rafmacalaba/datause-encoder-data huggingface dataset:
# **2. EVALUATING THE NEED FOR A SOCIAL POLICY**

2.1 There are two basic justifications for social policy intervention in the infrastructure sectors. First, access to basic services is considered essential to achieve a minimum level of household welfare; without adequate sanitation, for example, it is difficult to sustain good health. Second, it is known that access to basic services provides a platform that helps families to escape from poverty; for example, by facilitating labor force participation via improved health or reduced time spent on domestic chores such as collection of water and wood, or by expanding possibilities for household-based microenterprises.

2.2 In order to define the priorities of any social policy for the infrastructure sectors, it is important to begin with an empirical analysis to identify the areas of most pressing social need. In many countries, governments set themselves the objective of providing universal access to infrastructure services, often defined as universal service coverage at affordable prices that enable all citizens to cover their basic consumption requirements. To evaluate the extent to which this objective has been met in today’s Argentina, the study examines household survey data on coverage and expenditure across the income spectrum.

## **ACCESS**

2.3 Household data on service coverage show that access to electricity in Argentina is almost universal, both in the Metropolitan Area and in the provinces (Figure 1). As far as telecommunications are concerned, universal coverage has also been reached, since although only 72% of households have a private telephone, 97% live within 10 blocks of a public telephone service, which is considered to be an adequate substitute.

2.4 A significant coverage deficit, however, still remains for water and sewerage. In the case of water, the deficit is concentrated in the Metropolitan Area where 15% of households currently lack the service, in contrast to the provinces where the service is already virtually universal. In the case of sewerage, 30% of the population nationwide still lack access to this service. Given the importance of these services from a public health perspective, there is a clear justification for making coverage expansion a central element of the country’s infrastructure social policy.

2.5 Finally, the natural gas service also presents a significant coverage deficit with coverage levels of around 70% nationwide. LPG cylinders provide a widely available substitute for this service, however it is five times more expensive than natural gas per equivalent unit of energy. Ironically, its usage is concentrated among the lowest income groups, which are often unable to finance the US$53 connection charge for natural gas, as 7
The other possibility is a change in the composition of trading partners. Indeed, as shown in figure 4, over the sample period, the number of zero trade flows is quite stable until 1990, around 45% for the poorest tercile and 15% for the richest tercile.<sup>18</sup> Then the number of zero trade flows decreases sharply and fell by half. In contrast to the richest tercile, the average distance of zero trade flows for the poorest tercile is consistently higher than for positive trade flows, but the gaps narrow in later years.

The effect of this expansion of trade and its implication for the average distance of trade is shown in figure 5 for the poorest tercile. The figure disaggregates the ADOT for the lowest tercile into the two components: (i) “traditional”, i.e. existing trade partners with already positive trade flows in 1970-1974 (intensive margin); (ii) “new” trade partners with positive trade flows since 1975 (extensive margin). We also report the weights of each trading partner (or each margin) in total trade.

Figure 5: Average Trade Distance of Poorest countries with Traditional and New Trade Partners <!-- Start of picture text --> 50% 8000<br>40% 7 000<br>30% 6000<br>20% 5000<br>10% 4000<br>0% 3000<br>Nber obs. Import Value<br>ADOT - Trad. Partners ADOT - New Partners<br>ADOT - 39 Poors (kms)<br>Ratio (New Partners/Total)<br>1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> Two patterns are evident: first the regionalization of trade is partly reflecting the closer distance of the “new” partners that are significantly closer than the existing partners and they have an increasing weight in the 18 We observe in the mid-1980s a slight increase in the number of zero trade flows compared to preceding years mainly for the poorest country’ trade. This slowdown in trade growth around the mid-1980s is also visible in Felbermayr and Kohler (2006, figures 3a and 3b based on Rose’s database) and in Helpman, Melitz and Rubinstein (2008, figures 1 and 2, based on Feenstra’s database). As these new (and temporary) zero trade flows also concern geographically close partners, this results in a decrease of the unweighted average distance of zero trade flows during the 1980s.

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burden of TCs in the EU ETS. One of the main findings of the study done by Jaraite et al. (2009) lies in the fact that the most significant TCs arise in the early phase of the implementation. Within this early implementation phase, TCs incurred by organizations in relation to baseline setting and the functioning of the EU ETS market represent the two most significant sources. In absolute terms, the initial costs tend to be higher for large organizations compared to medium and small organizations. However when TCs are apportioned in terms of CO2 emissions, the finding is the opposite: early implementation costs for large organizations represent 3€cts/tCO2 and 51€cts/tCO2 for small ones (Jaraite et al., 2010, p. 196). This finding highlights the importance of project size and (potentially) unequal distribution of administrative costs borne by participants of different sizes. The same study provides estimates for MRV costs: 2€cts/tCO2 for large companies and €1.51/tCO2 for small size firms (Jaraite et al., 2010, p. 198). Due to the (large) nature of projects, large and medium companies tend to spend more on monitoring compared to small organizations. In small organizations, half of the MRV costs are devoted to verification. On average, implementation and MRV costs represent 8€cts/tCO2, less than 1% of the allowance price, which was €11.60 as of 23 January 2009 when the study was conducted (Jaraite et al., 2010, p. 201).

Trading costs tend to be variable as opposed to MRV and implementation costs since they are proportional to the amount traded. Amongst the 27 responding organizations, 11 Irish companies proceeded with trading: 6 sold allowances and 5 bought allowances. All the companies traded through an external broker. The study does not provide a quantification of those costs, but cites a brokerage fee per ton of traded allowance from 10€cts in January 2005 to 6€cts in August 2006 (Jaraite et al., 2010, p. 200).

# **6. Factors influencing transaction costs**

The findings from our review strongly underscore that it is problematic to draw general conclusions about the nature and scale of TCs. All TCs seem to be case- and context- specific. The question then is what makes all the cases so unique. To address this aspect, we re-visited all the cases in order to better understand the drivers and specific contexts that determine the nature and scale of TCs. As shown in Table 5, determinants of TCs can be classified in three categories: (1) endogenous factors that are inherent to the projects (e.g. characteristics of the technologies), (2) exogenous factors which relate to different contexts including the institutional and legal frameworks in which TC are occurring, and (3) intrinsic factors such as the conceptual choices and the methodologies used by the researchers. These aspects are briefly elaborated and discussed in the following sections.

**Table 5: Identified factors influencing the nature and scale of analysed TCs studies**

|**Endogenous factors**|**Exogenous factors**|**Intrinsic factors to the approach**<br>**used**|
|---|---|---|

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the last week did you or another adult in the household skip a meal due to lack of money or resources?”; and (2) “was there a time in the last week when you or another adult in the household went an entire day without food due to lack of money or other resources?”. The responses are binary, 1 for “Yes” and 0 for “No”.

We also consider measures related to social distancing and school attendance. For social distancing, the survey question is “Have you adopted social distancing/self-isolation?” We use two measures of educational disruption. When analyzing wave 1, we construct a measure of school dropout based on “Have children in the HH attended school (primary/secondary) before school closures?” and “Have children in the HH engaged in ANY learning or educational services since school closures?". Because the first of these two questions is generally not asked after the first wave, we only consider the latter of those questions when analyzing all waves of data.

We take the mean of each well-being measures at the regional (state/province) level by month to create a panel of regions.<sup>4</sup> Appendix Table 1 shows the number of regional observations by month in 2020. All variables have data beginning in April 2020. However, the panel data are highly unbalanced with most observations concentrated in June, July and August, implying that the HFPS surveys covered most countries in the three months.

The summary statistics of the regional-level HFPS well-being measures for wave 1 and all waves in 2020 are reported in panels A and B of Appendix Table 1.

# 2.2 Explanatory Variables

The main explanatory variables are a set of real-time indicators including night-time lights, air pollution, and EVI (a vegetation index) measured from satellites, Google mobility data, and the popularity of Google search of certain keywords. These are listed in Table 2. Summary statistics of real-time indicators for wave 1 and all waves in 2020 are reported in Appendix Table 2A and 2B, respectively.

The measures of night-time lights, air pollution, and EVI are all obtained from Google Earth Engine. The night-time lights data are taken from the VIIRS monthly composites produced

> 4 Each survey wave is assigned to a unique month.

8
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# **_Africa-specific effects?_**

Another interesting policy question is whether the conventional Africa-specific negative effect is observed. A number of cross-country studies, throwing a dummy variable for Africa in the model, have demonstrated that African countries share some unobserved characteristics, which usually have adverse effects on economic development.

Sachs and Warner (1997) attributes Africa’s slow growth to poor governance, landlockness and the dynamic Dutch disease syndrome associated with abundant natural resources. It is true that many African countries do not have physical and effective access to the sea. Iimi (2007) also indicates the extent that natural resources are properly linked to growth is dependent on governance; many African countries are lagging behind in this area. Glaeser _et al_ . (2004) shows that human capital is more important for growth than the institutions. On the other hand, it may be just because of geographic conditions, such as temperature and precipitation, along with structural poverty traps (Bloom, _et al_ ., 2003).

In our results, there is no clear indication that African countries bear high procurement costs because of “unobserved African factors.” The estimated country-specific fixed effects are different from sector to sector. In our _limited_ sample countries, Africa’s road procurement costs are not systematically increased by unobservables. The normalized country dummy coefficients for Ethiopia and Ghana are relatively low compared with other regions (Figure 20).<sup>19</sup> But the Swaziland-specific effect may be among the highest.

In the water and sewage sector, Tanzania’s project costs may be systematically high. However, this cannot be over-generalized because of our narrow country coverage. In the electricity sector, it is found that most African countries in our sample, such as Nigeria, Tanzania and Uganda, bear additional infrastructure project costs associated with some unspecified elements in our models. This is consistent with the conventional “Africaspecific” adverse effect view. Further analysis is required to examine what it is.

> 19 This does _not_ mean that Africa’s project costs are relatively low. Rather, it means that there is no evidence that African countries would have a common unobserved factor affecting the public procurement results.
|**Table 2: Exten**<br>|**t of training samp**<br>|**les and F1 scores for**<br>|**winter crops**<br>|||
|---|---|---|---|---|---|
|**Year**|**Total**<br>**sample size**|**Extent o**<br>**Winter cereal**|**f training sample for c**<br>**Winger rapeseed**|**ollected**<br>**Summer crops**|**F1**|
|2019|2,481|508|271|1702|99.3|
|2020|3,155|543|263|2349|97.0|
|2021|3,644|699|292|2653|99.9|
|2022*|2,132|354|116|1662|95.1|

_Note_ Results for 2022 are preliminary as field data collection is still ongoing. The F1 score is TP/(TP+0.5*(FP+FN)).

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High density values indicate that locality _l_ has a comparative advantage in many goods that are closely related to product _i_ . This product is thus densely connected to its locality’s product structure. As indicated by Kali et al. (2013)<sup>8</sup> and Hidalgo et al. (2007), density is considered a proxy for product spillovers emanating from consistent specialization, such as knowledge externalities and economies of scale and scope spillovers.

# **II Empirical strategy**

## **Empirical specification**

Our specifications focus on the impact of product-level density of links to the local product space on the export performance of Chinese firms between 2000 and 2006 compiled by the Chinese Customs Trade Statistics (CCTS).<sup>9</sup> Our dependent variable is the log of the export value of product _k_ from firm _f_ in locality _l_ in 2006.<sup>10</sup> This is regressed on the 2000 value of the same variable, the first year in our sample, and the density indicator for locality _l_ and product _k_ in 2000, as presented in Section 2. Our specification is We consider, in line with Bernard et al. (2010), three broad determinants of firm productlevel export performance: factors that are specific to products but common to all firms; factors that are specific to firms but common to products; and factors that are idiosyncratic to firm-product pairings. The first product-specific category corresponds to factors such 10
_Discussion_ _19_ Table 9. Percentage of **regional** population covered by reports to WHO or sample surveys, by year

|_Region_|_1970_|_1985_|
|---|---|---|
|World<br>|28|59|
|Developed countries<br>|74|76|
|Developing countries<br>|10|53|
|Industrial market economies<br>|98|100|
|Industrial nonmarket econo-<br>mies<br>|29|32|
|Latin America and Caribbean<br>|62<br>|53|
|Sub-Saharan Africa<br>|0<br>|0|
|Middle East an( North Africa<br>|14<br>|2<br>|
|Asia|2|71|

Note: For 1985, the U.S.S.R. is not counted as covered. China and rural India in 1985 are the only cases covered by sample surveys rather than registration. Urban India is not counted as covered.

expectations. For example, substantial deaths from diarrhea at older ages were judged improbable. Nevertheless, on the average across reporting countries in 1985, 40 percent of all deaths from this cause occurred at age 65 and older, and the proportion exceeded 80 percent in countries like Norway and Japan, with presumably reliable reports. Misclassification cannot be excluded as an explanation, but cannot be confirmed with available data.

Further potential problems exist with the regression-based estimates, where they are used for 1970 and 1985 for countries with no reported data, as well as for 2000 and 2015 for all countries. Both statistical and substantive criteria could be applied in assessing these results.

Statistically, the equations for major causes of death produced patterns for 1970 and 1985 consistent with those produced by Hakulinen and others (1986a) for 1980, using the same equations but different life tables and regional groupings. Hakulinen and others argued that their results had many similarities to previous results by Preston (1976), who estimated equations that were not age-specific. They also argued that the procedure was insensitive to choice of life tables, which appears borne out by the similarities between their results and these.

To assess the regression procedure, the equations were applied to the reporting countries for 1985 to see whether predictions would match reports. This comparison is permissible because the equations for major causes had been separately estimated (by Hakulinen and others) with other data; the procedure is less informative regarding the equations for specific causes, which were estimated using the reported data in the comparison. Table 10 shows, however, that the reported distribution by major causes is more doselv approximated by predictions than is the distribution by specific causes. For the major causes, indices of dissimilarity between percentage distributions range from 5 to 13 among the four regions in Table 10. For specific causes under infectious diseases, these indices range from 12 to 34; for specific causes under circulatory system
ELMO statistic can be more readily compared across settings (because in each village the size and number of population groups is controlled for) and so the finding of a much lower salience for group differences in Sugao based on this statistic is fairly robust. A further broad finding from Tables 9-11 is that if anything, group differences in Sugao are becoming _less_ important over time – in contrast to Palanpur where the reverse was observed.

Considering the results for Sugao for each year in turn, a few additional observations can be noted. In 1942, the most important breakdown of the village population in terms of both the conventional and the ELMO decomposition corresponds to a caste-based grouping. In the case of the conventional decomposition, the unsurprising finding is that the highest between-group contribution obtains when the village is divided up into the largest number of groups (Table 9). In the case of the ELMO decomposition, the closest one comes to a partitioning of the village population along the income distribution is when the village is divided into 4 groups (Maratha versus Dalits versus Muslims versus the rest of the village). By 1958 and even more so by 1977, caste differences seem to become less pertinent, and there is some suggestion that occupational characteristics are becoming more salient. In 1958 the largest ELMO statistic obtains when the village is divided into two groups comprising scheduled caste agricultural labor households (as opposed to all scheduled caste households) on the one hand versus the rest of the population. In 1977, the highest ELMO statistic obtains when the population is divided into three groups, distinguishing agricultural labor and traditional occupation households from white collar service employed households from the rest. In this last year however, as already noted, all decomposition results are particularly low, relative not just to Palanpur but also to the earlier survey years. Group differences in Sugao (at least based on the characteristics presented in Tables 9-11) appear to be of only minor importance.

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# **2. Data**

# **_2.1 3S Episodes_**

The Sudden Systemic Stops (3S) identified in the macro literature are not mild recessions or contractions, but rather periods of output collapses of severe magnitude of about 10% from peak to trough. The dating and identification of 3S episodes in the macro literature follows the classification in Calvo et al. (2006a, b) who use a sample of emerging markets that are integrated in world capital markets and tracked by JP Morgan to construct its global Emerging Market Bond Index (EMBI). Calvo et al. (2006a, b) first characterize output collapses by a pre-crisis peak (period displaying the maximum level of output preceding a trough), trough (local minimum following the onset of a crisis) and full recovery point (period in which the pre-crisis peak output level is fully restored). Next they classify a 3S window as one that is marked by (a) _a capital flow window_ where there is a large fall in capital flows exceeding two standard deviations from its mean that overlaps at any point in time with (b) an _aggregate-spread window_ containing a spike in the aggregate EMBI spread exceeding two standard deviations from its mean. If either the pre-crisis peak or trough of a previously identified output collapse episode falls within the 3S window, it is classified as a 3S collapse.

While Calvo et al. identify 22 3S collapses through the 1980s and 1990s, we are restricted by the availability of firm level data to 9 3S episodes over the 1990s.<sup>7</sup> Table 1 shows the 9 3S episodes in our sample, with the dates of the pre-crisis peak, crisis trough and recovery point as identified by Calvo et al. (2006a). While the identification of these crisis episodes follows the 3S classification, they coincide with large banking and currency crises over the 1990s as identified by other studies, including Laeven and Valencia (2008).

# **_2.2 Identification of Phoenix Miracles_**

The literature has identified Phoenix Miracles as crisis episodes where there is a collapse in both output and credit but output recovers relatively quickly without a recovery in credit. For our sample, we start with 9 3S episodes over the 1990s that have been identified as potential Phoenix

> 7 Ecuador and Morocco had 3S collapses over the 1990s but we do not have these in our sample due to the unavailability of corporate balance sheet data in these countries from Bloomberg. These countries are not covered in other firm-level datasets such as Worldscope either.

7
Experimentation with a wide variety of individual, maternal and household characteristics reveals similarly-large percent increases in infant mortality when salinity is increased from the 5th to the 95th percentile. Overall, our results suggest that the impact of salinity variation on infant health in coastal Bangladesh is comparable to the impact of other variables that have been assigned major importance by previous research (e.g., maternal age and education, gender of household head, wealth, toilet facilities, drinking water sources and cooking fuel).

# **Conclusions**

In this paper, we have used new monitoring data to investigate the impact of drinking water salinity on infant mortality in coastal Bangladesh. Our work also incorporates many determinants of mortality and morbidity that have been identified by previous research. We use probit and logit analysis to estimate mortality probability models for infants whose age is less than two months. Our database combines spatially-formatted individual and household survey information from the Bangladesh Demographic and Health Surveys for 2004 and 2007 with spatially-formatted salinity measures for 2001-2009 provided by the Bangladesh Soil Research Development Institute. Our approach is based on extensive medical research that has established significant links between the salinity of drinking water consumed during pregnancy, hypertension and preeclampsia in pregnant women, and postpartum infant morbidity and mortality. We focus particularly on testing the post-natal impact of pre-natal salinity exposure during each month of pregnancy.

Controlling for many other determinants, our probability model estimation for infant mortality finds high significance for salinity exposure during the last month of pregnancy and no significance for exposure during the preceding months. The implied impact is comparable in magnitude to the estimated 15