rafmacalaba/datause-encoder-data huggingface dataset:
investments are complementary and that they increased economic development in Ethiopia. He finds starkly different patterns of bundled infrastructure investments on sectoral employment compared with only road investments. Roads alone cause service sector employment to increase at the expense of agriculture and, especially, manufacturing employment. In contrast, the interaction of roads and electrification causes a strong reversal in manufacturing employment. This paper adds to the strand of literature initiated by Moneke (2020) by studying the impact of internet, electricity and transport, and their complementarities, for other African countries.

There is a growing body of literature using quantitative spatial general equilibrium models to assess the impacts of infrastructure. Allen and Arkolakis (2014) develop a general equilibrium framework to determine the spatial distribution of economic activity and uses to assess the impact of the US interstate highway system. Michaels et al. (2011) look at changes in sectoral employment as an outcome that captures the underlying infrastructure-induced effects.<sup>3</sup> Bustos et al. (2016) and Fried and Lagakos (2020) study the general equilibrium implications of electrification via its effect on productivity. Several papers provide policy counterfactuals for future road and border infrastructure improvements for the Belt and Road Initiative (Lall and Lebrand, 2020; Bird et al., 2020); in Bangladesh (Herrera Dappe and Lebrand, 2019); and between Bangladesh and India (Herrera Dappe et al., 2021). The paper contributes to this strand of the literature by assessing the spatial general equilibrium impacts of several planned transport investments and trade facilitation measures in neighboring countries that are at different stages of development.

The paper is structured as follows. Section 2 presents the data and descriptive statistics. Section 3 presents the empirical strategy and results. Section 4 develops a spatial generalequilibrium model to produce counterfactuals for more domestic and regional integration through future infrastructure investments. Section 5 concludes.

# **2 Data and descriptive statistics**

## **2.1 Data**

This paper uses household survey data that have been georeferenced, new spatial infrastructure data, and district characteristics in order to study links between access to infrastructure and the structure of local economies as well as the complementarities between types of infrastructure.

**Infrastructure** We collected new information on road network expansions, access to the electricity network, and access to Internet fiber backbone from various sources (Table 1).

> 3See Redding and Turner (2015) and Redding (2016) for surveys of the literature on the impacts of transport infrastructure and the growing use of models to quantify these impacts.

5
# **Appendix Table 9. Evidence for mothers sorting to high preschool access districts**

||Net migrat<br>|ion of mothe<br>ki<br>|rs with pres<br>ds<br>|chool-aged<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Net change in private preschool density|0.087<br>(0.055)||||
|Net change in public preschool density||-0.688<br>(0.476)|||
|(Lagged) Net change in private preschool<br>density|||-0.067<br>(0.055)||
|(Lagged) Net change in public preschool<br>density||||0.102<br>(0.474)|
|Observations|1,733|1,733|1,732|1,732|

Notes: Sample is composed of a panel of districts over PODES survey years. We aggregate the number of preschool-aged eligible mothers in our constructed panel and regress the net migration of eligible mothers between PODES survey years on the net and/or lagged net change in private/public preschool densities. Number of observations indicate the number of district-year observations in the panel of districts.

# **Appendix Table 10. Annual cost of attending private and public preschools**

||Pri<br>|vate<br>|Pu<br>|blic<br>|Private-<br>|
|---|---|---|---|---|---|
||Mean|SD|Mean|SD|Public|
|Registration fee|15.47|(38.58)|6.19|(13.53)|9.27<sup>***</sup>|
|Other scheduled fees|12.92|(33.79)|4.80|(8.86)|8.12<sup>***</sup>|
|Exam fees|0.23|(1.66)|0.01|(0.05)|0.22<sup>**</sup>|
|Books/writing supplies|5.57|(11.16)|2.61|(5.24)|2.95<sup>***</sup>|
|Uniform and sports supplies|5.54|(10.35)|5.16|(8.35)|0.38<br>|
|Transportation costs|3.87|(18.50)|0.25|(1.57)|3.63<sup>***</sup>|
|Food/housing costs|13.96|(29.43)|9.40|(15.98)|4.57|
|Special courses|0.93|(13.70)|0.00|(0.00)|0.93|
|Other school expenses|1.22|(7.14)|0.00|(0.00)|1.22<sup>***</sup>|
|Observations|4|30|7|6|506|

Notes: Sample is obtained from IFLS 3 (2000) conditional on enrollment in indicated public or private preschools. Means are reported in IDR 10,000 increments and are adjusted for inflation using national CPI with 2010 base year (FRED). The exchange rate in 2010 was 1 USD for 9,090 IDR (FRED).

77
Tij = the estimated volume of freight flows between origin i and destination j Ai = the balancing factor for origin i that ensures compliance to Oi Bj = the balancing factor for destination j that ensures compliance to Dj Oi = the constraint value for origin i (i.e. total supply) Dj = the constraint value for destination j (i.e. total demand) ƒ is the decay function where: ƒ(Cij,ß) = exp(‐ß.Cij) in case of an exponential function ƒ(Cij,ß) = Cij<sup>‐ß</sup> in case of a power function where: Cij = the distance between origin i and destination j (the resistance measure) ß = the decay parameter The availability of both supply and demand data enables the use of a doubly‐constrained gravity model (de Jong and Van der Vaart, 2010) where total flows from a district (the origin) equal the total supply from that district, while flows to a district (the destination) equal the total demand at that district. This ensures adherence to the market equilibrium condition where total supply equals total demand (Ivanova, 2014).

Equations (4) and (5) above hold for a doubly‐constrained gravity model if the constraint equations (6) and (7) below are satisfied through an iterative procedure: For the India FDM, the road distance matrix was used to determine the flow data for the non‐rail component of freight. A detailed national road network was constructed. This allowed road travel times to be estimated between the various origins and destinations, penalized for the type of road. A lower resistance was given to national roads, so freight collates towards these highways, the logic being assumed improved travel time on highways. This refers to Cij in Equations 3 to 5, which typically refers to distance, but can be adjusted based on estimated travel time or costs. For the India FDM, it is adjusted by ranking roads through reducing the travel distance for highways and increasing the travel distance for rural roads.

In a doubly‐constrained spatial interaction model where both the origins and destinations are known but the derived freight flows over the transport network are unknown, the problem is essentially confined to the estimation of a suitable decay parameter. In terms of actual flows, only rail freight‐ flow information is available (which in most instances accounted for only a small market share); the distance decay parameter could therefore not be derived (for all commodities) from actual data. Decay parameters utilized in other gravity models are also typically not published, these could therefore not be used as a starting point, as commodities tend to exhibit similar distribution characteristics. Decay parameters for the South African FDM were however available. At the outset of the South African gravity‐modeling exercise, distance‐decay parameters were developed informed by the decay parameter principles discussed above as well as known flows such as rail flows and large industry flows.

24
Figure 6: Change in sales by informality (a) Change in sales vs. 2019 according to the type of (b) Change in sales vs. informality informality <!-- Start of picture text --> 0 -20<br>-10 -8<br>-25<br>-20 -17 -17<br>- 20<br>-30 -30<br>-40 -36 -38<br>-50 -47 - 49 -45 -46 -44 -46 -35<br>-60 -40<br>Fully formal Necessity De Soto Parasite<br>-.2 0 .2 .4 .6 .8<br>Round 1 Round 2 Round 3 Informality PCA score<br>Change in sales<br><!-- End of picture text --> Notes: Controls for size, sector, age, country, gender of the owner, and severity of the crisis. Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).

when weighted by size (Figure A8), implying that there is still a role for informality as an additional factor explaining firm performance during crisis.

: _Regression framework_ Results from Table 1, suggest that even after external firm attributes such as size, sector, age, and gender of the owner, fully informal firms (a value of one on the informality index) suffered ten percentage points higher decline in sales relative to formal firms (column 1). If we further condition by initial labor productivity (column 2), the magnitude does not change much. This indicates that even though an important part of the performance of firms can indeed be explained by their characteristics, as the coefficient on informality reduces significantly from the unconditional (-36%) to the conditional specification, there is still a role for informality by itself.<sup>19</sup> Within the types of informal firms, those driven by necessity have worse performance, an average four-percentage points difference against parasite firms (columns 3 and 4). Recovery exhibits a similar pattern, with fully informal firms showing a much worse performance, even after controlling for initial productivity (columns 5-8).

To understand the role of each firm attribute, we extend our regression framework to also include interactions of these traits with informality, one at a time. The results from our split regressions, presented in Table B7, suggest that female-led informal firms perform relatively worse.<sup>20</sup>

> 19The results for the unconditional specification are not shown here but are available upon request.

> 20In the fully-interacted specifications, the coefficients of female-led are negative for necessity and De Soto firms, while for parasite firms, the coefficient associated with female-led is positive but not statistically significant (Table B7).

16
**Table 2: Structure of GATS Commitments, all Members, Mode 1**

|**Sector**|**Total**<sup>a</sup>|**Ma**|**rket Access**<br>|**(%)**|**Nation**|**al Treatm**<br>|**ent (%)**|
|---|---|---|---|---|---|---|---|
|||Full|Partial<sup>b</sup>|Unbound|Full|Partial<sup>b</sup>|Unbound|
|**Business Services**||||||||
|Legalservices|59|20|60|10|20|60|14|
|Accounting/auditing/book-keeping|70|34|41|24|37|39|24|
|Architecturalservices|64|58|25|17|55|31|14|
|Medical& dentalservices|51|37|27|35|47|20|33|
|SoftwareImplementationservices|71|61|24|15|65|17|18|
|Dataprocessing services|69|59|25|16|62|20|17|
|R&Dservices (naturalsciences)|31|65|16|19|65|19|16|
|Advertising services|54|57|31|11|57|24|19|
|Managementconsulting services|65|63|23|14|69|18|12|
|**Telecommunication services**||||||||
|Voicetelephone services|83|10|78|12|27|63|11|
|Privateleased circuitservices|74|15|78|7|35|58|7|
|Electronicmail|68|35|59|6|56|40|4|
|Onlineinfo & databaseretrieval|70|34|55|11|59|32|9|
|**Audiovisual services**||||||||
|Motion PictureProjectionService|17|53|29|18|59|24|18|
|**Distribution**||||||||
|Retailing services|47|30|53|17|28|55|17|
|**Educational Services**||||||||
|Adulteducation|34|53|41|6|50|44|6|
|**Financial Services**||||||||
|Insuranceintermediation|61|18|52|30|45|20|35|
|Lending ofall types|93|24|35|41|42|18|40|
|**Health Related, Social Services**||||||||
|Hospitalservices|41|44|2|54|51|2|46|
|**Tourism Services**||||||||
|Travelagencies|103|54|18|27|59|15|26|
|**Recreational Services**||||||||
|Entertainmentservices|39|51|10|38|54|10|36|
|News agencyservices<br>Source<br>:Based on data provided by t|24<br>he WTO S|71<br>ecretariat|21<br>, updates S/|8<br>C/W/99, Marc|63<br>h 3, 1999|33<br>|40|

<u>Source: Based on data provided by the WTO Secretariat, updates S/C/W/99, March 3, 1999</u> a Total number of Members with commitments in Mode 1. b Includes horizontal limitations.

In general the commitment level across WTO Members and service sectors is quite heterogeneous. In Table 2, commitments range from 17 commitments for motion picture projection services to 103 commitments on travel agencies. Only a few service sectors are committed by at least two-thirds of WTO Members. To give some examples, in 2000 only few service sectors particularly amenable to electronic transactions (other business services, professional services, financial services) had commitments by significantly more than half of the WTO membership<sup>54</sup> . But except for some business services (i.e. advertising and management consulting services), the GATS Mode 1 commitments on these three rather liberal service sectors are often only partial commitments. In some sectors, nearly two-thirds of the commitments guarantee full market access (i.e. news agency services) whereas in others, like voice telephone services, most commitments are of a partial nature. Accordingly, the lack of mode 1 commitments also applies to the most liberal sectors like business and computer services that – due to their generally low level or regulation - are known to be

> 54 See MATTOO & SCHUKNECHT (2000), p. 14 and MATTOO & SCHUKNECHT (2001), p. 17.

16
_Gender Equality and Economic Development: The Role for ICTs_ 24

# **References**

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Barro, Robert and Jong-Wha Lee (1993). “International comparisons of educational attainment.” _Journal of Monetary Economics_ . Vol. 32, pp. 363-394.

Barro, Robert J. and Jong-Wha Lee (1994). “Sources of Economic Growth.” _Carnegie-Rochester Conference Series on Public Policy_ .

Barro, Robert J. and Jong-Wha Lee (2001). “International data on educational attainment: updates and implications.” _Oxford Economic Papers_ , Vol. 3, pp. 541-563.

Barro, Robert J., and Xavier Sala-I-Martin (1995). _Economic Growth_ . New York: McGraw-Hill.

Bledsloe, Caroline (1990). _Critical Perspectives on Schooling and Fertility in the Developing World._ Washington D.C., National Academy Press.

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Broersma, Lourens and Robert H. McGuckin (1999). “The Impact of Computer on Productivity in the Trade Sector: Explorations with Dutch Microdata.” Research Memorandum GD-45, Groningen Growth and Development Centre, October.

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country and the size of the country‘s shadow economy, using a sample of transition countries for which estimates of the informal sector as a percent of official GDP are available from Schneider and Klinglmair (2003). _Figure 1_ measures the values based on data sets for the years 1999 and 2000. The results indicate a strong negative correlation between both variables (-0.657), significant at the 0.01 level. In order to increase the number of observations, in _Figure 2_ we include countries that are not included in the WVS 1999-2000 but that were part of WVS 1995-1997 (19 observations in total). We observe also in _Figure 2_ a strong negative correlation (Pearson _r_ = -0.551), significant at the 0.01 level. Thus, transition countries with low tax morale show a clear tendency to have a large shadow economy. Indeed, a simple linear regression in both figures suggests that a decrease of tax morale by 1 unit would lead to an increase of the shadow economy of roughly 20 percentage points. Moreover, the variable tax morale can explain more than 30 percent of the total variance of the variable size of shadow economy.

However, these analyses give only information about the raw and not the partial effects. The observed correlation might be explained in terms of factors that affect the size of the shadow economy. It is important to investigate the causes as a whole with their interdependencies. An investigation that focuses on a simple correlation has a somewhat limited validity. Thus, multiple regressions help us to disentangle the effects of other factors from a possible tax morale effect. Multivariate studies indicate that tax morale has a very strong impact on the size of the shadow economy using a crosssectional and a panel approach (see Torgler and Schneider, 2007a, 2009).

# **III. HOW IMPORTANT ARE INSTITUTIONS AND GOVERNANCE QUALITY?**

## _1. Theoretical Considerations_

As Cowell (1990) notes, ―... the issue of evasion is, unlike other illegal activities, inseparably bound up with the instruments of fiscal control that the government attempts to use in carrying out its economic policy‖. While reducing evasion improves the government‘s revenue, it is a broader issue for the development of a civil order (Knack and Keefer, 1997). But, as discussed reducing tax evasion is not simply a matter of 11
is used to ensure a nationally representative sample for each country, with oversampling corrected accordingly.<sup>6</sup> This approach generates margins of error that are generally in the 3–4 percent range at the 95 percent confidence level, with a mean error margin of 3.3 percent.<sup>7</sup> Note, however, that because these surveys have a clustered sample design, the margin of error varies by question. It is therefore possible that the margin of error is greater for certain questions. We also note that the margins of error in China and India tend to be lower than the average (by 1.6 to 2.6 percentage points). However, in China in 2005–06, the food insecurity question followed some fairly detailed questions on income and welfare, which may have primed respondents to be more likely to answer “yes” to the food insecurity question. Although we were aware of this problem in China, there may be similar problems in other countries. It is certainly possible that the first wave of the GWP (2005–06) contains greater measurement error than subsequent waves because Gallup faced a steep learning curve in conducting such an ambitious global survey (we address this issue below in a sensitivity analysis).

# **<<B>>** _The Gallup World Poll question on food security_

Although these general characteristics of the GWP surveys are pertinent, we now turn to the specific question of interest, which is phrased as follows:

> 6 In a handful of cases, certain sections of the population are oversampled (see appendix S3). For example, urban areas were oversampled in Pakistan, Russia, and Ukraine in at least one year, and in the August–September 2009 survey in China, the provinces of Beijing, Shanghai, and Guangzhou were oversampled, possibly because of the unusual switch to telephone surveying. In other contexts, it appears that Gallup oversampled more educated groups (Senegal, Zambia), and in some developing countries, certain parts of the country were not sampled at all because of ongoing political instability or other accessibility problems.

> 7 Thus, if the survey were conducted 100 times using the same procedures, the “true value” around an assessed percentage of

> 50 would fall within the range of 46.7 percent to 53.3 percent in 95 out of 100 cases.

7
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households in terms of living standards.

Recently, a “vulnerable” class, located between the poor and the middle class, has been defined (López-Calva and Ortiz-Juarez 2014). Vulnerability relates to the risk of falling into poverty over time due to adverse events. For instance, a vulnerable household may not have sufficient resources to continue to acquire all the necessities to maintain an adequate living standard over a reasonably long period. Even in countries like Kazakhstan where extreme poverty is very low, the presence of a large vulnerable group may prevent households with median income— those in the middle of the income distribution—from becoming middle class. Assessing vulnerability, Ferreira et al. (2013) used per capita income of US$10 to US$50 a day in 2005 PPP terms to define the middle class in Latin America and the Caribbean.

Can the US$10–US$50 range be exported and applied directly to Kazakhstan? While the range is solidly grounded for Latin America, it may not be appropriate elsewhere. For instance, the same method applied to Nigeria estimates the lower threshold at US$3 per capita a day (Corral, Molini, and Siwatu 2015).

For this reason, we have devised an approach to estimate country-specific thresholds for identifying the Kazakhstan middle class. Our method, which is based on an absolute income approach, estimates lower and upper bounds based on how Kazakhstan citizens perceive their own status relative to their reported income. One module of the 2013 Kazakhstan Household Survey (see Section 2) dealt directly with self-declared social class. Our analysis focuses on 2013 because the module appeared only in that round of the survey, but we have extended the analysis to 2014 and 2015 and have also looked back to 2003, 2006, and 2010 to better describe the profile of the middle class and analyze possible changes in the Kazakhstan middle class over a longer period.

In what follows, Section 2 discusses the Household Budget Survey of Kazakhstan. Section 3 details the econometrics method for estimating the lower and upper income bounds that define the middle class. Section 4 highlights the characteristics of the middle class, and Section 5 explores factors beyond income that could influence the self-perception of being middle class. After controlling for these factors, income remains the most important determinant of middle-class 3
- 9 -

Figure 1: Households' Asset Portfolio <!-- Start of picture text --> l ~~~~~~~~~~~~~~~~~~I<br>Money and Banku<br>sDDeposits (money<br>on- inanciaClamsonMuua plus quasi-money) p<br>HouseholdS Shares<br>\ ~~~~~Financial<br>\ Assets \ . FBonds<br>\X / 4|Claims on Mutual N<br>\ / Funds Assets | ~~~~~~~~~~~Money and Bank<br>F u n ds.A ss ets D e p o sits (m o n e y<br>Com s A s Nplus quasi-money)d<br>FinanciF I A<br>Assets e n Shares<br>S| ares Bon Non-Fin. \s SharesFBonus Non-FIn~ Shares Bonds Assetsn. Sh|res Bonds Bonds l<br>B 500 100iquid 100 100 50 Non Financial<br>Financial p Insurance Assets<br>Assets Companies Assets Non-traded<br>\ ~~~~~~~~~~~~~~Financial Asset:s<br>|Non-traded loans,|<br>debt, and equity|<br>Table 2: Portfolio Composition<br>Demand for Assets Household Sector 11 Contractual Savings hMutual Funds<br>CSaras Bonds Non1F1 5 Sha50s Bonds No50Fin. Sha1es 5onds Non0n Sha0s Bonds No0+ln.<br>A 100 100 100 100 100 100 0 0 0 0 0 0<br>B 100 100 100 50 50 50 50 50 50 0 0 0<br>C 150 100 50 50 50 50 100 50 0 0 0 0<br>D 100 100 100 0 50 100 100 50 0 0 0 0<br>E 160 130 10 70 70 10 90 60 0 0 0 0<br>F 150 100 50 50 50 50 0 0 0 100 50 0<br>Notes: I With banks<br><!-- End of picture text -->