rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

The academic debate on the impact of infrastructure on development outcomes has been ongoing for several decades, at least since Aschauer published his seminal paper in 1989 (Aschauer, 1989). It has fueled considerable interest in policy circles because of its relevance to address some of the main development challenges of the time: by 2022, one billion people live more than two kilometers away from an all-season road, 685 million people around the world lack access to electricity and 2.1 billion to clean cooking facilities, and 2.6 billion people do not use the internet. Even when services are available, they are often not of sufficient quality and reliability for households and businesses. Of course these gaps mostly concentrate in the developing world. For example, 16 of the top 20 electricity access deficit countries are located in Sub-Saharan Africa (IEA et al., 2023), and there are 42 and 52 times more kilometers of paved roads per square kilometers of land in Europe than in Latin America and Africa respectively (Fay et al., 2017). Lack of access to these critical infrastructures in turn often means that people are being curtailed from opportunities to learn, receive good quality healthcare, access good job openings, or develop a business.

While these facts may lead to the simple conclusion that more investment in infrastructure is needed, they also raise complex questions. What is the most efficient way to extend access in each sector? For electricity, should grid extension be prioritized, or is it more a pressing to provide reliable electricity supply to those who already have access? For transport, how do returns from investment in rural roads compare with those directed towards upgrading critical interurban arteries or alleviating congestion in cities? At the root of these issues lies a fundamental question: given the limited resources available, do investments in some sectors have higher social and economic returns than in others? And, are these trade-offs different depending on the context, the initial level of development for example?

To start providing answers to this question, this paper performs a meta-analysis of the infrastructure research done over the last three decades regarding the transport, energy, and digital or information and communication technology (ICT) sectors, casting a wide 2
effort could increase if the increase in hours spent in one type of work exceeds the reduction in the other, and decrease otherwise. Still, since single-equation estimates are asymptotically consistent but inefficient (Wooldridge, 2010, p.188), if the estimated coefficients on these incentives based on the single-equation method are statistically significant in one regression (say, tutoring) in the same direction as those in the simultaneous equation framework but insignificant in the other (say, work hours), these conservative estimates obtained from single-equation estimation method could provide some hint on the direction of change for total effort.

The integral in (3) is amenable to maximization by maximum simulated likelihood (MSL) estimation.<sup>15</sup> Appendix 1 provides the derivations for the likelihood function as well as the firstorder derivatives that can be used to maximize the likelihood function. We write our own program to maximize this likelihood function in Stata (see, for example, Gould, Pitblado and Poi, 2010) using Halton draws with antithetics.<sup>16</sup> We use Stata’s maximization method d1 and the combined Berndt-Hall-Hall-Hausman and Broyden-Fletcher-Goldfarb-Shanno algorithms. We discuss the choice of variables in the next section.

# **IV. Data Description and Background on Teacher Incentives and Effort**

# **IV.1. Data Description**

Our analysis draws primarily on data from a school survey designed by one of us;<sup>17</sup> it was fielded in conjunction with the Laos Expenditure and Consumption Survey 2002-2003 (LECS3)

> 15 See, for example, Train (2009) for a textbook treatment of maximum simulated likelihood estimation. An alternative maximization method is Gauss-Hermite quadrature (Butler and Moffitt, 1982), which can reduce computer time but results can be more unstable. We tried Gauss-Hermite quadrature in a previous draft and find qualitatively similar results. A Stata program that estimates the likelihood function in (3) is available upon request. 16 We obtain the Halton draws with antithetics using the user-written routine “mdraws” (Cappellari and Jenkins, 2006). Halton draws with antithetics are found to increase performance of the estimation results for multivariate probit models compared to Halton draws without antithetics (Sandor and Andras, 2004).

> 17 One of the authors, Elizabeth King, designed this survey with Dominique van de Walle. Keiko Miwa (World Bank) and staff in the Ministry of Education, Lao PDR, were also involved in the final design and pilot-testing of the survey.

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24 Scope for improving Data Quality

**5.**

29.

29. Clearly, if all the data had been extracted from audited annual accounts with full explanatory notes, the reliability which could be attached to the figures would be greatly enhanced. However, this would not have eliminated such grounds for reservation as:

- (i) Badly controlled accounting systems.

- (ii) Accounts qualified by the auditors.

- (iii) Inadequate policies for treatment of depreciation, loan amortization, foreign exchange movements and the impact of hyper inflation.

- (iv) The concealment of subsidies.

- (v) The lack of breakdown of interest paid as between government and other lenders.

30. Even in developed Western countries, accounts-based comparisons between different enterprises in the same industry pose many awkward problems. Where the comparisons are across public sector enterprises from a broad mix of developing nations, the difficulties are magnified appreciably. These could no doubt be eased somewhat through examination of accounting systems and discussions with chief accountants and auditors. This could be a lengthy and costly procedure.

31. Provided a reasonable degree of cooperation was assured, perhaps chief financial officers could be requested to complete a return designed to elicit cash flow data central to the objectives of the exercise. In the case of roads, this would include the usual information on road revenues and expenditures (exclusive of any general revenue adjustments), together with an estimate (if feasible) of the shortfall in regular road maintenance. For transport enterprises, it would include:

- (i) total revenue (showing clearly any current government subsidies)
Ferracane and van der Marel (2018) the DR variable remained insignificant.<sup>25</sup> . Table 5 also adds a third control variable which is the World Economic Forum’s Network Readiness Indicator (NRI). This variable measures a country’s digital preparedness by looking at the propensity for countries to exploit the opportunities offered by information and communications technologies (ICT). It measures the exporting countries enabling environment for digital trade. As expected, the WEF variable comes out as positively significant in all columns.

Table 6: Robustness check baseline equation (2) using OECD Digital STRI as control

||**(1)**<br>**Digital+**|**(2)**<br>**Digital**|**(3)**<br>**Publishing**|**(4)**<br>**Telecom**|**(5)**<br>**IT & Info**|**(6)**<br>**Business**|
|---|---|---|---|---|---|---|
|MODEL**CB**OP|-0.038|1.173***|0.127|-0.034|2.048***|0.139|
||(0.885)|(0.005)|(0.793)|(0.902)|(0.000)|(0.617)|
|MODEL**CB**CT|0.296|-0.339|0.401|0.079|-0.955***|-0.046|
||(0.239)|(0.354)|(0.334)|(0.703)|(0.001)|(0.854)|
|MODEL**CB**LT|-0.236|0.086|-1.792***|-0.166|-0.116|-0.401|
||(0.445)|(0.838)|(0.002)|(0.602)|(0.771)|(0.529)|
|MODEL**DR**OP|-0.404|-1.667***|-1.092**|0.152|-1.702***|-0.145|
||(0.188)|(0.000)|(0.010)|(0.642)|(0.000)|(0.717)|
|MODEL**DR**CT|-0.131|1.078***|-0.258|-0.024|1.482***|-0.260|
||(0.624)|(0.002)|(0.505)|(0.911)|(0.000)|(0.359)|
|MODEL**DR**LT|-0.344|-1.422***|-0.039|-0.114|-1.434***|0.147|
||(0.306)|(0.001)|(0.920)|(0.734)|(0.000)|(0.740)|
|OECD DSTRI|-2.259**|2.465*|-0.106|3.562***|3.475*|1.783|
||(0.036)|(0.070)|(0.962)|(0.009)|(0.062)|(0.356)|
|WEF NRI|1.045***|1.607***|1.819***|1.203***|1.591***|1.763***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp + imp|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|2898|2898|2898|2898|2898|2898|
|P-R2|0.988|0.969|0.963|0.967|0.937|0.976|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level. Fixed effects for exporter and importer are applied. CB refers to the cross-border transfers of data, whereas DR refers to the domestic data processing. Digital STRI denotes the OECD’s Digital Services Trade Restrictiveness Index for which is the grouping of Infrastructure and Connectivity is used. The OECD Digital STRI variable is taken for the importer’s side and is 25 Note however that in Ferracane and van der Marel (2018) the authors use a different identification strategy that is not based on the gravity model and uses imports as opposed to exports, as this paper does.

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the UEUS/LFS for those who are engaged in self-employment jobs. The monthly wage is overall higher among public employees than private employees. In addition, monthly wages are higher in larger towns, as the average wages are ETB 762 in small towns and ETB 1,292 in Addis Ababa. We also calculate hourly wages by dividing earnings by the number of hours worked.

# **4 Empirical Strategy**

Our primary econometric approach is pooled ordinary least squares (OLS) and 2SLS at the individual level. The baseline pooled OLS model for individual _i_ in town _j_ at year _t_ (either 2012 or 2013) is where _yijt_ , is a binary indicator of a labor outcome for individual _i_ (for example, one if self-employed and zero otherwise), _Popjt_ indicates the log population of town _j_ at year _t_ , and _β_ 1 is the parameter of interest indicating the probability change in the outcome with respect to the change in a log town population. _Xijt_ is a vector of individual or household characteristics for control.

We control for a set of individual and household characteristics. Those include individual’s age, sex, marital status, and household size. Their educational attainment is controlled by the following categories: no education, primary incomplete, primary complete, secondary incomplete, secondary complete, higher education, and adult education. The summary statistics are reported in Appendix B.

_γt_ is a year fixed e↵ects to deal with year-specific e↵ects since the UEUS and the LFS data were collected in di↵erent years. The standard error is clustered by the town level.

We rely on IVs to reduce an endogeneity bias. There are three sources of possible endogeneity problems in our models. The first stems from the correlation of unobserved town characteristics with town population and labor outcomes. Another problem is reverse causality, that is, workers move to towns where labor outcomes would be better. These two problems are econometrically equivalent. The third problem is about how to distinguish location e↵ects from the sorting of workers by observed and unobserved skills. We deal with the first and second problems by employing an IV approach that is commonly used in the literature (Chauvin et al. 2017; Ciccone and Hall 1996; Combes et al. 2010; Duranton 2016). We instrument the current town population by the populations 12
As noted earlier in the paper, estimates of the implications of the Modalities formulae for the tariffs facing individual members are probably more important for policy than estimates of the tariffs levied. Evaluating the former is quite straightforward for an individual country, while estimating the implications for barriers faced requires an assessment for all other countries.

**Annex Table 4. Implications of the Modalities for agricultural tariffs faced, %**

||Base|Formula|Formula plus<br>flexibility|
|---|---|---|---|
|Australia NZ<br>|17.0<br>|10.0<br>|13.6<br>|
|Argentina|9.0|6.2|7.8|
|Bangladesh|14.8|12.6|14.5|
|Brazil|18.9|10.0|13.9|
|Canada|9.0|5.2|6.8|
|China|16.7|9.7|13.7|
|EU-27|16.9|10.9|13.9|
|Korea and Taiwan Pr.|16.0|10.8|12.8|
|HK & Singapore|18.4|12.6|17.2|
|India|10.1|7.2|8.9|
|Japan|14.0|9.9|12.7|
|Cambodia|12.8|8.5|12.2|
|Mexico|4.2|2.3|3.1|
|M.E. & N. Africa|9.7|5.9|7.8|
|Nigeria|2.6|2.4|2.5|
|Pakistan|13.2|8.5|11.8|
|Selected LDCs|5.9|4.7|5.4|
|South Africa|10.8|8.0|9.2|
|Sri Lanka|12.9|9.4|11.0|
|Thailand|23.7|13.3|19.3|
|Turkey|9.0|5.6|6.9|
|United States of America|14.0|8.5|11.4|
|**World Bank Classification**||||
|All countries|14.6|9.0|11.9|
|High income countries|15.0|9.3|12.2|
|Developing countries, non LDC|14.3|8.7|11.6|
|LDCs|7.4|6.5|7.1|
|**WTO Classification**||||
|Developed WTO|15.0|9.2|12.2|
|Developing WTO non LDCs|14.3|8.7|11.7|
|_Normal Developing WTO_|13.7|8.8|11.2|
|_RAM WTO_|15.8|9.2|13.1|
|_SVE WTO_|15.6|8.0|12.6|

Annex Table 4 shows some quite substantial reductions in the tariffs facing WTO members. The average tariff facing agricultural exporters would decline by more than one-third—from 14.6 to 9.0%—through application of the formula without exceptions. The reduction in the tariff facing industrial countries is quite similar to that facing developing countries—5.6 percentage points in the former and 5.6 in the latter. Even in 20
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- Barnett, S. and Ossowski, R. (2003). Operational aspects of fiscal policy in oil-producing countries. _in J. Davis, J. Ossowski, and A. Fedelino, eds., Fiscal Policy Formulation and Implementation in Oil-Producing Countries, (Washington, D.C.: International Monetary Fund_ .

- Baunsgaard, T., Villafuerte, M., Poplawski-Ribeiro, M., and Richmond, C. (2012). Fiscal frameworks for resource rich developing countries. _Staff Discussion Note, International Monetary Fund_ , page 12/04.

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- Baxter, M. and King, R. (1993). Fiscal policy in general equilibrium. _American Economic Review, vol. 86_ , pages 1154–1174.

- Berg, A., Portillo, R., Yang, S., and Zanna, L.-F. (2013). Public investment in resource-abundant developing countries. _IMF Economic Review, 61_ , pages 92–129.

- Berms, R. and Irineu, C., F. (2011). The current account and precautionary savings for exporters of exhaustible resources. _Journal of International Economics, vol. 84_ , pages 48–64.

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51
was reduced by about 4 percentage points of GDP. as well as in Mexico (2 1/2 OV. Even if cuts in non-interest public spending were important, they were rarely "growth-oriented", since they often concentrated on capital expenditure. To the extent that these cuts hit infrastructure capital rather than "white elephants", they lowered the productivity of complementary private-sector capital, the profitability of private investment and future output growth.

# (Table 6)

# 4. Interest Rates and GOP Growth

Much of the increase in public debt ratios has been unrelated to deficits in the government budgets. As shown in Table 2, the difference of real interest rates over real GDP growth accounts for one third in the lowest case (Italy) and two thirds in the highest case (Mexico) of the relative debt increase in recent years (since end-1983). Thus, for a country like Mexico, you can easily imagine a disaster scenario in spite of its primary budget surpluses: debt service replaces investment and hence reduces output growth and the tax base; this process feeds cn itself until no resource base is left from which to service the public debt. It is thus important to know to what extent public finance can contribute to higher growth and lower interest rates, and to what extent it cannot.

Pooled time-series/cro **s** s ection regression estimates reported in the 1988 OECO Survey of Belgium identify the budget deficit/private saving ratio as a significant determinant of real bond yields. The ratio explains a third of the increase in bond yields from 1979 to 1983 as well as a third of their subsequent decline. A further third is explained by the US bond yields, indicating that their determinants, e.g. the US deficit/savings ratio, are important concerns for the OECD at large and all the more so for developing countries where the foreign share in public '.bt is higher.

In countries such as Mexico, however, real bond yields have failed to decline with falling operational deficits (which corrects for the inflation component in the government's interest outlays). This may be due to the fact

-8-
price database. Coincidentally, the size of the GAEZ grids and the sparsity of the market geo-location data prevent us from finding multiple markets within any grids.

Finally, we compute expected per capita income gains by taking the product of crop prices and the simulated production gains for each crop-grid. We aggregate this for all grids ( _G_ ) within a country to estimate an expected change in income for individuals at the poverty line. That is, for a representative farmer at the 1.90 USD extreme poverty line (2011 ppp) within a specific country, we compute the expected percentage income growth ( _E_ ( _θ_ )) from all crops combined.

Here, _AEPL_ represents the annualized extreme poverty line (i.e., 1.90 * 365 USD), and therefore _E_ ( _δθc_ ) represents the expected change in income for farmers around the poverty line from adopting groundwater irrigation to reduce the crop water deficit.

# **6 Results**

Table 4 presents the simulation results we obtained using the two different approaches— SD and SRHS—when all three conditions are met. Each row in the tables corresponds to a unique combination of model and crop choice.

The mean gains in production are much higher than the median gains, suggesting the distribution of production gains is right-skewed. Therefore, we use median production gains to discuss our results.

Using the two approaches, our simulated median gains in production for different crops range between 29.57% and 33.28% for barley, 76.35% for millet, 107.96 and 123.26% for maize, 108.09% and 114.64% for rice, 89.49% and 129.42% for sorghum, and 27.97% and 53.08% for wheat. Refer to the map of production gains in Figure 6 and Figure 7 for the SD and SRHS approaches, respectively.

Note we were not able to run simulations for millet using the SRHS approach, due to very few grids being irrigated. With the exception of barley, the simulated gains using SRHS are higher than when using the SD approach. These results suggest large production gains can be realized through groundwater irrigation in SSA, especially in the production of maize and rice. For a country-wise overview of our results, refer to the median production gains by country in Figure 9 and Figure 11 for maize and rice, respectively. For the results using the SRHS approach, refer to Figure 8 and Figure 10 for maize and rice, respectively.

Furthermore, note that 24.91% of all GAEZ grids are existing croplands. Among these croplands, 21.96% satisfy all three conditions considered in our simulations. Since we do lose a substantial portion of grids by imposing a depth constraint of 7m, we check for 14
41 participation in the recovery programs.34 The use of targeting mechanisms<sup>to reach the</sup> poor is a natural outcome of this debate and has been the<sup>focus of much attention.</sup> Kanbur (1987), Ravallion and Chao (1987), Besley and<sup>Kanbur (1988), and</sup> Thorbecke and Berrian (1988) provide an extensive discussion of the<sup>theoretical basis of</sup> targeting. The objective in targeting is to make transfers to poor households<sup>such that their</sup> income level after the transfers is just above the poverty line.<sup>If the income of the</sup> households was perfectly measurable and poor households could be identified,<sup>the transfer</sup> program would target funds to these poor households only. This extreme<sup>case called</sup> "perfect targeting" (since it assumes that each poor household<sup>can be perfectly identified) is</sup> represented in Figure 3. If a household is below some poverty line<sup>Z, then an amount is</sup> transferred to the household such that its new income just equals Z. Any<sup>point below the</sup> 45- line implies that the household receives a (positive) transfer, and<sup>any point above it</sup> shows that the household receives a negative transfer, i.e., it is being taxed.<sup>The total cost</sup> of the poverty reduction program in such a case is simply the area<sup>of the triangle OZC."</sup> The amount needed to finance this program is simply equal to<sup>the area OZC. If this</sup> transfer scheme is to be self-financed, the same amount must be obtained<sup>by taxing other</sup> households. This is depicted by the solid line below the _45_ line where<sup>the households that</sup> are above Z are taxed. As mentioned earlier, the use of targeting mechanisms<sup>is conditional</sup> on the identification of the poor. In this extreme case, the size of<sup>the transfer program is</sup> based on the assumption that it is possible to identify the poor below<sup>the poverty line Z.</sup> I See for example, Cornia et al. (1987), World Bank (1990b), UNDP<sup>(1990).</sup> 35 This assumes that the distribution of income is uniform.