rafmacalaba/datause-encoder-data huggingface dataset:
**Learning Outcomes and School Cost-Effectiveness in Mexico: The Pare Program**

# Gladys Lopez-Acevedo

# **1. Introduction**

To understand better the qualitative dimension of basic education, it is necessary to analyze student learning outcomes and school effectiveness. What factors influence them? How responsive is student learning to these factors? What impact can learning improvement interventions have? This paper attempts to address some of these questions.

Empirical studies of student learning achievement in Mexico are scarce. Interest, however, regarding its determinants and the impact of interventions to improve it is increasing. In January 1995, the Ministry of Education presented the Programa de Desarrollo Educativo 1995-2000 (PED), which contains a series of targets and general guidelines in order to improve the coverage, efficiency and equity of the Mexican educational system. In fact, the PED recognizes the importance of research and evaluation in its strategy to improve quality of education. In view of this policy, the Ministry of Education has collected databases that can be useful for this purpose. These include Carrera Magisterial, PARE, and TIMSS (Third International Mathematics and Science Study) databases among others.

Due to data constraints, this paper is unable to do a comprehensive and in-depth analysis of the learning achievement issues in Mexico. The following analysis, therefore, should be regarded as an exploratory rather than as a conclusive study. Available data are used to highlight certain ideas about learning improvement interventions. As mentioned, there has been very little study in Mexico that examined this issue. There are, however, many international studies that looked at this question. An excellent summary of this literature can be found in Fuller and Clarke (1994), and Hanushek (1995).

The early studies on learning outcomes showed that the student's socio-economic and cultural background predominantly determines differences in test scores. These led to the conclusion that there was little that government can do by way of direct educational policy and government interventions to improve learning outcomes. More recent results and experience, however, indicate that school factors do matter and that they can play a more critical role than previously thought. Moreover, "education production function" studies indicate that the magnitude of production inputs varies substantially. Some inputs have larger marginal effects than others do and, in some places, the effects of some of the factors are not statistically significantly different from zero, while in others the same factors have shown substantial impact.

Table 1 summarizes the various educational inputs that have been empirically analyzed, the number of studies reviewed and the "confirmation percentage" for each of 2
reported in panel B are approximately twice as high (in absolute value) as the ones shown in panel A.

In Table 1 panel B we find that the estimated coefficients of the incidence variable reported in columns (1) and (2) are fairly similar. According to these results, civil conflict in country _i_ at time _t_ correlates negatively with GDP per capita growth which ranges from 2 to 3 percentage points. In column (3) we use night light as an economic indicator. The estimated effect of conflict on night light is statistically significant and is equal to -0.075. What does this number mean in terms of GDP per capita growth? One way to think about this is the estimates from Henderson et al. (2012) who argue that the relationship between GDP and light can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0.25 percent. If we apply this model to our result from Table 1 panel B, the conflict reduces GDP growth by almost 1 _._ 9 percentage points. This is strikingly similar to the 2 percent found in column (1).

Table 1: Conflict Incidence and Economic Performance: Country-level data

||**Pan**|**el A**||
|---|---|---|---|
||(1)<br>GDP growth|(2)<br>GDP growth|(3)<br>Night light growth|
|Confict Incidence|-0.011**|-0.016***|-0.032*|
||(0.004)|(0.004)|(0.016)|
|Observations|8,004|8,076|3,924|
|R-squared|0.097|0.124|0.267|
|Country FE|YES|YES|YES|
|Year FE|YES<br>**Pan**|YES<br>**el B**|YES|
||(1)|(2)|(3)|
||GDP growth|GDP growth|Night light growth|
|Confict Incidence|-0.020***|-0.029***|-0.075***|
||(0.005)|(0.005)|(0.024)|
|Observations|8,004|8,076|3,924|
|R-squared|0.100|0.130|0.268|
|Country FE|YES|YES|YES|
|Year FE|YES|YES|YES|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The dependent variable in columns (1) and (2) is the GDP per capita growth computed using Penn World Table data and World Bank data respectively. Column (3) uses growth of night light per capita form the National Oceanic and Atmospheric Administration (NOAA). Panel A uses as “conflict incidence” a dummy that takes a value of one if in country _i_ at time _t_ the number of battle related deaths is higher than 0. In Panel B “Conflict incidence” dummy takes a value of one if the number of battle deaths is above a threshold of 0.008 fatalities per 1000 population.

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Promoting Democracy in Fragile States Table 3: Effects of programs on enthusiasm for participation

||Index<sup>_‡_</sup>|Rally|Register|Voted|Slogan|Bought news|Petition<br>|Issues card|
|---|---|---|---|---|---|---|---|---|
|(Control mean)|_−_0_._05|0_._24|0_._97|0_._94|0_._42|0_._45|0_._58|0_._64|
||(0_._04)|(0_._01)|(0_._01)|(0_._01)|(0_._02)|(0_._02)|(0_._02)|(0_._01)|
|Civ. Ed. ATE|0_._22<sup>_⇤⇤_</sup>|0_._11<sup>_⇤⇤⇤_</sup>|0_._00|_−_0_._03|_−_0_._01|0_._09<sup>_⇤⇤_</sup>|0_._17<sup>_⇤⇤⇤_</sup>|_−_0_._01|
||(0_._07)|(0_._02)|(0_._01)|(0_._02)|(0_._05)|(0_._03)|(0_._03)|(0_._02)|
|Sec. Com. ATE|0_._03|0_._02|0_._01|_−_0_._00|_−_0_._08<sup>_⇤_</sup>|0_._05<sup>_†_</sup>|0_._09<sup>_⇤⇤_</sup>|_−_0_._02|
||(0_._05)|(0_._02)|(0_._01)|(0_._01)|(0_._03)|(0_._03)|(0_._03)|(0_._02)|
|Combined ATE|0_._28<sup>_⇤⇤⇤_</sup>|0_._09<sup>_⇤⇤⇤_</sup>|0_._01|0_._02|0_._01|0_._07<sup>_⇤_</sup>|0_._15<sup>_⇤⇤⇤_</sup>|0_._09<sup>_⇤⇤_</sup>|
||(0_._06)|(0_._02)|(0_._01)|(0_._01)|(0_._03)|(0_._03)|(0_._04)|(0_._03)|
|_N_|1426<br>|1384|1419|1406|1426|1368|1426|1426|
|_R_<sup>2</sup>|0_._10|0_._14|0_._05|0_._06|0_._11|0_._15|0_._14|0_._08|

> _‡_ First column outcome is a standardized, inverse-covariance-weighted average of the other columns’ outcomes. Standard errors account for clan-level clustering.

> _†_ significant at _p < ._ 10; _⇤p < ._ 05; _⇤⇤p < ._ 01; _⇤⇤⇤p < ._ 001

_negative_ effect on the Herfindahl index. In Table 5, the outcomes are vote shares for the three national-level party candidates (the LP, UP, and CDC) as well as for parochial candidates (“Other”) from the polling place data. Less parochial voting would be reflected by decreases in voting for the “Other” candidates in favor of a national party candidate. In Table 6 the outcomes are voteshares for the different types of candidates presented as part of the voting vignette exercise in the survey. Again, less parochial voting should be associated with increased voteshares for candidates that make national appeals over regional, ethnic, or religious appeals. The results agree in part with our hypothesized effects, but not completely. With respect to diversity of vote choice, we see that the control group levels of voter homogeneity are quite high: Herfindahl indices of circa 0.4 to 0.5 are consistent with votes going predominately to two or three candidates. There is noisy indication that the civic education program caused such homogeneity to _increase_ , contrary to the expected effect. When we look at the changes in party voteshares in Table 5, this concentration appears to have been due to a coordinated shift (of about 10 percentage points) in votes away from the “parochial” candidates toward the UP. For the security committee and combined interventions, there is some suggestion of increases in voter diversity (that is, decreases in 27
in India has grown by 6 percent between 2001 and 2011 census rounds (ORGI, 2011) as villages evolved into towns, resulting in a divergence in the urban-rural classification between the two surveys. From a poverty measurement perspective this could matter because growth of smaller towns has an impact on rural poverty (Gibson et al., 2017). Second, larger villages and towns are more likely to be selected in the NSS, whereas differently sized villages have an equal probability of being sampled into the CPHS. More specifically, the NSS draws FSU locations based on population size. In comparison, the CPHS selects rural villages from the rural strata using simple random sampling; for urban areas, CPHS stratifies cities into four groups based on their population and then draws urban FSUs using simple random sampling. Within the FSUs from the CPHS, households have unequal sampling probabilities as households on the main street may have a higher likelihood of selection into the sample relative to other households (see Pais and Rawal, 2021; Dreze and Somanchi, 2021 for details).

Third, the NSS-2011 survey implemented a second stage stratification process, selecting a greater fraction of households in state-regions that had a higher proportion of non-agricultural occupations in rural areas and urban households with mean per capita consumption expenditure between the 1<sup>st</sup> and 6<sup>th</sup> decile based on the NSS’ 2009-10 expenditure survey. The CPHS in contrast, randomly selects households in rural and urban areas without second-stage stratification, with higher urban draws compared to rural. Despite comparatively larger urban samples, the absence of a second stage stratification in the CPHS means that representation of households from both ends of the income distribution is left to chance. In the NSS, representation of urban households from the 1<sup>st</sup> to 6<sup>th</sup> deciles of the distribution is embedded into the sampling design.

Fourth, the CPHS defines households as the physical unit where a group of individual members reside; whereas the NSS defines household as a group of individuals who normally live together and share a common kitchen. The CPHS’ definition implies homeless people or families living in construction sites are excluded in the survey. This choice could potentially further contribute to under-coverage of the poorest households in the CPHS.

Fifth, unlike the NSS, the CPHS does not conduct a listing exercise. Instead, it uses projections of household and population growth from India’s census organization to construct sampling weights. The NSS does conduct a listing exercise at the start of every round and uses this frame to estimate household level weights. Population weights in the NSS are calculated as the product of the household’s sampling weight and its household size; in CPHS population weights are based on the population projections and not the number of household members observed in the survey.

_Differences in instruments._ Sixth, the NSO uses a more detailed consumption mod9
REACHING PEOPLE AT THE PERIPHERY: CAN THE WORLD BANK'S POPULATION, HEALTH, AND NUTRITION OPERATIONS DO BETTER?

by

## Richard Heaver

# 1. nIom criau

Poor people in third world countries tend to live at the geographic cr social periphery. They **are** often poor beauseY they live at the periphery--for example, in rural **areas far** from markets, job opportunities, and social services. They also often live at the periphery because they are poor--because the better off have squeezed them onto marginally productive rural land or into urban slums **at** the geographic center but social periphery of their societies. The poor **at** the periphery tend ro be those with the highest rates of mortality and morbidity and the lowest rates of family planning acceptance; they are uherefor- the most important client group for population, health, and nutritic 4) programs.

At the same time, because spending on PHN services !P most developing countries is biased toward urban and elite groups, the poor at the periphery get less than average from their governments. The poor are also the hardest group to reach, not only because they **are** often physically distant, but also because they are culturally and socioeconomically distant from third world bureaucrats or doctors (not to mention Bank staff members). Finally, because they are poor and distant, these clients are often also politically peripheral, and hence the clients that public health services may have least incentive to reach. For all these reasons, the Bank is concerned that its PHN operations shouid do everything possible to reach out to this group.

This paper looks at the Bank's past and present PHN work to see whether the approaches being used are likely to be effective in reaching clients at the periphery, and to suggest some directions for the future. The focus is on design and management of direct efforts to deliver services and education aimed at behavioral change, and hence on the interface between worker and client. Excluded from the scope of the paper are PHN projects and project components aimed at strengthening referral and support systems-for example hospital services, vaccine production or drug supply logistics-even though these are important to the success of service delivery efforts at the periphery. Also excluded from the scope of the paper are Bank-financed efforts to assist the poor through changes in pricing and production policies--for example, in the areas of drug production **and** pricing or food subsidies--which can also be important complements to direct outreach to the poor.

Urfortunately, the state of the art in the PHN sectors **is** such that there are no established criteria for judging whether the Bank's PHN projects are sufficiently responsive to client **needs.** The best way of establishing such criteria might be to look closely at the characteristics of those projects which show the highest levels of contraceptive prevalence
0.02, whereas the changes in producer surplus and SNAP consumer surplus are around 0.1 for each marginal dollar of SNAP.

Third, our baseline calculations assume that non-SNAP consumers face the same change in prices as SNAP consumers for simplicity. However, as shown in Section 4, price responses are stronger in stores located in regions with higher SNAP participation rates.<sup>54</sup> The exact incidence calculations would vary across different regions. For non-SNAP consumers shopping in stores with a smaller proportion of SNAP consumers, we would expect them to face a smaller decrease in consumer surplus and vice versa. Furthermore, SNAP consumers and non-SNAP consumers also buy different goods within the same store. Using income-group specific weights for products based on the Nielsen Consumer Panel to construct alternative store-level price indices, we find that price responses are smaller for high-income groups, but the difference in price response between the top and bottom income quartile is only about 0.01, which does not affect incidence calculations substantially.

Fourth, if we account for the distortionary cost of taxation as in Hendren (2020) by using efficient welfare weights, surplus to SNAP consumers could be weighted 1.5-2x more than surplus to non-SNAP consumers who are richer. Likewise, we may assume producer surplus is redistributed to shareholders of retailers who are richer than SNAP consumers. Implementing these weights would raise the economic efficiency of SNAP-benefit changes. On the other hand, the relative burden for non-SNAP consumers may particularly fall on those consumers whose incomes are just high enough to make them ineligible for SNAP, although the absolute burden for any single non-SNAP consumer is found to be small.

# **5.5 Multi-Product Pricing**

To better understand the price and quantity movements seen in Table 6, we extend pass-through formulas to a multi-product setting. In the case of perfect competition, we show in Appendix Section K that we can rewrite the equilibrium condition with demand _Q_<sup>_D_</sup> _j_<sup>(</sup><sup>_p_)</sup> for good _j_ as a function of a vector of prices _p_ = ( _p_ 1 _, ..., pn_ ) and obtain the pass-through formula as shown below:

> has also found a range of estimates of about 0.03-0.17 (Hoynes and Schanzenbach 2009).

> 54Table 6 also shows that the quantity response is higher in regions with higher SNAP participation rates. This result provides further evidence consistent with equation (9), which shows that the quantity response is larger when the proportion of SNAP sales (proxied for by the SNAP participation rate empirically) is higher. This large heterogeneity in the quantity response across regions may contribute to the large standard error for our estimate of the real-sales response in Table 3, which aggregates across regions.

31
Table 1. Nominal and real (food) hourly wages for several occupations Annual after-tax wage divided by the number of effective annual hours of work, March 2009

||Building l|aborer 1/|Skilled indust|rial worker 2/|Engin|eer 3/|
|---|---|---|---|---|---|---|
||Nominal<br>after tax<br>wage (in $)|Real food<br>wage|Nominal<br>after tax<br>wage (in $)|Real food<br>wage|Nominal<br>after tax<br>wage (in $)|Real food<br>wage|
|New York|16.6|16.6|29.0|29.0|26.5|26.5|
|London|9.7|15.4|19.0|30.4|22.1|35.2|
|Beijing|0.8|1.3|2.3|3.8|5.8|9.5|
|Delhi|0.5|1.7|2.1|6.9|2.9|9.1|
|Nairobi|0.6|1.5|2.0|4.7|4.0|9.2|
|Rich vs.<br>poor<br>(unweighted<br>ratio) 4/|20.4|10.9|11.0|5.8|5.8|3.3|

Note: Food prices are estimated from a basket of 39 food products with weights reflecting West European consumption patterns. New York food prices are set equal to 1. Real food wage (in New York food prices) is estimated by dividing the nominal after-tax dollar wage by the food price index (not shown here). Annual number of hours worked is equal to the weekly number of hours of work given for each profession and country separately (UBS, 2009, pp. 34-35) multiplied by 52 weeks, and reduced for the number of official and paid vacation days per year for each country (UBS, 2009, p. 30).

1/ Unskilled or semi-skilled laborer, about 25 years of age, single.

2/ Skilled worker with vocational training and about 10 years of experience, working in a large company in the metal working industry, approximately 35 years of age, married, two children.

3/ Employed in an industrial firm in the electrical engineering sector, university or technical college graduate with at least 5 years of experience, about 35 years of age, married, two children.

4/ Rich are New York and London; poor are Beijing, Delhi and Nairobi.

15
Policy Research Working Paper 9814

# **Abstract**

This paper explores the relationship between the use of service inputs, participation in global value chains, and firm productivity. Services play the role of both an intermediate input in production and a coordinator. Using a detailed Indian firm-level data set from 1990–2017, the paper estimates the productivity premium associated with varying - depths of global value chain integration and different inten sities and types of services used in the production. The study finds that firms in global value chains have a productivity premium between 13 and 22 percent relative to domestic firms, with some variation based on the depth of global value chain integration and the sector to which the firm belongs. Both the type of service inputs used (composition of services) and the origin of services (whether sourced domestically or from abroad) matter for firm performance. While higher aggregate service input use (as captured by the share of expenditure on service inputs) is not necessarily associated with an increase in productivity, increased use of complex services and information technology services is associated with higher productivity. The use of imported services is associated with higher productivity. Moreover, firms that are more deeply integrated in global value chains benefit more from importing services.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice and the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rmanghnani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team