rafmacalaba/datause-encoder-data huggingface dataset:
21 determinants and the recent globalization trends, the adoption of a fully-fledged IT regime results in higher output income per capita for industrial and emerging economies. However, under dynamic models the estimated long-run output impact of inflation targeting in emerging markets is lower than the results reported for the static models. This is because the persistence coefficient of output is considerably lower ( β 1 is lower than 0.4) in emerging markets economies than in industrial countries ( β 1 is greater than 0.8). While the evidence suggests that industrial economies gained more from globalization, emerging markets seem to have benefited from adopting inflation targets as well. One likely interpretation is that the disinflation process of the last two decades in emerging markets has made the output benefits far outweigh the output costs by setting high interest rates to attract capital flows in a global world but that the full effect of adopting the regime have not been fully felt by emerging markets as in industrial economies. We argue that this might be due to the long lags until the full effects of greater credibility are felt in the real economy and the fact that emerging markets adopted the regime much later than industrial economies.

Firm-level data in Schmukler and Vesperoni (2006), for example, examine the effects of globalization on the debt structure of firms operating in seven emerging economies. They find that with financial liberalization long-term debt decreases and the maturity structure shifts to the short-term. This sort of _collateral benefit_ contrasts with the one in this paper and occurs at the microeconomic level. Another extension is to revisit the links between trade and financial openness in the empirical models. Aizenman (2008) proposes a mechanism through public finance in which trade opening leads to financial opening. These extensions, however, are left for further research.
satisfactorily for various constrains and very'little has been accomplished in sectoral planning or coordination. Individual line ministries have their own planning departments, which operate in more or less complete isolation of one another.

The River Research Institute (RRI), which includes the Surface Water Modeling Control (SWMC) unit, is a research and data generation unit for hydraulic and river studies. It has limited function for collecting broad water resource data.

Major investments in the water sector are made by the Ministry of Water through the Water Development Board (BWDB) and by the Ministry of Local Government and Rural Development through its Local Government Engineering Department (LGED). Besides other water related ministries have their own investment programs. There is very little coordination of activities between all these agencies.

# Box 2: **Water** Management Institutions in **Different** Countries

In China, seven commissions cover the six major international river basins and one lake basin. They are centralized agencies under the Ministry of Water with important planning and regulatory functions.'<sup>8</sup> The Yellow River Conservancy Commission also has specific responsibility for flood management in the lower Yellow River. In India the Damodar Valley Corporation has the responsibility of developing water and managing flood protection, irrigation and power. There are some other agencies in India like the Narmada Control Authority, Bhakra-Beas Management Board, Ganges Flood Control Commission with limited functions. In Sri Lanka, the Mahaweli Authority has been a fairly powerful authority for both development and management of land and water resources. Malaysia has the area-based authorities for Muda and Kemubu irrigation projects.

Some of the basin authorities, like the Tennessee Valley Authority (TVA) in the United States, have very broad charter to develop multiple resources including water, power, navigation, etc. Others have more narrowly focused functions such as operation and maintenance of main-stream facilities for water supply and power generation. Another common use of basin authority is to simply coordinate basin planning, operation and regulatory activities. In France, river basin committees have operated successfully for over twenty-five years doing long-term planning for developing water resources. Regulation and enforcement over there are conducted by various national ministries, while operation and maintenance of different components of the water system are left primarily to regulated private sector entities and public utilities.

People's participation in water resource planning, execution, operation and maintenance, monitoring and evaluation have been limited in Bangladesh, although there is good scope for this. For example, in Mirzapur a community based program for installing handpumps and latrines has worked very well. Moreover, women (a disadvantaged group) were

**18** See Water Resource Management in Asia, by Frederiksen et al, World Bank Technical paper No. 212, Washington D.C. 1993.

13
29 standard MICS wealth index, and for the WHS we used all the questions on assets available in the WHS survey.

Averages are presented for each quintile of households; because the number of births and child deaths are not equal across household wealth quintiles, there are typically more children in the lower wealth quintiles. The poorest quintile thus contains 20 percent of households but typically more than 20 percent of children.

In addition to presenting the quintile means, the 2018 HEFPI database, like previous HEFPI databases, also includes the concentration index and its standard error (Kakwani et al. 1997). This captures the degree of inequality (by wealth) in each indicator. A negative value indicates, on average, higher values among the poor; a positive value indicates, on average, higher values among the better off. The minimum is -1, and the maximum is +1. The concentration index and its standard error were computed using individual-level data via the Stata module CONINDEX (O'Donnell et al. 2015, 2016), with per capita household consumption or income or the wealth index as the ranking variable; it is therefore not affected by the fact that the quintiles are quintiles of households. When quintiles were very small, the quintile mean is not included in the data set. A quintile-specific datapoint is excluded if the sample size in any quintile was less than the following thresholds: 250 per quintile for infant and child mortality estimates and 25 per quintile for all other indicators; this follows the practice of Gwatkin et al. (2007).

# <u>Data-quality checks</u>

On the health equity side of the HEFPI data set, our quality checks involve checking population and quintile specific rates from DHS and MICS against rates published by WHO’s Health Equity Monitor, MICS reports, DHS STATcompiler and World Bank’s ‘Health, Nutrition and Population Statistics by Wealth Quintile’ databank whenever our and the publishers’ indicator definitions align (e.g. for the infant mortality and diarrhea treatment indicators on STATcompiler,
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the formal sector: the bulk of these expenditures are utilities typically provided by large institutions which cannot evade taxes (Figure A3). Appendix B provides more details on the methodology. Table B4 shows for each country the original names of the places of purchase, their expenditure shares and our formality assignment.

We will investigate the robustness of our results to two alternative formality assignment rules. The first alternative is to assign a non-binary probability to each category that it pays VAT on its sales. We use the share of retailers that pay VAT in each category in the Mexican census to obtain a for each These are probability category. probabilities depicted in Panel C of Figure 1.<sup>12</sup> This enables us to take into account the fact that some small retailers pay taxes – either because a subset of small retailers are fully formal or because all small retailers pay VAT on a subset of their transactions. The second alternative changes specialized stores (category 4) to be fully informal (leaving all other categories unchanged). This is the category for which there is arguably the most uncertainty about its true formality status.

Our formality assignment rule enables us to measure the informal budget share of each household, defined as the share of its total expenditure purchased from informal retailers. In what follows we also consider within-product informal budget shares. We use the UN’s detailed COICOP classification of products, which is available at the 2-digit level (12 products), 3-digit level (47 products) and 4-digit level (117 products).<sup>13</sup>

# **2.3 Extended Sample for Food Expenditures**

We complement our core dataset with microdata from the World Bank’s Global Consumption Database (GCD). The GCD compiles household expenditure surveys across countries and harmonizes product categories across all surveys at the 2-digit COICOP product level. 14 Merging this dataset with our core sample, we obtain consumption data for 89 low and middle income countries which account for over 50% of the world population. While this extended sample does not contain place of purchase information, we can use it to characterize food consumption levels and food Engel curves. Food products are defined as all items pertaining to the COICOP 2-digit category ‘food and non-alcoholic beverages’. This category is a good proxy for the set of products which

> 12We assign the following probabilities that the place of purchase is formal to each category: 10% in category 2; 20% in category 3; 50% in category 4; and 90% in category 5. Other categories are unchanged from the baseline assignment rule.

> 13We use crosswalks to convert survey-specific product categories to COICOP categories when necessary. This could not be done for three countries: Brazil, Chad and Peru. For these countries, we use survey-specific product categories at the 3 and 4 digit levels.

> 14 Aggregate statistics are available at http://datatopics.worldbank.org/consumption. Appendix B.3 provides further information on the dataset and our merge.

9
one of the clearest features of the development of manufacturing and service activity in developing countries, yet discussion of urbanisation is strangely absent from the economic analyses of growth and development.

# _Sectoral concentration_

There is a substantial but rather old development economics literature on the changes in countries’ production structures typically associated with development, deriving from Chenery’s “normal” pattern of sectoral change (Chenery 1960; Chenery and Taylor 1968). Using a trade framework, the specialization of countries has been researched by a number of authors, for example Leamer (1984, 1987) and Schott (2003), who find “development paths” linking the structure of countries’ production to their factor endowments.

These studies are, however, based on sector aggregate data, and seem to miss key aspects of modern specialisation. What do we know about specialisation during development in the 21<sup>st</sup> century? Globalization has created the possibility of trade in new products and services, and of a much finer pattern of specialisation. The impact of new technologies is perhaps best illustrated by the experience, already referred to, of India (and Bangalore in particular), which is able to export labor services embodied in information technology and services. The finer pattern of specialisation arises with the growth of production networks. Component parts and semi-finished goods can cross borders multiple times, and countries are able to engage in “vertical specialisation”, just producing one very narrowly defined part of the product. Data on these activities can be hard to obtain, as they do not correspond to the standard commodity classifications of trade. However, one of the fastest growing elements of world trade has been trade in parts and components, now accounting for around 30% of world trade in manufactures (Yeats 1998).

A striking feature of growth has been the fact that many countries have done well in a 5
**Table 3- Impact of the African Diaspora on intra-African exports**

||OLS|IV-OLS|OLS|IV-OLS|
|---|---|---|---|---|
|VARIABLES|(1)<br>Ln(Exports)|(2)<br>Ln(Exports)|(3)<br>Ln(Exports)|(4)<br>Ln(Exports)|
|**Migrants_ij (log)**|**0.0267**|**-0.0111**|**-0.373***|**-1.155*****|
||**(0.579)**|**(-0.104)**|**(-1.725)**|**(-2.867)**|
|**Migrants_ij*Ethnic Diff**|**0.0673**<br>**(1.170)**|**0.230*****<br>**(2.857)**|||
|**Migrants_ij*Distance**|||**0.0579****<br>**(2.067)**|**0.194*****<br>**(3.656)**|
|Ethnic Difference|-0.898**|-1.736***|||
||(-2.358)|(-3.684)|||
|GDP_i (log)|0.674***|0.661***|0.661***|0.621***|
||(4.005)|(3.540)|(3.990)|(3.676)|
|GDP_j (log)|0.261|0.219|0.251|0.109|
||(1.408)|(1.145)|(1.370)|(0.588)|
|FTA|0.328**|0.280*|0.289**|0.0819|
||(2.292)|(1.866)|(2.025)|(0.534)|
|Distance (log)|-1.654***|-1.571***|-2.139***|-2.375***|
||(-9.839)|(-6.486)|(-9.668)|(-6.545)|
|Common colony|0.865***|0.438**|0.320|0.200|
||(3.970)|(2.541)|(1.580)|(0.967)|
|Common language|0.477**|0.525***|1.034***|0.383*|
||(2.244)|(3.091)|(4.695)|(1.820)|
|Contiguity|0.571***|0.964***|0.690***|1.009***|
||(2.829)|(3.917)|(3.568)|(3.688)|
|Constant|-12.33***|-9.132***|-13.45***|-13.81***|
||(-6.711)|(-4.941)|(-7.514)|(-7.350)|
|MR terms|yes|yes|yes|yes|
|Year FE|yes|yes|yes|yes|
|Country FE|yes|yes|yes|yes|
|Sargan (p-val.)||0.798||0.116|
|Underidentification<br>test||0.000||0.000|
|(Kleibergen-Paap p-val.)|||||
|Observations|1,881|1,881|1,899|1,899|
|R-squared|0.605|0.601|0.600|0.573|

Notes: Robust t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Observations are clustered by country-pair. We instrument both the endogeneous variable _Migrants_ij_ and the interactive variable _Migrants_ij*Ethnic Difference_ with our instruments and the interactive terms of each instrument with _Ethnic Difference_ .

Turning to the variables of interest, we see that the coefficients on _Migrants_ are negative and significant but that the interaction term between _Migrants_ and the log of distance is positive and significant. Given that the net effect of migration on exports is the sum of these two coefficients, the result suggests a non-linear effect of migrants on exports, turning positive for values of the log of distance larger than 5.95. Only 13 pairs of countries (representing about 1% of country-pairs in our sample) have a distance lower than this threshold. Above this value, the positive effect of African migration on intra-African exports grows larger the more distant are the countries of origin and of destination. Migrants have thus a positive effect on 18
sector. From the perspective of our paper, what made a vital difference to emerging market countries were the steps they took after 2001 to self-insure, of which the most visible manifestation was the building up of IR—although, as we shall see, other equally important measures were taken.

# **DEVELOPING COUNTRY RESPONSE TO MACROECONOMIC VULNERABILITY**

Putting the crises of the 1980s and the 1990s together, emerging markets have been on a steep learning curve. In this section we link financial stability to the broader context of the macro tradeoffs facing emerging markets and developing countries. In the 1980s, most emerging markets operated with low financial integration and rampant capital controls, strong preferences for exchange rate stability, relatively low levels of international reserves-to-GDP ratios, and active monetary policy. These patterns were modified substantially during the 1990s and 2000s.

# **Navigating the Trilemma**

A compact way of capturing the response of developing countries is to base it on the macroeconomic policy trilemma. This trilemma, or the ability to accomplish at most two out of following three policy objectives – financial integration, exchange rate stability and monetary autonomy- is a key implication of the Mundell-Fleming macroeconomic framework. A lingering challenge in applying it is that, in practice, most countries rarely face the binary choices articulated by the trilemma. Instead, countries choose the degree of financial integration and exchange rate flexibility. Aizenman, Chinn and Ito (2010) allow for this by constructing three indexes measuring the trilemma dimensions of each country. Applying these indexes, Aizenman et al. (2011) validated that the weighted sum of the three trilemma policy variables adds up to a constant, where all the weights are positive.<sup>12</sup> This result confirms the notion that a rise in one trilemma variable is traded off against a linear weighted drop in the sum of the other two.

12 The analysis was applied to a panel of the trilemma indices of 50 countries (32 of which are developing countries) during 1970-2006 period for which there is a balanced data set (the requirement of a balanced panel reduced the number of countries in the sample substantially). The study applies a linear regression, testing the hypothesis that the linear sum of the three indices (without a constant term) adds up to a positive constant, where all the regression coefficients are positive. The regression analysis confirms this hypothesis, with R<sup>2</sup> well above 0.9, for three sub-groups: Industrial Countries, Non-Emerging Developing Countries, and Emerging Market Countries. The overall results are robust to the possibility of allowing structural, endogenously determined breaks in the data (the years of 1973, 1982, 1997-98, and 2001 were identified as candidates for structural breaks, and tested for the equality of the group mean of the indexes over the candidate break points for each of the subsample groups).

.

9
Despite their high ranking on quality of place, some creative cities face unique challenges in quality of life, as diseconomies of scale also exist, including high costs that result from concentration, size, and congestion.<sup>28</sup> For example, salaries and housing prices in Silicon Valley are significantly higher than in many other places. Working by the Bay often involves hours of commuting and a stratospheric cost of living. Rents are steep, parking a nightmare, and the political climate sometimes inhospitable to business. Alleviating these problems is not going to be easy as firms there are located in an area containing more than one hundred cities with different governments and rules (Newscientistjobs.com 2003a). Traffic congestion also is a major problem for many creative centers. Places like New York, Boston, Washington DC, and San Francisco experience near gridlock. These factors have led at least a few creative firms to relocate out of these places to mid-size metros where transportation mobility is still relatively easy (CEOs for Cities 2000).

# **_III. Case Studies of Dynamic Cities_**

To illustrate how the factors discussed above may work together in dynamic cities to create unique attractions to specific clusters, two case studies are presented here in greater details. San Diego is a story of an emerging biotech R&D center with a combination of pioneer research and business savvy, while Boston represents a mature model of university-based creative growth in software and biotech.

# **San Diego**

San Diego presents an interesting example of creative milieu, as it is located outside of the usual high-tech U.S. regions in northern California and the northeast. In a matter of a decade or so, San Diego has reduced its dependency on tourism and defense production to emerge from a severe recession and become one of the nation’s fastest growing, diverse and knowledge-based economic regions (Porter and Monitor Group 2001).<sup>29</sup> With a focus on research, the city now has

> 28 A somewhat different concept from quality of place, quality of life may refer to such important issues as quality public education, reliable health care, assured public safety, and a clean and attractive natural environment, as shown by results of a survey of high-tech communities in the U. S.  In addition, the availability of recreational opportunities is another top driver of quality of life (IntelliQuest 1999).

> 29 During the early 1990s, the region experienced a recession that was attributable to the basic restructuring of the local economy.  See SANDAG (2001).  But today, most agree that, San Diego ranks just behind San Francisco and Boston as a biotech center in the U.S. (Newscientistjobs.com 2003b).

18
**Table 3 continued from previous page**

|||Abidja|n|Gr|and Bas|sam||Samp|le|
|---|---|---|---|---|---|---|---|---|---|
||Male|Female|Dif|Male|Female|Dif|Male|Femal|e Dif|
|Has a professional Network|0.82|0.75|0.07*|0.71|0.82|-0.11|0.80|0.76|0.04|
|Network size (top coded at 3)|1.91|1.65|0.26**|1.83|1.81|0.02|1.90|1.68|0.22**|
|**_SD_**|_1.35_|_1.32_||_1.49_|_1.23_||_1.38_|_1.31_||
|Proportion of male contacts in the top 5 contacts|0.55|0.42|0.13**|0.54|0.43|0.11|0.55|0.42|0.13***|
|**_SD_**|_0.41_|_0.41_||_0.42_|_0.38_||_0.41_|_0.40_||
|**Role Model and support outside the family**||||||||||
|Male role model|0.72|0.34|0.38***|0.66|0.33|0.33|0.71|0.34|0.37***|
|Female role model|0.07|0.34|-0.27***|0.09|0.28|-0.19|0.07|0.33|-0.26***|
|Role model in EICT|0.09|0.07|0.02|0.08|0.05|0.03|0.09|0.07|0.02|
|Can ask for professional advice from people around<br>him/her (outside the family)|0.84|0.74|0.10***|0.80|0.81|-0.01|0.83|0.75|0.08***|
|**Gender attitudes and agency**||||||||||
|Gender attitudes [0,1]|0.48|0.46|0.01|0.46|0.44|0.01|0.47|0.46|0.01|
|**_SD_**|_0.18_|_0.18_||_0.16_|_0.16_||_0.18_|_0.18_||
|Agrees that women’s most important role is to cook and<br>take care of her househol|0.52|0.47|0.05|0.63|0.56|0.06|0.54|0.48|0.05*|
|Agrees that household expenses are the responsibility of<br>the husband|0.70|0.61|0.09*|0.72|0.60|0.12|0.70|0.61|0.10**|
|Agrees that by nature men and women have diferent<br>abilities in diferenta areas|0.85|0.78|0.07**|0.78|0.78|0.00|0.84|0.78|0.06**|
|Agrees that at work, men cope better with difcult condi-<br>tions than women|0.34|0.37|-0.03|0.22|0.32|-0.10|0.32|0.36|-0.04|
|Perceived ability to take decisions alone if desired<br>(score=[0,1])|0.72|0.67|0.05**|0.72|0.65|0.07|0.72|0.66|0.06**|
|**_SD_**|_0.25_|_0.26_||_0.26_|_0.22_||_0.25_|_0.26_||
|Attitudes towards domestic violence [0,1]|0.07|0.09|-0.02***|0.07|0.11|-0.04|0.07|0.09|-0.02**|
|**_SD_**|_0.15_|_0.19_||_0.16_|_0.18_||_0.15_|_0.19_||
|N|1175|938||225|190||1400|1128||