rafmacalaba/data-use-mentions-extended huggingface dataset:
max-entropy approach advocated by Jaynes (1957).

The reweighting procedure consists of two steps. First, we use assets, demographic and education variables observed in the NFHS-2015 (as well as the CPHS) to reweigh all CPHS rounds from 2015 to 2019<sup>17</sup> . Second, we use demographic, education and labor market indicators observed in the PLFS rounds of 2017, 2018 and 2019 to further adjust the sampling weights in each round of the CPHS<sup>18</sup> . The second reweighting step allows us to account for changes in socio-economic indicators over time.

For the selection of target variables (on which to reweigh), we prioritize non-expenditure indicators that exhibit comparatively large biases in the CPHS relative to the benchmark surveys that are assumed to be nationally representative. An example of such a target variable is the share of undereducated adults (comprising of illiterate and below primary levels of education). We deliberately do not include all indicators that are shared between the CPHS, PLFS and NFHS in the set of target variables. This facilitates convergence of the max-entropy procedure (Zhang and Yoshida, 2022), and more importantly, sets aside a set of indicators that can be used to validate the reweighting exercise.

The adjusted sampling weights are obtained by matching the weighted means of the target variables between the CPHS and the benchmark representative surveys at the state-rural or urban levels (max-entropy minimizes distances between the weighted means obtained in the CPHS and the benchmark surveys). Following existing practices (e.g. Chen et al., 2018; Haziza and Beaumont, 2017; Kolenikov, 2014), the adjusted individual level weights obtained are winsorized at the 0.25<sup>th</sup> and 99.75<sup>th</sup> percentile level. We achieve national level representation by multiplying the resulting normalized weights with the rural and urban population populations of each state. The population estimates are obtained from the NFHS-2015 for 2015 and 2016 rounds; and from
public health research in order to provide a cross-sectional picture of an evolving political economy of actors’ interaction.<sup>34</sup> Methodologically, stakeholder mapping relies on in-depth interviews with major stakeholders (therefore a large s/N sample to population proportion), but it can be used jointly with other quantitative or qualitative social science methods. It has also been found useful as a preparatory tool for micro-level household surveys and formal modeling.<sup>35</sup> For the analytical purposes of this paper, we do not present the complete stakeholder mapping and analysis. Instead, the empirical section uses a number of selected survey questions that tap exclusively into general questions of equity and efficiency assessments in relation to priority setting and litigation.<sup>36</sup> _Why not micro-level surveys with larger sample size for analyzing priority setting and health litigation?_ Priority setting and the Constitutionally mandated right to health are ultimately patientcentric issues, ideally addressed with micro-level data. However, stakeholder mapping and macro-level political economy analysis are more useful as initial analytical building blocks for several reasons: 1) the issues of health litigation and priority setting have been only recently emerging in developing countries; their salience is not yet high enough among the general public, and the pool of claimants is very small compared to the general population; 2) stakeholder analysis and mapping analyze _aggregated_ preferences of mobilized groups as opposed to _diffuse interests_ of citizens, therefore being more relevant to short term policy making dynamics; 3) patient focus groups can be a substitute for microlevel surveys especially as the problem is complex, vaguely specified and would benefit from interaction among participants; often, however, stakeholder analyses help identify the policy issue and preferences with greater precision, and guide the design of subsequent micro-level survey instruments whenever analytically necessary.

_Data collection_

> _34_ For an extensive methodological review, see: <u>Ruairí Brugha</u> and  Zsuzsa Varvasovszky. “Stakeholder Analysis: a Review,

> _“_ <u>Health
contributions to employment and growth hold. Henceforth, we will use the term establishment and firm interchangeably.

Finally, our data is based on survey data rather than individual country census. However, our enquiries with the survey implementation team reveal that mean response rates across our sample of countries is 70% which suggests that we have a representative sample of firms across economies. While we recognize fully the above data limitations, we believe that this initial crosscountry analysis is useful in understanding the relationship between size, job creation and growth in developing countries. Given the various data caveats, the data presented in this paper is best used as cross-country evidence on the role of small firms in job creation.

# **III. Summary Statistics**

In this section, we first preview the evidence on the relation between firm size and aggregate employment and job creation shares across countries. Where possible we contrast our findings to the U.S. evidence in earlier literature. We then present detailed tables and charts across the entire size distribution across country income groups. Like all survey data, our data is subject to the usual sampling errors for surveys and the data caveats discussed in section II.

Hence, in presenting the summary statistics, we report medians across different sub-populations of firms.

# **A.** **_Firm Size and Contribution to Employment_**

Table 1 shows the contribution to employment across the size classes in each country.

Both the employment shares and the size classes are defined in the year before the survey. The sum of all the employment contributions across the size classes in an economy should add to 100%. The summary statistics show that the large firms (>99 employees) are the largest 9
perhaps the poorest-are underrepresented. However, because of the high replacement rate in Abidjan, INS decided to keep the 1680 households in Abidjan that had been surveyed in 1992 as part of the sample, and to reject those that had been surveyed in 1993. Presumably, therefore, the Abidjan sample suffers less from this bias.

Another worrisome finding is the relatively large number of households in the 1993 survey that reported zero value for the sum of purchased expenditure and imputed value of consumption of home production (Appendix C). About one percent of households fall in this category. Some of these households did report receiving transfers of food from people outside the household, but is unlikely that transfers from outside the household would be the sole source of food. The relatively large number of households with zero food expenditure suggests that there may been problems with implementation of the survey or with data entry.

## **Decline in household size**

A troubling finding is the decline in household size between 1993 and 1995 (table 19). This would tend to raise per capita expenditure, all other things being equal, assuming that per capita household expenditure and household size are inversely correlated as is usually the case. Mean household size fell from 5.9 persons in 1993 to 5.5 persons in 1995, with the fall particularly acute in the West Forest. For Cote d'Ivoire as the whole, the 1993 estimate is not too dissimilar to the 1988 estimate of 6.2 persons. For export crop farmers, the 1988 estimate was 6.8, compared to the 1993 estimate of 6.7 and the 1995 estimate of 5.9 persons (table 20). The fall in household size is particularly large among export crop farmers who grow cocoa: household size declined from 7.1 to 5.9 persons between 1993 and 1995 (table 21). In contrast, food crop farmer household size did not decrease: mean household size rose slightly from 5.5 to 5.7 persons. Both
(Under constant returns to scale the coefficient on the effective depreciation rate should be 0). This last result carries through in all subsequent equation specifications.

The second set of equations, (5) - (8), shows the effect of incorporating all the other variables as well into the growth equation. In all cases, the efficiency index has a positive and highly significant impact. Both financing variables are also statistically significant (most of the time) and have the expected negative sign. That is, higher public debt levels are associated with lower rates of growth, as are higher levels of government consumption. Another consequence of introducing both variables is that the growth elasticity of public capital regains it statistical significance, albeit the size of the impact is somewhat smaller than before.

A number of tentative conclusions can be drawn with respect to the relationship between public capital and economic growth on the basis of equation (7) in Table 6, which yields the best fit for the entire cross-country sample.17 [In the comparative statics exercises described next, it is convenient to remember -- from equation 3 earlier -- that the investment ratios in Table 6 may be interpreted as the natural log of the steady-state capital-output ratios; i.e., (sz/X) = exp(kz) = (KZ /Y)*.]

> **1lt** would have been preferable to use total (i.e., internal and external) public debt as a measure of the burden of financing public capital, but comparable data on internal debt is not available for many countries.

> 17As usual, in drawing such conclusions from cross-country data it is implicitly assumed that all countries share the same structural parameters (i.e., the production function elasticities), and only vary with regard to the behavioral parameters.
response to the nature of the shock; but, also, it shows that providing all/most extreme poor households with a lower transfer (i.e. columns 5 and 6) tends to deliver higher impacts than providing a more generous transfer to only a subset of them (columns 1a and 1b), even when poor households cannot be perfectly identified (i.e. need to rely on a PMT score).<sup>23</sup> _Table 9 : North Macedonia – Proportional loss shock_ <!-- Start of picture text --> Proportional Shock<br>Targeting → Household<br>Eligibility ↓<br>PMT Ex- PMT Post<br>Means<br>Means Perfect Imperfect ante or Shock or<br>Orginal Testing PMT Ex- PMT Post<br>Expansion Testing (Ex- Universal Loss Loss Perfect Perfect<br>Beneficiaries (Post Shock ante Shock<br>Ante Poor) Indicator Indicator Loss Loss<br>Poor)<br>Budget ↓ Indicator Indicator<br>(1a) (1b) (2) (3) (4) (5) (6) (7) (8) (9) (10)<br>Poverty Rate Pre-shock: 0.169; Post-shock: 0.219<br>(1) 0.09% GDP -0.021 -0.023 -0.024 -0.035 -0.027 -0.027 -0.027 -0.025 -0.025 -0.022 -0.024<br>(2) 1.0% GDP -0.048 -0.088 -0.097 -0.110 -0.037 -0.061 -0.059 -0.042 -0.045 -0.040 -0.045<br>Poverty Gap Sqr. Pre-shock: 3.523; Post-shock: 4.205<br>(1
annual data for the 1991-2015 period, this study finds that infrastructure development<sup>6</sup> reduces poverty in India. It finds a positive and unidirectional causality running from infrastructure development to poverty reduction. Sasmal and Sasmal (2016) also investigates the impact of public investment on poverty alleviation in India. Using panel data analysis, this study shows that Indian states where public expenditures on the development of infrastructure (e.g., road, irrigation, power, transport, and communication) is higher, per capita income is also higher, and the incidence of poverty is lower. Chotia and Rao (2017b) examine the relationship between infrastructure development, rural-urban income inequality, and poverty in BRICS countries (Brazil, the Russian Federation, India, China, and South Africa) using Pedroni’s panel co-integration test and panel dynamic ordinary least squares (PDOLS) method. It finds that infrastructure development and economic growth lead to poverty reduction in BRICS countries; the rural-urban income inequality aggravates poverty further.

Some studies examine the impacts of infrastructure investment on other forms of inequality. Fan and Zhang (2004) investigate non-farm productivity inequality in rural areas across Chinese provinces. The study finds that investments in rural infrastructure and education are a major driver of productivity growth in China, and non-farm rural productivity varies across Chinese provinces depending upon the level of investment in infrastructure and education. The lower productivity in western China is found to be associated with a lower level of rural infrastructure and education in that region. Similarly, <mark>Démurger</mark> (2001) provides empirical evidence of inequality across the Chinse provinces in response to infrastructure investment. Using panel data from a sample of 24 provinces for the 1985-1998 period, it finds that provinces with higher infrastructure endowment also had better economic performance (measured in terms of provincial GDP). The study also concludes that transportation infrastructure is the main factor in explaining provincial economic inequality. Cohen and Paul (2004) investigate cross-state variations of public infrastructure in the united states
Since each survey is designed slightly differently, care must be taken when comparing across different countries. Table 6 summarizes the comparability of the different indicators across the countries and the survey year for each country. In most cases, the two survey years have comparable data and it is possible to compare the evolution of the indicators within a country. The notable exception is Bolivia where slightly different data were collected in the two survey years and the comparison of adequate food or adequate care measures through time is not possible. Comparisons across countries are more difficult. There are two sets of countries which have similar information. The first is comprised of Cambodia and Zimbabwe and the second of Ethiopia and Nepal. If the fact that the first set collects information for under 24‐month olds and the second for under 36‐month olds is not a concern, then all the four countries can be compared.

Currently many household level surveys have information on aspects of environment and health but the information regarding food and care aspects of nutrition is less robust. Understanding the synergies from the adequacies and nutrition would benefit from more detailed data collection especially of food and care components. Information on food security is crucial in complementing food diversity and minimum acceptable diet components to construct a holistic adequate food measure. Information on the knowledge of the caregiver regarding best child care practices is missing in all the surveys that we reviewed.

Another important consideration is that currently most studies are cross‐sectional and with few questions regarding the timing of various actions or information on past conditions. The implication is that we can only assess the child’s current situation and not the child’s cumulative experience. The fact that a child meets the recommendations for his or her age at the time of the survey, does not imply that he or she has always met the age appropriate adequacy definitions. So for example in the majority of our studies, for a child that is a year old, it is not possible to know whether the child was exclusively breastfed until 6 months and
taking appetite of DFI boards and shareholders must match the risk involved in changing the narrative and trajectory of fragile states. Only this will reduce the risk that fragile states remain an obstacle to achieving sustained global prosperity.

# **Bibliography**

- Basu, Kaushik. 2014. “Fiscal Policy as an Instrument of Investment and Growth.” _World Bank Policy Research Working Paper No. 6850_ , Washington DC: The World Bank

- Boulding, William, and Markus Christen. 2003. “Sustainable Pioneering Advantage? Profit Implications of Market Entry Order.” _Marketing Science_ 22 (3): iii–436.

- Carter, Paddy. 2015. “Why subsidize the private sector?”, London: Overseas Development Institute.

- Collier, Paul. 2007. _The Bottom Billion: Why the Poorest Countries Are Failing and What Can Be Done About It._ Oxford: Oxford University Press.

- ———. 2017. “Culture, Politics, and Economic Development.” _Annual Review of Political Science_ 20 (1): 111–25.

- DFI Working Group on Blended Concessional Finance for Private Sector Projects. 2017. _Summary Report._ October. Washington, DC: International Finance Corporation (IFC).

- LSE-Oxford Commission on State Fragility, Growth and Development. 2018. “Escaping the Fragility Trap.” LSE-Oxford Commission on State Fragility, Growth and Development. London and Oxford, UK.

- Golder, Peter N., and Gerard J. Tellis. 1993. “Pioneer advantage: Marketing logic or marketing legend?” _Journal of Marketing Research_ 30 (2): 158–70.

- Fernandes, Ana Margarida, Caroline Freund, and Martha Denisse Pierola. 2016. “Exporter Behavior, Country Size and Stage of Development: Evidence from the Exporter Dynamics Database” _Journal of Development Economics_ 119 (C): 121–37.

- Hauser, John, Gerard J. Tellis, and Abbie Griffin. 2006. “Research on Innovation: A Review and Agenda for Marketing Science.
to substitution into foreign currency-indexed loans. Kraft and Galac (2011) estimate a simple VAR model based on Brzoza-Brzezina et al (2010). The impulse response functions suggest that most of the decrease in domestic currency loans is compensated for by increased foreign currency loans. They also look at the effect of the central bank measures to increase the price of foreign currency (-linked) lending to borrowers who are not hedged against currency risk, from mid-2006 to early 2010. The conclusion is similar, at least in the pre-crisis period, but this time it is the increase in domestic currency lending that compensates for decreased foreign currency (-linked) lending.

It is interesting to note that neighboring Serbia chose to issue central bank bills despite these arguments. Ultimately, high interest rates did limit loan demand because the elasticity of loan demand was not in fact zero. But this approach, while relatively transparent in the sense that interest rates on central bank paper were formed in the market, allowing monetary policy to use an interest rate channel, was also quite costly and may have increased capital inflows and appreciation pressure.

The Croatian approach, in contrast, relied on high and uniform reserve requirements on bank deposits in both domestic and foreign currency, coupled with a marginal reserve requirement on increases in bank foreign liabilities. Remuneration of required reserves was kept

> 2 For time series data on financial system shares through mid-2007, see the CNB Macroprudential Bulletin #6, p. 29. 3 Naturally, any form of sterilization that succeeded in limiting the money supply would raise interest rates, ceteris paribus. There would always be a large positive differential between the interest rate on bank loans in Croatia and the interest rate on similar loans in the euro zone (unadjusted for risk). However, if sterilization were done via the issuance of central bank paper at market rates, the central bank would pay this market rate on its liabilities, whereas sterilization through increased reserve requirements at least would not create large interest costs for the central bank.
# **4.2 Results**

Table 3, columns (1) to (7) reports baseline least-squares estimates of equation (3) for the whole sample as well as for separate regions and countries.<sup>24</sup> All specifications control for firm _×_ product _×_ destination and origin _×_ year fixed effects (equation (3)). We also replicate Table 3 replacing the latter set of fixed effects with firm _×_ product _×_ year fixed effects (equation (4)). To conserve space the results are reported in Table A.6 in the online appendix Section C.1. On the whole sample the elasticity of the exporter price to the log of the bilateral RER is positive and significant and implies that the average firm in our sample raises its price by 1.4 percent (respectively 0.9 percent) following a 10 percent depreciation of its home currency in column (1), Table 3 (resp. Table A.6). This elasticity is quantitatively close to the estimates reported in the literature on industrial and emerging countries, which typically lie between 0.05 and 0.2.<sup>25</sup> Thus, although our elasticities are estimated on a sample of developing countries, we find a degree of exchange rate pass-through which is very much consistent with those found for industrial countries. Moreover, like those found in the existing literature, our estimates also reflect limited PTM on average and very high levels of pass-through into export price (86 percent in column 1, Table 3). The estimated elasticities might be low due to measurement error arising from using annual data (Fosse, 2012). An alternative explanation is that these firm-level estimates hide a great deal of heterogeneity. If large firms adjust more to exchange rate variations at the price margin pass-through may be lower on aggregate.

Table 3: Pricing-to-market: Baseline

||(1)|(2)|(
96 Another innovative approach is being tested under USAID's TIPPS project in Zimbabve. A business analysis is being undertaken vith. CIMAS, the country's largest health Insurance company vith 135,000 clients and 2,100 member companies. The company anticipates that it vill be able to reimburse for family planning with little or no increase in premiums based on a preliminary assessment.

Tvo different projects in Nexico involve innovative approaches to increase the number and types of outlets that offer family planning products. The Enterprise Program's Micro-enterprises Project. is assisting private physicians to set up family planning clinics, and Enterprise is absorbing all of the investment risk in the first fev monthr by guaranteeing the physicians' salary; however, the project's risk is systematically reduced over 12 months after vhich donor support is totally withdrawn and the clinics must operate on their own without a subsidy. This type of project should be carefully assessed and vill be discussed further in Section V.

Enterprise's Self-Financing Project in Mexico is training private and social security physicians in IUD insertion and selling contraceptives to them at cost to ensure that the lov income populations they serve can afford and will pay for the contraceptive. No other subsidy is contemplated. Uhile the latter raises the issue of vhen or if the contraceptive subsidy can be vithdravn, the incentive structure of the project is to encourage the private sector to operate in response to market forces.

Tvo Enterprise projects, one in Thailand (Family Planning Products Market Survey and Marketing Capability Evaluation), the other in Indonesia (the YKB Entr2preneurial Development Project) propose to assist local NGOs in improving their management, financial viability, and effectiveness in distributing family planning. Both efforts are aimed at making these enterprises more competitive

**al.wp:Vaily rlzmning Pp e lwhole)**