rafmacalaba/datause-encoder-data huggingface dataset:
# **4 Analysis**

We analyze the intent-to-treat effect of Malawi’s public works program using householdlevel data from two rounds of post-intervention surveys. Recall that our design includes two levels of randomization: village-level randomization varied PWP availability and payment structure, while household-level randomization varies eligibility to participate conditional on PWP availability. Our main results pool across the four variants of the intervention in order to estimate the effect of having any public works opportunities in one’s village, and the additional effect of being a treated household within a PWP village. Therefore, we capture the direct effect of PWP availability on treated households, and the indirect effect of the program on untreated households in PWP villages. The indirect effect is important in the context of rationing.<sup>9</sup> Using data from the lean season (survey round 2), we pool across treatments and estimate the equation where the indicator PWP _v_ is a village-level indicator for the availability of any PWP program and Topup _i_ is a household-level indicator that equals one if the household was randomly selected to be offered (“treated”) the program and zero otherwise. The coefficient _β_ 1 captures the indirect effect of the program on untreated households in PWP villages. The coefficient on the interaction term _β_ 2 captures the marginal effect of being randomly selected for PWP in a village that had a PWP program – that is, the effect of being offered the opportunity to participate in program. The sum of the two coefficients _β_ 1 and _β_ 2 captures the total effect of PWP on treated households compared to households with no PWP in their villages. All specifications include district and week-of-interview fixed effects; standard errors are clustered at the village level. As of survey round two (February 2013), all villages with PWP programs had completed Cycle 1.

In half of the PWP villages, Cycle 2 took place in the lean season, after survey round 2 and before survey round 3. Therefore, analyses of the pre-harvest data (survey round 3) estimate the effect of the lean season PWP compared to the standard harvest season program

> 9One limitation of our design is that we capture the indirect effect only at the implicit coverage rate as implemented in our study, approximately 18.8%. EAs are defined by NSO to include an average of 200 households. Our randomization ensures that ten of the 16 surveyed households are offered PWP. At the overall coverage rate of 15%, another 28 households, or 15% of the remaining 184, would be included in the program. Therefore, in PWP villages, an average of 37.6 out of 200 households have the opportunity to participate.

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8 are privately owned, the state-owned banks control more than 50 percent of banking assets.

**_<u>Table 1:  Overview of recent financial sector evolution in Russia</u>_**

||<br>Jan-01|<br>Jan-02|<br>Jan-03|<br>Jan-04|<br>Jan-05|Jan-06|
|---|---|---|---|---|---|---|
|**Number of Institutions**|||||||
|**TOTAL**|**1311**|**1319**|**1329**|**1329**|**1299**|**1253**|
|Banks|1,274|1,271|1,276|1,277|1,249|1,205|
|_of which licensed to take deposits_|_1239_|_1223_|_1202_|_1190_|_1,165_|_1,045_|
|Non-Bank Credit Institutions|37|48|53|52|<br>50|<br>48|
|State Banks|-|-|-|20|<br>21|<br>-|
|100 percent Foreign Banks|22|23|27|32|33|41|
|Moscow Banks (City and Region)|628|639|663|679|<br>671|<br>646|
|Regional Credit Institutions|683|680|666|662|<br>628|<br>607|
|**Branch Statistics**|||||||
|**TOTAL # of Branches**|3,793|3,433|3,326|3,219|3,238|3,295|
|Sberbank|1529|1233|1162|1045|1011|1009|
|# of branches outside of Moscow|3,430|3,113|3,023|2,927|2,956|3,003|
|_Branches per institution_|2.9|2.6|2.5|2.4|<br>2.5|<br>2.6|
|_Population per Branch_|-|-|-|-|-|43,642|
|**Other Indicators (in%)**|||||||
|M2/GDP|21.1|23.2|25.8|29.8|<br>31.0|33.33|
|Banking sector assets / GDP|-|35.3|38.3|42.3|42.0|45.0|
|Domestic Credit / GDP|24.1|24.9|26.3|26.6|<br>21.4|<br>20.6|
|Credit to domestic real sector / GDP**|11.6|14.8|16.6|20.3|22.9|25.2|
|Deposits / GDP|17.2|19.2|21.2|21.9|23.3|26.3|
|Household deposits / GDP|-|7.6|9.5|11.5|11.7|12.7|

_Source: CBR, IMF *) annualized figure_ _**) this indicator has undergone major updating throughout 2002-2005 based on the 2005 CBR report_ Sberbank is still by far the largest institution, with 54.1 percent of total retail deposits and about one-third of total loans outstanding. The remaining 20 state banks are much smaller and usually specialize in a particular market niche or region. The only exception is Vneshtorgbank, which accounts for about 6 percent of total bank assets. Nonetheless, the weight and preferences enjoyed by publicly-owned banks are holding back the development of private sector banking in Russia. Banks under foreign control have grown substantially in recent years, but they represent only 7.6 percent of total bank assets. Although the interest of the latter to enter the booming Russian economy has increased, they fear the existing legal uncertainty and claim to face significant informal barriers to their operation and expansion.

Overall, the level of penetration of banking services is low, and competition is insular. Banking institutions, in general, offer three kinds of services: depository, lending, and
populations do not necessarily have to experience increasing dependency ratios or lower productivity.

8. _Entrepreneurship and startups_ . In our discussion of innovation, we did not look at the supply of entrepreneurs and the environment in which they were operating, something that Baumol had been emphasizing for more than a decade when we began our work. Lazear and others (2014) have looked at the links between entrepreneurship and demographics, noting that younger workers may have more creativity, but that experience in management is required to get the business acumen necessary for entrepreneurs. Separately, Lazear (2005) has also shown that entrepreneurs tend to have varied educational backgrounds with a balanced set of skills. By contrast, many middle-income countries have been obsessed with science and technology, often focusing narrowly on producing STEM students. We underestimated the importance of start-ups to the process of growth and innovation, and ignored the needed entrepreneurial climate. We had almost no discussion of intellectual property rights, something that has come to dominate today’s trade negotiations involving middle-income countries.

Metrics like the Global Entrepreneurial Index provide a body of useful empirical benchmarks that could now make this a fruitful avenue to explore. It makes for somber reading for many Asian countries. Indonesia (120), India (104), the Philippines (95), and Thailand (68), all rank below levels that would inspire optimism. Malaysia (53) has also slipped in the rankings in recent years.

9. _External commitment and regionalism_ . We were conscious of neighborhood effects and the impact of China on East Asian economies, but we mainly saw this operating through the channels identified in the literature on economic geography—on “regionalization” rather than “regionalism”. As a result, we neglected the value of regional institutions and organizations in pre-committing middle-income countries to a long-term reform trajectory and the impact this could have on economic development. In a world where the WTO and other global rules have stalled, these external commitments are likely to be regional in nature. The added advantage of that is that it could be easier for groups of countries within a region to escape the middle-income trap together than for individual countries to do so on their own.

The economic success of countries integrating into the European Union has persuaded us that external commitments have a far more significant role to play than we thought. In fact, convergence within Europe has been one of the extraordinary stories of growth in this century. A recent assessment of the European economic model (Gill and Raiser, 2012) provides clues about how this might have happened: “If you can be a part of the formidable European convergence machine, you do not need to be extraordinarily 18
23 the rural poor – either gain or lose less from higher prices than the middle groups. And the rich lose little, since they spend a small proportion of their incomes on food to begin with.

Our analysis also suggests that the record of the social protection response by the Brazilian government was mixed. Although overall the increases in Bolsa Família and BPC benefits were welltargeted, their volume appears to have been insufficient to fully protect the poor – especially the urban poor – from the negative welfare consequences of higher food prices. Although the growth in Brazil‟s social protection system has played an important role in poverty reduction over the last two decades, there is still scope for further improvements.<sup>16</sup> Pre-existing social protection programs may in some cases be rapidly scaled up (like Bolsa Família and BPC) thus enabling the government to mitigate some of the adverse consequences of higher food prices on the beneficiaries. However, these programs were designed primarily to combat long-term, “structural” poverty, and not as short-term risk-management instruments. Conditional cash transfer programs (and social pensions like BPC) are not counter-cyclical instruments. The targeting mechanism in place may not necessarily be appropriate to identify those hit by a crisis (new poor). With the benefit of hindsight, it is unsurprising that small increases in their transfer amounts proved insufficient to fully protect the poor against higher food prices. Whether an alternative social protection instrument should be put in place to fulfill that need in future episodes of price volatility – and if so, how it should be designed – are interesting policy questions going forward, both for Brazil and for a number of other countries in Latin America.

The analysis in this paper also highlights the need for collecting additional data, and points to a number of further research questions. We close by highlighting three examples of such additional work, which would permit a more accurate estimation of the overall change in household welfare due to changes in food prices, given by equation (5). First, it would be great if one of Brazil‟s excellent household surveys (either the POF or the PNAD, both of which have recently undergone reforms) included a more detailed module on agricultural production and sales at the household level. This would enable researchers to compute net purchases of agricultural commodities for all households involved in family farming.

> 16 On the role of Brazil‟s social protection system in the recent process of poverty reduction, see Ferreira et al. (2010) and Veras et al. (2006).
are substitutes for improved nutrition.

We investigate how some of these mechanisms contribute to the overall impact of cash and in-kind food transfers on children’s learning in the context of the _Programa de Apoyo Alimentario_ (PAL), one of Mexico’s flagship anti-proverty transfer programs. To identify impacts, we leverage the randomized controlled trial that was implemented during the program’s rollout in 2003, in which 208 villages in southern Mexico were randomized into three groups: program-eligible households received either cash transfers, in-kind food transfers, or no transfers (a control). In-kind food transfers were of a similar value to the cash transfer, and both represented around 12% of household consumption pre-program. We follow approximately 4,000 children whose families were part of this experiment into primary school by merging individual-level experimental data with a nationwide census of standardized test scores from 3rd to 6th grade. Test score data spans the years 2007 through 2013, which allows us to study children’s learning 4 to 10 years after transfers began. Unlike Mexico’s other wellknown anti-poverty program, _Progresa/Oportunidades_ , PAL transfers were not conditional on school attendance.

Previous research (Cunha, 2014) has shown that, in the short-run (approximately one year after transfers began), in-kind and cash PAL transfers both increased total household consumption by similar magnitudes, a result attributable to the fact that the cash transfer was largely inframarginal. However, certain in-kind food items were extramarginal and binding which induced children receiving in-kind transfers to consume more micronutrients than children who received cash transfers. Many children were deficient in some key micronutrients that have been shown to support brain development (Black, 2003) - namely, vitamin C, iron, and zinc - and the in-kind treatment induced more children to consume above the Recommended Daily Allowance (RDA) of these micronutrients. By linking individual-level information on consumption from the post-experiment survey in 2005 with test score results in primary education, we can thus directly assess whether improvements in the nutritional intakes translate into improvements in subsequent performance in school. In addition, we use both the household survey data that were collected as part of the experiment and administrative data that were collected concurrent with the primary school exams to study how PAL impacts other determinants of school performance, such as child labor, school quality, and parental time investment.

Our main finding is that, 4 to 10 years after transfers began, in-kind food transfers had no impact on test scores while cash transfers negatively impacted test scores, relative to the control. Transfers, whether in-kind or in-cash, did not impact the likelihood that children took the tests, which rules out the possibility that these results are driven by sample selection.

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Martin (2000), however, conduct the required analysis for the TIMSS 1999 sample and there is little reason to suspect the results would not extend to India.

The methods and results discussed here should not be taken as advocacy for dispensing with TIMSS altogether and using their publicly released items to place tested children on international distributions. TIMSS provides a level of analysis and robustness checking that independent researchers cannot easily replicate. We view the methods presented here more as a bridge between current practices and TIMSS-like comparability rather than an alternative. Even in this case, a larger pilot that compares TIMSS results with those obtained by the methods suggested here would yield important information on the biases inherent in our equating methods.

# **6 Conclusion**

The educational administration in India has often shaken off the bad news emerging from the primary educational sector on the grounds that the Indian system is based on the rigors of selection. A gruelling primary schooling would weed out all but the best performers, who would then graduate onwards to secondary schools and receive a higher quality education. One response to the poor testing results from the primary level has in fact been to point to India’s position in the global economy and the comparable performance of its top firms and professionals to their international counterparts. In essence, if the schooling system is so poor, how is it that India has all these top global performers?

But this misses the point. Both positions are sustained by the data. Children from these two states clearly fail any potential Millennium Learning Goal. If results are similar for the rest of the country, over 17 million 14 year-olds, around 80 percent of the population, are either not enrolled or cannot pass the lowest international benchmark. But India’s massive population and wide variance in achievement also ensure that Indians are amply represented in the worldwide cohort of top performers. One out of fourteen children who pass the advanced benchmark in the TIMSS sample are Indian, a ratio only four other countries can match. For every ten children in the United States who pass the advanced benchmark—and only 7 percent do—there are four who pass it in India.

How this situation plays out over the next decade has much to do with how production 19
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patterns that characterize youth employment movements in the sample countries, with a particular focus on those issues discussed in Section 2 above.

_The importance of turnover: “shopping around” behavior_ Similarly to the OECD, we find evidence of high turnover among the young in Latin America. The propensity to change sector, as summarized in Table 2, shows that in all three countries, the highest propensity to move is among workers between 19 and 24 years old. Argentina has the highest turnover for young adults with 37.7 percent (as compared to 27.8 percent among prime-aged adults and 22 percent among youth), followed by Mexico with 29.4 percent and Brazil with 19.9 percent. There is not a consistent pattern across the three countries for the second highest turnover. Prime-aged adults are more likely than youth to change jobs in Argentina while the opposite emerges in Mexico and the propensities are equal in Brazil.

Young people from poor families seem to change sectors more often than non-poor youth. Table 2 shows that on average poor workers have a higher number of people who leave a sector as a share of the total number who move. In Argentina, for example, young adults in the first quintile have a turnover rate of 43.7 percent compared to 28.8 percent of those in the fifth quintile. In Brazil, turnover among poor young adults is 23.6 percent and among non-poor young adults is 19.6 percent and in Mexico is 32.8 percent versus 28.3 percent. The same pattern can be found in workers under age 19.

Women’s turnover is significantly lower than that of young males. Within gender, young adults still have the highest turnover rates, and overall, young men have the highest rate of turnover of all age-gender groups. For example, the propensity for young males in Argentina to change jobs is 41.5 percent, 21.6 percent in Brazil and 32.1 percent in Mexico versus young females where propensity is 34 percent, 18.1 percent and 26.8 percent respectively.

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potential endogeneity of access to urban infrastructure and profits. The productivity variable takes a value of 1 if the firm’s profits per worker is in the top 10 percent of the sample’s distribution of productivity.<sup>36</sup> Finally, we use an OLS model to estimate the correlation between employment type and the urban environment in one country. Taking advantage of available geospatial data and the 2017 Census for Mozambique, we explore how the urban environment – crowding, neighborhood economic development, and cell phone coverage – correlates with employment types. To do so, we create a dataset where each observation is an urban _bairro_ (neighborhood) in Mozambique, resulting in a sample with 212 _bairros_ in five cities _._ Using data from the 2017 Census, we create variables for the distribution of employment type within a _bairro_ and average ownership of a vector of durable goods in each _bairro_ . We use publicly available geospatial data to generate a vector of six urban variables: two measure neighborhood economic development (night light luminosity and electrification), three measures of crowding (population density, building density, and urban density), and one measure of cell phone coverage (derived from cell tower density). The methodology to generate each urban variable is provided in Annex 2. The regression specification is as follows: (5) gg gg gg gg gg gg bbii bbii bbii ii ffii The dependent variable is the share of total employment that is in employment type YY = αα + θθ UU + ββ XX + γγ II + εε _g’_ in _bairro b_ in city _c_ . To remain consistent with our definitions used to generate the employment type variable with the GMD and IES data, we use Census data on job category and employment to define _g’_ ( _g’=_ low-educated self-employed, high-educated self-employed, unpaid family worker, employer, low-educated employee, and high-educated employee).<sup>37</sup> The spatial distribution of each employment type across the five cities is provide in Annex 2.

We redefine _Ubc_ and _Xbc_ with _bairro_ -specific variables derived from publicly available geospatial data and Census data. The vector _Ubc_ has six elements: population density, night light density (luminosity), average electrification, cell phone tower count, proportion urban, and building density. The spatial distribution of each urban variable across the five cities is provided in Annex 2. The vector _Xbc_ is a vector of durable goods that are used to proxy _bairro_ poverty, namely the share of the _bairro’s_ population that owns each of the following: radio, landline phone, laptop, iron, electric stove, refrigerator, and car. The term _Ic_ is now the city fixed effect. We use robust standard errors.

# **4. The Structure of Urban African Labor Markets**

> 36 <mark>The 10 percent cutoff follows Grimm et al. (2012).</mark>

> 37 <mark>The Mozambique 2017 Census does not provide information on social insurance or firm registries. Instead, drawing from Fields (2015), we use education level as a proxy for formal and informal.</mark>

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# **References**

- Acemoglu, D., He, A., and le Maire, D. (2022). Eclipse of rent-sharing: The effects of managers’ business education on wages and the labor share in the us and denmark. Technical report, National Bureau of Economic Research.

- Adhvaryu, A., Fenske, J., and Nyshadham, A. (2019). Early life circumstance and adult mental health. _Journal of Political Economy_ , 127(4):1516–1549.

- Adhvaryu, A., Molina, T., Nyshadham, A., and Tamayo, J. (2023). Helping Children Catch UP: Early Life Shocks and the Progresa Experiment*. _The Economic Journal_ , page uead067.

- Anderson, M. L. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the abecedarian, perry preschool, and early training projects. _Journal of the American statistical Association_ , 103(484):1481–1495.

- Atkin, D. (2016). Endogenous skill acquisition and export manufacturing in mexico. _American Economic Review_ , 106(8):2046–2085.

- Baker, M., Gruber, J., and Milligan, K. (2019). The long-run impacts of a universal child care program. _American Economic Journal: Economic Policy_ , 11(3):1–26.

- Bazillier, R. and Girard, V. (2020). The gold digger and the machine. evidence on the distributive effect of the artisanal and industrial gold rushes in burkina faso. _Journal of Development Economics_ , 143:102411.

- Bedi, A. S. and Marshall, J. H. (1999). School attendance and student achievement: Evidence from rural honduras. _Economic development and cultural change_ , 47(3):657–682.

- Benshaul-Tolonen, A. (2019). Local industrial shocks and infant mortality. _The Economic Journal_ , 129(620):1561–1592.

- Blattman, C. and Dercon, S. (2018). The impacts of industrial and entrepreneurial work on income and health: Experimental evidence from ethiopia. _American Economic Journal: Applied Economics_ , 10(3):1–38.

- Borusyak, K., Jaravel, X., and Spiess, J. (2024). Revisiting event-study designs: robust and efficient estimation. _Review of Economic Studies_ , page rdae007.

- Boudreaux, K. C. (2011). Economic liberalization in rwanda’s coffee sector: A better brew for success. _Yes Africa Can_ , page 185.

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