rafmacalaba/data-use-mentions-extended huggingface dataset:
At the time of the IHS2 survey (2004/05), 43 percent of young people 15-24 years of age had resided in another village or neighborhood (Panel A, Table 1).<sup>2</sup> These numbers show how common geographic mobility is among young people in Malawi. Compared to working-age adults, rates among adults over 50 years of age are lower. About one-fifth of working-age adults have resided in their current communities for fewer than five years (Panel B, Table 1). Among younger Malawians (between 15 and 24 years of age), the rates are even higher, showing that younger adults move with high frequency. Nearly one-third (33%) of women aged 15-24 have been in their current community for fewer than five years, a finding consistent with marriageassociated migration; for men this statistic is 23%. Many of these moves occur across district lines, as shown in Panel C of Table 1. Among men in particular, almost half of movers had resided previously in another district.

Never-married women and men are less likely to have resided in a different community or village, compared to their ever-married counterparts. Among never-married men, 80% resided in the community for five or more years, compared to 66% of married men. For women, the rates are 82% and 57%, respectively. This suggests that marriage contributes to relocation. Evermarried young movers are more likely to have moved within the district (more locally), whereas among the never-married, a larger share of moves are across districts.

Young adults report a variety of reasons for their moves. Generally, local moves are dominated by marriage for men and women. These moves are within the district and therefore

> 2 The examination of rates of migration across age groups captures two effects: aging and cohort effects. The former captures the effect that older people have had more years exposed to the possibility of a move; once a person has moved, s/he is forever identified as having
fixed effects, likely due to its nature of being a stock variable.

Taking the estimates from the last column as the preferred results, the estimates suggest that (1) a subsidy of ¥10,000 increases EV sales by nearly 16% ; (2) an increase of 1,000 charging ports increases EV sales by 20%; (3) the exempt from driving restriction does not seem to impact EV sales; and (4) the green plate policy increases EV sales by nearly 37%. The coefficient estimate on the price variable implies the own-price elasticity of -2.5 for a vehicle at the average price of ¥158k. The implied price elasticity is in line with the estimates from recent studies on EVs in other countries. For the US EV market, Li et al. (2017) estimates a price elasticity of -1.3 based on the initial stage of EV sales from 2011-2013 using a similar empirical framework. Xing et al. (2019) provides an elasticity estimate of -2.7 using a rich random coefficient discrete choice model with the second-choice data from US consumer surveys. Based on California data, Muehlegger and Rapson (2019) reports a price elasticity estimate from -3.1 to -3.9 among households with an annual income less than $100k. Springel (2019) estimates the price elasticities for EVs to be between -1 to -1.5 in the Norwegian EV market. In the policy analysis section below, we further discuss the implications of our parameter estimates and compare our findings with those in the literature.

# **4.2 Alternative Specifications**

We estimate several alternative model specifications to examine the robustness of our findings. First, we replace consumer price in Equation (1) with its logarithm, leading to a log-linear specification. The semi-log specification estimated above implies the price elasticity being larger in magnitude for more expensive vehicles while the log-linear specification implies a constant price elasticity across products. The results are presented in Table
Table 4: Impacts on Firm Performance

|||Pr|ofts|||Business inputs||
|---|---|---|---|---|---|---|---|
||(1)<br>Last year’s proft<br>(Unconditional)<br>(IHS)|(2)<br>Last year’s proft<br>(Conditional)<br>(IHS)|(3)<br>Last 30 days proft<br>(Unconditional)<br>(IHS)|(4)<br>Last 30 days proft<br>(Conditional)<br>(IHS)|(5)<br>Value of owned equipment<br>(Unconditional)<br>(IHS)|(6)<br>Value of inventory stock<br>(Unconditional)<br>(IHS)|(7)<br>Value of raw materials<br>(Unconditional)<br>(IHS)|
|Treatment (0/1)|1.644|0.596|-1.754|-2.747|1.540|0.826|-0.834|
||(1.11)|(1.00)|(1.58)|(1.75)|(0.93)|(1.06)|(0.95)|
|Psychometric score above median (0/1)|1.977*|-0.182|0.792|-0.541|2.278**|1.127|0.880|
|2|(1.02)|(0.86)|(1.48)|(1.69)|(0.92)|(1.04)|
This is consistent with our model which predicts that the incidence of the policy would be heterogeneous among incumbent workers (who have a job offer and stay or are poached) but not among new entrants (who come from unemployment and have low bargaining power).

Furthermore, to make sure that our exposure measure _TCRit−_ 1 is not contaminated by the policy itself, we only use one post-policy year 2013 and one pre-policy year 2012 in the benchmark regression. Later we perform a robustness check where we define the tax cut rate based on wages two years before, formally _TCRit−_ 2 = 14 _,_ 500 _/wit−_ 2, and then we use data from 2014 and 2012. In the benchmark specification we also focus on full-time, full-month workers, to minimize measurement error in wages, and present robustness checks which include part-time workers.

The results of the wage regressions are reported in Table 6. Column (1) estimates wage effects for new entrants using equation (11). The change in the wages of new entrants is economically small and statistically insignificant. This is in line with the prediction of the model that suggests that the effect of the policy on new entrants should be limited.

Table 6 also shows the estimates for the incumbent workers for whom we can calculate the tax cut rate. Column (2) suggests that the average impact of the tax cut on wages among incumbent workers is positive. The coefficient showing the treatment effect post policy in relation to the tax cut rate ( _β_ 1) is 0.22 (s.e. 0.09). This implies that a $1 increase in the tax cut would result in a 22 cent increase in wages on average, or that average pass-through is 22% with firms capturing 78% of the tax cut on average.

We also examine heterogeneity in this treatment effect. We estimate the following equation, using the notation of equation (12): where we interact all coefficients in equation (12) with _Qj_
# **4.5 Tariff and Trade Data**

Trade data and tariff rates are taken from the GTAP 9.1 database. For tariff rates, GTAP uses the MAcMap-HS6 database, described in Guimbard, Jean and Mimouni (2011). The ASEAN members have committed themselves to a maximum tariff of five percent within ASEAN. The five percent maximum tariff has reportedly been achieved among the original six members of ASEAN (The Philippines, Brunei, Indonesia, Malaysia, Singapore and Thailand). The remaining four members, Cambodia, Myanmar, Laos and Vietnam, have committed to implement the maximum five percent tariff, but have been allowed a longer adjustment period. Consequently, we adjust all tariffs above five percent among ASEAN members to five percent in our benchmark. The tariff rates and the ad valorem equivalents of the barriers on FDI are listed in table 3.

# **4.6 Social Accounting Matrices**

The core structural data of the model comes from the GTAP 9.1 dataset.<sup>**27**</sup> Exports and imports and by trading partner of The Philippines from this source are available in tables 2.

# **4.7 Share of Market Captured by Foreign Direct Investors in Services**

In the business services sectors of our model, it was necessary to calculate their market share in The Philippines of each region of our model. Except for telecommunications and insurance, for the shares of the sector captured by the aggregate of foreign firms, we used data from the final Census of Philippine Business and Industry for 2012.<sup>28</sup> To allocate the shares among foreign firms, we used data from the Central Bank of the Philippines in their report “Net Foreign Direct Investment Flows by Country of Origin, 20142016, in millions of dollars.”<sup>29</sup> This report provides data on aggregate foreign direct investment (FDI) in The Philippines by source country for the years 2014-2016. We assumed
number of outages should be negative to match the concavity stressed in the theoretical model, and indeed we find that its coefficients are negative and significant in both columns. The threshold values indicated below mean that

> 15 A ―core‖ set of sectors is systematically included in all country surveys so that the missing ones represent relatively few firms overall. (See Dethier, Hirn and Straub 2010, for a detailed description of the surveys). We consider Sports Goods, Other manufacturing, and Mining and quarrying as industrial sectors that do not rely heavily on electricity (the cost share of electricity in these sectors is less than 3.5 percent in the full sample of countries) and assign to these sectors, as well as to Accounting and finance and Advertising and marketing a value of _S0=0_ .  Symmetrically, we assign a value _S0=1_ for firms operating in IT services, Hotels and restaurant, and telecommunications (average cost share of electricity above 8.6 percent in the full sample for the first two).

> 16 A more general issue is the fact that surveys cannot provide information on firms that were not born, because of credit constraints or unreliable public power supply.  Dethier, Hirn and Straub (2010) address this ―camels and hippos‖ self-selection issue in details, stressing in particular that econometric models like the one in this paper only provide information about the effect of constraints on the sample of existing firms, and that the analysis of entry would require different models. Note however, as pointed out there, that self-selection is hardly ever likely to be complete, so some informative variation should remain in the data.
- Bhan, Maharaj Kishan, Rajiv Bahl, and Nita Bhandari. 2001. Infection: how important are its effects on child nutrition and growth? In Reynaldo Martorell and Ferdinand Haschke (eds.). 2001. _Nutrition and Growth_ , USA: Nestec Ltd and Lippincoot Williams and Wilkins, (Nestle Nutrition Workshop Series, Pediatric Program, Vol 47). (note: place of publication not specified in the book)

- Black RE, Brown KH, Becker S. Alim ARM, and Huq 1.1982. “Longitudinal studies of infectious diseases and physical growth of children in rural Bangladesh. II. Incidence of diarrhea and association with known pathogens.” _American Journal of Epidemiology_ 115:315-324.

- Census of India 2001. _Provisional Population Totals, Series 1, India, Paper 1 of 2001_ . <u>(http://www.censusindia.net/results/)</u>

- Das Gupta, Monica. 1987. “Selective discrimination against female children in rural Punjab, India”, _Population and Development Review_ 13: 77-100.

- Das Gupta, Monica, and P.N.Mari Bhat. 1997. “Fertility Decline and Increased Manifestation of Sex Bias in India”, _Population Studies_ 51(3): 307-315.

- Deolalikar, Anil B. 2004. _Attaining the Millenium Development Goals in India_ , Washington DC: The World Bank, Human Development Unit, South Asia Region (mimeo).

- Dibley MJ, Goldsby JB, Staehling NW, Trowbridge FL. 1987a. Development of normalized curves for the international growth reference: historical and technical considerations. _American Journal of Clinical Nutrition_ 1987 46:736-48.

- Dibley MJ, Staehling N, Nieburg P, Trowbridge FL. 1987b. Interpretation of Z-score anthropometric indicators derived from the international growth reference. _American Journal of Clinical Nutrition_ 46:749-62.

- Dyson, Tim, and Mick Moore. 1983. “On kinship structure, female autonomy and demographic behavior in India”, Population and Development Review 9(1
on child development, it is important to examine whether up-to-date empirical evidence suggests overall positive, negative, or null impacts. Our study summarizes the evidence of maternal work on child and adolescent health and intellectual development from the past 20 years. We note strengths and limitations of the studies and highlight common threads relevant to different facets of policy design, including considerations for emerging economies.

# Broad coverage of contexts and measures

Using a variety of search terms, we retreived 1,181 studies from databases that cover medical, psychological, economic and other social science literature. We identified a total of 613 relevantly-titled articles published between 2002 and 2021, and further limited them to 312 which examined the links between maternal work and development of children ages 0 to 18 years. We further narrowed our selection to 80 papers, which utilized statistical methods to plausibly establish causal impact. Within these studies, we limited our analysis to a set of 26 articles, which used the most rigourous methods: instrumental variables, sibling fixed effects, and multiple analytic approaches (most commonly individual fixed effects and lagged dependent variables combined).

Twenty-six causal papers cover a range of contexts: 21 papers are from Western countries (Australia, Denmark, Italy, Norway, the United Kingdom, and the United States) and 5 are from non-Western: three lower-middle income (the Arab Republic of Egypt, India, and Indonesia) and one high income (Chile). Among papers on high-income countries (all Western countries and Chile), 5 focus on low-income population s (such as mothers of welfare in the United States), and 22 rely on nationally representative surveys or censuses.

2
. The advantage of these aggregate indicators is that they span a much larger set of countries than most individual indicators, permitting cross-country comparisons of between 155 and 173 countries, depending on the aggregate. More important, the aggregation method we use provides quantitative measures of the precision of both the aggregate governance indicators and their components, allowing formal statistical testing of hypotheses regarding cross-country differences in govemance along these dimensions.

(Stanford University), in an effort coordinated by the Center for Intemational Development at Harvard University.

In the context of the East Asian financial crisis, Kaufmann, Mehrez and Schmukler (1999) find that investor perceptions of future financial instability had significant explanatory power for future actual volatility, over and above available macroeconomic data.

2
pipeline effects and a changing firm environment.<sup>8</sup> The method estimates auxiliary, reduced-form models for each of the decomposition factors in each year, before aggregating them through microsimulations. We then obtain the decomposition through counterfactual simulations of male and female salaries in which each source of pay differences is shut down at a time. The simulations can also be extended forward to quantify pipeline effects. Rather than assume that data are drawn from a steady state, as in traditional wage decomposition methods, our approach accounts for the constant in and that are in changes hiring, firing, growth, shrinkage typical To apply our methodology, we assemble a new panel containing 27 years of all personnel records from the World Bank Group (WBG), a multilateral finance organization with more than 16,000 employees in 2015. The panel length, data size, recency, and measurement precision afforded by these personnel records offer the perfect setting for the specific purpose of this paper, which is to analyze the withinfirm dynamics of gender pay gaps. The firm is a policy-relevant unit of analysis because firms increasingly bear the legal responsibility of eliminating gender pay gaps.

Our application of the decomposition method to the WBG offers a striking example of how past hiring stocks can affect pay gaps today. Our data show a decline in the aggregate gender gap from 50 cents on the dollar in 1987 to 23 cents on the dollar in 2015. For the mean salaries in our data, this amounts to an annual difference of US$27,400. However, we find that three-quarters of the 2015 gap was due to historical differences in the types of jobs at which women and men were hired. By contrast, less than 10 percent of the pay gap is due to differences in entry salaries; about 5 percent is due to differences in salary growth (including promotions and raises) and

> 8The Fortran and Stata programs that implement the methodology are available at https://github.com/clemjoub/Firm-Pay-Gaps-Decomposition

4
the training dataset. We use proportions of 10, 20, 33, 25, 50, 67, and 75 percent of the crop-cut measures, with the rest of the sample serving as the test set in each scenario. These simulations are performed using the EACI 2017 dataset, which has the largest sample of crop-cut plots.

To assess the robustness of statistical analyses using imputed crop yields, our approach involves several key steps. First, for the within-survey imputation procedure, we train the ML model on one-third of the plots that have crop-cut data and test the model on the remaining two-thirds. This allows us to evaluate the effectiveness of the model in accurately imputing yields within the same survey. Secondly, for the survey-to-survey imputation procedure, we train the machine learning model on the entire crop-cut sample from EACI 2017 and then test its performance on the crop-cut sample from EACI 2018. This step further our understanding of how well the model can generalize and impute data across different survey years. Lastly, we invert the training and test samples to experiment with backcasting. In this process, we train the model on the EACI 2018 crop-cut sample and test it on the EACI 2017 sample. This reverse application provides insights into the model’s capability to predict past yields based on more recent data, further testing the limits of the imputation model’s robustness.

# **4.2 Imputation framework**

## **_Using machine learning (ML) for the imputation model_**

In recent years, ML techniques have increasingly been incorporated into economics research, demonstrating significant utility in various applications. As noted by Athey (2018), ML methods have shown remarkable success in predictive problems, much like the one we address in our study. Introducing ML into agricultural economics enables the modeling of key variables, such as crop yields, which are influenced by a complex array of factors including soil quality, weather conditions, timing of inputs, and other management choices, often characterized by non-linearities and interactions.

Moreover, ML algorithms
8|Chengdu|1.6%|
|9|Qingdao|1.4%|
|10|Ningbo|1.4%|

_Source_ : China Statistical Yearbook (2003) However, the perspectives we gain from analyzing organizational changes in the urban context must be tempered by four considerations.

First, the MLSOEs in the major East Coast cities are losing ground to other firms and in certain subsectors are not the dynamic leading edge of the industrial economy. Many of the privately owned, foreign owned or joint venture firms are growing more rapidly and are aggressively experimenting with organizational as well as other innovations. Indeed, it can be 9
relationship of the teacher monthly earnings differential (relative to comparable workers) and harmonized student test scores—and reveals a positive association between the two. Moreover, we observe a similar positive association in Figure 4 that plots the teacher hourly earnings differentials against student test scores. These associations are statistically significant at conventional levels (Table 11).<sup>32</sup> If we drop Namibia—the country with the highest test scores—from the sample, the association with monthly earnings differentials remains, but not the association with hourly earnings differentials. As Table 11 shows, controlling for GDP per capita makes little difference to the coefficient and no difference to the statistical significance.

When we use the rate of learning poverty as a proxy for student performance, we also find an association (this time negative, consistent with the harmonized test score results) between the teacher earnings differential and the proportion of children living in learning poverty. However, because of incomplete data coverage, only 6 countries are included in the analysis and the results are not statistically significant (Table 11).

In our final examination of the association between student learning and earnings, we use microdata from 10 Francophone African countries which include test scores for students and selfreported monthly salaries of teachers (see Appendix 1 for details).<sup>33</sup> We estimate the model both without and with a set of controls (Table A22 Columns 1-6 and 7-12 respectively). We find strongly statistically significant, positive associations when pooling the data across countries in both specifications. While there is some variation depending on the exact specification used at the country level, on the whole, the microdata are consistent with the notion that better paid teachers are in schools with higher performing students.<sup>34</sup> This may or may not be the result of a causal relationship: we cannot, for example, rule out that the best teachers are paid more and are sent to schools with better students—although our controls do capture
research within - existing health intervention programmes methodological improvements are much Tnre likely to result from indepth study over many years linked to input-output monitoring of programe efficiency than from a "one-off' survey.

However, there are various issues related to health services which would form a more appropriate focus for investigations based on single-round or repeated single-round surveys. Access and coverage, for example, can be much more clearly identified as outcomes of investments in particular health programmes and so are more appropriate for economic evaluation than impact on health status. For the same reason, data on these issues can also be more easilv compared across countries. Although the dangers of transferring results of evaluations between countries without reference to differing country situations remains, the impact of such differences on the results are themselves also of interest to health planners. (How do different strategies of health service delivery affect numbers of population covered?). Data collection could concentrate both on areas where improvements In the methodology of economic evaluation might result, and on other useful economic/planning information relating to health services (for eacL country and to make international comparisons). Possible areas of interest would be: - data on patient costs for use of health services: differences between urban and rural areas, differences between age and sex groups; - data on the patient costs of different health financing mechanisms eg.user fees, tax financing and social security; - data on patient utilisation of health services/programmes: differences between urban and rural areas, differences between age and sex groups; effects of fees, transport costs and time cost; - data on the determinants of demand for health care; comparing demand for different types of providers (public, private-for-profit, traditional healers, charitabl **e** tc.); - data on the adequacy of care: relating adequacy to different factors such as level of facility, educational level of health worker, availability of drugs, availability of supervision and management.

Such household-base **d** ata would need to be complemented by data