rafmacalaba/datause-encoder-data huggingface dataset:
reliable measurement is more difficult, and that the reforms themselves may well induce output and employment to shift to the informal sector.

A part of the reason why the faster post-reform growth has not yielded comparably higher rates of poverty reduction is that this higher overall growth has been accompanied by a rise in inequality. As in any developing country, the gap between urban and rural living standards is an important dimension of overall inequality. The urban mean has risen over time relative to the rural mean. The trend rate of growth in mean consumption based on the NSS since 1958 has been 0.87% per annum (standard error of 0.10%) for urban areas versus 0.65% (standard error of 0.14%) for rural areas.<sup>31</sup> Figure 4 plots the ratio of urban mean consumption to the rural mean over time, which rose from 1.15 around 1960 to 1.30 around 2000. Fitting a linear trend to the post-1958 series in Figure 4 implies that the ratio increases by 0.03 per 10 years, and this is significant at the 1% level (t=4.01; n=37). So inequality increased between urban and rural areas.

What has happened to inequality within urban and rural areas separately? The Gini indices calculated from the relevant NSS rounds, but without an adjustment for the difference between the uniform versus mixed recall period, suggest that inequality within rural areas has tended to decline while that within urban areas declined up to about 1980 with a tendency to increase thereafter (Figure 5). However, this is no longer true once we control for the mixed reference periods of the several NSS rounds since the 1990s, which have a dampening effect on measured inequality; as can be seen in Figure 5, which also gives the predicted values when we control for the differences between surveys in their recall periods. We find evidence of an increase in inequality within both rural and urban areas after 1991, with a clear rising trend emerging in the post-1991 period (upon controlling for the influence of mixed reference period in several of the post-1991 rounds), which replaced a flat inequality level in urban areas and a declining trend in rural areas during the earlier (pre-1991) period (see Table 2 and Figure 5). _Growth elasticities of poverty reduction_ Table 3 gives our estimates of the elasticities of all three poverty measures with respect to: (i) consumption per person from the NSS; (ii) consumption per person as estimated by the

> 31 The rural mean was rising relative to the urban mean during most of the 1950s (Figure 1). We exclude this period from the calculation since it is so unusual.

18
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Policy Research Working Paper 7520

# **Abstract**

This paper revisits the extent of seasonality in African livelihoods. It uses 19 years of monthly food prices from 20 markets and three years of nationally representative household panel surveys from Tanzania. Trigonometric specifications are introduced to measure the seasonal gap. When samples are short and seasonality is poorly defined, they produce less upward bias than the common dummy variable approach. On average, the seasonal gap for maize prices is estimated to be 27 percent; it is 15 percent for rice.

In both cases it is two and a half to three times higher than in the international reference market. Food price seasonality is not a major contributor to food price volatility, but it does translate into seasonal variation in caloric intake of about 10 percent among poor urban households and rural net food sellers. Rural net food-buying households appear able to smooth their consumption. The disappearance of seasonality from Africa’s development debate seems premature.

This paper is a product of the “Agriculture in Africa—Telling Facts from Myths” project managed by the Office of the Chief Economist, Africa Region and the Jobs Group of the World Bank, in collaboration with the Poverty and Inequality Unit, Development Economics Department of the World Bank, the African Development Bank, the Alliance for a Green Revolution in Africa, Cornell University, Food and Agriculture Organization, Maastricht School of Management, Trento University, University of Pretoria, and the University of Rome Tor Vergata. 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://econ.worldbank.org. The authors of the paper may be contacted at lchristiaensen@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 or the governments they represent._ Produced by the Research Support Team
however, played a relatively larger role in mothers’ education.

# **Estimates from Lewbel’s Heteroskedasticity Based Identification**

The estimates from the Lewbel approach are reported in Table 5.<sup>36</sup> The evidence is broadly consistent with the main conclusions reached above on the basis of sensitivity analysis and the MB-NIPW estimates, but there are also some differences. The effects of family background have increased substantially over the reform period for the men, as found above from a host of other approaches. The larger effect of high educated farmer parents compared to the low educated nonfarmers remain valid for the women in both generations, but does not hold for the sons. The estimates also imply that the effects of family background on women may have increased somewhat for the reform generation, in contrast to the finding from other econometric approaches that they remain stubbornly similar across generations. We, however, note that since these are IV estimates they are not relevant for the whole population, and part of the difference in the results may reflect that fact that they are local average treatment effect, rather than average treatment effect estimates in Tables 2-3.

# **(8) Parent’s Education and Non-Farm Occupation: Substitutes, Complements or Separable?**

There are some plausible arguments in favor of the proposition that positive feedbacks between education and occupation may be important, especially in the post-reform period; education helps in finding better quality non-farm jobs, and the expected returns to education for children may be higher as nonfarm jobs depend on network. Given higher

> 36Note that we do not report any Hansen’s J test even though the models are overidentified. It is unlikely that the effect of parents’ socio-economic status is constant within a group defined by parents’ education and occupation. The effects may vary, for example, by ethnicity (Hahn or minority), by political connections (party affiliation). With heterogenous effects, it is not meaningful to use overidentification test for validity of the instruments (Angrist and Pischke (2009)).

34
<mark>wish to earn their college degrees. Furthermore, employers might use Saber Pro scores to screen applicants for a professional job position (Saavedra, 2009). Nonetheless, schools and universities cannot retain students based on their scores.</mark> <mark>Saber 11 and Saber Pro share three components, which allow us to compare the “change” in students’ learning: native language, quantitative reasoning and foreign language. “Language” in Saber 11 is comparable to “Reading” in Saber Pro as both evaluate academic competences in Spanish (i.e., reading skills). In this paper we use “Native Language” to refer to both. “Mathematics” in Saber 11 is comparable to “Quantitative Reasoning” in Saber Pro as both evaluate basic competencies in quantitative problem solving. In this paper we use “Quantitative Reasoning” to refer to both. The foreign language component in both tests evaluates proficiency in English. For all three cases, the level of difficulty is higher in Saber Pro, capturing the fact that students should develop their basic academic skills during higher education. We do not compare scores in both tests in a direct way. Instead, we use the individuals’ position on the distribution of scores (z-scores), analyzing the changes from one test to the other.</mark> <mark>ICFES provided researchers the possibility of linking both tests using a unique key that identifies individuals. These keys are available for Saber 11 from the first semester of 2002 to 2010, and for Saber Pro from the first semester of 2007 to 2011. However, we do not use data from Saber 11 from the second semester of 2007 on due to changes in the survey structure, and from Saber Pro from the first semester of 2011 backwards given that quantitative reasoning, native language and foreign language were not being assessed.</mark> <mark>We restrict ourselves to students attending on-campus programs at their 4</mark><sup>th</sup> <mark>year of college or above, who at the time of the test had completed at least 75% of their academic programs, whose identification key allows tracing them in both Saber 11 and Saber Pro, and with no missing values in the variables of interest. This comprises 63% of the full sample of college students who took Saber Pro in the second semester of 2011 (56% of the education majors and 64% of those in other</mark> 5
poverty in 2006, and similarly with the period 2004-2008. The estimated consumption transition dynamics are then provided in Table 5 for the US and Table 6 for Vietnam.

The vulnerability index for the US in the period 2004-2006 is around 13 percent (=3.4/26.4), which remains more or less the same two years later for the period 2004-2008 (Table 5). However, the vulnerability index for Vietnam somewhat decreases over these same periods, hovering around 7 percent (2004-2006) to 8 percent (2004-2008) (Table 6). This points to the role of economic growth in reducing vulnerability: during the period 2004-2008, the growth in the median household income is 4 percent for the US, but the corresponding growth in per capita consumption is 84 percent for Vietnam.

As discussed earlier with Proposition 5, if the rate of change of vulnerability is assumed to be equal for both periods 2004-2006 and 2004-2008 regardless of the different time intervals, another alternative is to estimate the vulnerability line directly for each period rather than making the adjustments for this line from another period. For example, using household survey data for Vietnam in the period 2004-2006, we estimate the vulnerability line associated with a vulnerability index of 10 percent to be D 3,758,400 in 2006 prices; repeating this exercise for data in the period 2004-2008, we estimate the vulnerability line to be roughly 20 percent higher at D 4,500,800 in the same prices. Clearly, rising consumption levels in Vietnam reduce vulnerability, thus drive up the vulnerability line in the period with higher consumption levels if the same vulnerability index is to be fixed for all periods.

Tables 5 and 6 also indicate that for the periods 2004-2006 and 2004-2008, Vietnam sees slightly more economic mobility than the US, with the proportion of the immobile population decreasing from around 68 percent (=11.3+33.4+23) to 63 percent, while the corresponding figures for the US are 77 percent and 74 percent. But the distribution of economic growth is quite 22
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# **Dynamic Cities and Creative Clusters**

# **Weiping Wu**

# **Virginia Commonwealth University**

In recent years, a subset of knowledge-intensive industries that are reliant on individual creativity and talent, known as creative industries, have received increasing attention. An important hallmark of these industries is the continuing innovation and development of products and processes, as often measured by the number of patents awarded. Overall, the average rate of patenting per employee in centers of creative industries is about twice as high as for all metropolitan in the United States (Cortright and Mayer 2001).<sup>1</sup> These knowledge-intensive production and service activities also are almost exclusively city based, making urban places the central organizing unit of the creative economy (Florida 2002). Creative industries tend to cluster in large cities and regions that offer a variety of economic opportunities, a stimulating environment and amenities for different lifestyles.

This paper focuses on the interactions of the clustering of creative industries, urban policy, and urban outcomes. The set of creative industries include those with output protectable under some form of intellectual property (IP) law. More specifically, the paper investigates the industries of software, multimedia, video games, industrial design, fashion, publishing, and biotech research and development (R&D). Drawn primarily from the U.S., the cities that form the basis for the empirical investigations are those where policy induced transitions have been most evident, including Boston; San Francisco; San Diego; Seattle; Austin; Washington DC; Dublin (Ireland); and Bangalore (India). The key research questions motivating the paper include: What types of cities are creative? What locational factors are essential? and What are the common urban policy initiatives used by creative cities?

The paper first explores the importance of the external environment for innovation and places it in the context of a national innovation system. Based on a study of development in the selected cities, the paper then isolates the factors and policies that have contributed to the local clustering of particular creative industries. Next the paper presents more in-depth case studies of two successful creative cities – Boston and San Diego, followed by a discussion on whether

> 1 Some key indicators of innovation and creativity include patents per worker, venture capital funding per worker, fast growth firms, and initial public offerings.  See Porter and Monitor Group (2001), Cortright and Mayer (2002).
initial exports to the BCA-imposing region may be diverted to other importers. This weakens the scheme’s ability to incentivize comprehensive carbon taxes in the L countries.

# **References**

Asheim, G. et al. 2019. The Case for a Supply-Side Climate Treaty. _Science_ , 365, 325-327.

Collier, P. and A. J. Venables. 2014. Closing Coal: Economic and Moral Incentives. _Oxford Review of Economic Policy_ , 30, 492–512.

Eichner T. and R. Pethig. 2017. Self-enforcing Environmental Agreements and Trade in Fossil Energy Deposits. _Journal of Environmental Economics and Management_ , 85, 1–20.

European Commission. 2020. The Just Transition Mechanism: Making Sure no one is Left Behind.

Flannery, Brian P., Jennifer A. Hillman, Jan W. Mares, and Matthew C. Porterfield. 2020. Framework Proposal for a US Upstream GHG Tax with WTO-Compliant Border Adjustments: 2020 Update. Resources for the Future Report 20/14. October 2020.

Fowlie, M. 2009. Incomplete Environmental Regulation, Imperfect Competition, and Emissions Leakage. _American Economic Journal: Economic Policy_ , 1 no 2, 72-112.

Harstad, B. 2012. Buy Coal! A Case for Supply-Side Environmental Policy. _Journal of Political Economy_ , 120, 77 – 115.

Helm, D., C. Hepburn, and G. Ruta. 2012. Trade, Climate Change, and the Political Game Theory of Border Carbon Adjustments. _Oxford Review of Economic Policy_ , 28, 368–94.

Hsiao, A. 2021. Coordination and Commitment in International Climate Action: Evidence from Palm Oil. Unpublished, Department of Economics, MIT.

Jensen, M. F. 2020. How Could Trade Measures Being Considered to Mitigate Climate Change Affect LDC Exports? Unpublished.

Karp, L., S. Siddiqui and J. Strand. 2016. Dynamic Climate Policy with both Strategic and NonStrategic Agents: Taxes versus Quantities. _Environmental and Resource Economics_ , 65, 135-168.

Kortum, S. and D. Weisbach. 2016. Border Adjustments for Carbon Emissions: Basic Concepts and Design. RFF Discussion Paper 16-09. Washington D.C.: Resources for the Future.

Kortum, S. and D. Weisbach. 2017. The Design of Border Adjustments for Carbon Prices. _National Tax Journal_ , 70, 421-446.

Lazarus, M. and H. van Asselt. 2018. Fossil Fuel Supply and Climate Policy: Exploring the Road Less Taken. _Climatic Change_ , 150, 1–13.

Marcu, A., M. Mehling and A. Cosbey. 2020. Border Carbon Adjustments in the EU: Issues and Options. ERCSR Roundtable on Climate Change and Sustainable Transition.

28
and for the Theil index _T_ (see e.g. Shorrocks, 1980): where _MLDk_ and _Tk_ measure the mean-log-deviation and Theil index for population sub-group _k_ , respectively. Note that the between-group inequality components of both the mean-log-deviation and the Theil index equal the difference between the arithmetic- and the geometric mean income levels. They differ only in the weights used in the geometric mean; the mean-log-deviation weighs the subgroup means by their population shares whereas the Theil index weighs them by their incomes shares.

An inspection of the three sub-group decompositions tells us that the Theil index will be most sensitive to the top tail of the income distribution.<sup>6</sup> To illustrate the significance of the top tail to total inequality consider the limit where the population share of top income earners tends to zero ( _λ →_ 0) while their income share tends to some positive value ( _s >_ 0). It can readily be seen that the between-group inequality component of the Gini coefficient tends to _s >_ 0 in that case, while the within-group inequality among top income earners tends to zero, i.e. _G →_ (1 _− s_ ) _Gini_ 1 + _s_ . It follows that the between-group inequality component for the mean-log-deviation tends to log (1 _− s_ )<sup>_−_1</sup> , while also here (as with the Gini) the within-group inequality among top earners tends to zero (yet it does not discount the contribution of within-group inequality among non-top earners), i.e. _MLD → MLD_ 1 _−_ log (1 _− s_ ). The Theil index stands out as the only of the three inequality measures where the within-group inequality among top earners does not vanish (i.e. makes a positive contribution to total inequality) while the between-group inequality component will tend to infinity (when _μ_ 2 tends to infinity as _λ →_ 0 while _s >_ 0).

# **2.2 An alternative to top income data: Challenges**

As stated in the previous section, DB-2 (the top income database) typically takes the form of tax record data. These data have at least two advantages: (1) it directly observes realized incomes (which makes the estimation of _F_ 2( _y_ ) or any income statistics such as inequality among top earners rather straightforward), and (2) it provides a count of the number of top income earners, which makes for

> 6Hence it is expected that any efforts made to fix the top tail of the income distribution by bringing in complementary data (top income database) will be rewarded the most by the Theil index.

6
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Policy Research Working Paper 9883

# **Abstract**

Across a wide variety of regions and contexts, surveys have found high rates of disagreement within couples on matters of household decision making. Using a unique data set from a spousal survey of 421 agricultural households in the Philippines, this paper finds that 50.2 percent of couples disagree about who makes any given decision in the household. The paper systematically explores the empirical relevance of theoretical explanations from the existing literature for this spousal disagreement. Spouses are no more likely to agree on specific decisions compared with general decision making, are more likely to agree on the decision-making process, and are less likely to agree on decision making for activities in which both take part. Moreover, women are more likely to report that their husbands were involved in decision making when speaking with a female enumerator. The findings suggest that intrahousehold disagreement is not driven by differing interpretations of which decisions count as “major,” or by asymmetric information. Although the paper finds evidence of enumerator effects, their magnitude is small and cannot explain the observed rates of spousal disagreement over decision making. Rather, spousal disagreement appears to stem primarily from systematic gender differences in interpreting what it means to be a decision maker. The paper discusses the implications of the findings for the measurement of intrahousehold decision making in household surveys.

This paper is a product of the Gender Innovation Labs of East Asia and the Pacific Region and Africa Region. 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 authors may be contacted at hjohnson1@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 or the governments they represent._ Produced by the Research Support Team