rafmacalaba/data-use-mentions-extended huggingface dataset:
baseline specication indicate that dierences in natural hedge across commodities have a small but non-negligible impact of natural hedge on trade patterns. This is encouraging considering the typically poor t of Heckscher-Ohlin regressions.

The results presented in columns (2), (3), (5), and (6) show that the measures of revealed factor intensities matter for trade patterns. Column (2) introduces the interaction of the baseline measures of factor intensity with a country's measure of factor abundance. The regression in Column (3) replaces these interactions by the interaction of the measure of the overall factor intensity of a commodity and a country's relative output per worker, which is a measure of a country's overall factor abundance (see Romalis (2004)). This measure is also the _PRODY_ measure introduced by Hausmann et al. (2007) and controls in a general way for the possibility that richer countries tend to export similar products, beyond what can be explained by relative factor endowments. The results of these two columns show that, in fact, countries tend to concentrate a relatively larger fraction of their exports in goods that are intensive in their more abundant factors. All estimated coecients are positive and statistically signicant, except for the one for capital intensity in Column (2), but the sign and signicance of the main coecient remain unaected. There is, however, a decline in the magnitude of the main coecient in the regressions for global trade shares, which is about half the size of the baseline when controlling for overall factor intensity in column (3). Part of this decline is due to the smaller sample of countries for which there are data on the factor endowments.

17
and experiences it is important to consider the role of historical data in the WHS. For example it would certainly be pertinent to record how long people have lived in their present environment (ie length of residence). Similarly, information on past housing conditions, water supply, and sanitation facilities may be needed. In order to distinguish widowed and divorced mothers it may be necessarv to include a simplified marriage history in a questionnaire.

# 4.7 Community variables

So far the discussion has tended to assume that information on health and its determinants should be collected about individuals or households. However, many aspects of environmental risk and the availability and accessibility of services will be common to wider aggregates. Moreover the interuption of disease transmission may only occur once services are used widely throughout an area. Common and contextual variables also have the further analytic advantage of being clearly exogenous to household decisions about the use of services (Schultz 1985).

It could be verv valuable to supplement the household survey with collection of information directly from clinics. One area of interest would be in the provision of services. For example data could be collected on location, opening hours, availability of essential drugs and the history of service provision (Hobcraft 1985). Moreover clinic records could provide important supplementary information on morbidity. Exactly what might be possible would depend on the national context, but one area of interest could be life-threatening diseases with a low prevalence in the community.

It could be useful to compare information obtained on general morbidity through household questionnaires with community level data from use of the Delphi technique (Levine 1984, WHO 1986). This technique involves using panels of knowledgeable individuals to obtain information about the most frequently occuring and most important illnesses that affect the population in their area. This combination of household questionnaires complemented by key informant discussions is a well tested and attractive approach.

34
be relatively low despite the imperfections in the domestic institutional environment. But the marginal cost of extending credit to small firms is likely to rise steeply, so that the volume of lending to such firms may be very insensitive to fluctuations in bank funding costs induced by monetary policy. In short, Tobin’s description of the effects of easing monetary policy under conditions of high liquidity preference as “pushing on a string” may be an especially apt analogy in the case of LICs, and the effects of tightening policy are also likely to be limited. A survey of studies of the strength and reliability of monetary transmission in LICs, by Mishra and Montiel (2013), and the empirical evidence based on a large panel of countries by Mishra et al. (2014) are consistent with this perspective.

The challenges created for monetary policy by weak monetary transmission could conceivably be overcome if the strength of monetary policy effects on such variables as inflation, real output, and the exchange rate could be reliably estimated, since weak effects could be offset by stronger policy measures. However, the strength of monetary transmission in LICs has proven difficult to estimate because of data limitations (Li et al. 2016). Several investigators have focused more narrowly on the extent of pass-through from policy rates to bank lending rates. Saborowski and Weber (2013), for example, find that although changes in policy rates tended to be transmitted almost one-forone into retail bank lending rates in advanced economies, pass-through in developing countries was only in the range of 30-45 percent. Abuka et al. (2015) find similar evidence for Uganda in relation to advanced economies, and that pass-through was particularly weak in less financially developed Ugandan districts. But they find evidence that increases in policy rates were associated with a reduced supply of bank credit, suggesting that a bank lending channel was operative in Uganda, although it was weaker than in advanced economies.

# 2.5. Shortcomings in the analytical capacity of central banks

Because monetary policy affects the economy with lags,
Policy Research Working Paper 9952

# **Abstract**

This paper examines the effect of foreign direct investment on local structural transformation and human capital accumulation in China, exploiting variations in foreign direct investment inflows across manufacturing sub-sectors caused by China’s foreign direct investment deregulation and initial sectoral composition patterns across China’s cities and provinces. Using a panel of city-level data from 1990 to 2005, the paper shows that manufacturing foreign direct investment inflows greatly accelerated city-level structural transformation and human capital accumulation. By expanding access to the global market, foreign direct investment created a huge pull factor that drew excess labor away from farms into factories and services. Foreign direct investment has promoted high school and university enrollment by paying a higher wage premium for skilled workers and pushing up the skill premium. The positive effect on structural transformation is largely driven by export-oriented foreign direct investment, while market-seeking foreign direct investment has a much larger effect on college enrollment. High-skill foreign direct investment has a larger effect on college enrollment than low-skill foreign direct investment.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at yanliu@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank
<br>do not crowd out green/clean technology investments.|
|Tugcu & Tiwari<br>(2016)|A panel bootstrap Granger<br>causality analysis of Konya<br>is used for BRICS countries<br>for 1992 to 2012.|The relationship between green technology and TFP<br>growth is less substantial than that between non-green<br>technology and TFP growth. In some cases, green<br>technology has a positive effect on TFP growth.|
|Hottenrott et al. (2016)|A two-stage least squares<br>regression analysis of firm-<br>level panel data of Germany<br>covering the period from<br>2000 to 2008.|Firms can achieve higher TFP gains from adopting new<br>technologies if they adapt their organizational<br>structures. Such complementarity effects may be of<br>particular importance for the adoption of GHG<br>abatement technologies.|
|Rubashkina et al.<br>(2015)|The instrumental variable<br>estimation approach is used<br>for manufacturing sectors of<br>17 European countries<br>between 1997 and 2009.|There is a positive impact of environmental regulation<br>on the output of innovation activity, as proxied by<br>patents, thus providing support in favor of the “weak”<br>Porter hypothesis.|
|Horvathova (2012)|A measure of environmental<br>performance is applied to the<br>firm-level data from the<br>Czech Republic.|The effect of environmental performance on financial<br>performance is negative in the short run but it is<br>positive in the long run.|
|Böhringer et al. (2012)|<br>Econometric analysis is used<br>for a panel dataset of German<br>manufacturing sectors.|The environmental regulations stimulate environmental<br>investment to be compatible with the pursuit of
discriminate against women in their lending practices. For example, in Pakistan, banks require two male guarantors who are not family members, and will not permit woman guarantors; almost all women borrowers are required to have the permission of their husband to access a loan, even in group lending schemes; and unmarried women are generally not considered credit worthy (Safavian, 2012).

Women might face greater challenges than men in accessing formal finance due to several potential factors. On the one hand, female entrepreneurs might choose to enter less capital intensive industries that require less debt. They might also be less inclined to seek external financing because of their own perceptions that women might find it more difficult to secure bank loans (Coleman, 2000). Alternatively, female loan applicants, relative to male applicants, might constitute weaker loan applications. For instance, women face relatively greater difficulties in completing complicated loan applications (Buvinic and Berger, 1990). They also have lower financial literacy rates (Lusardi and Tufano, 2009), which may make it harder for them to navigate the loan market. A study using loan-level bank data also shows that women randomly assigned male loan officers (and visa versa) are less likely to return for a second loan, pay higher interest rates, and receive lower loan amounts (Beck, et al., 2013).

In addition, women have been found to possess weaker business backgrounds than men, including a lack of relevant education (especially technical) and a lack of business experience (Carter et al., 2003; Menzies, Diochon and Gasse, 2004). On average, women may find it more challenging to provide collateral (Buvinic and Berger, 1990) and personal guarantees (Coleman, 2002) and may have weaker credit histories (“reputational collateral”). These findings suggest that women, on average, might possess lower credit scores, which are important for modern lending technologies (Narain, 2009).

6
are considered to have light to moderate work intensity. We unpack the potential heterogeneity in the effects of exposure to excess heat by industry by estimating the model for two broad industry classifications: manufacturing and services, where services include retail and other services. Manufacturing firms account for about 57 percent of the WBES data and comprise a variety of firms engaged in productive activities, from wood and furniture products to manufacturing machinery, electronics, and communications equipment. Retail and services account for the remaining 43 percent of our sample. Firms in retail are those engaged in construction, retail, hotels, and hospitality services. Other services account for about 21 percent of the sample and include firms engaged in wholesale and other services.

Table 5 presents the results by broad sector classification of manufacturing, and services (+retail). The effect varies by sector, with negative and statistically significant effects on the productivity of manufacturing firms located in both temperature categories where the WBT is above 24.9, while the results for services are not
_Sources_ : Original estimations based on migration data from Özden et al. 2011 and UN DESA 2020; and GDP data from the Penn World Tables (PWT) 10.0 (Feenstra, Inklaar, and Timmer 2015).

_Note_ : The figure traces the destination concentration of emigrants for low-income (panel a) and middle-income (panel b) countries as they have developed. The horizontal axis represents the increase in incomes and the vertical axis represents the increase in migrant concentration, measured by Herfinhdahl-Hirschman index, relative to the baseline year. The baseline year, for each country, is defined as the year in which per capita GDP in constant 2017 US dollars in purchasing power parity terms (2017 PPP$) was at its lowest level since 1960. Each lightly colored lines represents the trajectory for a particular country. The solid lines show the locally linear fit between increases in incomes and changes in destination concentration compared to the baseline year. Low-income countries were among the bottom third circa 1960 in terms of per capita GDP (2017 PPP$), and middle-income countries were among the middle third. Estimates exclude six outliers in terms of changes in emigration rates: three low-income countries/economies (Bosnia and Herzegovina, Grenada, and West Bank and Gaza) and three middleincome countries (Antigua and Barbuda, Dominica, and Saint Kitts and Nevis). CI = confidence interval.

On the other hand, the response is somewhat muted in middle-income countries. The concentration of migrants across destinations increases only slightly (figure 5, panel b). They increase emigration to the same high-income destinations as they grow, concentrating on corridors that offer higher returns to migration.

These patterns are consistent with global integration connecting low-income countries with many other highreturn destinations. But for middle-income countries that are already well-connected in the global economy, further development does not lead to increased migration or newer destinations, but continued or increased emigration to the same high-return destinations.

Furthermore, economic integration could be accompanied by policy integration, which can further lower barriers to migration.
# **5.4 Informal Sector Training (IST) for HEs**

105. It is difficult to obtain a comprehensive view of informal sector training (IST) in Tanzania because of the fragmentation and dispersal of its coverage. Government policies recognize the importance of IST, but these policy pronouncements have not been translated into concrete programs or investments **_._** Moreover, no single agency has the responsibility for collecting information about IST programs. The only evaluation identified was an early assessment of the ITEP pilot training program conducted by VETA/GTZ. This evaluation did not systematically determine the costs and benefits of the program, and was conducted too early to ascertain labor market outcomes and impact on income. This section relies on a review done by Johanson and Wanga (2009).

106. Demand-driven traditional apprenticeship is the most common form of IST, but it is not well organized in Tanzania, compared to those offered in other countries in the region, particularly in Kenya, Zimbabwe, or West Africa. Traditional apprenticeship has weaknesses – it is generally regarded as static and does not keep up with advancement in technology, although instances were found of innovation in the content of apprenticeship, particularly in areas where technology is changing fast (e.g., in vehicle maintenance). Apprenticeship training in the informal sector, however, has been effective in imparting trade skills to thousands of mostly male youth, but less to female workers. Figure 5.1 shows the result of a 10-year old survey of apprenticeships in Dar es Salaam, which found that only about one in four apprentices were female, half of whom were trained in tailoring, catering, childcare, and hairdressing (Nell and Shapiro, 1999). In the 2006 ILFS, 8 percent of women, compared to 16 percent of men, reported having participated in apprenticeship training programs. In general, apprenticeship training was found to perpetuate traditional gender-based occupational segregation.

**Figure 5.1:  Distribution of Apprentices in Dar es Salaam, by
19 of Dar es Salaam, which is its own region in the regional data. The simulation shows a large increase in poverty in Dar es Salaam, contrary to the decline observed in the survey data.

**Figure 8**

<!-- Start of picture text --> Simulated changes in poverty<br>using growth rates by region<br>1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year<br>Mainland Tanzania Dar es Salaam<br>Other urban areas Rural areas<br>.45<br>.4<br>.35<br>.3<br>.25<br>Fraction below poverty line<br>.2<br><!-- End of picture text --> The simulated poverty increase in Dar es Salaam is the product of a steep decline in GDP per capita in the official figures, which is not compatible with the 43% increase in mean per capita consumption seen in the HBS for the capital. While details of the methodology behind the regional growth data are not available, it appears likely that the regional figures were calculated using approximations from national data. Indeed, the official figures imply that GDP growth rates for Dar are identical with those of 17 other regions (out of 19) in 1999 and with 11 other regions in 2000. Such approximations are unlikely to offer an accurate picture in a rapidly changing region. In Dar, population growth was extremely rapid; figures calculated from the 1988 and 2002 censuses show that population grew at a 4.4% annual rate. As a result, it is likely that the approximations used to calculate regional GDP underestimate the growth of output per capita in Dar. A reasonable alternative is to substitute the overall urban growth rate (used in the simulation described in section 5.3). Appendix Table A8 and Figure 9 below shows results from simulations using regional growth rates, but substituting overall urban growth rates for Dar.
_|_35%_|_11%_|_22%_|294|245|75|156|695|
||_Top 10%_<br>_consumption_|_Not Monetary_<br>_Poor_|_36%_|_44%_|_16%_|_19%_|505|618|221|266|1,389|

Source: Authors’ estimates based on GHS Wave 1-3 Note: For each age group the respective maximum number of deprivations is considered. If a child is not deprived along any dimension, it is considered not deprived, if it is deprived along one or more dimensions it is considered deprived, irrespective of missing information on some other dimensions. If a child is for instance not deprived along four out of possible five dimensions applicable in its age category with missing information on one deprivation, it is excluded, as it is unknown whether the child is indeed not deprived of any dimension or is in fact deprived in the dimension where the information is missing.

68
the house is not affordable even to a household earning USD 20,000. And yet, the PIR of 2.5 would indicate that it is affordable.

Like the EIR, the PIR is commonly used due to its simplicity. However, it also has limitations. First, it is focused on for-sale housing; the correlation to rental prices is not direct. Second, it does not take into consideration variations in access to housing finance or financing terms, which directly impact a household’s purchasing power. While recognizing the limitations of the two ratio measures discussed above (i.e., EIR and PIR), it must be acknowledged that they are simple to use and are relevant indicators of affordability at the _aggregate level_ , both over time and across markets. However, caution is advised when using them in their current form to assess affordability at the _household level_ (Herbert, Hermann, and McCue 2018).

# 3.4 Data Resources

The approach used for measuring housing affordability in a specific location depends heavily on the availability of data at the market and household levels. This is often an obstacle to creating nuanced affordability measures and criteria for housing programs. For the purposes of measuring affordability in the developing country context, the most likely sources of information include national censuses, household income and expenditure surveys (HIES), and customized housing surveys. These data sources vary in availability, quality, and frequency from country to country.

**_National Censuses._** Almost every country conducts a national census on a regular basis (e.g., every 10 years). Data points related to the housing sector in censuses commonly include population, basic demographics, number of households, and household size. In some cases, the census includes a specific housing module that may include data on the number of housing units, housing type, housing quality, and tenure. Typically, housing income and expenditure, housing rents and prices are not collected as part of a national census in developing countries
studies, for those concerned with health planning and evaluation mortality is only one aspect of the health situation. Information on morbidity and on coverage and use of health progra_es and services is probably of more significance.

Against the background of disillusionment with questions about recent deaths in the household, particularly among investigators with a background in medicine, it is important to emphasise that alternative methodologies for the retrospective collection of information on mortality have performed far better. Questions addressed to respondents about deaths among clearly-defined categories of their kin normally yield useful data. In particular the World Fertility Survey convincingly demonstrated that collecting detailed birth histories from women and asking about the ages at which deaths of their children occured could provide upexpectedly high-quality estimates (Hobcraft 1984). Such birth histories also provide information about the constitutional factors affecting early mortality (such as the age and parity of the mother and the length of birth intervals) and represent a framework within which dated information on child rearing practices that influence health and mortality (such as breastfeeding and weaning) can be collected. To minimise costs a WHS core questionnaire might collect truncated birth histories covering only the 5 years before the survey. A full birth history could be an optional module. Obtaining reliable data about adult mortality is more difficult. Questions about orphanhood and widowhood often, but not always, perform well (see, for example, Timaeus 1987). Methods for measuring long-term trends in mortality are now well established (Brass and Bamgboye 1981). In addition a number of approaches have been suggested that can define the period of exposure to risk much more closely. These include the supplementary questions proposed by Brass and by Chackiel and Orellana (1986) and procedures for analysing intersurvey changes in orphanhood (eg. Timaeus 1986).

In **some** countries the WHS could provide an opportunity to develop methodologies for assessing causes of death. This information is valuable both for health impact evaluation and for setting health priorities. It is generally agreed that open-ended questions addressed to relatives of a dead person do not provide