectively. The negative coefficient indicates conditional convergence within the set of largely developing economies in the sample. We also interacted SCI with the lagged log of GDP per capita to explore whether the SCI may affect the speed of convergence.<sup>10</sup> The coefficient of the interaction term is statistically insignificant across all estimations. The similar magnitude of the effect of SCI in the panel estimates and the cumulative effect between 2005 and 2018 as captured by the cross-sectional OLS estimates may suggest that the effect of the SCI on growth may be permanent.

The coefficients of the other control variables (not presented) are not statistically significant across most specifications apart from trade, ranging from 10 to 1 percent level, depending on the specification.

9 The system GMM estimates may have the issue that the instruments are weak. We investigate this issue further. We collapsed the data into 2 year averages leading to 7 time periods in order to lower the time dimension (T) with respect to the number of countries. We then follow the literature (Bazzi and Clemens, 2013; Kraay, 2015) and produce confidence sets by “inverting” test statistics that are valid regardless of whether instruments are strong and when they are weak). These include the Anderson-Rubin (AR) test statistic, Conditional likelihood ratio (CLR) test statistic, and the K and J overidentification test statistic (K-J). For tractability, we use only two lags for the system GMM estimations. Specifications with and without controls are employed. We also unbundle the system GMM into “difference” and “levels” equations. We find the following. The positive effect of SCI on growth appear to be driven primarily by the cross-country variation captured in the “levels” equation. The confidence sets (95% level) show that the range of possible values for the coefficient of lagged SCI are largely positive. However, the confidence sets are unbounded. Thus, we cannot disregard the possibility of weak instruments. These findings are available upon request.

> 10 Not reported, but available from the authors upon request.

21
||(0.078)|(0.083)|(0.084)||
|---|---|---|---|---|
|**Determinants validated by only one method**|||||
|Household asset score|-0.060|0.019|-0.056|0.020|
||(0.040)|(0.041)|(0.042)|(0.044)|
|Village Female Labor Force Participation|-0.185|0.617*|-0.405|0.436|
||(0.335)|(0.345)|(0.352)|(0.375)|
|Relative cost of migration|-0.015|-0.002|-0.018|-0.008|
||(0.014)|(0.010)|(0.015)|(0.010)|
|Household non-agricultural income|0.000**|0.000|0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Percentage of remittances Respondent would send home if migrated|-0.002*|-0.001|-0.002|-0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Highest male education in the household (Years of schooling)|-0.009|-0.025|-0.002|-0.025|
||(0.010)|(0.028)|(0.010)|(0.030)|
|Proportion of male adults in the household|-0.223|0.146|-0.246|-0.020|
||(0.262)|(0.217)|(0.273)|(0.238)|
|Proportion of female adults in the household|0.059|-0.252|-0.258|-0.304|
||(0.248)|(0.260)|(0.270)|(0.280)|
|Area of land owned by the household|-0.000|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Index of communication|0.517***|0.382***|0.283**|0.217|
||(0.129)|(0.131)|(0.139)|(0.143)|
|Years of schooling|0.027|0.052**|0.032|0.022|
||(0.027)|(0.026)|(0.028)|(0.029)|
|Household agricultural income|0.000|-0.000|0.000|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|PM's income from main job (logged)|-0.043***|-0.002|-0.023*|0.010|
||(0.011)|(0.008)|(0.012)|(0.009)|
|Share of underemployment in household|0.058|-0.070|0.084|-0.052|
||(0.076)|(0.080)|(0.080)|(0.085)|
|Respondent non-agricultural income|-0.000***|-0.000|-0.000***|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Household enterprise operating period|-0.000|-0.000|-0.000|0.000|

23
Census of Agriculture, many counties in the South derive 50-70 percent of their agriculture income from groundnut. The first level of value-added activities, such as shelling, are performed locally as are many groundnut products manufacturing (Fletcher 2001). Groundnut policies have played a major role in maintaining rural income in these regions of the US.

The foundation of U.S. groundnut policy is the U.S. peanut program, which traces its roots to the 1930s. Until the signature of the New Farm Bill in May 2002, the pillars of the system were production regulation through quotas, high producer support prices, and import control. The groundnut support program existed as a two-tier price support program. The support price for edible groundnuts was $610 per short ton paid for production under quota. Other groundnuts (“additionals”) could be either exported at world prices or sold to the domestic crush industry and were eligible for a lower support price ($132 in 2001). The quota farmgate price tended to be higher than the prevailing export prices as shown in Table 6.

**Table 6. Producer support prices and international prices**

|**Prices faced by p**|**eanut produ**|**cers under**|**former pe**|**anut prog**|**rams**(US ce|nts/pound)|
|---|---|---|---|---|---|---|
|**Item**|**1993/94**|**1994/95**|**1995/96**|**1996/97**|**1997/98**|**1998/99**|
|Quota price|38.8|33.9|33.9|30.5|30.5|30.5|
|Additional price|6.6|6.6|6.6|6.6|6.6|6.6|
|Average farm price|30.4|28.9|29.3|28.1|28.3|28|
|CCC export price|20|20|20|20|20|20|
|Export unit value|33|28.6|29.2|32.2|32.7|32.8|
|Rotterdam c.i.f. Price|37.1|29.2|33.6|31.6|36|29|

Source: Skinner (1999) The domestic support as measured by the aggregate measure of support (AMS) for U.S. groundnuts was estimated at around $330 million during that year or $31 million more than the average AMS for 1996-2001. The average cost of aggregate support in 1996-01 stood at $206 per metric ton of groundnut produced in the U.S. (Table 7).

**Table 7. U.S. aggregate measurement of support (million dollars)**

||1986-88|1995-96|1996-97|1997-98|1999-2000|2000-01|
|---|---|---|---|---|---|---|
|AMS Million US$|347.2|414.6|299|305.76|300|330|
|AMS/ Metric Ton of US Production|203.3|264.1|180.0|190.5|172.7|222.8|

Source: Skinner (1999), Hart and Babcock (2002) for 2000-01 AMS and USDA database for production data With the 2002 farm bill, groundnut production quotas were eliminated (with a quota buyout) and the groundnut price support program converted to a system of direct and countercyclical payments, and a price floor _cum_ production subsidy (non-recourse loans with marketing loan provisions). The key features of the new program are detailed as follows: (i) All current groundnut producers have equal access to a marketing loan program, under which producers can pledge their crops as collateral to obtain a marketing loan rate equal to $355 per short ton. Producers may repay the loan at a rate that is the lesser of USDA-set repayment rate plus interest or the marketing loan rate plus interest, or they can forfeit the loan.

(ii) For producers with a history of groundnut production, a new direct and fixed payment of 15
empirical procedure that not only permits simultaneous estimation of two unobserved fixed latent factors (household and school effects), but also allows alternative assumptions regarding their covariance to be handled in a transparent fashion. This provides (strict) bounds on the variance contributions of interest. Furthermore, in our empirical implementation, we validate (cross-test) our results showing that extreme assumptions about the between-factor covariance can be ruled out.

The rest of this paper consists of four sections. Section 2 sets out a general framework to account for IOE, which also provides a window on findings of previous studies. It also describes the empirical strategy we use to identify the household and school factors, treating them both as unobserved effects, and proposes a simple mechanism that offers a unified variance decomposition strategy, covering the full range of alternative assumptions regarding the between-factor covariance.

Section 3 applies the proposed approach to extensive test score data on over 1,000,000 school-aged children from three East African countries (Kenya, mainland Tanzania and Uganda). It compares variance-decomposition results from three choices of the initialization parameter, each of which corresponds to an intuitive characterization of the between-factor covariance. Using both unconditional and conditional models, we show that household and school circumstances _jointly account_ for nearly half of the variance in test scores (normalized by age). However, confirming the limitations of past studies, households cannot be considered the primary or only source of IOE.

More specifically, we find the upper-bound variance share attributable to schools is generally as large as the upper bound attributable to households, which in itself is indicative of a positive association between the latent factors. And under our preferred model specification, we find evidence of substantial positive sorting whereby higher ‘quality’ households tend to be matched to higher quality schools. In Section 4 we validate the main findings using alternative estimation procedures and investigate heterogeneity in the variance decomposition. We find systematic patterns in the level of inequality and the magnitudes of the variance components across different sub-groups and geographical locations, including a larger contribution of sorting in more disadvantaged locations. Section 5 concludes and reflects on our findings.

# **2 Analytical framework**

# **2.1    Accounting model**

A general framework for the analysis of inequality in education splits the proposed process 4
employment, which _increases_ unemployment (Lazear, 1990; Hopenhayn and Rogerson, 1993). But a large number of empirical studies provide strong evidence that the net impact of strict employment protection laws on unemployment is to increase it (Blanchard and Wolfers, 2000; Addison and Teixeira, 2001; Botero et al., 2004; Blanchard, 2006; Feldmann, 2009; Blanchard et al., 2013). This effect is shown to be particularly strong for structural unemployment as well as for youth and female unemployment.

There also appears to be a strong positive correlation between strict employment laws and the duration of unemployment (Blanchard et al., 2013). The reason is that strict employment protection, by decreasing the risk of being dismissed, increases the bargaining power of employed workers and thus pushes up wages, raises the cost of labor and reduces firms’ demand for new hires. The impact of strict employment protection on the probability of being hired seems to be particularly strong for workers with no experience or no strong credentials indicating their productivity level, such as new labor market entrants or the long-term unemployed.

Figure 10: Stricter employment protection legislation is associated with a higher labor cost and a higher unemployment rate across the OECD <!-- Start of picture text --> Employment protection and labor cost in OECD countries, 2013<br>Employment protection and unemployment rate in OECD countries, 2013<br>BEL<br>DEU<br>KOR AUTFRA PRT<br>NLD<br>IRL SVK<br>ITA ITA<br>GBR JPNFINAUS CHE SWE<br>LUX<br>FRA<br>USA CAN ISR ESTHUN NORDNKPOL CZEPRTSVN USA CANGBR FIN ESTHUN DNKPOL SWE BELSVNNLD<br>NZL SVK AUS ISL<br>CHLIRL NZL ISR CHE NOR AUTCZE DEU<br>ISL LUX CHLJPN<br>KOR<br>-1.5 -1 -.5 0 .5 1 -1.5 -1 -.5 0 .5 1<br>Strictness of employment protection legislation Strictness of employment protection legislation<br>coef = 6925.372, (robust) se = 2805.0144, t = 2.47 coef = 1.4542047, (robust) se = .83376995, t = 1.74<br>0<br>10<br>5<br>0<br>-5<br><!-- End of picture text --> Source: OECD (2014b).

Note: Greece and Spain are excluded from the regression because they are outliers; the relationship is stronger when they are included. The analysis controls for the effect of GDP per capita on the unemployment rate and labor cost. The Employment protection legislation index of OECD ranges from 0 (least stringent) to 6 (most stringent). Labor cost refers to the total gross labor cost before taxes (in USD PPP exchange rate). Unemployment rate is the annual rate of unemployment in percent.

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**Figure 2.17 Morocco: Evolution of patent filings, 2013‐2017 (in units)**

<!-- Start of picture text --> 2105<br>1923<br>1240<br>1118 1096<br>1021 1003<br>803 743 797<br>315 353<br>224 237 182<br>2013 2014 2015 2016 2017<br>National filing Foreign filing Moroccan filing<br><!-- End of picture text --> _Source_ : OMPIC, Annual Report (2017).

As discussed in more detail in the context of the new growth model proposed in this study, increasing innovation capacity is essential for Morocco. In particular, innovation can help Moroccan firms face import competition from China and other low‐wage countries in some manufacturing industries, both at home and in third‐party markets.

# **2.3.5 A Loss of Competitiveness in Some Key Production Sectors**

Wage rigidities in Morocco’s labor market have translated into a high cost of labor. A high minimum wage makes unskilled labor expensive – in effect, “pricing out” the country (as documented earlier) from some labor‐intensive manufacturing industries. Skilled labor, in addition to the mismatch problem highlighted earlier, is also expensive. According to estimates by the World Bank (2008 _)_ given the level of productivity in Morocco, unit labor costs are about 30 percent higher than in competing countries. High relative unit labor costs (ULCs, defined as the ratio of labor compensation to labor productivity) have weakened the country’s competitiveness. Indeed, analysis of the development of unit labor costs in the manufacturing sector shows that cost competitiveness – as measured by the ULC‐based real effective exchange rate (REER) – has deteriorated in comparison to Morocco’s major trading partners and competitors (Agénor and El Aynaoui, 2015, International Monetary Fund, 2017).<sup>1</sup> In particular, sectors with high relative ULCs, notably the textile and leather sector, have lost global market share, compared to emerging sectors, such as mechanical and metallurgic manufacturing, chemicals, and electronics, which have much lower relative ULCs. This is illustrated in Figure 2.18, which shows the evolution of the ULC‐based REER for various industries over the period 2000‐14. More generally, Morocco’s exports remain fairly sensitive to the REER. According to estimates by the International Monetary Fund (2017), a 1 percentage point depreciation in the REER leads to a 0.2‐0.4 percentage point increase in merchandise exports.

In addition, the relatively rigid exchange rate regime has hampered the appropriate adjustments in the real exchange rate, thereby also contributing to the deterioration of 22
exercise for other parts of the world. The Sahel region and the Horn of Africa are important regions, that are different than the countries we have examined here, in terms of both agro-ecology and governance.

# **3.3 Heterogeneous Impacts across Household Types**

Given that budgets are limited, it would be very useful for interventions that aim to mitigate the impacts of droughts to be able to identify ex-ante (at a low-cost) children in households that are most likely to be affected. While this may seem straightforward, there are both conceptual as well as measurement challenges involved. From a conceptual perspective, should interventions target children that are in the poorest households, or children close to the stunting threshold, or children with profiles that have historically exhibited the largest drought impacts?<sup>26</sup> If children that are close to the stunting threshold are also more vulnerable to drought shocks, and in the poorest households, then supporting this group of children would address all three concerns. However, as we show below, these categories may not fully overlap.

From a measurement perspective, the set of surveys that capture child anthropometrics, as well as monetary poverty measures, both of acceptable quality, is very small. The DHS data on child anthropometrics are of reasonable quality, but the surveys do not have measures. any economic We are left with using the wealth indices, which are imperfect, for reasons discussed earlier.

Brown et al. (2017) show that around half of all undernourished children and underweight females, in their sample of 30 countries in Sub-Saharan Africa, are found in the top three DHS wealth quintiles. They use this fact to infer that a large fraction of poor individuals do not live in poor households, as defined by the wealth index. While this is certainly plausible, in our view, at least part of what they attribute to intra-household inequality or local health effects could merely reflect the error associated with measuring a household’s wealth status.<sup>27</sup> In any event, the facts they document, rather than their inferences regarding those facts, are important for our purposes. Figure 2 (which excludes outliers) shows that there is significant overlap in height for age z-scores across the five wealth quintiles for all the five countries in our study. Consequently, the DHS wealth quintiles may not be a good proxy for stunting.

Wealth quintiles are, however, the best available proxy for household wealth in the

> 26The incentives of policy makers and other organizations may be consistent with approaches that meet particular targets, for example, a given reduction in stunting rates. However, the threshold of a negative 2 standard deviation difference from the median is not based on biology or economics.

> 27The data that are needed to address these issues are not available. In the future, there may be surveys that provide good quality measures of anthropometrics and economic variables.

16
In October 2009, the FSS made recommendations to the non-banking financial companies<sup>36</sup> to include applying debt to-income (DTI) ratios for all the metropolitan areas (Table A.3).

# TableA.3. DTI Ratios of Non-banks, October 2009

|||Crrnt r<sup>a</sup>|**DTI ratio**<br>Edd|<sup>b</sup>|**DTI**|
|---|---|---|---|---|---|
|Collateral value|Loan amount|ue coveage|xpane cov|erage|adjustments|
|||Speculative area|Seoul (excluding<br>speculative area)|Incheon,<br>Kyunggi||
|More than KRW600 mil.<br>(holding title for 3 months or|More than KRW50 mil.|**40%**|**50%**|**60%**|‐|
|<br>less)|KRW50 mil. or less|‐|‐|‐||
|More than KRW600 mil.|More than KRW100 mil.|**40%**||||
|(holding title for more than 3<br>months)|KRW50 mil.–KRW100 mil.|**50%**|**50%**|**60%**|<br>Fixed interest<br>rates : + 5%p|
||KRW50 mil. or less|‐|‐|‐||
|KRW300 mil.– KRW600 mil.|More than KRW100 mil.|**40%**|**50%**|**60%**|<br>Amortization<br>schedule: +|
||KRW50 mil.–KRW100 mil.|**50%**|||5%p|
||KRW50 mil. or less|‐|‐|‐|<br>Credit|
|KRW300 mil. or less|||||ratings: ‐<br>5%p or + 5%p|
|Surface area more than 85|More than KRW100 mil.|**45%**|**55%**|**65%**||
|m<sup>2</sup>|KRW50 mil.–KRW100 mil.|**55%**|||<br>Income: ‐|
||KRW50 mil. or less|**‐**|‐|‐|5%p|
|Sf l th 85<sup>2</sup>|More than KRW50 mil.|**55%**|**55%**|**65%**||
|urace area ess an m|KRW50 mil. or less|‐|‐|‐||

_Source_ : FSS Press Release, October 2009.

a. Supervisory regulation (in excess of KRW600 million, mortgage loans taken out less than 3 months subsequent to title transfer) and financial companies’ internal guidelines (other loans) b. Maximum DTI ratio for apartments valued in excess of KRW300 million: 60% or less for Seoul 70% or less for Incheon and Kyunggi Province (currently 60% or less for speculative areas) Maximum DTI ratio for apartments valued at KRW300 million or less: 70% or less for Seoul 75% or less for Incheon and Kyunggi Province (currently 70% or less for speculative areas)

> 36  Non-banking financial companies refer to mutual finance companies, savings banks and specialized credit financial companies.

23