rafmacalaba/datause-encoder-data huggingface dataset:
benefit analysis; we also compute and report these results in section 5.4. In addition, once we have specified a proper likelihood on the data, it is natural to consider jointly modelling the potential outcomes to get more detailed causal inference on the distribution – of treatment effects in the style of Imbens and Rubin (2015) we pursue this as an extension to our main analysis (additional details and results provided in section 5.4).

We do note that the approach described above differs from the way that Bayesian statisticians or analysts in other fields would tend to approach this same data. Modern Bayesian data analysis differs from the Frequentist econometric approach in more respects than just the addition of priors; in particular, Bayesian analysis typically uses richer likelihood models that are tailored to the specific functional forms of the outcome variables in question, since much is typically known about them (e.g. productivity is bounded below at zero and thus likely right-skewed, export probability is binary, etc). However, we pursue a Bayesian analysis using Gaussian likelihoods because in the absence of priors these likelihoods deliver the OLS estimates of the regression coefficients, making it easier to compare the frequentist and bayesian results and to understand the role of our informative expert and firm priors.<sup>9</sup>

# **5 Results**

## **5.1 Take-up and usage**

Take-up and usage of the program was high, especially given the requirement for firms to pay US$1,000-$2,000 to participate. 88 of the 100 firms assigned to treatment are recorded in the administrative data as starting the intervention, and 83 have positive hours of consulting recorded. Figure A1 shows that this take-up rate is high compared to the medians of the prior distributions for take-up of the participating firms (58%), academics (63%) and policymakers (73%), and in line with the mean of our pre-specified literature prior (80%). Moreover, of the 83 firms with recorded hours, 94% (all but 4 firms) received at least 100 hours of consulting, with the median firm that took up the program receiving 195 hours. Appendix Figure B1 shows the distribution of total hours of consulting, and we see that half of all firms actually received slightly more than the 190 hours initially promised by the program.

Table 2 disaggregates the consulting received by area. Of the 83 firms with recorded activity data, 72 had hours in all three areas of consulting, 8 in two areas, and 3 in only one area. The most common two areas were the compulsory commercial strategy area,

> 9There are also substantial challenges with implementing the analysis with the richer likelihoods, which we document in Appendix G.

19
on the expectation that they can increase profitability, the buyers may pay the government a price higher than the discounted stream of profits that the Treasury would receive under continued public ownership. Second, PEG often impose a burden on the Treasury, which will now be alleviated.9' Third, facing fiscal imbalances, governments would be pressured to use the proceeds from the sale to effectively reduce the budget deficit.

From a fiscal point of view, *hese arguments are compelling for divesting an enterprise. However, there is no assurance that these expectations would mate-ialize. For example, because of favoritism, governments may strike a deal in which the private sector purchaser actually pays a price lower than the Treasury would receive under continued public ownership. Productivity and profitability gains may not materialize, especially if the private owner is able to attain comfortable profit margins in sheltered markets. The PE being divested may have been a positive contributor to the government budget. Governments may perceive the proceeds form the sale as windfall revenues, thus increasing public expenditure.

# THE EMPIRICAL GAP

In view of the uncertainties surrounding the efficiency and fiscal consequences of divestiture, empirical verification is crucial. However, as Jones et al. note: "Nowhere in the world are we able to find even a single 9/ Judging from several studies in LDCs, there is substantial evidence in support of the notion that PEe impose a heavy burden on the government budget. See, for example, Floyd et al., 1984, and Nair ard Filippides, 1988.
emotional and cognitive stimulation intersects with lack of proper nutrition and other critical inputs to increase the likelihood of long-term risks to development (Yousafzai et al., 2013).

All of these risks operate in conjunction with rising levels of food insecurity, which was estimated to be 37 percent prior to the pandemic and has increased as a result of the pandemic (National Nutrition Survey, 2018; World Bank, 2021a). Many under-resourced households suffer from high rates of malnutrition leading to high rates of child stunting and wasting, with an estimated 40 percent of children stunted and 29 percent of children underweight (National Nutrition Survey, 2018). These health and nutritional risk factors to development are highly associated with other risk factors, such as living in a rural area, and low maternal educational status (Khan et al., 2019), which leads to a cumulative negative impact on development.

# Early learning opportunities outside the home, through early childhood education, are also few

Enrollment in early childhood education is very low for children aged 3-5 in Pakistan, further curtailing access to early stimulation and learning opportunities for young children. According to 2019-20 PSLM data, just 18.9 percent of 3- to 5-year-olds nationally are enrolled in ECE (Tomlinson et al., forthcoming). The greatest level of coverage is seen in Punjab, where just over one-quarter of eligible children (27.0 percent) are enrolled, whereas in Balochistan, only 5.2 percent of eligible children are enrolled (Tomlinson et al., forthcoming), putting 95 percent of children in this province at a considerable disadvantage in terms of school readiness. Low-income and rural children are especially disadvantaged in terms of access to ECE—and gender inequality begins to emerge as well. Children in Pakistan who lack ECE experiences show lower rates of ontrack development than children who attend ECE programs (e.g., 52 percent vs. 74 percent respectively in Punjab, 2017-18) (Tomlinson et al., forthcoming).

Very little is known about the barriers to ECE enrollment in Pakistan, including at what age parents in Pakistan think their children should enroll and whether that correlates with actual behaviors. It is known through studies elsewhere that parents’ beliefs about child development are malleable and, when shifted, improve parents’ investments in their children and school readiness levels (Dizon-Ross, 2019). To our knowledge, the relationship between caregiver beliefs about when children should enter school and actual enrollment has not been explored in Pakistan. Although ECE enrollment requires more than parental belief in education—for example, program proximity, affordability, and quality—accurate perceptions of what young children need to develop and learn are important, given that beliefs drive investments. Compounded by insufficient early stimulation at home, especially for very young children, caregivers’ beliefs about young children’s needs may be a key to behavioral and subsequent outcome changes.

7
to the backbone. We assume that access before 2008 was null everywhere, an assumption that is supported by World Bank data on access to Internet, which reports that less than 4 percent of individuals in Sub-Saharan African countries (including high-income countries) had access to Internet in 2008. We confirmed our figures by cross-checking them against World Bank indicators reporting the percentage of the population using Internet.<sup>8</sup> Figures A6 and A7 in the appendix show the access to Internet in Ethiopia and Kenya.

**Isolated vs Bundled investments** Tables A1 reports the number of districts for Ethiopia and Kenya with bundled and isolated investments for all years. Given that the small number of districts with electricity but no paved road, we assume that there is no district with electricity but no paved road.<sup>9</sup>

**Employment** We are interested in structural transformation, which we interpret as changes in sectoral employment, in line with the literature (Herrendorf et al., 2014). We derive sectoral employment shares from Demographic and Health Surveys (DHSs), which produce harmonized survey data with GPS coordinates for most surveys and are available for several rounds per country. The DHS is a repeated cross-section of enumeration areas (EA), with approximately 20–30 households enumerated per EA. Four rounds of survey data are covered in Ethiopia (2000, 2005, 2011, 2016) and four in Kenya (1991, 2003, 2009, 2018). For Ethiopia, the DHS rounds included 12,751 individuals in 2000, 14,052 in 2005, 21,080 in 2011, and 19,157 in 2016, from approximately 650 EAs, which differ per round. Djibouti and Somalia did not conduct DHSs.

We use DHS data for which we have access to the occupation of the individuals as well as a proxy for their location. In order to construct the shares of employment per sector, we use respondents’ answers to questions about their current occupation. We first compute the shares of nonworking individuals and then group all working individuals into three sectors: agriculture, manufacturing, and services. We aggregate individual responses within each EA and then generate an unbalanced panel of districts that had at least one EA during a survey round. The DHS–provided GPS coordinates for EA locations are not perfectly reliable because of the common random displacement applied to GPS coordinates for anonymity.<sup>10</sup> We aggregate EAs per geographic location.

> 8The World Bank reports access to Internet (percent of population) for most countries for a long time period. See https://data.worldbank.org/indicator/WeT.NET.USER.ZS.

> 9Table A2 shows that increasing the electricity threshold to 80% leaves only one district with electriciy but no paved road, the charter city of Dire Dawa in Ethiopia, which is connected to the road network. Removing the electricity alone indicator reduces the risk of capturing data errors.

> 10DHS coordinates of rural (urban) EAs are randomly displaced within a 0-10 kilometer (0-5 kilometer) radius

8
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_5. Government’s budget constraint_ (27) _:_ _6. Previous underlying network investments:_ _In these constraints, Pdj is defined as in_ (8) _, Pj as in_ (9) _, κdi is given by_ _and bi comes from equation_ (13) _,_

# **4 Model Calibration**

We now give values to the model’s different parameters and exogenous variables. Except when noted, this calibration is performed using Brazilian data for the year 2010.

**Exogenously Calibrated Parameters.** Table 3 shows the parameters taken from the literature and their values. Let us start with the ones related to the household’s problem. The elasticity of substitution across sectors _ρ_ is set to 1;<sup>11</sup> otherwise, we cannot guarantee that the model equilibrium is unique (see Appendix D.1). The elasticity of substitution _σ_ for goods within the sector is set to 5 so that the elasticity of sectoral trade flows with respect to trade costs equals 4 as estimated by Simonovska and Waugh (2014). The parameters of the utility function _ωd_ are set to match the gross value added (GVA) share for each sector. Parameter _ν_ is estimated from the Brazilian consumer expenditure survey (POF) for 2008-2009 as one minus the average expenditure share in electricity. Following Allen and Arkolakis (2014), we map the parameter _θ_ to the expenditure share in housing, which equaled 25% for the POF 20022003 wave and 21% for the 2008-2009 period. This implies a value of _θ ∈_ ( _−_ 0.27, _−_ 0.33). Consequently, we take the intermediate value _θ_ = _−_ 0.3.<sup>12</sup>

The production function parameter _βd_ is obtained as one minus the share of energy consumption in production. The elasticity of trade costs to investment _γ_ is another key parameter for our results. We choose an intermediate value among those estimated by previous literature and set it to 0.47.<sup>13</sup>

> 11Notice that this implies that the country-wide sectoral GVA shares are constant but not the labor ones. Furthermore, policy in our model can change both the sectoral GVA and the sectoral labor shares across locations. 12 <u>κ</u> If κ is the expenditure share in housing, _θ_ = _−_ 1 _−_ κ<sup>.</sup>

> 13Santamaria (2020) and Allen and Arkolakis (2022) estimate values of 0.84 and 0.4 for _γ_ , respectively. Esti-

18
defined as debt changes that are not the result of public investment in infrastructure) does not reduce PII.

<mark>In South Asia, with a focus on Bangladesh, India, and Sri Lanka, Jha and Arao (2018) find</mark> that both total and private investment in infrastructure depends on the country’s level of economic development, market size, access to finance, and quality of institutions. The study suggests that the promotion of infrastructure investment requires greater leveraging of the private sector, which depends on the quality of public institutions and an effective regulatory framework for enforceability of contracts and resolution of insolvency. Public-private partnerships (PPPs) are the vehicles for private sector participation in infrastructure development in developing countries. Sharma (2012) examines the determinants of PPPs in infrastructure and finds that the size of the economy and per capita income are major determinants of PPPs in developing countries, along with macroeconomic stability, governance, and quality of regulation. In the Indian case, <mark>Jadhav and Choudhury (2019) examine the determinants of PPPs in the infrastructure sector for India, where they find that</mark> market size, macroeconomic stability, exchange rate, and governance are major determinants of PPPs in the infrastructure sector. Similarly, Nataraj (2007) finds that the private sector plays a major role in developing the telecom and energy sectors in South Asia, but it has a limited role in providing water and sanitation services because these services are considered the government’s responsibility in the region.

There exist large variations in infrastructure provisions among the different African countries. While a few countries (the Seychelles, the Arab Republic of Egypt, Libya, South Africa, and Mauritius) are endowed with relatively higher infrastructure facilities, the rest of the region is considered poor in terms of infrastructure provisions (AFDB, 2018). Some studies attempt to understand the reasons behind the disparity in infrastructure development in African countries. <mark>Owusu-Manu et al. (2019) show that foreign investors contribute significantly to</mark> infrastructure development in the region, although they find a significant negative relationship between FDI and GDP. With an analysis <mark>of</mark> public external debt servicing on infrastructure expenditure in Zambia over the period 1970-2014, Kapindula (2019) finds that the total debt service has a significant and negative impact on construction value added in the long run. In contrast, gross domestic savings and government expenditure have a significant and positive effect on construction value-added in both the short-run and the long-run.

6
network in a way that the country aggregates used by UNCTAD (2007) do not. Pearce (2007) also takes an intuitive approach to analyzing air transport connectivity, and his measure better captures the huband-spoke nature of the network. However, our approach is more rigorous in the sense that it is embedded in a consistent model of the global air transport network. We also apply our method to a much larger dataset: over 200 countries and territories, as compared to the 47 considered by Pearce (2007).

To capture the various dimensions of performance in a single numerical indicator—the _Air Connectivity Index (ACI)_ —we define connectivity as the importance of a country<sup>5</sup> as a node within the global air transport system. A country is considered to be better connected the stronger is the overall "pull" it exerts on the rest of the network. A country's connectivity score is higher if the cost of moving to other countries in the network is relatively low. It is considered to be less well connected if the dispersion of those costs is high. These two factors are both important in the context of a network industry. Combining them means that, for example, a spoke country with a strong connection to only one other country cannot have a high connectivity score: although the cost of moving from one country to the other is low, the dispersion of costs across all markets is very high. A regional hub with strong connections to a moderate number of destinations receives an intermediate connectivity score, since costs are relatively dispersed across the remainder of the network, although to a lesser extent than for a pure spoke country. Finally, a global hub with strong connections to many other countries in the network receives a relatively high connectivity score.

This approach to defining and measuring connectivity flows naturally from a generalized gravity model framework familiar from the regional science literature, since the pioneering work of A.G. Wilson (Wilson, 1970; Roy, 2004) which proposed a general bilinear formulation of bilateral flows. It also nests a

> 5 Given the breadth of the dataset we use for the empirical analysis in this paper, the term "country" is used loosely to refer to both countries in the strict legal sense and other territories with independent air services.

3
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<!-- Start of picture text --> @ (CT -Iniemational -Iniemational Gateways<br>Mam Fixed Transmission Network<br>i \CT-GSM Coverage<br><!-- End of picture text -->
# **Figure 1. GDP, unemployment, and inflation**

# **A. GDP in major advanced economies**

<!-- Start of picture text --> Index, 2007Q1=100<br>115<br>Euro Area<br>Japan<br>110<br>UK<br>US<br>105<br>100<br>95<br>90<br>2008 2010 2012 2014 2016<br><!-- End of picture text -->

# **C. Unemployment rate in NIRP economies**

<!-- Start of picture text --> Percent Latest 2008Q2 Post-crisis peak<br>16<br>12<br>8<br>4<br>0<br>E. 2016 inflation forecasts for NIRP<br>economies<br>Percent<br>3 Latest Jan-16 Jan-15<br>2<br>1<br>0<br>-1<br>Euro Area Sweden Hungary Denmark Switzerland Japan<br>Switzerland Japan Euro Area Denmark Hungary Sweden<br><!-- End of picture text -->

# **B. Inflation in major advanced economies**

<!-- Start of picture text --> Percent<br>Current<br>3<br>Target<br>2003-07 average<br>2<br>1<br>0<br>-1<br>Euro Area Japan UK US<br>D. 2016 growth forecasts for NIRP economies<br>Percent<br>4 Euro Area Sweden Japan<br>Switzerland Denmark Hungary<br>3<br>2<br>1<br>0<br>F. 2020 inflation forecasts for NIRP<br>economies<br>Percent<br>3 April 2016 April 2010<br>2<br>1<br>0<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16<br>Switzerland Japan Euro Area Sweden Hungary<br><!-- End of picture text -->

# **D. 2016 growth forecasts for NIRP economies**

Sources: Haver Analytics, Bloomberg, Consensus Economics, World Bank. A. Last observation is 2016Q1.

B. Inflation measured as year-on-year changes in the price index for personal consumption expenditures for the United States and headline consumer price indexes for others. Last observation is June 2016.

C. Peak is 2013Q1 for euro area, 2009Q2 for Sweden, 2012Q1 for Hungary, 2010Q1for Denmark, 2009Q4 for Switzerland, and 2009Q3 for Japan. Last observation is June 2016.

- D. Consensus Economics growth forecasts for 2016 over time. Last observation is June 2016.

- E. Consensus Economics inflation forecasts for 2016 over time. Last observation is June 2016.

- F. Consensus Economics inflation forecasts for 2020.

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# **Table 9 OLS: Are school and teacher characteristics correlated with scores?** **<u>Dependent variable is percent correct score, Grade 4, UP</u>**

||**Hindi**|**Math**|**Hindi**|**Math**|**Hindi**|**Math**|
|---|---|---|---|---|---|---|
|**Teacher-pupil ratio**|**5.00**|**5.00**|**5.0**|**4.9**|**4.9**|**4.9**|
||**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|**(0.00)****|
|Index of infrastructure|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.91)|(0.70)|(0.88)|(0.62)|(0.85)|(0.62)|
|% non-sc teachers|-0.03|-0.05|-0.03|-0.05|-0.03|-0.05|
||(0.56)|(0.15)|(0.56)|(0.15)|(0.54)|(0.13)|
|% male teachers|0.01|0.01|0.01|0.01|0.01|0.02|
||(0.88)|(0.70)|(0.89)|(0.71)|(0.84)|(0.66)|
|% teachers with college deg|0.05|0.01|0.05|0.01|0.06|0.02|
||(0.27)|(0.72)|(0.29)|(0.83)|(0.23)|(0.62)|
|% teachers withgraduate deg|0.05|0.02|0.05|0.02|0.05|0.03|
||(0.25)|(0.56)|(0.25)|(0.57)|(0.21)|(0.47)|
|Av. Years of service|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.68)|(0.60)|(0.63)|(0.76)|(0.51)|(0.86)|
|Av. days in service in lastyear|0.00|0.00|0.00|0.00|0.00|0.00|
||(0.91)|(0.41)|(0.92)|(0.40)|(0.96)|(0.30)|
|% teachers with pre service<br>training|0.01|-0.02|0.01|-0.02|0.07|0.04|
||(0.80)|(0.64)|(0.80)|(0.64)|(0.32)|(0.41)|
|% teachers doingmultigrade|0.00|0.03|-0.01|0.03|0.00|0.03|
||(0.94)|(0.53)|(0.94)|(0.54)|(0.95)|(0.51)|
|**% teachers actively engaged in**<br>**teaching (mean over 4 visits)**|**0.13**|**0.14**|**0.15**|**0.17**|**0.12**|**0.13**|
||**(0.05)***|**(0.01)***|**(0.05)***|**(0.00)****|**(0.09)**<sup>+</sup>|**(0.03)***|
|% teachers present in school<br>(mean over 4 visits)|-|-|-0.02|-0.05|-|-|
||-|-|(0.73)|(0.28)|-|-|
|% contract teachers|-|-|||0.10|0.11|
||-|-|||(0.26)|(0.10)<sup>+</sup>|
|Block fixed effect|Yes|Yes|Yes|Yes|Yes|Yes|
|n|2553|2553|2553|2553|2553|2553|

Robust p values in parentheses clustered at GP level, * significant at 5%; ** significant at 1%;<sup>+</sup> significant at 10%. Other controls include all student family background characteristics that are in table 8 and block fixed effects.

24