rafmacalaba/datause-encoder-data huggingface dataset:
|earthquake<br>wealth<br>quintile|household<br>s|%<br>Assets<br>losses<br>(June)<br>(1)|% Assets<br>losses<br>(February)<br>(2)|Variation<br>(1) – (2)|Main assets lost<br>(February)|% Agric.<br>assets<br>losses<br>(June)|Main agric.<br>assets lost<br>(June)|
|---|---|---|---|---|---|---|---|
|Poorest|250|19.6|17.6|-2|Radio,<br>cell phone, iron|12.1|Poultry, goats,<br>swine, cattle|
|2|259|39.0|38.2|-0.8|Television, radio|22.2|Poultry, swine|
|3|239|57.3|51.9|-5.4|Television, radio|31.8|Poultry, swine|
|4|254|74.0|79.5|5.5|Television, radio<br>Television,|6.7|Poultry, swine|
|Richest|244|70.5|86.5|16|radio,<br>fan|0.0|Poultry, goats|
|Total|1246|51.9|54.6|2.6|Television, radio|15.8|Poultry, goats,<br>Swine|

Source: Own computations using June 2010 (EFSA II) surveys. Notes: The sample is restricted to the households residing in the six strata that cover areas directly affected by the earthquake. *Among households practicing agriculture before the earthquake.

# _(g) Displaced people and host households_

Table 7 presents some statistics on mortality and displacements. Overall, 12.4% of households declared having a member dead or missing as a consequence of the earthquake. 12.8% of households declared that at least one member of the household were displaced: this percentage is higher among the richest (14.8%) than among the poorest (8.4%). Among these households reporting at least one displaced member, the mean number is 2.8 people per household, with few variations according to household wealth. In June 2010, the majority of those people were still displaced.

**Table 7. Dead/missing and displacements due to the earthquake, by pre-earthquake quintile of wealth**

|Pre-<br>earthquake<br>wealth<br>quintile|Number of<br>households|% with<br>dead/missing<br>people|% with<br>diplaced<br>people*|Number<br>of<br>persons<br>displaced<br>(mean)|Among ho<br>Number<br>of<br>children<br>displaced<br>(mean)|useholds w<br>Number<br>of adults<br>displaced<br>(mean)|ith displac<br>Number<br>of<br>elderly<br>people<br>displaced<br>(mean)|ed persons<br>Some<br>people<br>still<br>displaced|<br> <br>All<br>people<br>still<br>displaced|
|---|---|---|---|---|---|---|---|---|---|
|Poorest|250|9.2|8.4|2.8|1.5|1.2|0.1|4.8|81.0|
|2|259|12.4|10.8|2.9|1.2|1.7|0.0|3.6|85.7|
|3|239|14.2|12.6|2.5|1.1|1.3|0.0|6.7|83.3|
|4|254|13.0|17.7|3.0|1.2|1.5|0.4|2.2|75.6|
|Richest|244|13.5|14.8|2.7|0.9|1.7|0.0|5.6|80.6|
|Total|1246|12.4|12.8|2.8|1.1|1.5|0.1|4.4|80.6|

18
# **Industrial Policy, Information, and Government Capacity**<sup>**1**</sup>

William F. Maloney Gaurav Nayyar

## **JEL classification: O25, O10**

**Keywords: industrial policy, market failures, government failures**

> 1 EFI World Bank.  Corresponding author: wmaloney@worldbank.org. Our thanks to Shanta Devarajan, Caroline Freund, Navin Girishankar and Aart Kraay for helpful comments.
financial institutions, leaving them no option but to rely on their own funds or borrow from informal networks (Shinozaki, 2022).

Similarly, many informal workers were adversely impacted by the pandemic. Workers whose jobs require face-to-face interactions – such as street vendors, domestic helpers, and drivers – experienced large reductions in working hours and incomes (Octavia, 2020). Reports from motorcycle drivers, for example, revealed that their incomes had fallen by as much as 70 to 80 percent since the start of the pandemic (Octavia, 2021). The need to travel to earn a daily wage also put informal workers at a greater risk of contracting the virus, especially women who are more likely to use public transportation than men (Setyonaluri & Samudra, 2020). Although the government’s social assistance program for the poorest Indonesians benefited some informal workers, many did not qualify for the in-kind and cash transfers because they either earned incomes above the eligibility threshold or had assets to their name (Octavia, 2020). Notably, there was no specific program designed to assist informal workers partly due to the lack of a universal database that could be used to identify and target recipients (Octavia, 2020; Pitoyo et al., 2021).

Nevertheless, not all informal enterprises and workers suffered because of the pandemic. A series of small MSME surveys conducted between March 2020 and May 2021 by the ADB revealed two distinct groups: those who were disrupted by the pandemic on one hand, and those who benefited from the pandemic on the other hand (Shinozaki, 2022). Several factors influenced outcomes. First, informal enterprises and workers in sectors that were considered ‘essential services’ – such as ICT, finance, and health and social services – fared much better than enterprises and workers in other sectors (Shinozaki, 2022). Second, informal enterprises and workers who were able to leverage the digital economy were shielded from the worst effects of the pandemic (Shinozaki, 2022). Informal enterprises that were primarily engaged in e-commerce, particularly the online buying and selling of goods, fared relatively well (Pitoyo et al., 2021). Likewise, informal workers who had access to social media (e.g., Twitter) and messaging platforms (e.g., WhatsApp) 19
economies in East Asia, including China and Japan, fell into recession at the end of 2008 and reduced intra-regional trade. It has been reported that about 10 percent of container ships have been idle during this period. Some shipping companies expect the decline in volume in 2009 may reach 20 percent. Chronic port congestion may be lessened at some ports for some period in the region. Full recovery in the United States, Japan and Europe over the short term is unlikely. As a result, exports of the developing economies in East Asia will not likely return to the high levels in 2008.

The decline in demand for international transportation suggests a decline in the expected rates of return in new investment in port infrastructure.. This may hinder some of the port development projects that had been viable prior to the crisis. However, since infrastructure projects require a long gestation period and cost recovery is expected over a long time period; demand and rates of return should be considered from a long-term perspective. One should note that the demand for ports in the region does not only depend only on demand in the United States or Japan. Over the long term a shift from external to internal and regional demands may take place.

As long as port development projects are viable in the long-run, investment in ports likely provides a good policy target in the region both in terms of creating macroeconomic demand and improving supply side growth prospects. Investment in port infrastructure in developing economies in East Asia could have a strategic role in this regard. The following sections of this paper center on the benefits and costs of increased port capacity within the context outlined above.

# **2. Port Infrastructure, Transport and Trade Costs: Survey**

Trade costs are widely defined as any costs which increase the prices of traded goods during the delivery process from the exporters (or producers) in exporting countries to the final consumers. Developed countries face substantially high international trade costs: estimated about 74 percent in terms of _ad valorem_ tax equivalent<sup>5</sup> , including transportation costs, policy barriers,

> 5 Defined as international trade costs divided by the value of the imported goods in the country of origin.

9
The data are all from surveys that were designed to be nationally representative (typically with sampling weights—which are used throughout this analysis) with sample sizes ranging from 1,141 households in Papua New Guinea to over 10,500 in Uganda.<sup>18</sup> Most of the surveys were carried out in the 1990s but four of them were fielded after 2000.

The asset indicators used in each of these datasets are similar to that in situations in which the asset index approach is typically used, for example the analysis of Demographic and Health Survey data. The indicators cover household ownership of consumer durables such as a radio, a television, a bicycle, a car; and characteristics of the dwelling in which the household lives such as characteristics of the flooring, the roofing, main source of drinking water, type of toilet facilities, main source of lighting and cooking fuel. The datasets contain between 12 (Uganda) and 29 (Nicaragua) indicators of asset ownership and between 4 (Ghana) and 12 (Albania) indicators of housing characteristics (Zambia is an exception at 37). The full list of indicators for each dataset is in Appendix 2.

# **3) Results**

In this section we begin by assessing how household rankings differ when using the alternative approaches to measuring economic status; we then assess how gradients in outcomes differ; and we end by assessing the variation in household attributes such as rural/urban location, size and composition.

# _a) Relative rankings_

We use two approaches to compare household rankings. The first compares the simple correlation of household rankings across the different measures. This gives an indication of the difference across the entire population. The second estimates the share of households that are simultaneously ranked in the poorest quintile by different measures. This focuses on the way in which these types of aggregations are often used, namely identifying households in the poorest tail of the economic status distribution.

> 18 In rare cases, parts of the country were excluded.  For example the Brazilian survey only covers the Southeast and Northeast regions of the country.  In Uganda one region of the country was not sampled because of security reasons. Surveys typically used cluster sampling: robust standard errors are used for inference in this paper.

17
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**Table 5: Correlations at the Firm Level**

||**Formality**|**Credit**|**EXT**|**INF**|**Banks**|**Size**|**Age**|**Fowned**|**Exporter**|**Manager's**<br>**education**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Formality**|1.000||||||||||
|**Credit**|0.136|1.000|||||||||
|**EXT**|0.087|0.430|1.000||||||||
|**INF**|-0.080|-0.079|0.027|1.000|||||||
|**Banks**|0.071|0.326|0.703|-0.057|1.000||||||
|**Size**|0.198|0.243|0.194|-0.105|0.156|1.000|||||
|**Age**|0.089|0.157|0.089|-0.072|0.083|0.203|1.000||||
|**Fowned**|0.093|0.029|-0.029|-0.088|-0.011|0.280|-0.025|1.000|||
|**Exporter**|0.109|0.129|0.123|-0.082|0.106|0.467|0.043|0.244|1.000||
|**Manager's**<br>**education**|0.156|0.182|0.146|-0.033|0.098|0.350|0.121|0.165|0.207|1.000|

_Sample restricted to “Credit” regression._ 26
<!-- Start of picture text --> 128° 126° 124 ° 122 ° This map was produced by the Map Design Unit of The World Bank. The boundaries, colors, denominations and any other information 116° CANADA 114°<br>shown on this map do not imply, on the part of The World Bank<br>Group, any judgment on the legal status of any territory, or anyendorsement or acceptance of such boundaries. FRASER RIVER BASIN<br>SELECTED CITIES<br>Burns Lake MAJOR ROADS<br>INTERNATIONAL BOUNDARIES<br>Prince George<br>C A N A D A<br>CANADA<br>Fraser<br>River<br>Basin<br>Jasper<br>Bella Coola<br>Williams Lake<br>Little Fort<br>Calgary<br>Revelstoke<br>Winter Lillooet<br>Harbour<br>Kamloops<br>Powell River<br>Kelowna<br>PACIFIC Cranbrook<br>OCEAN Vancouver Hope<br>0 25 50 75 Kilometers<br>0 25 50 75 Miles UNITED STATES<br>128° 126° Victoria 122 ° 120 ° 118° 116°<br>c<br>54°<br>52°<br>50°<br>ompso<br>We<br>54°<br>52°<br>50°<br>N h<br>n<br>t<br>e a<br>d<br>st<br>h<br>Roa<br>l<br>o<br>ko<br>Fr<br>c<br>e<br>T<br>as<br>il<br>er<br>n<br>i<br>h<br>C<br>o<br>V<br>a<br>n<br>c<br>o<br>u<br>v<br>e<br>r<br>I<br>sl<br>a<br>n<br>l<br>u<br>d<br>m<br>s<br>b<br>C<br>i<br>e<br>a<br>n<br>u<br>r<br>Q<br>e<br>s<br>a<br>F<br>r<br>JANUARY 2005 IBRD 33725<br><!-- End of picture text -->
# **6 Discussion and Conclusions**

In this paper, we studied how unconditional cash and in-kind welfare transfers impacted the standardized test scores of primary school children from rural and marginalized areas in Mexico. We merged individual-level data from the randomized controlled trial of the _Programa de Apoyo Alimentario_ with administrative panel data on standardized test scores taken 4 to 10 years after transfers began. Despite the fact that both transfer modalities increased household consumption, and that in-kind transfers induced children to consume more key micronutrients, we find that in-kind transfers did not impact student learning, while cash transfers significantly reduced student learning.

There are many possible reasons why the improved micronutrients intake might have not led to long lasting impacts on test scores. First, for the children in our sample the improvement in micronutrients was not accompanied by a significant increase in the overall caloric intake. Due to the high share of children who were consuming less calories than the recommended daily allowance at the baseline, one possible explanation is that the quality of the intakes might not be sufficient to improve the cognitive function if the overall calorie consumption does not achieve a minimum threshold, and children do not improve their anthropometric measures. Second, most of the previous evidence on the beneficial effects of iron supplementation on cognitive outcomes is based on interventions that target individuals diagnosed with iron deficiency anemia (IDA) with high doses of iron. At the opposite, in our study all children from PAL beneficiary households are potentially exposed to improved intakes, but the doses do not seem sufficient to benefit those with IDA at the baseline. In fact, we did not observe any statistically significant reduction in anemia in the follow-up survey. Finally, previous evidence shows that zinc supplementation per se is not sufficient to improve cognitive development. Powell et al. (2005) find for a group of undernourished children in Jamaica that zinc supplementation increases cognitive development only when complemented by psychosocial stimulation. This result together with the evidence from other studies that analyze the combination of micronutrient supplementation and psychosocial stimulation (see Attanasio et al., 2018) is potentially consistent with the hypothesis that nutrition interventions only improve the cognitive development of children who have adequate stimulation. This might not be the case for children in the PAL evaluation sample, where the quality of stimulation both at home and in school is likely to be poor. Moreover, our results show that parents did not increase the time they devote to their children, particularly in the cash group.

The explanations discussed above cannot fully explain the whole set of results in our study. In fact while in localities that received the transfer in-kind we observe an improvement in 23
Early case studies for countries in Latin America find results in line with those from the cross-country empirical literature. Foreign bank presence through the mid-1990s was linked to lower interest margins, overhead costs, and profitability of domestic banks in Argentina (Clarke et al., 2000). In Colombia, foreign bank presence was linked to declining non-financial costs for domestic banks (Barajas, et al., 2000).

Case study evidence from individual countries in Eastern Europe and Central Asia also points to increased competition as a result of foreign bank entry. Based on stochastic frontier analysis, foreign banks in Hungary were found to be more cost efficient than domestic banks, except in the medium-size range (Kiraly, et al., 2000). In Poland, foreign banks were found to be more cost efficient than domestic banks, except those domestic banks that had a high share of foreign customers (Nikiel and Opiela, 2002). At the same time, foreign banks (and domestic banks that catered to a foreign clientele) were not necessarily more profit efficient than other banks. In all, the results from Poland suggest that foreign bank entry contributed to increased competitiveness, but in specific market niches.

Regional studies for Latin America and Eastern Europe yield more ambiguous conclusions than country case studies. While a study on Argentina, Chile, Colombia, Mexico, and Peru reveals that increased foreign presence coincided with reductions in operating costs which, in turn, help to narrow spreads (Martinez Peria and Mody, 2004), another study that used the H-statistic as the measure of competition, including the same countries along with Brazil, Costa Rica, and El Salvador, concludes that foreign bank presence weakened competition (LevyYeyati and Micco, 2007). For Eastern Europe and Central Asia, while some studies based on cost estimations for nine countries from 1995 to 1999 fail to confirm that foreign banks are more cost efficient than domestic banks (Green et al., 2004, 2003), a series of other studies yield opposite results. For example, one study based on 319 banks across ten countries finds that greater foreign bank presence is associated with lower non-interest income, profits, and interest rates. Stochastic frontier analysis revealed foreign banks to be more cost and profit efficient than domestic banks, especially state-owned domestic banks (Bonin et al., 2005). Data envelope analysis on a larger set of banks from 17 countries also confirms that foreign banks were more efficient than their domestic counterparts in the last half of the 1990s (Grigorian and Manole, 2006). Moreover, a stochastic frontier analysis of 562 banks in Eastern Europe and Central Asia from 1993 to 2000 11
-31 -

# Table 8

_Comparison of Cosrs of Defined Benefit and Defined Contribution Pension Plans with more than 100 participants in the United States, in 1988._ (1988 dollars per covered employee)

|Source of Cost<br>Factor|Defined<br>Benefit|Defined<br>Contribution|Cost Ratio<br>(DB/DC)|
|---|---|---|---|
|Investment<br>Advice||||
|& Management fees|38.60|6.94|_5.5_|
|Actuarial fees|8.74|0.38|23.0|
|Accounting fees|2.15|1.06|2.0|
|Salaries and Allowances|5.47|_0.58_|9.4|
|Contract Administrator fees|6.90|1.82|3.8|
|Legal fees<br> <br>|3.05|_0.58_|5.3|
|Valuation/Appraisal<br>fees|0.41|0.27|_1.5_|
|Trustee fees and expenses|10.37|3.81|2.7|
|<br>Other Administration|70.79|10.92|6.5|
|TOTALCOSTS|146.48|26.36|_5.56_|
|Non Investment Mngmt. Costs|107.88|19.42|5.55|
|**p**articipants projected 1988<br>(millions)|26.530|29.118||

Source: Trends in Pensions 1992, Table A-15.

In 1988 defined benefit plans with more than 100 participants managed 2.21 times more assets per covered employee than defined contribution plans, which are newer. This is not the source of the difference in cost, as the total cost per million dollars of assets in defined benefit plans is 2.51 times that in defined contribution plans, for plans with more than 100 participants.

If costs are compared as a percentage of assets, DC plans are more expensive than DB only when the factor is smaller than 2.21. These costs do not consider the net expected value of the guarantees issued by the Pension Benefit Guarantee Corporation in favor of defined-benefit plans. 4.3 Life Insurance (annuities) in the United States Defined-contribution plans in the United States usually pay a lump-sum at retirement. To obtain lifetime costs comparable with the Chilean system, we must consider that the worker needs additional pension services after retirement. One important case occurs when the worker chooses to buy an
information provided by the surveys. The combination of all these reasons and limitations motivated the collection of primary data with which to conduct this analysis of the road and transport services situation in Uganda.

# **_Should the quest to achieve the 100 percent of access to rural roads at less than 2 kilometers be continued?_**

Across the LIC and in particular Sub-Saharan Africa (SSA), governments and their development partners now face a dilemma: should they open more rural roads to achieve a complete RAI in a country or should they drop the aim of the full RAI because of sustainability problems and concentrate more resources on maintenance of the existing road network?

**_Road impact does vary_ .** Over the last thirty years a large number of road impact studies have been carried out and, perhaps unsurprisingly, in view of the different circumstances involved, a wide range of impacts have been found. Impacts range from an apparent negative one on agricultural production, where the area under crops fell by 52 percent on the project road and 44 percent on a control road,<sup>6</sup> to situations of very substantial impact, for example in the case of a new 85 km mountain road in Madagascar where rice production increased by 160 percent and coffee by 70 percent (Mitchell and Rakotonirina, 1977).

The impact of roads investment on the economy seems to depend upon a range of factors: (i) the magnitude of the change in transport costs, (ii) the competitive nature/current behavior of the transport and distribution markets, and (iii) the response of different parts of the economy to changes in transport costs and quality of transport (spillover effect).

**_Change in transport costs due to passability/impassability._** Large changes in transport costs, per km, could occur with new construction if this involves a large proportionate change in trip length for diverted traffic or a change in transport mode, say from human or animal transport to truck.  Much smaller changes in transport costs (per km) will occur with rehabilitation or maintenance. The HDM (Highway Development Model) road planning model is the main method of estimating changes in transport costs

> 6 A road without any intervention.

9
water markets ( _National Irrigation Market_ scenario). This scenario equalizes irrigation water SPs both within and between all WMA’s and thus establishes a national general equilibrium SP; <u>Scenario III</u> introduces increased competition for water from predicted expansions in non-agricultural uses and rapid urbanization through rural-urban migration. This scenario however, does not liberalize water markets (i.e. does not allow transfer of or trade in water between irrigation and competing non-agricultural uses, i.e. for industrial, mining, services and domestic purposes). It also maintains current inter-basin water transfers unchanged ( _Water-Restricted -Urbanization_ scenario).

Urban residents consume substantially more water resources than rural residents, implying that urbanization and industrial expansions will greatly increase urban water demand over the coming decades. This is expected to heighten competition for scarce water resources between urban users (residents and urban-based industries) and agriculture increasing the opportunity cost of subsidizing irrigation water, and may warrant a reallocation of water resources from agricultural to non-agricultural and domestic use. This establishes the potential gain from liberalizing water markets to allow water trade between irrigation agriculture and non-agriculture sectors.

<u>Scenario IV</u> liberalizes water markets allowing for market-based water transfers out of irrigated agriculture to municipal areas to meet the growth in demand for domestic and industrial use introduced under scenario III. This scenario ( _Water-Liberalized Urbanization_ ) is expected to transfer significant amounts of water out of irrigation agriculture leading to declines in agricultural GDP, rural employment and incomes. The net impacts on the national economy will ultimately determined by the magnitudes of offsetting gains from expansions in urban-based non-agricultural sectors’ income and employment.

23