rafmacalaba/datause-encoder-data huggingface dataset:
||PPPC = PPP / Population|||
|---|---|---|---|
|UVI|Unit value of import|IFS (see note 3)||
|Copperchl|Copper price for Chilean TOT|IFS||
|TOT|Terms of trade|IFS and calculated (see note 4)|1970- 1995|
|DREF|Dummy variable for reforms|constructed (see note 5)|1970-1995|
|DPOL|Dummy variable for political upheaval|constructed (see note 5)|1970-1995|
|EDI|mean years of education for the population<br>age over 15|constructed from<br>(a) Barro & Lee 1997 and<br>(b) Psacharopoulos & Arriagada<br>(see note 6, 7, 8)|1970-1995|
|EDV|variance of education for the population age<br>over 15|constructed|1970-1995|
|EDS|standard deviation of education for the<br>population age over 15|constructed|1970-1995|
|EDZ|coefficient of variability of education for<br>the population age over 15|constructed|1970-1995|
|GINI|GINI index of education for the population<br>age over 15|constructed|1970-1995|
|THL|Theil index of education for the population<br>ageover 15|constructed|1970-1995<br>_|
|SDL.|Standard Deviation of Logs of education for<br>the population age over 15|constructed|1970-1995|

**End** Notes for the data sources and data calculations

# 1. WDI

World Bank, _World Developm7ent Inidicators (WDI), CD-ROM._ 2. Nehru & Dhareshwar, 1993 Nehru, Vikram, and Ashok Dhareshwar. 1993. "A New Database on Physical Capital Stock: Sources, Methodology and Results." Revista de Analisis Economico 8 (1): 37-59.

Nehru, Vikram, Eric Swanson, and Ashok Dubey. 1994. " A new database on human capital stock: sources, methodology and results." Journal of Develoment Economics.

The two variables of (I) total physical capital (CAP) and (2) human capital stock: total mean years education (EDU) are quoted from the above papers.

# 3. IFS

International Monetary Fund. _International Financial Statistics Yearbook (IFS)._ 4. The terms of trade

- For Brazil, Colombia, India, Korea, Malaysia, the Philippines, Thailand, and Venezuela, the terms of trade are quoted directly for the IFS. For Chile, China, Mexico, and Peru, the terms of trade are not available from the IFS, and they are obtained through the following processes.

- a. For Chile, the terms of trade are substituted with the ratio of the copper price (Copperchl) to the unit value of imports (UVICHL), let 1990 =100.

- b. For China, the terms of trade are substituted with the terms of trade of Asia.

- c. For Mexico, the terms of trade are substituted with the terms of trade of Venezuela.

- d. For Peru, the terms of trade are substituted with the terms of trade of Chile (see 4.2a)

5. The dummy variables for economic reforms and political upheavals

- The dummy variables for economic market reforms (DREF) and for political upheavals (DPOL) are constructed based on the country stories, which summarize both the economic and the political processes occurred in the 12 countries during the historical period from 1970 to 1995.

6. Barro & Lee

- Barro, Robert and J.W. Lee, "International Measures of Schooling Years and Schooling Quality," AER, Papers and Proceedings, 86(2), pp. 218-223. Data file "School4.raw" contains the data on educational attainment at various levels for the population over the age 15.

29
the one at the new steady state, the researcher would back out distortions that are more severe than those that are actually underlying the economy in 2011, point at which the prole of distortion adopts it lowest estimated value and stabilizes. Had the researcher been able to construct the life-cycle of a cohort of rms, the imputed degree of distortions would have been milder, and closer to the actual degree of distortions in 2011, given that the life-cycle of the cohort is closest to the cross-sectional life cycle consistent with the steady state associated with the distortions of 2011.

# D Self-Employment and the Number of Firms: Evidence and the Model's Predictions

The paper stresses the behavior of the average rm size as the relevant empirical counterpart to assess the implications of distortions on the rate of entrepreneurship and the rm-size distribution. However, being an entrepreneurial model of rm entry and exit, it is useful to review evidence that more directly speaks to this margin of adjustment.

To this end, gures D.1 and D.2 report the dynamics of the rate of self-employment and the number of rms along China's and Chile's growth accelerations. Both these metrics have merits and limitations in capturing the notion of a rm in the model. Self-employment, on one hand, better reects entrepreneurial activity from individuals that are on the margin of entrepreneurial activity or seeking for work in the labor market, but is less likely to reect the innovation and growth potential of that entrepreneurial rms exhibit in the model. The number of rms, on the other hand, is subject to the opposite trade-o. Stemming from China's Annual Survey of Industries, which covers rms beyond a certain size, it captures rms with a certain number of employees and stock of capital, but also captures businesses with a more sophisticated ownership structure whose survival is less linked to an occupational choice from the entrepreneur. Since, as we show below, both measures exhibit a similar behavior, we argue they provide empirical validity to the channels in the model.

Turning, then, to the results, let us begin with gure D.1, which illustrates the fraction of entrepreneurs in the model and the fraction of self-employed in the labor force for China and Chile. We see that in both cases the model captures the direction of change in the rate of self-employment, except for the 1995-2005 period in Chile, and the 1999-2001 period in China. Despite these non-monotonicities, we interpret 52
15 nonusers) risks presenting a misleading picture, since it will likely include some who were deterred from using services by the cost of care at the point of use.<sup>7</sup>

# **4. Some illustrations**

We illustrate our unified approach using data from the World Health Survey (WHS) and variations on the international dollar-a-day poverty line. Insofar as we can, we split category (5) (the non-spenders) into two groups: those who received care but did not pay anything; and those who did not receive care at all. The WHS has some attractions for this type of analysis: it was fielded using an identical questionnaire in 70 countries including very poor countries and very affluent countries, although out-of-pocket spending data were collected in only 51 countries<sup>8</sup> and the data are publicly available and easily accessible. The WHS does, however, have some limitations: it was fielded over 10 years ago; nonmedical consumption information data were collected in a very rough-and-ready fashion; and while there is no accepted best practice on how best to collect data on out-of-pocket spending on medical services, there are some oddities in the WHS approach which probably make it less than ideal (Lu et al. 2009; Heijink et al. 2011; Raban et al. 2013). The

> 7 Such households or individuals could be retained in the analysis and included either among one of the other categories or in a category of their own. However, in neither case is it obvious how to include them since the other categories are all distinguished from one another on the basis of their out-of-pocket spending relative to their total consumption net of the poverty line. Perhaps the best way to capture forgone care due to cost would be in an analysis of use (and nonuse) of services.

> 8 The countries are: Bangladesh, Bosnia and Herzegovina, Brazil, Burkina Faso, Chad, China, Comoros, Rep. of Congo, Croatia, Czech Republic, Côte d'Ivoire, Dominican Republic, Ecuador, Estonia, Ethiopia, Georgia, Ghana, Guatemala, Hungary, India, Kazakhstan, Kenya, Lao PDR, Latvia, Malawi, Malaysia, Mali, Mauritania, Mauritius, Mexico, Morocco, Namibia, Nepal, Pakistan, Paraguay, Philippines, Russia, Senegal, Slovak Republic, Slovenia, South Africa, Spain, Sri Lanka, Swaziland, Tunisia, Ukraine, United Arab Emirates, Uruguay, Vietnam, Zambia, and Zimbabwe.
TABLE 1: ADAPTIVE PRINCIPLES REFLECTED IN BANK DOCUMENTATION

|**Seven Principles of adaptive**<br>**programming (Teskey 2017)**|**Commitments highlighted in the Nigeria Governance Note that reflect**<br>**adaptive principles (World Bank 2017)**|
|---|---|
|1) Context is everything|“We will continue to base operations on a solid understanding of<br>technical challenges, but we will also strive to understand local contexts<br>and feasible reform processes as opposed to filling technical gaps or<br>deficiencies in our clients’ current approaches.”|
|2) There is no singular blueprint<br>for development – rather<br>flexible, responsive, adaptive<br>programming|“There will be a presumption in favor of flexibility and adaptation in<br>designing and implementing Bank interventions.”<br>“...we will be looking to scale up our support to local pockets of efficacy<br>where the likelihood of sustainable reforms is greatest.”|
|3) Best fit not good practice|“The Nigerian context calls for flexibility over the lifetime of projects<br>with<br>regard<br>to<br>technical<br>approach,<br>counterparts,<br>phasing,<br>communications, geographical scope and scale, and so on. Accordingly,<br>we will encourage flexible project design and encourage and reward<br>project restructuring based on solid supervision and implementation<br>facilitation.”|
|4) Enabling, not doing|“While recognizing the importance of leadership for reform, we will<br>expand the range of stakeholders included in the design and<br>implementation of our engagements, with a view to creating broader<br>support and sustainability for reforms.”<br>“Ownership of approaches will be tested throughout the delivery cycle<br>and, where it does not exist, exit strategies will be employed.”|
|5) Real‐time learning|“Information—baselines, evidence, and feedback—will be a central<br>feature of our work as we test and tailor approaches.”<br>“Our approach will be based on problem solving and unpacking<br>assumptions behind hypotheses.”<br>“We will supplement our focus on rules, legislation, manuals, and formal<br>institutional designs with a focus on implementing these rules and<br>institutional designs,etc.”|
|6) Long‐term<br>commitments<br>with staff continuity|n/a|

The paper proceeds as follows. Following this introduction, section 2 lays out further details regarding the contexts within which ‘adaptive’ implementation work was undertaken within a national health project in Nigeria. Sections 3 and 4 provide an extended summary of the key findings, providing the empirical and analytical foundations for section 5, which explores some specific implications of these findings for policy, practice and theory. Section 6 concludes. (Detailed tabular presentations of the empirical results in each of the six states visited are presented in the annexes.)

# 2. Contexts: Doing ‘adaptive’ work in a national health project

The SOML PfR in Nigeria has as its primary goal helping states achieve specific improvements in six pre‐defined health interventions (the ‘six pillars’): 5
Table 11: IV regression IT use on firm-level productivity considering China’s competition in the U.S.

|Dependent variable: log(sales/worker)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Computers-per-worker|0.079|0.389|0.384|0.345|-0.134|-0.447|
||(1.129)|(1.124)|(1.045)|(1.035)|(1.076)|(1.359)|
|Competition in U.S.*computers-per-worker|7.097*|5.742*|5.731*|5.772*|6.083*|6.864*|
||(3.738)|(3.307)|(3.268)|(3.213)|(3.144)|(3.772)|
|Competition in U.S.|-1.863|-1.393|-1.326|-1.412|-1.438|-1.843|
||(1.222)|(1.161)|(1.157)|(1.134)|(1.111)|(1.575)|
|F-frst stage IT|11.1|11.3|12.0|11.9|10.3|9.1|
|F-frst stage IT*competition|9.5|12.9|12.3|12.6|12.3|12.0|
|Sargan|0.90|0.72|0.58|0.68|0.60|0.64|
|Share of labor with Internet|-2.391*|-2.043*|-1.858|-1.678|-1.779|-3.191|
||(1.296)|(1.197)|(1.270)|(1.261)|(1.237)|(2.266)|
|Competition in U.S.*Share of labor with Internet|11.271**|10.056**|9.693**|9.422**|8.748*|12.062*|
||(5.648)|(4.625)|(4.582)|(4.577)|(4.514)|(6.614)|
|Competition in U.S.|-1.180|-1.075|-1.018|-1.021|-0.826|-1.902|
||(1.045)|(0.920)|(0.903)|(0.871)|(0.870)|(1.604)|
|F-frst stage IT|18.1|16.1|15.7|17.7|15.9|10.1|
|F-frst stage IT*competition|9.6|11.3|12.7|14.3|12.8|12.7|
|Sargan|0.91|0.86|0.90|0.98|0.88|0.99|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses Source: Authors’ calculations using data from ENTIC, INEGI

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

28
Wong (2020) documented pronounced backhaul effects in the spot freight rates reported by Drewry, especially for the US-China routes, where it costs three times more to ship a container from China to the US than from the US to China.

**Stylized Fact 2.** _Between 2002 and 2020, maritime trade was highly imbalanced. The containerized imports exceeded exports for 7 of 8 regions except for the Far East, for which the net exports increased from 10 to 30 million TEU. The largest relative imbalances are observed in the Middle East and Africa._

# **2.2 Industry Growth, Technological Changes, and Economies of Scale**

The shipping industry has responded to the growing demand for transportation by building larger vessels that presumably improve efficiency through economies of scale. For example, containerships have increased in size from 700-1,000 TEU in the 1960s to more than 23,000 TEU (Fan et al., 2018). Using the Clarksons SIN data, we document that the growth of the containerized maritime fleet has been remarkable in the last 25 years.<sup>6</sup> We plotted the trends in Figures 3 and 4 and summarize them as following.

**Stylized Fact 3.** _The total containerized fleet capacity has increased eight times between 1996 and 2021, with the average vessel size increasing 2.7 times. The total capacity share of the largest ships, of over 8,000 TEU, has increased from 0% in 1996 to 55% in 2021._

Indeed, while the containerized fleet capacity, measured in twenty-foot equivalent units (TEU), has increased eight times, the number of vessels has increased only three times. From Figure 4, we learn that smaller ships, with the capacity of under 3,000 TEU, were rapidly replaced with the larger ones. Specifically, the capacity share of the smaller vessels has decreased from 65% in 1996 to 18% in 2021. Instead, as pointed out above, the capacity of the largest ships, of over 8,000 TEU, has increased from 0% in 1996 to 55% in 2021. The largest ships in the industry these days exceed 23,000 TEUs.<sup>7</sup> 6Clarksons SIN provides a comprehensive list of container and dry-bulk vessel types and their total capacity for each year starting 1996. See section 3.3 for information about SIN.

7For example, the Korean carrier, HMM owns a dozen of 23,000+ TEUs containerships (HMM Vessel Fleet).

6
For the BIS data, we construct our measure of portfolio debt flows from the BIS International Debt Securities (IDS) data. It captures net issuance of debt securities (bonds) in a market other than that of the country where the borrower resides (Grui ́c & Wooldridge, 2012). This does not necessarily imply that the securities are held by foreigners, but can be taken as an approximation for external financing flows through debt securities.<sup>62</sup> Since the IDS data are compiled on a security-by-security basis, granular sectoral splits are easy to obtain; we thus construct these net issuances by sector using the same sector definitions as the BOP data.

For other investment debt, we construct our series from our BIS estimates as follows: First, we examine the underlying components of other investment debt. The primary instruments are loans (for corporates and governments) and currency and deposits (for banks and central banks). If loans are missing for corporates or government, or currency and deposits is missing for banks or central banks, we rely on the BIS Locational Banking Statistics (LBS) to fill in the data.<sup>63</sup> The BIS data captures cross-border lending from banks in BIS reporting countries.<sup>64,65</sup> This lending can be broken by instrument into loans, debt securities holdings, and other instruments. We use just the loan instrument in our measure, and so avoid capturing any bond holdings or equity investment made by banks. Since the BIS data will not capture official lending, we add IMF Credit to these series to capture that com-

> 62As discussed above, the assumption does not hold well for sovereign debt, particularly in advanced economies, but is otherwise appropriate for many economies.

> 63Interbank loan flows are automatically classified as deposits in the BOP data. Thus, all loans from BIS reporting banks to bank counterparties, including the central bank, would be captured in the currency and deposits instrument in the BOP.

> 64This captures about 95% of all cross-border interbank business (BIS, 2015).

> 65There have been some discrepancies noted in the past between the BOP ad BIS data due for a few specific cases, such as trustee accounts in Japan and custodial accounts in Switzerland. We give priority to the BOP data, which is well reported for these series.

74
productivity _e_<sup>_ω_</sup> , consistently with the misallocation literature.<sup>17</sup> Formally, we characterize this relationship with the following function: The key parameter governing the elasticity between _TFPR_ , the measure of distortions, and _TFPQ_ , the idiosyncratic productivity, is given by _γi_ . Our calibration section discusses how we estimate this elasticity in the Ghanaian firm-level data.

Optimization leads to the following expressions for labor demand and variable profits:

# **5.3 Firm Dynamics, Entry, and Exit in the Formal Sector**

Our characterization of the formal sector features the first-order margins of adjustments through which distortions affect micro and macro-level outcomes. These are an endogenous entry and exit of firms into the formal sector, and an innovation technology that allows for an endogenous component of firm dynamics.

Endogenous firm dynamics in the formal sector arise from a process of technological upgrading and downgrading similar to that in Atkeson and Burstein (2010). Specifically, a firm with current productivity _e_<sup>_ω_</sup> can upgrade to _e_<sup>_ω_+∆</sup> with probability _qt_ ( _ω_ ) and can downgrade to _e_<sup>_ω−_∆</sup> with probability (1 _− qt_ ( _ω_ )). The expected growth rate, given by _qt_ ( _ω_ ), is endogenous, as firms can allocate resources to innovation activities. The variance of the shock process (∆), on the other hand, is exogenous. The labor-denominated cost for attaining the desired probability _qt_ ( _ω_ ) is given by Notice that the innovation cost is scaled by the entrepreneur’s current productivity. This is an important assumption that allows the model to be consistent with the innovation patterns of large firms in the US, our target economy for the calibration. The scale parameter _η_ and the elasticity parameter _φ_ will be calibrated to replicate the properties of the size distribution and the life cycle of firms in the U.S.

The value of an operating formal firm with current productivity _e_<sup>_ω_</sup> is where _π_ 1<sup>_v_</sup> _,t_<sup>(</sup><sup>_ω_) is the indirect variable profit function under optimal factor demands given prices and</sup> distortions, defined in equation 7, _fc_ denotes the labor-denominated fixed cost of production, and

> 17 See, for instance (Bento & Restuccia, 2017) and (Fattal-Jaef, 2022) for cross-country estimates of the relationship between idiosyncratic distortions and idiosyncratic productivity

30
Other work has measured similar impacts of electrification. Kularni et al. (2007) have found that higher electrification rates lead to better educational outcomes in Nicaragua and Peru. Barkat et al. (2002) similarly found that electrification leads to higher literacy rates and increased school enrollment in Bangladesh. These results are confirmed more recently by Asaduzzaman et al. (2010), who also found that school children’s study times increase substantially for families who gain access to electricity. Asaduzzaman and Latif (2005) found that electricity demand by families with electricity access rises substantially with the number of school children. This suggests a strong effect on household electricity demand from school children’s need for evening light access. The two latter studies however indicate that schooling and electricity access may be endogenous in a more comprehensive model, thus calling for caution in interpreting results. Kanagawa and Nakata (2008) found that literacy rates in the state of Assam, Pakistan, among children above six years of age would increase substantially, from 63 to 74%, given complete electrification of the state (which was claimed to be reachable by 2012). Grogan and Sadanand (2009) modeled how changes in home production technology might affect fertility and women’s time use in Guatemala. Using difference-in-difference type estimators, household electrification substantially reduces cooking times and increases working outside the home among Guatemalan women (the probability of outside employment increases by nine percentage points). Dinkelman (2009) assesses the impact of the national electrification program in South Africa using two waves of community-data from rural areas in KwaZulu-Natal. Results indicate that female employment rises by a significant 9.5 percentage points in communities that receive an electricity project. This author also shows that electrification has larger effects on female employment in middlepoor communities and for women in their thirties and forties, who are less likely to live with young children requiring full-time care.

A thematically related paper, focusing as we do on children’s school attendance in Ghana, is Lavy (1996), which is based on data from the Ghana Living Standards Survey in 19871988. The focus was here more on schooling costs, assumed to be a positive function of distance to the nearest public school. Greater distance to the school is found to reduce school attendance; more in secondary than in primary school.

Other related studies have dealt with social impacts of improved access to other types of public goods. Banerjee et al. (2009) considered the impact of railroads on wages in China. Akee (2006) estimated the effects of road construction on wage employment and agricultural employment in the Republic of Palau.

5