rafmacalaba/data-use-mentions-extended huggingface dataset:
to migrating for work, but nonetheless find their work and time allocation are affected when other male household members do so. Mu and van de Walle (2011) and Slavchevska et al (2016), for example, discuss how rapid economic development and rural-urban migration led to lower engagement in paid work and greater on-farm work for women who remained in rural areas, but not for men. And when rural women do migrate, they are much more likely to be engaged in vulnerable, informal forms of work (ILO, 2018a).

Additional individual-level questions on migrants’ employment outcomes, including place and date of migration, would help shed light on how economic development is associated with work-related migration and outcomes of remaining household members, including women. Among the LSMS-ISA, currently only the 2015-16 Ethiopia survey asks individual-level questions on migrants spanning these topics. The 201314 Uganda LSMS-ISA asks on the second visit to the household whether any member from the roster has left, reasons why (economic/education/marriage/conflict), and where (region of the country, or abroad) they went. The question on locality of migration, if within the country, is asked at the district level. Having a similar, consistent set of questions across surveys and survey rounds is valuable in cross-country comparisons, as well as the longer-term effects of migration in these areas.

17
- _Factor-shift effect:_ Post-regulation production technologies may be more or less labor intensive, as more /less labor is required per dollar of output.

Morgenstern, Pizer and Shih (2002) empirically estimate a model incorporating these different effects of regulation on employment for four highly polluting/regulated industries using data for the period 1979-91. Importantly, they measure regulation by the plant’s expenditures on pollution controls, as self-reported in the Commerce Department’s survey on Pollution Abatement Control Expenditures (PACE).

While there are some differences across the four industries, Morgenstern et al. find that increased abatement expenditures generally do _not_ cause a significant change in employment at the industry level. Since these estimates do not consider the effect that regulation in one industry may have on other industries that make complementary or substitute products, the results should not be interpreted as relevant to the whole economy, but they do highlight the complex responses to the new regulations exhibited by plants in the four industries.

Interestingly, other studies have focused on air pollution control induced changes in employment, using measures of regulation tied to specific policy actions rather than to self-reported pollution control expenditures. Examples include the stringent air quality standards imposed in the South Coast Air Quality Management District in the 1990s, and the nationwide attainment/nonattainment designations used for criteria air pollutants, as well as the realignment of such designations following the 1990 Clean Air Act Amendments.

In an early study, Berman and Bui (1997) found small, statistically insignificant employment _gains_ associated with the more stringent South Coast air quality rules, consistent with the later findings of Morgenstern et al. In contrast, in the analyses of federal attainment/nonattainment designations, studies by Greenstone (2002) and Walker (2011) found adverse employment impacts associated with the regulations.

Importantly, all three of these studies use measures of regulation tied to specific policy actions rather than self-reported pollution control expenditures. The actual modeling is based on fixed effects, which only provides a relative indication of impact compared to the control group.
we find a strong socioeconomic gradient in obesity. Women living in poor and middleincome neighborhoods have a significantly higher probability of obesity than women in wealthy neighborhoods. Between 10 and 15 percent of this socioeconomic gradient in excess body weight can be attributed to differences in the diet quality of FAFH in the food environment. Interestingly, the same type of menus (for example, chicken with rice and _arroz chaufa_ , a fried rice dish) were most frequently consumed across socioeconomic groups, but diet quality differs greatly among the groups. The diet quality of the same menu is, on average, always much lower in neighborhoods of low socioeconomic status compared with high socioeconomic status neighborhoods. When analyzing variation within socioeconomic status, differences in diet quality explain 15 percent of the higher excess body weight in poor neighborhoods, while they explain only 10 percent in more well-off neighborhoods. Hence, women in poorer neighborhoods could benefit slightly more from an improvement in the diet quality of FAFH in their food environment compared with women in more well-off neighborhoods. This result points to the potential value of public health policies that help people, especially in poorer neighborhoods, to make healthier food choices away from home. The rest of the paper is organized as follows. Section 2 introduces the data sets. Section 3 presents the diet quality index defined for the study, while section 4 describes the empirical approach. Section 5 introduces the descriptive and empirical results, and section 6 concludes.

# **2 Data**

The data used in this analysis originate from four sources: (1) the Demographic and Family Health Survey ( _Encuesta Demográfica y de Salud Familiar_ ), which has been published as the continuous demographic and health surveys; (2) the Survey to Measure Nutritional Composition of Meals Most Frequently Consumed away from Home ( _Encuesta para Medir la Composición Nutricional de los Principales Alimentos Consumidos Fuera del Hogar_ , ENCONUT); (3) ENAHO; and (4) the National Survey of Family Budgets (ENAPREF).

The continuous demographic and health survey is part of
To quantify war-induced direct and indirect impacts on area and expected yield of winter crops, this paper uses a 4-year (2019-22) panel of 10,125 village councils (VCs) for which reliable crop cover information based on a machine learning model using ground-truthed training data for each of these years is available. To generate a measure of war-related disturbances that is more relevant for agriculture than the measures normally used in the literature, we identify location and extent of damages to agricultural fields via burning, explosion of ordnance <mark>, rockets, or aircraft</mark> , and soil compaction from movement of tanks or heavy artillery from changes on repeat imagery by medium-resolution optical sensors (Sentinel-2 and Planet). Sample images are displayed in the appendix.

2
0.81 to 0.99 and <u>((ˆ</u> _σσ_ ˆ _<u>δu</u>_<sup>(2)(2)</sup> <u>)</u><sup>)22ranging from 0.66 to 0.92.This in turn means that the household effect</sup> is relatively small, though it is in general not negligible. In all the ecozones, the magnitudes of intra-personal correlations as measured by _ρ_ ˆ<sup>(1</sup><sup>_,_2)</sup> were found high, ranging from 0.44 to 0.53. This strongly suggests the importance of the inclusion of intra-personal correlation.

After the predictions for standardized height and weight for each child in the census were made in each round of simulation, they were aggregated to the commune level in Cambodia to arrive at the prevalence of stunting and underweight. Because of missing data in the census and geographic datasets for a small number of communes, we obtained communelevel estimates for a total of 1,594 communes out of the 1,616 communes in Cambodia.

> 9We followed the definition of the ecozones previously used by WFP (2001).

> 10Other regression results are available from the author upon request.

15
27|
|Table|11:|Relation Between Work Status and Apprenticeship||
|||Training|28|
|Table|12:|Bank Education Loans to C8te d'Ivoire**.....**<br>**e........**|31|
|Table|13:|IBRD/IDA Lending for Training in C8te d'Ivoire||
|||(1982 -<br>1986)**....................**|**33**|
|Table|14:|Monthly Earnings by Employment Status|38|
|Table|15:|Monthly Earnings (W2) of Workers, According to||
|||Educational<br>Background**...... ......... ...............**|40|
|Table|16:|Means and Standard Deviations of Earnings Function<br>||
|||Variables**........................................**|**.**43|
|Table|17:|Earnings Function (lnWI)- All Employees|46|
|Table|18:|Earnings Function (lnW2)-<br>All Employees|49|
|Table|19:|<br>Earnings Function (lnW2)- AllEmployees<br>-<br>||
|||Corrected for Selection Bias|51|
|Table|20:|<br>Choice of Employment Status-<br>Probit Results|58|
|Table|21:|Annual Private Costs of VTE (Per
temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

63
We set out, in general terms, how the impact of school management can be decomposed into learning gains that arise because given actors (teachers, students and parents) become more productive, and learning gains that arise because different actors join the school. To explore why these _incentive_ and _selection_ effects might arise, we turn to a specific model that captures key features of education systems in Latin America.

This model has two main building blocks. The first is the education production function: we assume that student learning depends on teacher ability, teacher effort, and household effort. The second is the impact of management practices where, considering the personnel policy restrictions the public sector faces, we distinguish between operations and people management. Good people management practices enable managers to observe and contract on the performance of their employees, as well as to cultivate the intrinsic motivation of their staff. Good operations management practices enable managers to use resources efficiently and hence offer a higher level of teacher compensation and a more stimulating environment for students.<sup>2</sup> Our framework predicts that good people management practices increase expected test scores through two channels. A teacher with a given ability and intrinsic motivation to teach exerts more effort because these practices provide extrinsic, and cultivate intrinsic, incentives. Compounding this, good people management practices improve selection: a teacher with high ability and high intrinsic motivation prefers a school with performance pay over alternative employments because she anticipates that she will work hard and be rewarded for producing student learning. We focus on Latin American countries and find support for both mechanisms in our PISA data. Principals in schools with higher PISA-based people management scores (predominantly private schools) are less likely to report experiencing teacher shortages and also report higher levels of teacher motivation and effort, compared to principals in schools with lower PISA-based people management scores.

Our framework also predicts that good operations management practices increase expected test scores through two channels. There is no teacher incentive effect but the selection effect remains, now driven by the _level_ rather
positive and negative information, we now turn to the more general issue: the value of sharing information. Using data from the Argentine PCR we estimate the following equation for individual financial institutions:

# Pr. ( Deafult ) _i_ =  αINDIVIDUAL _i_ + βSHARED _i_ + e _i_

information could not be used for defining dependent variable. It was used for designing explanatory variables.

10
learning, variety, and quality effects. This channel of tariff reforms on productivity through intermediates appears to be stronger than the former channel. For India, Topalova and Khandelwal (2010) find that lower input tariffs played a larger role in boosting productivity than output tariffs during the liberalization process of the 1990s. Yu (2015) provides evidence on the matter for China. Following a related line of argument, Goldberg et al. (2010) find that lower intermediate input tariffs account for a large portion of output growth in India mainly due to the availability of increased varieties of inputs.

Another branch of the literature focuses on distortions in upstream services sectors – in the form of restrictive policies or implementation hurdles which reduce the scope for increased FDI.<sup>3</sup> When relevant intermediate inputs are services, rather than goods, FDI in upstream sectors can play a key role in increasing their availability and quality, or reducing their prices. Indeed, exposure to multinationals that typically display greater productivity levels in upstream sectors has also been linked with learning and with increased incentives to innovate in downstream sectors. For example, Arnold et al. (2016) found sizable effects on the productivity of the firms operating downstream, when foreign entry restrictions in upstream sectors were relaxed in India. Fernandes and Paunov (2012) use data for Chile showing analogous results, while Duggan et al. (2013) conduct the analysis

> 3 There is also a branch of literature that focuses on distortions to investment upstream in goods, as well as in services. See for example, Javorcik (2004).

2
on Serbian customs data.

# **4.3 Implementation Details**

In Section 3 we described the SCM in abstract terms. In practice our application of the SCM faced a number of challenges. We describe here briefly the details of implementation.<sup>38</sup> The primary challenge for estimation was that even the smoothed outcome variables exhibit a high degree of volatility. A different issue (with related implications) is that we use a relatively large number of conditioning variables, especially with respect to the composition of imports.<sup>39</sup> These two features of our analysis mean that we require substantially larger donor pools than is typical in SCM studies, because smaller donor pools were unable to successfully construct the synthetic

> 38 We perform our estimation using the _synth_ routine for STATA developed by Abadie et al. (2010) and use the full nesting procedure to determine the **_V_** matrix.  The procedure identifies through a regression the relative predictive power of the k covariates in the vector Z for the outcome variable and assigns larger weights to covariates with larger predictive power.  The procedure takes those weights as initial values for the importance of each covariate and then iterates until optimal weights V* and W* are obtained. The method iterates between solving for the V* matrix and the W* matrix until it reaches an optimal solution.  In our case we limit the number of iterations between V and W matrices to 50 in order to achieve a solution in a reasonable period of time.

> 39 We could simplify the analysis considerably by aggregating commodities into even larger bundles: primary products and manufactured goods, for example.  This would give the estimator fewer targets to hit and so imply a better statistical fit.  But more aggregation gives less confidence that the synthetic control firm constructed by the SCM procedure offers a meaningful counterfactual.  This is a trade-off but we err on the side of less aggregation in order to allow matching of synthetic control and treated firm at the level of
# **Appendix A: Internal and External Conditions for Innovations**

# _Intrafirm determinants of organizational innovations_

In a survey of literature, Wan et.al. (2003) noted the internal environment favorable to the survival of new ideas and innovations in an organization include (i) frequent internal communications, (ii) decentralized structure, (iii) organizational resources, (iv) belief that innovation is important and should be supported and rewarded, (v) willingness to take risks and (vi) willingness to exchange ideas. Of the list of six determinants, it would appear that the government through tax and other financial incentives would be able to provide incentives for firms to increase the organizational resources available for innovation. The other five determinants would depend much on the leadership and the prevailing culture within the firm, as well as the external environment -- to the extent that the external environment affects the internal culture or values of the firms.

# _The external environment for innovation and creativity_

Baumol (2002) lists the following as amongst the most important business conditions for technological innovation: (i) oligopolistic competition among large, high-tech business firms, with _innovation rather than price serves as the prime competitive weapon_ ; (ii) routinization of innovation to minimize the uncertainty of the process; (iii) incentives for productive entrepreneurship rather than innovative rent seeking behaviors; (iv) the rule of law; and (v) markets for technology selling and trading. As the above are all features of a capitalist or free market economy, Baumol argues for the free enterprise economy as the best system to foster innovations.

Baumol’s analysis is undoubtedly US-centric and takes for granted the existence of laws, institutions, market size and human capital that serve to facilitate innovation. Other conditions would include (i) adequate human capital (ii) society’s tolerance for learning through failure; (iii) bankruptcy laws which do not excessively penalize business failures; (v) ease of communications and travel for the dissemination of ideas;
reliance on consumer prices that effectively cover full cost. Corrupt elites may have an interest in going along with such policy change, because it might make them better off too.

We conclude with some basic thoughts about a sensible attitude towards measurement of corruption and policymaking.

# **Noise and Bias in Aggregate Indicators?**

Aggregate indicators of corruption tend to be based on perceptions of “experts” in firms, government and other institutions who provide views about the degree of corruption in different countries. The best know such indicators are Transparency International‟s (TI) Corruption Perception Index (CPI) and the World Governance Indicators (WGI) of the World Bank.

There are two potential issues with such indicators: noise (that the indicators only weakly reflect underlying levels of corruption) and bias (that the reflection is distorted by the influence of factors other than corruption). Given the illicit nature of the activity, it is unsurprising that even 2
**Polay nning, and Rd"c**

# **Public Economba**

There is a compelling fiscal rationale for encouraging greater reliance on taxing the consumption of electricity and telephone (ET) services.

ET taxes are easy to administer. Enforcement and collection of the tax is relatively inexpensive, since the tax can be added to comrnercial charges, and the services quickly turned off for nonpayment. It is not difficult to distinguish in most cases between business and personal use of these services. ET taxes avoid the problems of smuggling and evasion commonly associated with taxing the production or use of commodities that can be imported.

On equity grounds, in COte d'lvoire at least, ET taxes are clearly the most desirable excise taxes. Ranking alternative commodity taxes with high income elasticity, telephone services - clearly dominate and electricity consumption nearly dominates - the taxation of alcoholic beverages and public transportation.

These conclusions on the distributive impact of altemative indirect tax measures are reached through the application of the relatively new concept of marginal conditional welfare dominance. A commodity tax dominates others on social welfare grounds when a marginal shift in the balance of commodity taxation toward that particular comnodity enhances social welfare.

Using household budget data, such dominance can be established statistically and shown graphically without resort to normative considerations. T'is approach suggeats that ET services may be an underexploited tax base in many developing countries.

ET taxes may also meet the test of relatively high efficiency if they can be implemented thomugh a two-part pricing schedule that charges a flat fee for access to service and an additional escalating fee for marginal use.

This paper is a product of the Public Economics Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Ann Bhalla, room N10077, extension 60359.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fmdings out quickly
sup> measure of deprivation. This measure could be income-based<sup>poverty or, more commonly, an</sup> indicator of health, educational or nutritional<sup>status, or access to basic services, such as electricity</sup> or running water. Resources are then allocated in<sup>inverse proportion to average welfare, so that</sup> poor regions receive higher per capita transfers than<sup>rich ones. Alternatively, rich areas can be</sup> excluded from the program altogether. The simplicity<sup>of geographic targeting is an important</sup> advantage when lack of information or administrative<sup>capacity is a serious concern.</sup> This paper compares a number of geographic<sup>targeting indicators that have been discussed</sup> by policy-makers in Peru. These include the infant<sup>mortality rate, which is used to target the</sup> Municipal Compensation Fund, the main block<sup>grant from the central government to local</sup> governments in Peru; a composite "poverty" index<sup>developed by the Peruvian Social Fund</sup> (FONCODES), which FONCODES uses to target its<sup>projects; and an estimate of "imputed"</sup> poverty which combines census and survey data in<sup>an attempt to approximate money-based</sup> measures of welfare.

To test the potential impact on poverty of targeting<sup>with alternative indicators, I conduct a</sup> series of simulations which combine information<sup>on household expenditures from the 1994 and</sup> 1997 Peru Living Standards Measurement<sup>Surveys (LSMS) with district-level averages of various</sup> welfare measures. I use the results of the simulations<sup>to compare leakage rates, trace out</sup> concentration curves, estimate the impact on various<sup>poverty measures of the Foster-Greer-<