rafmacalaba/data-use-mentions-extended huggingface dataset:
that shocks engender _changes_ to granular units that may not be aggregated in a simple manner (Acemoglu, Akcigit, and Kerr (2016), Baqaee and Farhi (2019)).<sup>26</sup> Perhaps because of these challenges, economists studying developing economies have traditionally focussed their attention on analyses that either involve disaggregate units (households, firms, villages) or units that are very large (states, countries). In contrast, economywide empirical analysis at intermediate levels of aggregation is far less common.<sup>27</sup> Yet, there is a growing consensus regarding the importance of understanding broad changes in economic activity at meso-level scales. This paper’s objective is to contribute to the development of simple and transparent approaches that estimate changes in sub-national economic activity across sectors and business cycles. The results suggest that changes in nighttime light emissions may complement more traditional types of economic analysis, providing a spatial perspective on business cycles in near-real time.

26 There are two alternative approaches that ideally would be used together in a complementary manner. First, model and estimate the aggregation pathways using microeconomic data on household and firms (c.f. Browning, Hansen, and Heckman (1999)). Second, directly estimate changes at meso-levels of interest. While the latter cannot provide counterfactuals and is purely descriptive, it is considerably easier, and much more amenable to being conducted in scarce data contexts typical of developing countries. This is what we have attempted to do in this paper.

27 Analysis at intermediate levels has traditionally focussed on sectoral or specific concerns: e.g., poverty maps, remoteness and transport accessability, urban structure and its relationship to congestion and agglomeration, or agricultural productivity.

17
-25-

# Components of US Census of Manufactures Definition of Gross Value Added

- 1) Gross Value of Output = Gross Value of Shipments (excludes sales and excise taxes')

- 2) US Value Added = Value of Shipments minus Cost of Materials US Cost of Materials =

- a) all raw materials, semi-finished goods, parts, containers, scrap, and supplies put into production or used as operating supplies and for repair and maintenance during the year;

- b) electric energy purchased;

- c) fuels consumed for heat, power or generating electricity; d) work done by others on materials or parts furnished by manufacturing establishments (contract work);

- e) products bought and resold in the same condition;

- 3) No National Accounts Concept of Value Added derivable from the census;

1 see p. XXVII of General Summary volume.
import parity price. While most farmers sell their produce at the farm gate, others sell at nearby market centers and depending on the month in which sales are made, face either the harvest season (May to October) price or the lean season price (November to April). Apart from representing a price that farmers can potentially face in the maize market, the lean season maize price also represents the opportunity cost to farmers of purchasing maize if they are not able to produce enough maize to avoid household-level, seasonal maize deficits. The import parity price of maize is also considered in order to account for the government’s opportunity cost to home production of having to import maize. Accounting for all of these maize output prices will help to provide a broader picture of the expected profitability of fertilizer use.

The present paper also extends the scope of previous work on fertilizer profitability by using the NUE and profitability estimates that are likely more internally and externally valid than in previous studies from Malawi. For, example, the NUE estimates used in the official evaluation of Malawi’s FISP is assumed to be 16.8 on average (Dorward et al. 2008). These estimates are based on work from the late 1990s from the Malawi maize productivity task force (MPTF) and use data on master farmer fields to estimate NUE (Benson 1999). Arndt et al. (2015) use the estimates from the MPTF to employ a CGE model of benefits from FISP and find that with an NUE of 16.8, FISP generates an economywide benefit/cost ratio of 1.62 when spillover benefits are considered in addition to direct benefits from increased maize production. They find that the benefit-cost ratio falls below 1.0 (break-even level) at an NUE of 13. Harou et al. (2017) also use the MTPF data from the late 1990s to estimate fertilizer profitability in Malawi. The authors find that bundles of fertilizer distributed under FISP (100 kilograms) are almost always profitable on average in Malawi,
Rural Central Urban Central Rural South Urban South Rural<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text -->

**Revised** : national 1996/97 based basket PL with 13 location price deflators and 2002/03 and 2008/09 data partially re-estimated (set A and B table1)

An analysis of the distribution of real consumption (see Figure 6, below) obviates the potential disadvantages of considering only one measure of welfare, the poverty line, while overlooking the rest of the consumption distribution. The horizontal axis represents revised consumption measured as a percentage of the poverty line. The vertical axis represents the population percentage. Each point on the distribution function shows the share of the population below a specified consumption level.<sup>33</sup> The

> 33 Given our focus on the performance of the poorest segments of the population we cut off the distribution at 200% of the poverty line.

23
Table 9c: Difference in Difference Estimates of the Impact of PROSPERA on ENLACE Achievement Tests: All 6<sup>th</sup> Graders in 2008 in Medium, High or Very Highly Marginated Communities 2008.

|Variables|7th grade- 6th grade<br>score<br>9th grade -6th grade<br>score<br>Propensityscore matchingwith exact matchingon age<br>_Panel A: Spanish_|Increase in category<br>of score from 6<sup>th</sup>grade<br>to 12<sup>th</sup>grade<br>and state|
|---|---|---|
|Girls|2.41<br>6.389|-0.0608|
||(4.022)<br>(5.29)|(0.0335)|
|Boys|7.29<br>14.67**|0.0604|
||(5.358)<br>(6.501)|(1.79)|
|Observations|4,565<br>4,415|2,253|
||_Panel B: Mathematics_||
|Girls|7.478*<br>7.145|-0.0157|
||(4.23)<br>(6.623)|(0.0499)|
|Boys|9.99*<br>14.097*|0.01|
||(5.19)<br>(7.558)|(0.004)|
|Observations|3,072<br>2,908|1,494|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: Control variables
27

# **Box 5: Public-Private Partnerships in Social Sectors**

Loevinsohn and Harding (2004) review the experience with contracting approaches (mainly management and service delivery contracts) in the delivery of primary health care or nutrition services involving nonprofit organizations. Based on ten studies of mostly low-income countries, they show that rapid improvements were achieved in a variety of services and settings. The four cases with controlled before and after evaluations demonstrated large impact, with the median double differences ranging from 9 to 26 percentage points. Larger double differences were observed for parameters that are easier to change, such as immunization. Six of the ten studies compared contractor performance to government provision and all found that contractors were more effective, both in terms of quality of care and coverage of services. The concern that contracts are unlikely to enable service delivery on a large scale was not borne out: four of the examples involved millions of beneficiaries. In terms of cost, non-governmental entities performed better even when public institutions were provided similar amounts of resources. The programs were sustainable: of the nine programs, seven have been continued and expanded. The one study which examined equity found that contractors were able to significantly improve health services for the poor, actually saved them money, and did a better job than government provision.

Taylor and Blair (2002) review the experience with PPPs in managing and constructing public hospitals. In Australia, federal and state governments have introduced private participation in more than 50 public hospitals through different approaches, including build-own-operate (BOO) transactions, conversions or sale of hospital to a private operator, private management of a public hospital, build-own-leaseback arrangements, and collocations in which a private wing is located within or beside a public hospital. One example with positive results is the Mildura hospital BOO contract, awarded in 1999. Capital costs for the new hospital were 20 percent below those for public hospitals. All performance targets have been met, patient volumes increased by 30 percent in the first year, clinical services are provided at a lower
Table 5.3—Sample means of variables for face-to-face interviews and phone interviews

|Variable|Face-to-face|Phone|Sig.|
|---|---|---|---|
|Years of schooling|9.79|9.72||
|Repetitions in high school|0.15|0.13||
|College entry|0.40|0.29|***|
|Migration|0.16|0.73|***|
|TEEP province|0.60|0.67|***|
|Female|0.50|0.60|***|
|Log(consumer durables)|11.08|10.76|***|
|Mother’s years of schooling|7.48|6.45|***|
|Mother’s age|49.16|49.98|*|
|Number of siblings|5.52|6.22|***|
|Age|19.58|20.18|***|
|Number of observations|2,724|330||

Source: TEEP Tracking Survey.

34
on the Blanchard and Perotti (2002) identification method, employing quarterly data for GDP and government expenditure from the IMF’s International Financial Statistics database for the period 1990-2019. In computing the “pours” component, the value of the multiplier after four quarters is used (given by 0.88).

> 18 The fiscal reaction elasticity is estimated using a panel of 15 commodity exporters (4 advanced economies and 11 EMDEs). The fiscal multiplier is estimated for the same panel following Blanchard and Perotti (2002). Government size is represented by the average government expenditure as a share of GDP for each country. The availability of quarterly data is critical for the Blanchard and Perotti (2002) identification method. This method assumes that output responds to government spending within the period, but that government spending does not respond to GDP. In other words, all contemporaneous correlation is attributed to fiscal policy affecting GDP. See Ilzetzki, Mendoza, and Végh (2013) for a detailed discussion.

10
||(1)<br>Average<br>change<br>in sales<br>(%)|(2)<br>Average<br>change<br>in sales<br>(%)|(3)<br>Average<br>change<br>in sales<br>(%)|(4)<br>Average<br>change<br>in sales<br>(%)|(5)<br>Average<br>change<br>in sales<br>(%)|
|---|---|---|---|---|---|
|Increased use of digital platforms|8.68***<br>(2.88)|||||
|New investment in digital solutions||11.05***<br>(5.00)||||
|More work from home|||2.9<br>(7.84)|||
|More online sales||||18.28<br>(6.67)||
|Increased use of digital tech|||||8.77***<br>(2.77)|
|Country-sector fxed effects|YES|YES|YES|YES|YES|
|Basic characteristics|YES|YES|YES|YES|YES|
|Management|YES|YES|YES|YES|YES|
|Observations|837|882|643|603|889|

Table B6: Effects of digitalization on sales, nearest neighbor matched sample. _∗∗∗p <_ 0 _._ 01 _, ∗∗p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1 Robust standard errors in parentheses.

35
spending is large compared to studies that find the income elasticity of education spending (Acar et al. (2016); Huy (2012); Qian and Smyth (2011)). While the results of these studies are different across countries, levels of family income, and other household characteristics such as parents’ occupation, most of these studies find that the income elasticity of education spending is significantly less than one implying that education is a necessity item. For those groups of households that education is a luxury good, income elasticity is less than two. I find an income elasticity of more than three. Using family income, the estimated elasticity of 25
# **Non-technical Summary**

This paper investigates the impact of international migration on child health outcomes in rural Mexico using a nationally representative demographic survey. Identification of the health effects of migration is complicated by the fact that migrants are not randomly drawn from the general population. Hildebrandt and McKenzie use historic migration networks as instruments for current household migration to the United States in order to correct for the selectivity of migrant status. Children in migrant households are found to have lower rates of infant mortality and higher birthweights than children in similar non-migrant households. They then study the channels through which migration may affect health outcomes and find evidence that migration improves child health through raising the health knowledge of mothers, in addition to the direct effect on health of higher wealth after migration. However they also find that preventative health care, such as breastfeeding and vaccinations, is less likely for children in migrant households. This research contributes more generally toward a broader view of the benefits and costs of migration for Mexico than that offered by the amount of remittances and number of migrants which frame much of the policy debate. More detailed analysis of the effects of migration on Mexican communities holds the potential for informing this policy debate and for understanding the likely consequences of any policy changes on Mexico’s long-term development. The research provided in this paper enables clear identification of one such effect, showing migration has the potential to result in sizeable effects on child health.

- ii -
to acceptance of violence, with a slightly increasing trend in justification of IPV. While attitudes are incomplete and noisy proxies for social norms, this stable trend in conservative attitudes is indicative of the persistence of regressive gender norms in the region. _Fourth_ , a negative relationship exists between these conservative gender attitudes and their respective gender outcomes. Although this is not a causal relationship, their interdependence is important to note, given the difficulty in explaining the lack of progress in gender outcomes in the region.

The remainder of the paper is organized as follows. Section 2 discusses the related literature on social norms and women’s outcomes focusing on South Asia. Section 3 describes the secondary data sources used to explore these trends in the region, while Section 4 outlines the methods used to analyze them. In Section 5, we present the four stylized facts and discuss their implications. Finally, Section 6 concludes.

# **2. Literature Review**

Women’s economic participation may be influenced by several factors operating at different levels -from the macro to the individual. While there are both demand and supply-side factors that may predict and determine women’s labor force participation and employment outcomes, we focus primarily on barriers on the supply-side, especially those that interact strongly with social norms to influence FLFP.<sup>4</sup> Many studies argue that gender norms are behind the low levels of women’s economic participation (Heintz, Kabeer, and Mahmud, 2017; Bridges, Lawson, and Begum, 2011; Luci, Jütting, and Morrisson, 2012, Blau et al., 2013; Fernandez 2013, Fernandez and Fogli, 2009, Giuliano, 2017).<sup>5</sup> In some South Asian countries, economic growth has reshaped long-standing norms, as in Bangladesh, where women’s employment and mobility in cities have risen due to the growth of the garment sector (Heath and Mobarak, 2015). Nevertheless, many norms remain unchanged or have regressed along with this
14 Claeson 2004), the ‘Countdown to 2030 for Maternal, Newborn, and Child Survival’ (cf. Victora et al. 2015), the SDGs,<sup>6</sup> the STEPS<sup>7</sup> indicators and other NCD indicators used to track progress relating to the UN General Assembly’s 2011 Political Declaration on NCDs,<sup>8</sup> and the ongoing discussions relating to the measurement of service coverage in the context of UHC<sup>9</sup> (cf. Boerma et al. 2014a; Boerma et al. 2014b; Hogan et al. 2018). We include 18 indicators of health service utilization, of which 12 are preventative and 6 curative. The other 28 indicators are health and anthropometric outcomes for both adults and children.

# <u>Data search and data sources</u>

We set out to assemble as large a data set as possible of household surveys. To this end, we undertook inventories of the microdata catalogs of the International Household Survey Network<sup>10</sup> and the World Bank,<sup>11</sup> the Institute of Health Metrics’ Global Health Data Exchange,<sup>12</sup> and several household survey collections. We also searched for household surveys online. This search identified 1,153 surveys from 193 countries – see Figure 5. The surveys include country-specific surveys as well as multi-country household survey collections, notably the DHS, the ECHP, the Eurobarometer, the European Health Interview Survey (EHIS), the LSMS, the Multi-Country Survey Study on Health and Responsiveness (MCSS), the MICS, the Reproductive Health Survey (RHS), the STEPS, the World Bank’s Europe and Central Asia Household Health Survey, and the WHS. Table 4 summarizes the key details of these survey ‘families’. For 863 of the surveys identified, we were able

> 6 The SDG indicators are listed at
.095)||||
|Girls have a right to ask to use<br>a condom|||||
|treatment x post|0.122*<br>(0.064)|-0.066<br>(0.054)|0.797|2,314|
|treatment x post x Goal|0.030<br>(0.074)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 2. See notes from Table 3 for detail on table structure and control variables. Female friend somewhat or very likely to have STI is an indicator for believing that female friends are somewhat or very likely to have an STI (versus ‘not likely’). Over 15% of males have an STI is an indicator that a male responded that out of 100 of their male peers, more than 15 of them have an STI (the median response), and Girls have a right to ask to use a condom is a binary indicator that the male agrees that females have a right to ask to use a condom. The SRH perceptions index is the unweighted mean of the indicators after they have been standardized at baseline and endline separately to the mean and standard deviation among males whose connected female is in a ELA only community and was not assigned to the _Goal_ treatment. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *.

_Source._ Male baseline and endline data, balanced panel.

52
_likely to happen than not to happen. If you put 10 beans on the sheet of paper, it means you are sure the event will happen._ A practical issue is how many beans respondents have been given. The most common choices in the existing literature have been 10 and 20 beans (Delavande et al, 2009), although exceptions exist, such as the use of 12 stones by Luseno et al. (2003). Ten and twenty beans are easily interpreted as probabilities, with the trade-off being between less respondent burden (both in terms of time and cognition) with ten beans against the potential for allowing more precision with twenty beans. The 10-bean format forces respondents to round their probabilistic beliefs to nearest 10 percent while the 20bean format forces them to round to the nearest 5 percent. Manski and Molinari (2010) find that in the U.S. Health and Retirement Study, answers to expectations asked in percent chance format tend to be rounded to the nearest 5.

To evaluate how much difference the number of beans makes, half the sample of respondents (4 of the 8 treatment groups) were randomly chosen to receive 10 beans, and the other half 20 beans. For those given 20 beans, the introduction paragraph above was modified accordingly. After reading the introduction, the interviewer illustrated the concept further by using an example: they were told to suppose 5 black beans and 5 white beans were placed in a box, and then asked how likely it is that they would pick a black bean without looking.<sup>4</sup>

# **2.3. Questions to Measure Understanding of Probability**

The expectations module then began with five simple questions to measure whether boat owners understand the concept of probability. The five questions were as follows:

- 1) Imagine I have 5 fishes, one of which is red and four of which are blue. If you pick one of these fishes without looking, how likely it is that you will pick the red
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the outcome of interest is Y and the mediating factor (observed measured input) is M (it can be a vector of factors), the goal is to separately identify the intervention’s total indirect effect (a ∗ b) from the direct effect (c) from the following model: where a is the ITT estimate of NEP on a particular mediator (practices and beliefs), and b the marginal effect of mediator M on the outcome. The outcomes we consider is the child’s vocabulary score and the socio-personal development index, and the vector of potential mediators includes quality of the home environment, nurturing and disciplinary strategies, self-efficacy, perceived social support, and parental beliefs about the process of child development. In the case of NEP-B, the program was unable to shift significantly any intermediate indicators, so we focus on NEP-I.

We estimate the model in steps using a Monte Carlo simulation approach following Campos et al. (2017). First, we estimate the coefficient a by regressing the effect of treatment assignment on each mediator (eq. 5). Second, we obtain estimates of b from a regression of child language on treatment status (as in the ITT equation, controlling for child and household characteristics and health center fixed effects) and add one particular mediator at a time. We then compute the 95% 22