rafmacalaba/datause-encoder-data huggingface dataset:
intent-to-treat impact on the educational expenditures related to children within a household by using a regression discontinuity design and a differences-in-differences approach. The eligibility effect, which we allow to vary across households and children, is assumed to be a linear function of the ethnicity and gender of the potential recipient. The main finding is that unconditional cash transfers to elders lead to substantial improvements in children’s human capital investments. We also find that the program has stronger effects in indigenous populations as well as in female and rural populations. Our results are robust to a series of falsification tests, survey structures, model specifications and estimation methods.

# **References**

- Agüero JM, Carter MR, Woolard I. 2005. From flows to stocks: The impact of unconditional cash transfers on human capital. _Working Paper, University of Conneticuc_ .

- Amuedo-Dorantes C, Juarez L. 2015. Old-age government transfers and the crowding out of private gifts: The 70 and above program for the rural elderly in mexico. _Southern Economic Journal_ **81** : 782–802.

Azariadis C. 1993. Intertemporal macroeconomics .

- Baird S, McIntosh C, Özler B. 2011. Cash or condition? evidence from a cash transfer experiment. _The Quarterly Journal of Economics_ : qjr032.

- Barrientos A. 2003. What is the impact of non-contributory pensions on poverty? estimates from brazil and south africa. _Working paper, University of Manchester_ .

- Barro RJ. 2004. Sala-i martin x (2004) economic growth. _Aufl. Massachusetts Institute of Technology (MIT) ISBN: 0-262-02553-1_ .

- Behrman JR. 2011. How much might human capital policies affect earnings inequalities and poverty?

15
availability, and safety); (b) we provide evidence on the MNA region which has been understudied so far.<sup>10</sup> The rest of the paper is structured as followed. Section 2 presents the data. Section 3 presents the methodology, including both the construction of measures of accessibility, availability, and safety, as well as the empirical model. Section 4 presents and discusses the results. Section 5 concludes.

# **2. Data**

This section presents the type of data that have been collected to study the effect of the public transportation network in Amman, Beirut, and Cairo on the labor market outcomes of the population, and in particular women. Three types of data have been collected: the transit network of each city, the built environment audits to assess the safety of public transportation stops, and household surveys. For all three types of data, we restrict the analysis to the metropolitan areas (therefore do not include potential commuters living outside of these borders).<sup>11</sup>

## **_2.1. Transit network data and built environment audits_**

Transit network mapping and built environment audits were conducted between May and November 2022 for Beirut and Amman. The transport network map for Cairo was collected as part of the Cairo Mobility Assessment and Public Transport Improvement study which was completed in May 2022. The build environment audits for Cairo were collected in November 2022.

In each city, all fixed route-based public transport modes were surveyed for the transit network mapping.<sup>12</sup> The mapping of the public transit system, started with the collection of official routes (and related) information available from government entities. Known gaps within these official records were filled through desk research. Finally, a team of enumerators were deployed to map the actual routes (and capture other attributes like frequency, fares, etc.) using a mobile based application. The building blocks of the field work were public transit stations. Enumerators then conducted station surveys to identify all routes departing from a specific

> 10 In the MNA region social and cultural norms might lead to different results than what had been observed in developed countries

> 11 Note that metropolitan areas include both the city municipality and its surrounding important areas.

> 12 Route-based public transport modes are defined as modes whose routes have a fixed origin and destination, as well as travel through key defined landmarks along the route.

6
births in 1999 (Table 4.2). In 1999, the infant mortality rate was in line with the average for Brazil. However, the infant mortality rate in Pará is still much higher than in the South of the country, for example in Rio Grande do Sul infant mortality reached 18.4 percent of live born infants.

Advances in infant mortality reduction can be attributed to increase service delivery **.** The large advances in reducing the IMR that took place in Pará can be attributed to an improved health care system, increased access to treated water, economic growth, urbanization, and past investments in education, and other social programs. Hence, to further reduce the infant mortality rate in order to reach the national average of 35 percent, especially in the rural areas, further actions are called for. These include general livelihood improvements such as access to clean water and sanitation, high quality education and health care, and a daily caloric intake sufficient to cover the basic needs. Moreover, research shows that a 10 percent increase in income is associated with a 6 percent lower infant mortality rate (Filmer and Pritchett, 1997).

|**Table 4.2:**<br>|**Inf**<br>|**ant Mortali**<br>|**ty, 1970-99**<br>||
|---|---|---|---|---|
|**Region/State**|**1970**|**1980**|**1991**|**1999**|
|**North**|**113.3**|**71.1**|**51.0**|**34.1**|
|Rondônia|156.1|76.8|47.1|31.6|
|Acre|120.7|78.4|60.6|44.2|
|Amazonas|112.1|62.2|45.4|31.8|
|Roraima|113.3|71.1|51.0|38.3|
|**Pará**|**110.8**|**73.6**|**52.6**|**34.6**|
|Amapá|113.3|71.1|51.0|31.7|
|Tocantins|90.1|62.3|54.2|33.0|
|**Nordeast**|**179.2**|**131.3**|**82.5**|**53.0**|
|**Sudeast**|**94.0**|**63.4**|**32.2**|**24.4**|
|**South**|**84.0**|**54.1**|**29.0**|**20.7**|
|Paraná|98.6|65.6|35.6|24.3|
|Santa Catarina|85.4|55.6|28.8|22.2|
|Rio Grande do Sul|62.8|40.0|22.3|18.4|
|**Center-West**|**94.9**|**59.3**|**32.5**|**24.5**|
|**Brazil**|**123.2**|**85.2**|**49.5**|**34.6**|

_Source_ : data from 1970, 1980, and 1991 are from "Atlas do Desenvolvimento Humano no Brasil"/PNUD/ IPEA, 1999 from IBGE.

site: http://www2.ibge.gov.br/pub/Indicadores_Sociais/Sintese_de_Indicadores_Sociais_2000/.

Across the regions, large differences exist in infant mortality **.** In 2000, the infant mortality rate was lowest in the Metropolitan region (26.8) and Almeirim (22.6) (Table 4.3). This is mainly due to the easy access to hospital, prenatal care, education, clean water, etc. in these cities. In more remote and poorer areas where many services are lacking or difficult to access, such as Marabá and Paráuapebas, the IMR was nearly double that of the Metropolitan region in 2000.

The adult illiteracy rate took the same declining path as headcount poverty and infant mortality **.** In Pará, during 1991–2000, the illiteracy rate for people over age 15 fell 8.9 19
_to set the access price._ This is quite critical since often access prices are over half of the costs of downstream entrants. Imagine the following stylized situation. In order to provide one unit of final good, downstream firms need one unit of the upstream input that is produced by the bottleneck owner at a unit LRIC _co_ in exchange for a unit access charge denoted by _a._ If all firms in the downstream sector are similar (in terms of technology and thus of costs) and their products are identical, firms undercut each other until price competition drives to zero all extra profits. The price charged to final users ends up equal to the marginal cost of each firm, which amounts to the sum of the access charge and any other cost incurred in order to transform the intermediate good. If we denote the latter by c, the final price would be _p = a +_ c. The lower the access charge, the lower the final prices and the higher the total quantity consumed by the end-users.

Without any other source of distortion, the best that could be done is to follow a _marginal rule:_ the price to the final user (the consumer willingness-to-pay) should be set equal to the total marginal cost of production. The access price should thus be set equal to the marginal cost of production _(a =_ co) and in the end the consumer price would be p = _c0_ + _c._ On the other hand, distortions in the incumbent's retail prices typically exist for various reasons, for instance because the bottleneck involves also some unapportioned fixed costs, hence a marginal rule would not allow to recover them. Another type of distortion arises when the incumbent's prices do not reflect its cost structure, because the incumbent is constrained by some social obligations to charge identical prices in different geographic region - more on the actual importance of social obligations in Latin America later. In these circumstances, a marginal rule is _not_ the correct benchmark unless additional instruments are used simultaneously to relieve the access charge from additional tasks. Access charges purely based on LRIC are an appropriate benchmark when retail-level distortions are eliminated (for instance by tariff rebalancing) or dealt with using other instruments. On the other hand, the common practice to apply uniform mark ups to the LRIC estimates to recover un-apportioned costs does not reflect much economic analysis.

Table 9 summarizes the main options for access pricing under different situations.<sup>2 8</sup> A quick glance suggests that the rather complex theoretical contributions deliver one message: _the access charge is often performing too many tasks._ The main interest of the table is that it makes the point that different goals and policy objectives lead to alternative ways of

> on the definition of "appropriate" equipment. In contrast, top-down approaches can reflect complex networks and do not omit costs. Their downside is that they are more opaque, hence they may hide inefficiencies.

> 28 For more details, see the surveys by Armstrong (2001a) and by Valletti and Estache (1999).

23
# **<u>Table 7 The Threat Industries of China</u>**

|_SITC_<br>_code_|_Industry_|_Export growth from East Asia to China,_<br>_1990–94 to 2000–04_ (annual%)|_PRODY_|
|---|---|---|---|
|34|Gas, natural and manufactured|23.5|5791|
|56|Fertilizers, manufactured|−23.1|7120|
|_59_|Chemical materials and products n.e.s.|14.5|_15310_|
|_65_|Textile yarn, fabrics, made-up articles n.e.s., related products|6.9|_9409_|
|66|Nonmetallic mineral manufactures n.e.s.|14.4|11332|
|_69_|Manufactures of metals n.e.s.|9.3|_11894_|
|_71_|Power generating machinery and equipment|12.4|_11933_|
|_72_|Machinery specialized for particular industries|11.0|_11016_|
|_73_|Metalworking machinery|14.7|_9919_|
|_74_|General industrial machinery, equipment, parts n.e.s.|9.6|_12955_|
|_75_|Office machines, automatic data processing machines|29.2|_13021_|
|_76_|Telecommunications, sound recording equipment|14.1|_13138_|
|_77_|Electrical machinery, apparatus, appliances n.e.s.|27.0|_12124_|
|81|Sanitary, plumbing, heating, lighting fixtures n.e.s.|1.4|12068|
|_89_|Miscellaneous manufactured articles n.e.s.|7.5|_12878_|
|95|Arms, ammunition|−3.4|2196|
|Weigh|ted average|14.2|12205|

_Source:_ UNCOMTRADE _Note:_ The codes in italics indicate the industries on the top-20 list of the most rapidly growing export industries to China. n.e.s. = not elsewhere specified.

**_What is the impact of China on Southeast Asia in machinery?_** Regarding the impact of China on Southeast Asia in machinery, the largest competing export sector, two hypotheses are tested:

- whether the increasing role of China in manufacturing machinery affects the value of the exports of machinery from other developing countries in East Asia; the emergence of China as an important final assembly hub may be displacing the Southeast Asian countries (Indonesia, Malaysia, the Philippines, and Thailand) that used to be competitive in assembly

- whether developing East Asia will be able to take advantage of the increasing role of China as a powerful assembly hub by exporting machinery components; if Southeast Asian countries move up the technology ladder, they will be exporting components of machinery for assembly in China

The regression equations are shown in box 2.

23
# **Notes**

1Here and in the rest of the article, we define “foreign firms” as those with some foreign capital ownership (i.e., wholly foreign-owned firms as well as joint ventures; the latter includes equity and non-equity joint ventures and joint cooperatives).

> 2Taking the minimum of the conditional probabilities eliminates the problem that arises when a country is the sole exporter of one particular good; the conditional probability of exporting any other good given this one equals 1 for all of the other goods exported by that country.

> 3This dataset, constructed using original COMTRADE data, provides bilateral trade flows. The BACI dataset is downloadable from http://www.cepii.fr/anglaisgraph/bdd/baci.htm

> 4The flow dataset is constructed using an original procedure that reconciles the declarations of exporters and importers. The harmonization procedure enables us to extend considerably the number of countries for which trade data are available compared to the original dataset.

5The product-space framework has been used in different papers on industrial policy and economic development in developing countries. The countries covered include Chile (Hausmann and Klinger 2007b), South Africa (Hausmann and Klinger 2008), Ecuador (Hausmann and Klinger 2010), Algeria (Hausmann et al. 2010), and the Kyrgiz Republic (Usui and Abdon 2010).

> 6We thank an anonymous referee for suggesting various alternative approaches to measure RCAs.

> 7In the subsection “Robustness checks and alternative indicators”, we discuss the results reported in the appendix, in which we use alternative methods to compute RCA. First, we define a measure called “no deficit RCA” according to which a country cannot have an RCA in a product for which it is running a trade deficit. Second, we define a measure called “no outlier product” RCA, which is computed by relying on countries for which no single product weighs more than the upper quartile value (46%) of the distribution of product-level weights in the country’s export basket. Third, we define a measure called “restricted sample” RCA, which is computed by relying on the 60 countries for which the share in world exports is higher than the upper quartile value (0.17%) of the distribution of export shares.

> 8According to Kali et al. (2013), the key indicator is a weighted average of density across products measured at the location level. This differs from our density measure, which has both location and product

28
mean, not the actual mean. This reduces the likelihood of endogeneity and ensures that the variables are the arguments of a demand function not a production function.

**Table 2. Variable definitions, means, and standard deviations**

|||**19**|**93**<br>_Standard_|**1**|**998**<br>_Standard_|
|---|---|---|---|---|---|
|_Variablegroup_|_Variable_|_Mean_|<br>_deviation_|_Mean_|_deviation_|
|Regional|Northern Uplands|0.18|0.38|0.21|0.41|
|(omitted=MekongD|elta)RedRiver Delta|0.23|0.42|0.17|0.37|
||NorthCentral Coast|0.15|0.35|0.16|0.37|
||CentralCoast|0.10|0.30|0.11|0.31|
||Central Highlands|0.04|0.20|0.06|0.24|
||SouthEast|0.10|0.31|0.10|0.30|
|Child's|Boy|0.52|0.50|0.51|0.50|
|characteristics|Child'sage|5.12|2.84|5.69|2.77|
||Logequivalent HH<br>|8.12|0.52|8.38|0.55|
|Household<br>characteristics|consumption<br>Yearsschoolingofmother|6.45<br>|3.79<br>|6.68<br>|3.88<br>|
||<br>Safewater|0.20|0.40|0.31|0.46|
||OK sanitation|0.13|0.34|0.19|0.39|
||Vaccinationcoverage-<br>nonselfmean(%)|45.54|27.06|62.11|30.11|
|Community|Antenatal visitcoverage-<br>nonselfmean(%/o)|,39.45|28.35|49.98|28.92|
|<br>characteristics|Medicallytraineddelivery<br>coverage-nonselfmean(%)|'0.14|31.86|70.35|32.46|
||Facilitydeliverycoverage-<br>nonselfmean(%)|50.93|37.45|53.93|37.94|

# **5. Causes of the recent changes in child survival in Vietnam**

In this section, we apply the decomposition method outlined above to try to uncover the reasons for the faster decline in mortality among better-off children over the period 1993-98.

## **_Estimation results_**

Table 3 shows the parameter estimates of the model. The p-values are based on standard errors adjusted for clustering at the village level.1<sup>5</sup> Nonpoor children living in the central highlands and southeast have significantly better survival prospects than those

> **'5** This adjustment of the standard errors invalidates traditional likelihood tests. Hence the use of Wald tests in Table 3.

17
second, currency mismatches often result in an environment where domestic interest rates are much higher than the foreign interest rates and the exchange rate is fixed; and third, when the banks are eventually bailed out, their private external debts are often taken over by the government, adding to public indebtedness. This was the story of the Southern Cone Latin American countries, vividly described by Carlos Diaz-Alejandro in his 1985 classic (DiazAlejandro 1985), which to a large extent foreshadowed what happened in East Asia in 1997-98.

But the Russian story was somewhat different. While currency mismatches developed on the balance sheets of the banks themselves (as a result of borrowing externally in hard currency and investing in ruble government debt), the other two channels were absent as banks’ links with the Russian real sector were limited and the government let the banks collapse instead of bailing them out. Besides, Russian commercial banks were much smaller than those in East Asia or Argentina. Total household deposits just before the crisis were approximately 7 percent of GDP, compared to over 30 percent for East Asia; and credit to the private sector was a paltry 4 percent of GDP, compared to 30 percent for Central and Eastern Europe.<sup>20</sup> Moreover, over 75 percent of household deposits were held in the state-owned savings bank, Sberbank, under an implicit deposit guarantee. Under this configuration, a bank bailout would have been unlikely to contribute to a fiscal crisis;<sup>21</sup> the actual amount spent to prop up banks, including emergency loans to large banks before the meltdown, has been estimated at no more than 2 percent of 1998 GDP. This compares with public bailouts of over 10 percent in Hungary in the early 1990s, and a multiple of that figure in the Asian crisis countries.

Excluding Sberbank, the banking system was dominated by a few large private Moscowbased banks, well-connected politically and part of “Financial Industrial Groups” built around

> 20 Figures for household deposits from Sinegubko (1998). Central and Eastern Europe includes Bulgaria, the Czech Republic, Hungary, Poland, Romania, and Slovenia.  See IMF (2003, table 6.2).

> 21 The moral hazard argument of Dooley (1998); Burnside, Eichenbaum, and Rebelo (2001); and Kharas and Mishra (2001) explores the role of prospective fiscal deficits caused by implicit bailout guarantees to banks in the context of the Asian crisis.

20
US find that large institutions lend to larger, older SMEs with stronger financial ratios. By contrast, small financial institutions are found to rely more on soft information and lend to SMEs with which they have stronger relations.<sup>16</sup> Moreover, some studies show that SMEs are relatively less likely to receive loans from foreign banks, which are typically large.<sup>17</sup> Other studies focus on how an improvement in the institutional environment enhances lending to SMEs, with the underlying belief that better institutions (associated with more transparency and a better enforcement of the rule of law, typical of developed countries) reduce the need to engage in relationship lending. Consistent with this, Beck, Demirgüç-Kunt, and Maksimovic (2005) show that SMEs tend to benefit more than proportionally as the overall institutional environment improves. Similarly, Galindo and Micco (2005) find that the difference in bank financing between SMEs and large firms is higher in countries with worse creditor protection and less efficient judicial systems.

In this paper, we explore whether and to what extent the main tenets of the conventional wisdom—that banks in general are not interested in dealing with SMEs (due to their opaqueness) and that the small and niche banks that do engage with SMEs do so mainly through relationship lending—holds in reality. To do this, we use new data from bank surveys for a total of 48 banks (plus 1 leasing company) in 12 countries to characterize the degree, determinants, and types of bank involvement with SMEs. We use hard evidence collected via bank questionnaires as well as anecdotal evidence obtained

> bank bias in lending to SMEs, as indicated by the fact that the probability that a small firm borrows from a large bank is proportional to the market share of large banks.

> 16 See, for example, Haynes, Ou, and Berney (1999), Cole, Goldberg, and White (2004), Scott (2004), and Berger, Miller, Petersen, Rajan, and Stein (2005).

> 17 See Berger, Klapper, and Udell (2001), Mian (2006), and Berger, Klapper, Martinez Peria, and Zaidi (2008).

6
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**Table 2: Pairwise Correlation Matrix for Sub-Saharan Africa**

|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|(1) FVA_share|1.00|||||||||||||||
|(2) DVX_share|-0.26*|1.00||||||||||||||
|(3) GVC_participation|0.52*|0.69*|1.00|||||||||||||
|(4) LACEX_sk|0.12*|0.02|0.11*|1.00||||||||||||
|(5) LACEX_usk|0.13*|-0.05|0.05|0.97*|1.00|||||||||||
|(6) log_GDP|-0.11*|-0.05|-0.13*|-0.04|-0.04|1.00||||||||||
|(7) log_distance|0.08*|-0.04|0.02|0.24*|0.23*|0.08*|1.00|||||||||
|(8) log_population|-0.11*|0.05|-0.03|-0.31*|-0.31*|0.58*|-0.33*|1.00||||||||
|(9) HCI|0.21*|-0.18*|0.00|0.17*|0.21*|-0.08*|0.32*|-0.55*|1.00|||||||
|(10) Remit/GDP|0.09*|-0.05|0.02|-0.09*|-0.09*|-0.12*|-0.29*|0.06|0.01|1.00||||||
|(11) migration_share|0.01|-0.08*|-0.07|0.11*|0.08*|0.44*|0.19*|0.24*|-0.19*|-0.25*|1.00|||||
|(12) FCS|-0.03|0.03|0.002|0.01|0.02|-0.21*|-0.12*|-0.08*|-0.07|0.29*|-0.40*|1.00||||
|(13) non_oil_rich|0.001|0.02|0.02|0.15*|0.14*|0.20*|0.29*|-0.22*|-0.002|-0.11*|-0.01|0.13*|1.00|||
|(14) oil_rich|-0.22*|0.10*|-0.08*|-0.09*|-0.09*|0.35*|-0.24*|0.24*|-0.14*|0.03|0.06|-0.08*|-0.17*|1.00||
|(15) overall_LPI|-0.08|-0.08|-0.13*|-0.03|-0.02|0.59*|0.10*|0.32*|0.05|0.07|0.38*|0.01|0.06|0.05|1.00|

*shows significance at the 10% level at least. Note: _LACEX_usk and LACEX_sk denote labor content of export for unskilled and skilled workers respectively. HCI is human capital index, FCS is a dummy for countries’ fragility and conflict situation, and_ non_oil_rich and oil_rich _are dummy variables for oil rich and non-oil rich countries. Finally, LPI represents countries’ Logistics Performance Index._

23