rafmacalaba/datause-encoder-data huggingface dataset:
markets and their impact of the structure and dynamics of product markets. Still unanswered though is the impact of financial institutions on firm lifecycle in developing countries.

In this paper, we examine whether domestic financial development matters for explaining firm<sup>3</sup> lifecycles in a developing country like India. Specifically, we examine the relationship between plant size, age, and growth using detailed manufacturing census data from India and ask: how does local financial development influence the size-age relationship? Are there differences in the size-age relationship across different industry characteristics and between the formal and informal manufacturing sector and does this vary with the extent of local financial development? Does the role of local financial development on firm lifecycle vary with major regulation changes in India such as financial liberalization, changes in labor regulation, and industry de-licensing?

India offers an ideal laboratory for testing the role of institutions on firm lifecycle given the large persistent differences in institutions, business environment, and income across different regions in India (Ahluwalia, 2002). In an interesting comparison, the _Economist_ magazine matches India’s most populous state, Uttar Pradesh, to Brazil in terms of population, Qatar in terms of size of the economy, and Kenya in terms of its GDP/capita. Maharashtra, on the other hand is one of India’s richest states and is equivalent to Mexico in terms of population, Singapore in terms of GDP, and Sri Lanka in terms of GDP/capita.<sup>4</sup> Importantly, there is substantial and well researched heterogeneity in financial and labor institutions across these states. Overall however, India ranks quite low on different indicators of financial development. For instance, the Global Findex database ranks India as 77 of 147 countries in terms of

> 3 For brevity we use the terms firm and establishment interchangeably since the vast majority (over 72%) of firms in India are single establishment firms. We obtain similar results in other contexts when we restrict our analysis to firms with single establishments.

> 4 All the comparisons are based on 2009 values. See “Comparing Indian states and territories with countries,” _The Economist Magazine, June 21, 2011._

3
35 Report to USAID, R-643-AID, Santa Monica, California, Rand Corporation, June, 1971.

25. Schultz, T. Paul, "Interrelationships Between Mortality and Fertility', in Ronald G. Ridker (ed.), Population and Develogment -- The Search for Selective Interventions, The Johns Hopkins University Press, 1976, pp 239-289.

26. Schultz, T. Paul, "Fertility and Child Mortality over the Life Cycle Aggregate and Individual Evidence", American Economic Review, Vol. 68, No. 2, May 1978, pp 208-215.

27. Scrimshaw, Susan C. M., "Infant Mortality and Behavior in the Regulativn of Family Size", PoRulation and Development Review, (1979) 28. Simmons, George B., "The Policy Implications of the Relationship between Fertility and Socio-Economic Status", Population Planning Working Paper No. 24, University of Michigan, June 1977.

29. Simon, Julian L., The Effects of Income on Fertility, Carolina Population Center University of North Carolina at Chapel Hill, 1974.

30. Taucher, Erica, "Effects of Declining Fertility on Infant Mortality Levels: A Study Based on Data from Five Latin American Countries", Latin American Demographic Centre (CELADE), Report No. 82-2-222, Report to the Ford and Rockefeller Foundations, 1982.

31. Warwick, Donald P., "The Indonesian Family Planning Program: Government Influence and Client Choice", Pogulation and Development Review, Vol. 12, No. 3 (September 1986).

32. Wheeler, David, "Human Resource Development and Economic Growth in Developing Countries: A Simultaneous Model", Staff Working Paper No. 407. World Bank, 1980.

33. Winegarden. C. R., "Socioeconomic Equity and Fertility in Developing Countries", Economist (Leiden), vol 128, pp.530-557, 1980.

34. Zachariah, K. C., and Sulekha Patel, "Determinants of Fertility Decline in India -- An Analysis", World Bank Staff Working Paper No. 699, 1984.
reasons. First, enforcement reduces the scope for tax evasion and therefore directly impacts firms‘ effective tax rates. Second, many firms report facing large costs of dealing with the tax administration (World Bank 2017).<sup>1</sup> Third, reliance on size-dependent policies for tax enforcement has increased over time, as international institutions encouraged tax administrations to segment taxpayers (Kanbur and Keen 2014). To illustrate this trend, Figure 1 shows that over the past 20 years, more than 70 countries adopted special enforcement units for large taxpayers. While large taxpayer units hint at stringent enforcement at the top of the firm-size distribution, countries have also adopted enforcement policies targeted at small and medium firms.<sup>2</sup> Therefore how tax inspection and compliance vary with firm size and in countries around the world, is an empirical question.

The empirical analysis uses the comprehensive World Bank Enterprise Surveys (WBES), which contain firm-level data on self-reported tax inspection and tax compliance<sup>3</sup> for a sample of 125,000 firms in 140 countries. Identification is based on the idea that firm size is partially determined by technological factors (Lucas 1969; Kremer 1993). These technological factors pin down firms’ optimal scale of operation (Bain 1954, Burnside 1996, Kumar et al. 1999, Basu and Fernald 2016). If firms in an industry share a common optimal scale across countries, then the relative ranking of two industries’ scale in a plausibly undistorted market such as the US can serve as an instrument for the relative ranking of the same two industries’ optimal scale in a developing country’s distorted market.<sup>4</sup> We measure scale as the number of employees and take the average at three digit ISIC sectors. Identification relies on predicting the size ranking of industries at the 3-digit ISIC level in

> 1The World Bank 2017 Doing Business publication reports that " _on average taxpayers spend 25 hours complying with the requirements of an auditor, and go through several rounds of interactions during 10.6 weeks."_ 2Many countries implemented small and medium taxpayer offices alongside the large taxpayer office, which tailored audit algorithms to the evasion risks specific to smaller firms.

> 3The question on tax compliance is asked at the industry level. Since all our specifications are at the industry-country level, we do not require that the reported answers represent the firm’s own tax compliance. Instead we only require that the answers accurately represent the industry’s average compliance.

> 4This idea follows Rajan and Zingales 1998, who studied if industries reliant on external finance grew faster in countries with more developed financial institutions. They instrument an industry’s reliance on external finance with the external finance usage of the same sector in the US.

2
Table 4: Postnatal health investments as a function of ultrasound availability and firstborn sex

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||Mo|nths breast|fed|Receiv|ed _≥_one v|accine|Med<br>during|icines/speci<br>illness last|al food<br>year (Rs.)|
|_First girl_ * _Female_|-5.116**|-4.585**|-3.625**|-0.130**|-0.093**|-0.069|-28.473|-45.830|-95.486**|
||[1.861]|[1.820]|[1.536]|[0.051]|[0.039]|[0.048]|[42.037]|[37.982]|[43.982]|
|_First girl_ * _Female_ * _Post1_|4.499**|3.957*|1.859|0.113**|0.099*|0.076*|17.708|35.897|73.112|
||[2.011]|[2.128]|[2.207]|[0.049]|[0.048]|[0.042]|[64.694]|[48.992]|[58.856]|
|_First girl_ * _Female_ * _Post2_|5.672*|4.277|2.331|0.094*|0.073*|0.118**|67.488|103.445*|154.470**|
||[2.712]|[2.746]|[2.396]|[0.051]|[0.035]|[0.044]|[44.108]|[52.554]|[69.308]|
|_Female_ * _Post1_|-2.374*|-1.201||-0.005|0.001||9.401|12.427||
||[1.321]|[0.999]||[0.056]|[0.045]||[21.840]|[23.550]||
|_Female_ * _Post2_|-1.730|-0.798||-0.029|-0.015||-38.946|-58.308||
||[1.733]|[1.708]||[0.038]|[0.030]||[31.540]|[36.770]||
|_First girl_ * _Post1_|-1.142|-0.634||0.052*|0.050||-30.552|-21.272||
||[1.224]|[1.425]||[0.026]|[0.035]||[52.685]|[45.289]||
|_First girl_ * _Post2_|0.061|0.506||0.039|0.059||-54.144|-58.160||
||[1.871]|[1.746]||[0.039]|[0.049]||[59.750]|[53.773]||
|N||10,965|||20,562|||10,293||
|Baseline mean||18.931|||0.704|||186.426||
|_Xijt_||x|x||x|x||x|x|
|_1st Girl_ x Birth year FE|||x|||x|||x|
|_Female_ x Birth year FE|||x|||x|||x|
|_Female_ x State FE|||x|||x|||x|
|_Female_ x Birth order FE|||x|||x|||x|
|Birth order x State FE|||x|||x|||x|
|State-specifc time trends|||x|||x|||x|
|_Firstborn girl_ x State FE|||x|||x|||x|
|_Firstborn girl_ x Birth order FE|||x|||x|||x|

NOTES: This table reports investment effects for children of second- and higher-order birth order using the REDS sample. Each column is from a separate OLS regression. We control for _Female_ , _Firstborn girl_ , and fixed effects for birth year, birth order, and state in all columns. The vector _Xijt_ comprises mother’s age at birth, caste, religion, educational attainment of child’s parents, and mother’s birth cohort. Breastfeeding results are based on the last two surviving births of a mother and restrict the sample to children above age 2. Vaccination and health expenditure results are based on all surviving children of a mother. Standard errors in parentheses are clustered by state. Baseline mean refers to the average of the relevant outcome variable in the pre-ultrasound period. *** 1%, ** 5%, * 10%.
fronts (Dang _et al._ , 2023). In particular, the SPI explicitly offers standard _desiderata_ for a statistical index (i.e., simple, coherent, motivated, rigorous, implementable, replicable, incentive consistent). Conceptually, it consists of five pillars of data use, data services, data products, data sources, and data infrastructure, which can be further disaggregated into 22 dimensions. The SPI is also built on a clear mathematical foundation with a three-level nested weighting structure that offers desirable properties for an index such as symmetry, monotonicity, and subgroup decomposability (Cameron _et al.,_ 2021). Empirically, the SPI offers more than twice the number of indicators provided by the SCI. The SPI covers both low-income and high-income countries, while the SCI focuses on non-high-income countries alone. Finally, the SPI covers indicators related to the SDGs, while the SCI covers indicators related to the (older) MDGs.

Despite its infancy, to date the SPI has been adopted for measuring country statistical capacity in several high-profile policy reports such as _Sustainable Development Reports 2021, 2022, and 2023_ (Sachs _et al._ , 2021, 2022, and 2023) and the World Bank’s _World Development Report 2021_ (World Bank, 2021). In particular, the index was used to help highlight various gaps in countries’ data dissemination and openness, with higher statistical capacity levels being positively correlated with more NSO independence (World Bank, 2021). The index has inspired further research on assessing data openness and accessibility in MENA, a traditionally data-scarce region (EkhatorMobayode and Hoogeveen, 2022), or how best to construct measures for learning deficiency due to COVID-19-induced school closures (Azevedo, 2020), or to better understand how NSOs respond and adjust to the disruptions caused by the COVID-19 pandemic (Wollburg _et al._ , 2022). It also contributes to current thinking not only on improving the quality of NSSs, government use of data, and future official statistics (Radermacher, 2021; Asher _et al._ , 2022; Bersales, 2022), but also on other topics such as reducing GDP growth forecast errors (Gatti _et al._ , 2024), measuring public sector digital transformation (Dener _et al._ , 2021) and food and agricultural statistics (Bizier _et al._ , 2022), and selecting the appropriate context to measure student absenteeism and women’s empowerment (Yount _et al._ , 2022; Evans and Acosta, 2023).

We offer further discussion on the conceptual motivations and construction of the SPI in the Method section. We provide more details on the dimensions of the SPI, including ongoing data work, in Appendix A, Table S2 and a mapping of the SPI indicators to the SDG indicators in Table S3. We provide the latest update for the SPI country scores in 2022 in Appendix A, Table S4.

5
changed is the composition: in earlier periods labour reallocation out of agriculture was more important, while in more recent periods labour reallocation out of the state sector was more important.

Will factor reallocation continue to contribute to productivity growth in China over the next 20 years? The answer to this depends on the extent to which intersectoral differences in marginal products of factors remain, and the scope for future reallocation of factors to those sectors where marginal products are relatively high. In Figure 7 we present some very rudimentary evidence on this using more recent data for China. In the top panel we report the ratio of value added per worker in the private sector relative overall industry in China. The relative labour productivity advantage of the private sector in China has fallen somewhat over time, but remains quite high with the private sector having labour productivity around 2.5 times that of the industry as a whole. This is a comparison of average, rather than marginal products, and it is the latter that matter for intersectoral reallocation. However, if we assume a Cobb-Douglas production function for value added, the only difference between marginal products and average products is the output elasticity of labour. It seems clear from Figure 7 that the output elasticity of labour would have to be vastly smaller in the private sector than in industry as a whole for these labour productivity differences not to translate into sizeable differences in marginal products. We also note that although the share of the private sector in total employment has grown very rapidly, it still is quite modest at only 20 percent of industrial employment.

In the bottom panel of Figure 7 we report some evidence on the scope for reallocation of capital across ownership forms. We draw on data for large and mediumsized industrial enterprises reported in recent issues of the China Statistical Yearbook. This source provides information on value added and fixed assets, disaggregated by forms of ownership. Consistent with the top panel of this figure, we identify the private sector as all non-state and non-collective firms, and we again report the ratio of the average product of capital in the private sector relative to the total. Over the past several years the average product of capital in the private sector has been 50 percent higher than average, which is suggestive of non-trivial differences in marginal products across ownership forms. And as with labour, although the share of the private sector has increased sharply, it is still quite modest at about 30 percent.

27
- -51

By 1982, about 44 efficiency pricing studies had been completed and a further 25 were under way. Some 20 countries had Incorporated efficlency pricing principles, either fully or partially, Into their tariff structures. However, the spread of efficiency pricing has been slow In some countrles, because the concept has been resisted qulte strongly on the grounds that electric power Is a public service and therefore should be sold at a price sufficient only to recover historical costs.

# 2.6 Demand Forecasts

Since t,he energy (GWh) forecast Is the starting point for both power system and financial planning, the preparatlon of a realistic demand forecast Is a key element ln project success. There Is a normal tendency to be optimistic In forecastinb sales; hence, one would expect a priori to see a higher proportion of actual sales being lesa than the forecast value. Surprisingly, on average the overestimatlon of sales forecasts for projects approved from 1967-78 was comparatively small, I.e., actual sales at project completion were 6.4% lower than forecast for the period as a whole. However, as shown In Figure 4, there has been a definite downward trend In accuracy throughout the 1970s. Prior to 1973, forecasts were, on average, accurate In assessing the actual sales at completion of the project and for projects approved In 1969-70 actual sales at completion were actually higher than estimated at appraisal. This situation was thereafter was reversed as forecasts urned out to be more and more over-optimistic, until for projects approved In 1978 forecasts were on average overestimating sales at project completion by about 20%. This trend has continued for projects approved In the 1980s, with actual sales 17% less than originally forecast Close to half the projects approved between 1967-78 overestimated demand by 10% or more (particularly In the post-1973 perlod), compared to about 70% of projects In the 1980s. About 30% of projects overestimated demand by more than 20% during the 1970s and 1980s. On the other hand, only 13% of projects exceeded the forecast level of sales by more than 10% during both perlods. For projects Implemented
boundaries visible in the drone imagery. Four sub-blocks were then randomly selected and fully listed.

- For larger EAs with greater than 400 households in hosts, a fanning protocol was developed for listing based on a ground-truthing exercise described in detail below.

# _Ground truthing and developing the listing methodology for camps_

For the Rohingya camps, we had access to publicly available high-quality drone imagery from IOM’s Needs and Population Monitoring program for the Round-12 data that had been used as the sampling frame. The maps in theory could allow us to geographically locate the exact 200 blocks that had been sampled for the survey. In addition, the field team intended to use the IOM-assigned Block IDs (6-digit codes) and the Local Block Names specified in the NPM data to identify blocks inside the camps.

The field team was instructed to go to the campsite management office and ask staff or volunteers for assistance in locating the sample blocks in the camp. Printed IOM drone maps were provided to navigate inside the camps using prominent buildings and other geographical landmarks.

The first challenge faced in the field was inconsistencies in block names used across the camps. The field team had mixed experiences in being able to identify the correct blocks using the NPM Block IDs and Local Block Names provided. Some camp authorities were able to identify blocks using the IOM-assigned IDs, and some were identified using the local block names. However, in some camps neither of these options was feasible. This was explained by the following circumstances:

- The IOM NPM data used majhees<sup>35</sup> as their main point of contact and did the block mapping based on “majhee boundaries.” The majhee governance system is an informal system, and thus it was realized that these assigned boundaries and local names are relatively fluid and subject to change.

- Half of the camps in Ukhia and Teknaf were managed by UNHCR and the other half by IOM. Thus, while the NPM block IDs and names were familiar to IOM-governed site management, camp managements governed primarily by the UNHCR or the <mark>Relief and Repatriation Commissioner</mark> (RRRC)/army were not familiar with the information.

In addition, the field team also found it difficult to navigate the camps on the ground using the printed versions of the drone imagery, since they had no GPS support. After a validation exercise on the issues faced on the ground, the field protocols to identify sample blocks were revised.

The blocks successfully recognized by the respective camp management were fully listed. For blocks that could not be located with help from the camp management, the field team was given new instructions. The NPM data for the majhee blocks provided GPS coordinates for the centroid of each block. These coordinates were shared with the field team, who were instructed to locate the points using Google maps. Once they had

> 35 As discussed in the main text, new Rohingya refugee camps (established after the influx of August 2017) are governed by local leaders called “majhees.” A majhee block is defined by IOM as an area governed by one majhee.

18
income group, it ranges between 12% of total employment in Australia and 49% in Ireland. Within the lowand low-middle-income it ranges from 8% in Ethiopia and 39% in Thailand.

In general countries with lower GDP per capita (and consequently lower wages) tend to have a greater job intensity of exports. For example, while the Philippines had about 105.5 total jobs embodied in $1 million worth of exports in 2011, Belgium had only about 3.4 jobs embodied in the same value of exports. This is in line with standard trade theory, which predicts that exports of countries where labor is relatively cheaper would be more labor intensive, in line with comparative advantage.

There is substantial dispersion in the ratio of direct to indirect jobs embodied in exports across countries. For some countries, it is close to or above one, and thus direct employment plays a relatively more important role in supporting exports. For some countries the ratio is as low as 0.5, which means that compared to the jobs generated in the export industries, twice as many jobs are generated through domestic sourcing of intermediate inputs by the export industries. All of the high-income countries have direct-to-indirect ratios close to or above 1, with the exception of Poland, Italy and the Republic of Korea. A simple regression analysis across countries reveals a negative and mildly significant relation between this ratio and per capita GDP controlling for country size and geographic remoteness,<sup>19</sup> two natural determinants of the extent to which exports may use domestically produced inputs.<sup>20</sup> This suggests that backward linkages for exports are still relatively more important in richer economies.

> 19 Results are not reported here and are available from the authors upon request. In particular the analysis regresses the ratio on a constant, the log of per capita GDP in 2005 PPP terms, the log of the population and the log of market potential measure as a proxy for the country’s remoteness. GDP and population are taken from the World Development Indicators, while the market potential measures are taken from CEPII database and are computed using the Redding and Venables (2004) or the Head and Meyer (2004) method.

> 20 For example, Luxembourg has a very high ratio because a small economy that is integrated into Europe will naturally buy its imports from overseas. Among other explanations, Australia has a fairly low ratio because it is geographically remote and production must use more upstream inputs that are locally sourced.

29