rafmacalaba/datause-encoder-data huggingface dataset:
3

# **I.   INTRODUCTION**

Limited diversification in exports and broader economic structure has long been an underlying characteristic of many developing economies. Yet some have shown a remarkable economic transformation, especially over the past two decades. In particular, it has been argued that developing Asia has benefited significantly from diversification. This paper examines the claim with a comprehensive look at the facts, employing newly developed data sets covering diversification in both external trade and domestic production.

The paper focuses on two key questions. First, is diversification crucial to sustaining growth and reducing volatility? Put differently, does concentration in sectors with limited scope for productivity growth and quality upgrading, such as primary commodities, result in less broadbased and sustainable growth? And does lack of diversification increase exposure to adverse external shocks and macroeconomic instability?

Second, what precisely does diversification, in both external trade and the broader domestic economy, involve? How is it linked to broader structural transformation, including the process of quality upgrading? And which countries and regions have been more successful in promoting diversification?

Throughout, our focus is on Asia, and in particular on two groups of countries: “Emerging Asia”, comprising those economies generally classified as emerging markets; and “Frontier Asia”, comprising some economies that are still at lower income levels, but have experienced rapid growth and, in most cases, demonstrated a fair degree of macroeconomic stability over an extended period of time.<sup>2</sup>

> 2 Frontier Asia comprises: Bangladesh, Bhutan, Cambodia, Lao PDR, Maldives, Mongolia, Myanmar, Nepal, Papua New Guinea, and Vietnam.  Emerging Asia comprises: China, India, Indonesia, Malaysia, the Philippines, Sri Lanka, and Thailand.
vs international migrants; liquidity constraints; and migration as adaptation.<sup>4</sup>

# _A. Fast-onset events versus slow-onset changes_

The migration impacts of fast-onset extreme weather events (such as hurricanes, heavy rains, floods, and landslides) related to climate change are usually sudden and direct, resulting mainly in temporary movements over short distances (McLeman and Gemenne, 2018). A specific strand of the literature investigated the role of floods on migration, with mixed results: while Gray and Mueller (2012a) and Bohra-Mishra, Oppenheimer, and Hsiang (2014) document a lack of significant effects of floods on migration in, respectively, Ethiopia and the Philippines, Mueller et al. (2014) find that floods reduce the probability of migrating in Pakistan. Koubi et al. (2016) show that in Vietnam fast-onset shocks are more likely to trigger migration, whereas long-term environmental changes (such as salinization) _reduce_ the likelihood of migration. Robalino, Jimenez, and Chacón (2015) provide evidence that flooding and other hydro-environmental emergencies increase migratory flows in Costa Rica, but also find that more severe emergencies decrease migration. Kaczan and Orgill-Meyer (2020) argue that this may happen as floods and other fast-onset shocks rapidly deplete household assets and resources, leaving households unable to migrate. Using a global data set which combines climatic, census, and night-light data, Castells-Quintana, McDermot and Krause (2021) study the relationship between changes in weather patterns and the spatial distribution of population and economic activity, and find that worse climatic conditions are associated with higher urbanization. Their results suggest that while slow-onset changes in climate may lead to more permanent, and more long-distance, movements, sudden-onset events are primarily associated with temporary displacements and short-term migration to nearby urban areas. Koubi et al. (2022) use survey data from Cambodia, Nicaragua, Peru, Uganda, and Vietnam and document that less educated and lowerincome people are less likely to migrate after exposure to fast-onset shocks, compared to people endowed with higher education and more economic resources. Finally, even in the absence of sudden resource depletion, in poor countries the populations affected may not have enough monetary resources for longdistance migration (Zickgraf and Perrin, 2016). Cattaneo et al. (2019) emphasize that the main current insight of this strand of the literature is that the potential for fast-onset events to cause long-term, longdistance migrations appears limited, especially in the case of costlier international migration.

In contrast, slow-onset changes are more likely to induce migration than rapid-onset ones, but the

> 4 These five thematic subsections have been selected among many topics, due to their relevance and prominence for the assessment of data gaps we conduct.

5
While the objective of the interventions is of course ultimately to improve health, only direct information about health care utilization can teach us whether and how parents’ behavior changes and if there are problems with targeting such as use of care when the child is not actually sick.

**Classifying illness spell days.** The IMCI charts define an intuitive set of rules to determine need for care. For instance, cough and cold symptoms typically point to a viral infection and do not require medical care, unless they persist for 14 days or longer, which potentially indicates a more serious illness like tuberculosis. Diarrhea alone can be treated with home remedies, but in the presence of sunken eyes (dehydration) or blood in the stool (dysentery) requires immediate attention. Applying these rules to symptom records, every day in an illness spell can be classified as either an “early” day (i.e. before care should be sought) or a “care required” day (on or after the day at which care should be sought). We focus on “pre-care” spell days up to the first contact with the health system, since symptoms might be affected by treatment.As an example, consider a child who is coughing for three days, on the third day develops a fever and takes cough drops from a peddler, is prescribed paracetamol at the CSCOM on day 5, and symptoms subside after day 8. This is a five day pre-care spell with three care-required days (days 3-5). If the child had not received care, it would be an eight-day spell, with days 3-8 being care-required days. Appendix B describes the approach in detail.

Table 3 shows untreated days per child (possibly in several illness spells), and the percentage of days on which each symptom is present, in total and split into early and care-required days. On average, 16.3 of 59.8 observed days per child (27%) were (pre-care) illness spell days, and on six of those there was a need for care. Convulsions, lethargy, inability to drink, and vomiting should trigger immediate care, so they always occur on care-required days. The biggest contributor to need for care is fever (unusually hot skin). While cold symptoms and cough rarely in themselves indicate a need for care, they often occur in conjunction with other, more serious symptoms and therefore the spell day is still classified as “care required”.

18
22 significantly lower fertility compared to their rural counterparts. This is likely due to the fact that the impact of the type of residence is already captured by region fixed effects, which also account for differences in wealth, infrastructure, and remoteness that we are unable to pinpoint more precisely due to the lack of community-level variables recorded in the RDHS.

There is a lack of information recorded in RDHS data on migration history that would allow us to identify whether a respondent was an old or new caseload refugee. However, we know if a woman has always lived in the same community (NATIVE), thus controlling for migration and displacement. Women who never left their community of origin have significantly lower fertility compared to women who at least once in their lives shifted the place of residence. This finding supports evidence from a previous study on Rwandan refugees (Verwimp and van Bavel 2005), which finds that new caseload refugees had 1.04 times the fertility rate of Rwandan women who never migrated, after controlling for age, civil status, education, place of residence and occupation. It also fits well into the findings of Verwimp and Van Bavel (2011) from Burundi, where conflict-related displacement leads to a faster transition into motherhood.

# **b) Conflict and fertility in the long term**

Overall, the impact of conflict exposure becomes less pronounced in the long term (Table 6). The mean fertility rate in the past ten years of women who lost at least one child during the genocide (CHILDDEATH) was exp(0.12) = 1.12 times the fertility rate of women who did not lose children during the genocide. Women who lost at least one son (SONDEATH) have exp(0.13) = 1.13 more children than the reference group, while again the loss of a daughter in 1994 does not significantly influence fertility in the post-genocide period.

Unlike in the short term, sibling death in 1994 has no significant effects on fertility in the long term. These results may indicate that siblings are less important for livelihoods than own children, especially as siblings of women play less of a role in asset accumulation, land access, and old age security than do a woman’s own children. Alternatively, if sibling death captures the immediate exposure to violence at home, trauma, and victimization as argued above, these burdens appear to have been overcome in the subsequent five years.

In contrast, the negative impact of conflict on fertility for widows in 2005 (WIDOW x YEAR00) becomes even larger in the long-term compared to the short-term
**Figure 4. Averages of fiscal interventions, by pre-fiscal income decile**

<!-- Start of picture text --> 10000 90000<br>5000 80000<br>0 70000<br>-5000 60000<br>-10000 50000<br>-15000 40000<br>-20000 30000<br>-25000 20000<br>-30000 10000<br>-35000 0<br>1 2 3 4 5 6 7 8 9 10<br>Pre-Fiscal Income Decile (Income-Based PDI)<br>PIT CSS Cash Transfers<br>Pension Indirect Taxes NET In-Kind Edu.<br>NET In-Kind Health Net Position Final Income<br><!-- End of picture text -->

**Figure 5. Fiscal interventions as a share of final income, by pre-fiscal income decile**

<!-- Start of picture text --> 1 90000<br>80000<br>0.8<br>70000<br>0.6<br>60000<br>0.4<br>50000<br>40000<br>0.2<br>30000<br>0<br>1 2 3 4 5 6 7 8 9 10<br>20000<br>-0.2<br>10000<br>-0.4 0<br>PIT CSS Cash Transfers Pension<br>Indirect Taxes NET In-Kind Edu. NET In-Kind Health Net Position<br>Net Position (cash) Final Income (RHS)<br><!-- End of picture text --> _Note_ : The primary y-axis in Figure 4 denotes averages of fiscal interventions and the unit is Yuan. The primary y-axis in Figure 5 denotes benefits and taxes as a share of final income. The secondary y-axis in both Figures 4 and 5 denote average final income by decile and the unit is Yuan.

15
## Figure A.1: Trend of log of unit value 1984-2000

<!-- Start of picture text --> AlcoholTobacco Dairy Fruit Legumes<br>5.31727<br>1.2491<br>Maize Meat OilsFats OtherFood<br>5.31727<br>1.2491<br>Rice Sugar Vegetables Wheat<br>5.31727<br>1.2491<br>1984 2000 1984 2000 1984 2000 1984 2000<br>Log Unit Value<br><!-- End of picture text -->

# Year Graphs by good

Source: Author’s calculation using INEGI households surveys 1984-2000.

32
Policy Research Working Paper 7399

# **Abstract**

This study uses Twitter data to provide a more nuanced understanding of the public reaction to the 2011 reform to the propane gas subsidy in El Salvador. By soliciting a small sample of manually tagged tweets, the study identifies the subject matter and sentiment of all tweets during six onemonth periods over three years that concern the subsidy reform. The paper shows that such an analysis using Twitter data can provide a useful complement to existing household survey data and even potentially replace survey data if none were available. The finding show that when people tweet about the subsidy, they almost always do so in a negative manner; and there is a decline in discussion of topics about the reform subsidy, which coincides with increase in support for the subsidy as reported elsewhere. Therefore, the study concludes that decreasing discussion of the subsidy reform indicates an increase in support for the reform. In addition, the gas distributor strikes of May 2011 may have contributed to public perception of the reform more than previously acknowledged. This study is used as an opportunity to provide methodological guidance for researchers who wish to undertake similar studies, documenting the steps in the analysis pipeline with detail and noting the challenges inherent in obtaining data, classification, and inference.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. 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 may be contacted at jsseabold@gmail.com.

_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
# EMDEs.

# Data

The estimation relies on bilateral trade costs from the UNESCAP-World Bank Trade Costs Database. Following Novy (2013) and Arvis et al. (2013), bilateral trade costs are obtained as geometric averages of flows between countries i and j. They are computed according to the formula below:

# _(Xii Xjj)/(Xij Xji)_<sup>1/2 (</sup><sup>_σ_-1)</sup> ,

where _Xij_ represents trade flows between countries _i_ and _j_ (goods produced in _i_ and sold in _j_ ) and _σ_ refers to the elasticity of substitution. This measure captures international trade costs relative to domestic trade costs. Intuitively, trade costs are higher when countries trade more domestically than they trade with each other, i.e., as the ratio ( _Xii Xjj_ )/( _Xij Xji_ ) increases. Intra-national (that is, domestic) trade is proxied by the difference of gross output and total exports.

Trade costs thus computed implicitly account for a wide range of frictions associated with international trade, including transport costs, tariffs, and nontariff measures, and costs associated with differences in languages, currencies and import or export procedures. Trade costs are expressed as ad valorem (tariff) equivalents of the value of traded goods and can be computed as an aggregate referring to all sectors of the economy, but also specifically for the manufacturing and agriculture sectors.

# Estimation

Gravity equations are widely used as a workhorse to analyze the determinants of bilateral trade flows. Chen and Novy (2012) and Arvis et al. (2013) also employ a gravity specification to analyze the determinants of bilateral trade costs in a cross-sectional dataset. In line also with Moïsé, Orliac, and Minor (2011), this study estimates determinants of trade costs in a panel specification.

The regression equation takes the following form: _TCijt_ = β1 _RTAijt_ + β2 _tariffijt_ + β3 _LSCIijt_ + β4 _LPIijt_ where for any given country pair _ij_ , bilateral trade costs _TC_ observed at time _t_ are regressed on a wide range of candidate drivers. These include membership in a regional trade agreement (RTA); sector-specific bilateral tariffs; shipping connectivity (UNCTAD’s Liner Shipping Connectivity Index, or LSCI) and logistics (the World Bank’s Logistics Performance Index, or LPI); a proxy for trade policy uncertainty; and standard gravity indicators (distance, a common language, and a common border). In line with Osnago, Piermartini, and Rocha (2018), trade policy uncertainty is defined as the gap between binding tariff commitments and applied tariffs. To ascertain the role of policies aimed at facilitating trade, indexes of logistic performance and maritime connectivity are included.
the refugee population over a ten-year period.<sup>9</sup> Furthermore, in OECD countries, refugees are most often integrated into society and may be registered in the census.

Forced displacement is not limited to those fleeing conflict, but may also include individuals fleeing natural disasters or environmental change. Such migration may be international or domestic, and therefore, while forced, environmental migrants often do not fit the legal definition of a refugee. Individuals fleeing natural disasters, such as Haitians responding to the 2010 earthquake by fleeing to the Dominican Republic, may do so temporarily or on a longer-term basis. Documenting the temporary migration may be done through administrative data, such as information on border crossings, or through targeted surveys at the destination shortly after the natural disaster. Alternatively, longerterm migration may be captured in census data or through multi-topic surveys. Individuals forced to migrate due to environmental change are unlikely to be able to return. Researchers are devising a numbers of ways to link multiple types of data in order to assess and anticipate environmental migration. In Ghana, Van der Geest et al. (2010) link census data to vegetation data to examine the connections between internal migration and vegetation dynamics. Feng et al. (2010) examine the connection between crop yields and migration by merging crop data with census data. Others are modeling climate change patterns in an attempt to understand future migration trends (Barbieri et al., 2010; Hunter et al., 2011; McLeman, 2013).

Forced migration may also include displacement due to development policies or projects. These could include the introduction of conservation areas or biosphere projects, or large infrastructure projects, such as the construction of dams or deforestation. Similar

> 9. See the UNHCR website (http://www.unhcr.org/4a01417d6.html) for a list of the 25 industrialized countries and a complete explanation of the database.

41
(12) i i i iiggrrccrr ii where 11(LLeeL eeLLTTL nnssiittiiccnn)i is a dummy variable that equals one if a change in leader occurred = PP + ββgg + μμ + ee ii in year 11(LLeeL _t_ in country eeLLTTL _c_ nnssiittiiccnn) and i is the contemporaneous growth residual. Estimation with country fixed effect ( ), yields an estimate of gg = −0.5 ( _t-stat=-6_ ), verifying the endogeneity of leader ii transitions: a 1ppt lower growth rate increases the probability of leader transition by 0.5ppts.μμ ββ̂<sup>26</sup> To test the effect of this endogeneity on our methodology, Panel C of Table 1 re-produces all of the least-squares Monte Carlo estimates when leaders change endogenously with the growth rate, as in Equation 12 with = −0.5.<sup>27</sup> Despite being very statistically significant, the potential endogeneity of leader tenure has almost no effect on any of the least squares Monte Carlo ββ̂ estimates. Why? This form of endogeneity leads to an upward bias in average growth rates at long tenures due to sample selection bias (leaders with higher growth rates are more likely to survive). But its quantitative size is small, and so selection bias is miniscule at the average tenure of about 6 years. Moreover, it seems that in terms of RMSE, sample selection bias is a second-order issue relative to shrinking iid error noise.<sup>28</sup>

# **3. Data and estimates of variance components**

**Data sources.** Data on growth are taken from Penn World Tables (PWT) version 9 over 19512014 (the latest version at the time of writing, Feenstra et al. (2015), and data on leaders are taken from the Archigos 4.1 data set (described in Goemans et al. 2009).<sup>29</sup> We categorize countries as

> 26 Another potential interpretation is that the turbulence of the leader transitions causes lower growth.

27 That is, the probability of a leader change in year is 1/6 -0.5*growth (recall the growth rate is mean zero). 28 For example, even if endogeneity is 10 times as powerful in the data ( ), the RMSE of the LS leader estimate only increases by 0.06ppts, which is mostly due to biased variance estimates of the leader effect ( ). ββ= −5 29 ⋆ ≡ ln(Y ) − ln(Y ) , where Yt is real GDP per capita (rgdpna/pop in PWT9). If multiple leaders hold office in μμ σσ a year in the same country, we allocate that year to the leader in power for the most days. ii ii ii−1 gg 19
22 covers utility patents<sup>35</sup> and other types of U.S. documents, such as design patents<sup>36</sup> , plant patents<sup>37</sup> , reissues, defensive publications, and statutory inventions registrations granted between 1977 and 2001 and is aggregated by year and country of the first named inventor.

U.S. patents and patents in general have long been recognized as a very rich and potentially fruitful source of data for the study of innovation and technical change<sup>38</sup> . One key reason for this recognition is because each patent contains highly detailed information on the innovation itself, the technological area to which it belongs, the inventors (e.g., their country of residence), etc. Also, the number of U.S. patents granted to foreign inventors has consistently been increasing during the last three decades. More specifically, the percentage of U.S. patents awarded to foreign inventors has risen from about 20 percent in the early 1960s to about 46 percent in 2001. In addition, inventors from 106 foreign countries were granted USPTO patents in 2001. Hence, U.S. patents increasingly reflect not only inventive activity in the U.S. itself, but also around the world. Lastly, the number of patents potentially available for research is enormous. The stock of patents is currently in excess of 6 million, and the flow comprises over 150,000 patents per year (as of 1999-2000). In addition, patents have been granted in the U.S. continuously since the late 18<sup>th</sup> century. The current numbering and reporting system dates to the 1870s, which means that there are (in principle) over 100 years of consistently reported data (Hall, Jaffe and Trajtenberg (2002)).

However, the use of patent data to measure the level innovation activity is not without problems. The most obvious limitation is that not all inventions are patented and hence not all innovation activity is captured. One of the reasons for innovation not being patented is that not all inventions meet the criteria set by the U.S. Patent and Trademark Office (USPTO). USPTO requires that the invention be novel, nontrivial and have commercial application. Another reason for non-patented inventions is simply that the inventor did not take the initiative to apply for a patent, and may have decided to rely on secrecy to prevent duplication of the invention by competitors. Given that there is no known available data on inventions that are not patented, little is known of the extent to which patents are representative of the entire population of inventions.

> 35 Utility patents may be granted to anyone who invents or discovers any new and useful process, machine, article of manufacture, or compositions of matters, or any new useful improvement thereof.

> 36 Design patents may be granted to anyone who invents a new, original, and ornamental design for an article of manufacture.

> 37 Plant patents may be granted to anyone who invents or discovers and asexually reproduces any distinct and new variety of plant.

> 38 See Griliches (1990) for a survey of research using patent data.