rafmacalaba/data-use-mentions-extended huggingface dataset:
4 Farid, 1986 also examined regional differentials. The U.N. paper is particularly useful for updating the relationship between desired family size and education. Desired family size varies greatly between countries and across education groups. Desired family size is more likely to fall uniformly with education than is actual fertility. Only in the Cameroons and the Dominican Republic was desired fertility higher for those with some schooling than for those with no education. (See Table 2 and Figure 2). The differentials across education groups tended to be higher in the countries with higher base levels among the least educated. (Primarily in Sub Saharan Africa.) The percentage of women 40 to 49 who have more living children than their desired family size is substantial everywhere except in Sub Saharan Africa. In the most developed developing countries this percentage falls with education while in the poorest countries it increases with education. In the intermediate countries the pattern is irregular. This pattern probably arises from the difficulty of obtaining contraceptives even among the most educated in the poorest countries despite the reduced desired family size of this group. (The study did not provide data on the educational differentials in unwanted fertility classified by the availability of family planning.) While changes in desired family sizes across educational groups are clearly important, the behavior of women with respect to the "Proximate Determinants of Fertility "is also important because fertility preferences only affect actual fertility if breastfeeding, marriage, contraception or abortion behavior change. The WFS data have also contributed substantially to our understanding of how education affects these intermediate determinants of fertility. Singh, Casterline and Cleland have analyzed the educational differentials in three proximate determinants of fertility.(1985). Table 3
00365]|
|Household Home & WASH index|0.00812|0.00827|0.00817|0.00861|0.00896|
||[0.00164]***|[0.00175]***|[0.00161]***|[0.00164]***|[0.00169]***|
|Urban|0.0722<br>|0.0754<br>|0.0729<br>|0.073<br>|0.0732<br>|
||[0.00548]***|[0.00487]***|[0.00550]***|[0.00502]***|[0.00484]***|
|Use internet in the last 3 months|0.113|0.114|0.113|0.114|0.112|
||[0.00412]***|[0.00830]***|[0.00410]***|[0.00795]***|[0.00817]***|
|Washing machine|0.0328|0.034|0.0326|0.0338|0.0331|
||[0.00432]***|[0.00675]***|[0.00429]***|[0.00697]***|[0.00648]***|
|_Social norms & attitudes towards women_||||||
|Women participates in hhld decision making|0.0256||||0.0253|
||[0.00295]***||||[0.00457]***|
|High Wage Female Workers in the Public Sector (department level)||-0.115|
**Table 7:  Median Elasticities by Initial Spending and Efficiency Levels** _<u>Country episodes with spending increases</u>_

||**Low-Spending**|**High-Spending**|**Total**|
|---|---|---|---|
|_Learning-adjusted yea_|_rs of schooling_|||
|High-efficiency|0.01|0.04|**0.02**|
|Low-efficiency|0.19|0.14|**0.15**|
|Total|**0.10**|**0.07**|**0.08**|
|_Learning_||||
|High-efficiency|0.01|-0.001|**0.0001**|
|Low-efficiency|0.05|0.04|**0.05**|
|Total|**0.04**|**0.001**|**0.02**|
|_Expected years of sch_|_ooling_|||
|High-efficiency|0.02|0.04|**0.03**|
|Low-efficiency|0.07|0.09|**0.08**|
|Total|**0.04**|**0.05**|**0.04**|

_Source:_ World Bank calculations based on HCI, UIS, and IMF data.

The elasticities of learning outcomes and expected years of schooling also vary by category but follow similar trends. Low-efficiency countries have higher elasticities on both indicators, with elasticities declining as countries approach the frontier. For high-efficiency countries, the elasticities of both indicators are small, indicating that, in those countries, spending more resources on education has led to minimal improvement in outcomes. The differences in elasticities based on high or low spending are
91%, and declining shares in the higher-earning sectors. Male household heads and married men are largely overlapping groups and as a result are similar. In contrast to other men, they have higher income shares in the high-earning sectors, with the selfemployment income share increasing and the wage share decreasing.

"Other women" in Ghanaian families who were neither a household head or spouse gained the least among women. Their income share from farming increased, from 75 to 82%, and decreased from 22 10 17% in self-employment. This, together with the participation data shows that women other than the main income-eamers (presumably older and younger women) take over the agriculture duties usually done by the female head or spouse, the higher-earners in the family. Female household heads appear to have gained the most among women; their participation in farming was lowest, and it declined from 56 to 51%. Their self-ernployment income share was highest and increased from 34 to 43%. And as the only femaLles with significant participation in wage employment, their wage income remained significant, despite a decline from 10 to 6%. On the whole, married women and female heads appear to have gained as a result of increasing income from self-employment. These gains also appear to have been at the expense of more dependent women, young and old, whose income shares moved away from the highearning sectors.

Table 13 shows income share changes for Uganda and Ghana by household head type, male or female. These comparisons for Ghana differ from the above in that they include eamings from the whole household as opposed to above comparisons for household heads only. We see that female headed households gained considerably more in Ghana than they did in Uganda over the periods, with Ugandan female households 19
δδ

> 35 The municipal controls vary by year or by survey round because of limited historical municipal data reported monthly, and sometimes even yearly. Yearly variables are linked with DHS on the year and month of birth: if the child was born after June, the value corresponds to that of the concurrent year; if the child was born before June, it corresponds to the previous year.

> 36 For instance, Mansour and Rees (2012) find effects for the third trimester, Valente (2015) for the second one, and Camacho (2008) and Quintana-Domeque and Ródenas-Serrano (2017) only for early-pregnancy.

> 37 In the sample, only 154 cases (0.52%).

> 38 Despite this, because gestational length is negatively associated with in utero shocks, there could be some bias introduced for correlated outcomes; for instance, mothers who are not exposed to stress can mechanically have higher birthweight because of the longer gestational length (Matsumoto 2018). In a robustness check, I try the alternative proposed by Matsumoto (2018) of using three full months for each pregnancy trimester, although the

12
explained by the lesser need - made possible by the improved financial infrastructure - for enterprises to accumulate reputational capital in the form of non-salvageable (productive and/or nonproductive) capital assets necessary to signal the enterprises' commitment to honest and prudent behavior."<sup>3</sup> Unlike equity-financed industries, Carlin and Mayer find that bank-financed industries grow more slowly in countries with developed financial infrastructure and tend to undertake less R&D. Consistent with Carlin and Mayer's results, Hoshi _et al._ (1990, 1990b) also observe that industries dependent on bank finance tend to be physical capital intensive, smoke-stack industries. Furthermore, studying the financing arrangement of 250 US public firms, Houston and James (1995) find that firms with relationships to single banks tend to use less bank debt in 16
# **1. Introduction**

Household surveys remain underutilized to understand transport choices such as expenditure level and composition, the economic impacts of traffic accidents, and the impacts of environmental policies such as vehicle restrictions or fuel taxes. While better transport infrastructure and services improve mobility and economic opportunities, transport is also a main source of CO2 emissions and is responsible for multiple injuries and death with dramatic impacts for households.

Household surveys can help us to understand better how to improve mobility for households while reducing the negative environmental and safety externalities. First, household surveys provide insights on transport affordability. Transportation affordability has received limited attention compared to housing affordability while high transport costs reduce the mobility of low-income households and limit their access to jobs, education, and health services. Household surveys provide disaggregated data on expenditures on regular costs such as fuel and insurance and on durable goods like vehicles ownership. Second, they document transport choices made by households to go to work, reach health or school facilities, and reach markets to buy or sell products. Third, they document the extent to which transport is a constraint for agricultural and non-farm production. Limited mobility reduces agricultural productivity and access to markets and can deter individuals from creating non-farm activities, slowing down the structural transformation of the economies. Finally, they can provide information on the economic impacts of traffic accidents when happening to a household member. Household surveys may include questions on the incidence of traffic death, injuries, and resulting disabilities experienced by the households, which are otherwise difficult to obtain. The lack of harmonization of transportrelated questions and the small number of questions has limited their use in providing stylized facts to inform works on transport mobility for the poorest, infrastructure investment beneficiaries, and environmental and road safety policies.

This paper reviews a set of household surveys, focusing on World Bank Living Standards Measurement Study (LSMS) surveys, to show their potential in providing insights on multiple transport-related questions. It then discusses the limits of such surveys and suggest a standard set of guidelines to design
|1.6|1.1|0.4|-0.2|3.1|3.4|2.3|
|**kurtosis**|2.5|1.3|0.1|0.2|11.3|13.6|5.8|
|**observations**|1472|1472|1472|1472|1472|1472|1472|

Source: World Development Indicators data base, World Bank (www.worldbank.org).

# **Results**

We base our prediction of the required stocks and flows of infrastructure on an estimated long-run demand using a 52-country panel. The list of countries in the panel is listed in Appendix A. Justification for the use of international data is in the spirit of providing a benchmark against comparable country experiences. Advantage of the PMGE methodology is that the implicit assumption of homogeneity across the panel is explicitly tested for. The panel of countries was also limited to low- and middle-income countries, in order not to bias estimated infrastructure demand upward through the inclusion of high-income countries.

We estimated the following empirical specification of equation (3):

|ln<br><br><br>|_I_<br>_P_<br>⎛<br>⎞<br>⎜<br>⎟<br>⎝<br>⎠|,<br>,<br>ln<br>_y_<br>_i t_<br>_i t_<br>_YPC_<br>α<br>β<br>=<br>+|3<br>,<br>,<br>,<br>1<br>_k_<br>_i t_<br>_i t_<br>_i t_<br>_k_<br>_YS_<br>_Z_<br>β<br>δ<br>ε<br>=<br>+<br>
7_._864|[3_._774]|_<_0_._001|
|Married?|0_._779|0_._814|[0_._387]|0_._0699|
|_Children (6_⩽_age < 18)_|||||
|In school?|0_._968|0_._842|[0_._363]|_<_0_._001|

Sample households restricted to those whose applicant barely failed exam. “Agr.” = agriculture. Money in 2010 PHP/mo. Nationally representative data from 2006, inflated with CPI. Households: _N_ samp _,s<_ 0 = 460, _N_ ctry = 38 _,_ 453. Children: _N_ samp _,s<_ 0 = 433, _N_ ctry = 55 _,_ 642. Nationwide data weighted with frequency weights. Expenditures defined in Table 4b.

47
same positive effect on growth in all countries, regardless of their institutions and location. On the face of it, this is an extremely hard result to accept as a robust finding. Since most of the individual investments supported by aid fail in the countries with weak institutions – and we know little or no overall growth is recorded – it is hard to believe that aid has had a positive effect there.

On the other hand, we have to point out that the micro evidence alone cannot be decisive on this question, for the reason that money in general is fungible. When donors fund a project in China, for example, in general the donors are presented with highpriority projects that the government is likely to do anyway. The result of the donor support is that the project is done differently than it would have been without donor involvement and the government has resources freed up to pursue other, less high-priority activities (or to reduce taxes below where they would be otherwise). Thus, it is possible that every single donor-financed project is deemed to be successful, but yet there may be no overall net positive effect if the marginal projects funded by the government are all very bad.

It is for this reason that we find the combination of the micro evidence and the macro evidence most compelling. Our results make it very unlikely that aid works the same everywhere. And the fact that in several different macro data-sets researchers have found that growth is correlated with the interaction of aid and a measure of institutional quality or economic policies increases our confidence that aid is playing a useful role when it is targeted to low-income countries with reasonably sound institutions and policies. In terms of aid policy, our work provides additional support to the view that aid resources have the greatest impact on development when they are channeled to poor countries with sound institutions.

13
pricing policy--also likely placed downward pressure on the real price of other staple food crops.

10 Unfortunately, the aggregate data file we obtained for the 1985-88 LSMS data does not include the socioeconomic group variable so we cannot reproduce Grootaert's results using the INS deflators. In 1988, the poverty incidence, using his deflators, was .210 for export crop farmers and .197 for food crop farmers, based on the 75,000 CFAF poverty line.

11
data (and even with conflicting findings), is a clear indicator of the currently limited capacity of the LSMS-ISA data sets to provide a basis for meaningful analysis on climate change and migration. Figure 1 below provides an idea of the type of migration tracking that is possible using the LSMS-ISA data.

Let us focus, as a benchmark (see below for a comparative analysis of all LSMS-ISA surveys), on the Tanzanian National Panel survey, which was used by three out of the five studies above. All the available migration information in the questionnaires is essentially limited to a few questions in two sections in the Household Questionnaire, Modules B and G.<sup>14</sup> Module G, named “Children Living Elsewhere – Migration” contains some information on households responding affirmatively to the question: “ _Do you have any children 15 and older who live elsewhere (outside this household)_ ?” such as information on the

> 14 Cf. https://microdata.worldbank.org/index.php/catalog/76/related-materials. We use questionnaires from the second round

> (2008-2009) as the benchmark here.

27
regulator. The third-party inspector follows the same procedure described above, taking three samples and measuring approximately 200 individuals in total. The landings data are lower resolution than the electronic logbook data because third-party inspectors measure percentage juvenile from the sum of anchoveta caught by all sets on a fishing trip (average number of sets per trip is 2.2), whereas fishers measure percentage juvenile after each set in the electronic logbook data. However, unlike vessels in the electronic logbook data, the closures policy does not give third-party inspectors an incentive to misreport percentage juvenile because the regulator does not use landings data to determine closures during my study period. Third-party inspectors are from one of three international firms and tend to have more rigorous technical training in measurement and sampling than fishers (PRODUCE, 2018 _d_ ).

Even if percentage juvenile reported by vessels in the electronic logbook data is unbiased, I cannot calculate juvenile catch as percentage juvenile times tons caught because the weight of an anchoveta is approximately cubic in its length (IMARPE, 2019). I therefore obtained a supplementary electronic logbook dataset for a group of vessels that report length distribution data to their owners (but not to the regulator) (Englander, 2022). These vessels represent 56% of tons landed. For each two-week-of-sample by two-degree grid cell, I calculate the individuals-weighted average proportion of individuals in each half-cm interval caught by these vessels. Then I impute length distributions for sets by vessels that do not report these data to their owners. For sets with percentage juvenile above (below) the individuals-weighted average percentage juvenile for their two-week-of-sample by two-degree grid cell, I inflate (deflate) the proportion of individuals below 12 cm and deflate (inflate) the proportion of individuals above 12 cm so that the imputed length distribution for each set implies a percentage juvenile equal to the percentage juvenile reported for that set.

Next, I match all sets to landing events in order to correct the percentage juvenile, length distribution, and number of individuals caught by all sets. When the individuals-weighted average percentage
trucks, which is one of the factors that has led to significantly higher food prices in the country (e.g., ACAPS 2020; etc.).<sup>5</sup> Although it is difficult to precisely gauge the intensity of this disruption to supply chains, reports have suggested that immediately following the escalation of the conflict, imports in Al Hudaydah- the port that supplies nearly the entirety of the north- dropped to 40 percent of pre-conflict

> 5Importantly, domestic food production is unable to replace imported food made more expensive through higher transportation costs given that the country imported approximately 90 percent of its food prior to the conflict (e.g., WFP 2012).

6
per day) of gasoline were being smuggled out of the country ( _Platts Oilgram Price Report_ 2014). In May 2014, the government launched an operation jointly conducted by the police, Customs Department, the Malaysian Maritime Enforcement Agency, the Malaysian Anti‐Corruption Commission, and local councils to curb fuel smuggling. While the government moved gasoline and diesel to a “managed float” pricing system in December 2014 and ended price subsidies, fuel prices in Malaysia remain much lower than those in neighboring countries on account of tax differences, thereby continuing to provide incentives for smuggling. One of the most extreme measures taken to combat fuel smuggling is the nightly closure of the border between Colombia and República Bolivariana de Venezuela, which in August 2015 turned into an indefinite closure after a state of emergency was declared. The cost of border patrol adds to the already very large price subsidies for petroleum products in República Bolivariana de Venezuela.

Price controls have caused fuel shortages, which create black markets and push up effective prices paid. Periodic fuel shortages are common in countries with price subsidies in part because of diversion of subsidized fuels to alternative markets and in many cases also because of the delays in subsidy reimbursement payments by the government. In Nigeria, the National Bureau of Statistics reports that gasoline shortages in November 2015 pushed up the actual prices paid in one state to ₦187.5 (US$0.94) per liter, more than double the official price of ₦87 (US$0.44) per liter and much higher than the price that would have prevailed without subsidies in a deregulated market. Another consumer survey conducted by NOI Polls in Nigeria in March 2015 found that 90 percent of gasoline purchasers paid on average 31 percent more than the official subsidized price, and in the northeast region of the country, more than if the price of gasoline had not been subsidized (NOIPolls 2015). The fuel shortages that pushed up black‐market prices started in this case when fuel marketers charged that the government
**|||||||||||||
|**Transport nec**|30|.0%||2|1.9%||||||||||
|**Water Transport**|33|.0%||2|1.9%||||||||||
|**Air Transport**|30|.0%||2|1.9%||||||||||
|**Communica�on**|32|.0%||6|.6%||||||||||
|**Financial services nec**|29|.0%||6|.6%||||||||||
|**Insurance**|19|.0%||6|.6%||||||||||
|**Business services nec**|49|.0%||0|.0%||||||||||

* AVEs of NTMs in each region for agriculture and manufacturing, respectively are: Philippines: 34.3, 15.4; Australia, Korea, New Zealand: 23.5, 7.4; China: 6.1, 5.1; Japan: 23.6, 3.8; India: 26.2, 4.8; Rest of ASEAN: 26.7, 7.2; United States: 14.8, 3.3; Rest of World: 11.7, 3.7.

**Tariffs on all services are zero.

Source: GTAP 9 database for tariffs; Jafari and Tarr (2015) for FDI barriers; cross-border services non-tariff barriers are mapped into our sectors from estimates by Francois, Hoekman and Woerz (2007); time
entry through a series of linear regressions with the above determinants as explanatory variables: ln(Entrydi) = ηi + β∙Xd + γ∙ln(Incumbent Employmentdi) + γI∙Inputdi + γO∙Outputdi + γL∙Labordi + γC∙Chinitzdi + εdi.

12 There are several factors that we do not consider in this study: natural cost advantages (e.g., Ellison and Glaeser 1999), local industrial diversity (Jacobs 1970), and entrepreneurial culture (Hofstede 2001, Florida 2005, Falck et al. 2011). Perhaps most important, we also do not model Marshall‘s (1920) third agglomeration rationale of knowledge spillovers because the data used to typically measure knowledge spillovers (e.g., local patent citations) are not available consistently at the level of detail that we require in India. It should be noted that the customer/supplier and labor pooling rationales both overlap with knowledge flows to some extent. For example, Porter (1990) emphasizes that proximity to customers and suppliers can enhance innovation by increasing knowledge flows about which products are working and what new products are desired. Likewise, occupational sharing is often associated with the knowledge and skills of the workers. To some degree, knowledge flows will be included in these estimates.

17
firm-level exports during the pandemic.

In the second step, we compare the impact of the pandemic on firms that participated in GVCs with those that did not. GVC firms are defined as firms that both imported and exported in the Covid-19 pre-period (February 2019-November 2019). All other firms are categorized as nonGVC firms. Since in GVCs, tariffs and non-tariff barriers are effectively a tax on exports (because imports are essential for exports), to further understand the effect of import policies, we differentiate between GVC firms that faced non-tariff measures (NTMs) versus those that did not. To assess the level of NTMs faced by a firm, we rely on data from the World Bank Jakarta NTM database. This data improves on other NTM data by UNCTAD-ERIA, as it is at a higher frequency (monthly), more updated (up to December 2021), and has a time dimension (panel). This makes it appropriate for the purposes of this analysis because we can capture changes in NTMs real-time during the pandemic as opposed to annual data.

Indonesia makes an interesting case for multiple reasons. High levels of raw material exports have led to high forward participation in GVCs, but this has been on a downward trend in recent years (Figure 1). The backward participation is still in its infancy, with low level of foreign value 2