rafmacalaba/datause-encoder-data huggingface dataset:
specifically, Cantwell suggests that the entry of US affiliates provided a highly beneficial competitive spur in the industries where local firms had some traditional technological strength, whereas local firms in other industries - especially in countries where markets were too small to allow both kinds of firms to operate at efficient scale - were forced out of business or pushed to market segments that were ignored by the foreign MNCs.

Recently, some authors have also explicitly discussed the apparent contradictions between the earlier statistical spillover studies. In line with Cantwell (1989), Kokko (1994) argues that spillovers should perhaps not be expected in all kinds of industries. In,particular, foreign MNCs may sometimes operate in "enclaves", where neither products nor technologies have much in comnmon with those of local firms. In such circumstances, there may be little scope for learning, and spillovers may not materialize. Conversely, when foreign affiliates and local firms are in more direct competition with each other, spillovers are more likely. Examining data for Mexican manufacturing, he finds no signs of spillovers in industries where the foreign affiliates have much higher productivity and larger market shares than local firms; in industries without these enclave characteristics, on the other hand, there appears to be a positive relation between foreign presence and local productivity. Kokko, Tansini and Zejan (1996a) present similar findings for the Uruguayan manufacturing sector.

Another possible explanation for the divergent findings from the existing statistical tests is proposed by Kokko (1996), who focuses on effects of competition in Mexican manufacturing. The earlier studies have tested the hypothesis that productivity spillovers are strictly proportional to foreign presence, but Kokko (1996) argues that this is not always the case. Spillovers from competition, in particular, are not determined by foreign presence alone, but rather by the simultaneous interactions between foreign and local firms. Hence, it is possible that the spillovers are larger in case where a few foreign MNC stir up a previously protected market than in a situation where foreign affiliates hold large market shares, but refrain from competing hard with local firms. In fact, sometimes large foreign presence may even be a sign of a weak local industry, where local firms have not been able to absorb any productivity spillovers at all and have therefore been forced to yield market shares to the foreign MNCs.

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<!-- Start of picture text --> % of children<br><!-- End of picture text --> <!-- Start of picture text --> Child is currently exclusively breastfed Child is currently breastfed<br>100%<br>75<br>50 1 4<br>25 3 0<br>0%9, 2 0<br>Child is currently fed other milk Child is currently fed any milk (breastmilk or substitute)<br>100% 100 99) 4<br>75 ' 90 9<br>50<br>5 5<br>0%<br>Child is currently fed solid or semi-solid foods Age appropriate infant & young child feeding<br>100%9 5 38 100<br>75 4<br>50 1 4<br>25 0<br>0%11 a<br>0 3 6 9 12 15 18 21 24 0 3 6 9 12 15 18 21 24<br>Age (completed months)<br>© College (some or complete) © Elementary school or less<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS. Notes: Point estimates and 95% confidence intervals for the prevalence of feeding practices are shown for 2 month-interval age groups, where for example, children between the ages of 0 and 2 are positioned at 1 on the x-axis. The line shown is based on a locally weighted scatterlpot smoothing algorithm (loess) to more clearly show the average relationship between child feeding practices and child's age.
# **Table A.8. Predicted Mean (Log) Consumption Based on Imputation**

|||||**Se**|**cond rou**|**nd**|||||
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Model**<br>**1**|**Model**<br>**2**|**Model**<br>**3**|**Model**<br>**4**|**Model**<br>**5**|**Model**<br>**6**|**Model**<br>**7**|**Model**<br>**8**|**Model**<br>**9**|**True rates**|
|Bldh f 2015 2018/19|10.7|10.8|**10.8**|10.8|10.8|10.9|10.8|N/A|N/A|10.8|
|angaes, rom to|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|||(0.0)|
||12.1|12.1|**12.0**|12.1|12.1|12.1|12.1|12.2|12.1|12.1|
|Malawi, from 2016/17 to 2018/19|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||11.3|11.3|11.0|11.4|11.4|11.3|11.3|11.2|11.2|11.0|
|Nigeria, from 2012/13 to 2018/19|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||<br>13.7|<br>13.7|<br>13.7|<br>**13.7**|<br>**13.7**|<br>**13.7***|<br>**13.7**|<br>13.7|<br>13.7|<br>13.7|
|Tanzania, from 2019/20 to 2020/21|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
||<br>9.6|<br>9.7|<br>10.0|<br>9.8|<br>**9.8***|<br>9.7|<br>9.7|<br>9.7|<br>**9.8***|<br>9.6|
|Viet Nam, from 2014 to 2016|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|(0.0)|
|Food expenditures|||<br>Y||||||||
|<br>Non-food expenditures||||Y|||||||
|<br>Furnishings and household expenses|||||Y||||||
|<br>Health expenditures||||||Y|||||
|<br>Education expenditures|||||||Y||||
|<br>Utilities: water, kerosene, lighting||||||||Y|Y||
|<br>Household assets & house characteristics||Y|Y|Y|Y|Y|Y|Y|||
|Demographics & employment|Y|Y|Y|Y|Y|Y|Y|Y|Y||

**Note:** Empirical distribution of the error terms model with bootstrapped SEs is used. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. All estimates are obtained with population weights. The normal linear regression model with the theoretical distribution of the error terms employs cluster random effects. Imputed consumption per capita for the second round uses the estimated parameters based on the data from the first round. 100 simulations are implemented. True consumption per capita is the estimate directly obtained from the survey data. Estimates shown in boldface or with a “*” respectively fall within the 95% confidence interval or one standard error of the true rate.

54
# **3. An expository model**

An important point that has not received adequate attention in the literature on antipoverty policies is that heterogeneity in individual economic welfare at any given level of aggregate household welfare can restrict the scope for reaching vulnerable women and children using household poverty data. And this is the case even when there is a strong household income effect on individual welfare. To anticipate our empirical work, we shall identify individual welfare by nutritional status.

We elaborate this point in a simple expository model. The nutritional attainments of an individual (adult woman or child) are denoted _n_ , while the wealth of the household to which that individual belongs is _w_ . To keep notation simple, we can assume that _n_ and _w_ are both normalized by appropriate cut-off points (stipulated nutritional thresholds or poverty lines) such that a person is undernourished if (and only if) _n_ <1 and a household is poor if _w_ <1. These two random variables have a (continuous) joint density _f_ ( _n_ , _w_ ) .

As discussed in the prior section, the relationship between the two variables depends on a number of factors, including intra-household inequality, the local health environment (including water and sanitation), access to relevant health and nutritional knowledge, and child care. To keep our expository model simple, we collapse the heterogeneity into one composite factor denoted  , which we can take to be scaled such that it is bounded below by zero and above by unity. For concreteness, we might suppose that  is the share of the household’s total nutritional intake devoted to other household members. The expected value of individual nutritional status given _w_ and  is: It is assumed that the function _n_ (.) is strictly increasing in _w_ —the slope of this function with respect to _w_ is the aforementioned wealth effect on undernutrition—and that the function is strictly decreasing in  at given _w_ . (Continuing the previous example, we can have the special case _n_ (.)  1(  )  ( _w_ ) where  ( _w_ ) is aggregate household nutrition when wealth is _w_ .) Motivated by the existence of a wealth effect on nutritional attainments, it is understandable that a policy maker may be drawn to targeting wealth-poor households so as to reach nutritionally-deprived individuals. However, the common finding in the literature reviewed in Section 2 that the expected value of nutritional status rises with wealth does not necessarily mean that household wealth will provide a reliable indicator of individual outcomes for the purposes of policy. It makes more sense to focus on the 9
# _Relative Minimum Rates of Return_

Minimum rates of return and high market concentration aggravate herding behavior in the pension industry.<sup>50</sup> In order to limit the risk faced by contributors of mandatory DC pension systems, some emerging economies have imposed regulations on minimum rates of return, relative to the industry average, on investments made by pension fund administrators. In LAC, five reformed countries impose relative minimum rates of return on investments of pension funds, namely Argentina, Chile, Colombia, El Salvador and Uruguay (Table 16).<sup>51</sup> Even though this feature of the regulatory framework aggravates the herding behavior in the pension fund industry, herding has also been observed in other pension systems, including the developed ones with thousands of funds and behaviorally-oriented investment regimes, such as the UK.<sup>52</sup> In these cases, herding behavior resumes because managers are pressured by boards of pension funds to show satisfactory performances, which usually involves comparisons with peers and/or industry benchmarks, and tend to opt for defensive strategies so as to avoid underperforming relative to the benchmark and losing their affiliates. Even though portfolios may be similar across different pension fund managers due to herding, the average portfolio can and does drift over time.<sup>53</sup>

**Table 15:** Peru: Portfolio <u>composition of pension funds, by fund and instrument, December 2006 (percent)</u>

|**Instrument**||**Fund**||
|---|---|---|---|
||**1**|**2**|**3**|
|**Claims on the Public Sector**|29.4|19.0|8.0|
|Central Bank of Peru|1.8|1.3|0.9|
|Central Government|27.6|17.7|7.1|
|**Claims on the Financial Sector**|20.6|18.3|15.2|
|Mortgage bonds|0.1|0.1|0.0|
|Time deposits|6.9|5.4|4.4|
|Bonds of financial institutions|8.7|2.0|0.4|
|Shares of financial institutions|0.6|9.3|9.3|
|Debt|4.3|1.5|1.1|
|**Claims on the Corporate Sector**|43.9|53.4|72.1|
|Shares|9.2|33.2|64.5|
|Bonds|32.3|15.3|6.6|
|Units of investment funds|2.0|2.6|0.6|
|Others|0.4|2.4|0.4|
|**Claims on the Foreign Sector**|4.8|9.0|4.1|
|Units of mutual funds and shares|3.8|6.6|3.1|
|Debt|0.9|2.1|0.5|
|Time deposits|0.1|0.2|0.5|
|**Others**|5.9|7.0|4.3|
|**Total assets (LCU million)**|2,693.6|41,044.8|2,311.8|
|Source: SBS.||||

50 See, for example, Shah (1997), Vittas (1997, 1998), Queisser (1999), Srinivas and Yermo (1999), Srinivas, Whitehouse and Yermo (2000), and Olivares (2005).

51 In Peru, the legal framework refers to a minimum rate guarantee that is to be defined by the Ministry of Economy and Finance. The regulation, however, has not yet been issued.

52 The extreme herding behavior in the UK is well discussed in Blake et al (2000) and Myners (2002).

53 Even within a small band, each fund manager has the freedom to slightly deviate from the average, and the direction towards which managers find preferable to deviate does matter (Valdés 1999).

30
Europe, and Japan). Appendix Table 1 provides the list of economies included in each group. All values in the paper are reported in constant 2011 U.S. dollars.

To avoid capturing the trends in the largest economy (China) or in the most financially developed economies in the sample, we show results for the median economy and run regressions at the economy-industry-year level with economy-industry fixed effects.<sup>1</sup> Because bond issuance activity could have grown at different speeds across East Asian economies, we report our main regression results not only for all East Asian economies but also for three separate country groups: East Asia without China, advanced East Asia (Hong Kong SAR, Korea, Rep., Singapore, and Taiwan), and developing East Asia (Indonesia, Malaysia, the Philippines, Thailand, and Vietnam). We treat China differently because its capital markets are very large compared to those in other East Asian economies and underwent a different reform process over our sample period. In the mid-late 2010s, China opened its relatively closed capital markets to allow foreign investors to invest in domestic markets.<sup>2</sup> By 2016, Chinese equity and bond markets were the largest among East Asian economies and the second largest in the world after those in the United States.

# _2.2. Transaction-level Data_

To study the bond issuance activity in East Asia, we use comprehensive, transaction-level data on corporate bonds issued in domestic and international markets over 1990-2016. The data come from

> 1 We divide industries across nine main categories using the first digit of the Standard Industrial Classification (SIC) codes: agriculture, forestry, and finishing; construction; finance, insurance, and real state; manufacturing; mining; retail trade; transportation and utilities; wholesale trade; and other services. Only economy-industry-year observations with reported issuance activity (of any type) are included in the regressions.

> 2 Qualified Foreign Institutional Investors (QFII) were introduced in 2002, allowing certain foreign institutions to purchase equity and bonds issued onshore by Chinese firms. The Renminbi Qualified Foreign Institutional Investors (RQFII) were introduced in 2011 as an alternative for foreign investors to purchase China’s onshore securities. The opening of Chinese capital markets continued during 2014. Launched in November 2014, Shanghai–Hong Kong Connect allows global investors to purchase stocks and bonds listed in Mainland China through the Hong Kong Stock Exchange. The platform also allows institutional investors from Mainland China to access securities listed in Hong Kong SAR.

8
Policy ReseaRch WoRking PaPeR 4522

# **Abstract**

Environmental concern in developing countries has risen rapidly over the past decade. At the same time, decentralization and civic participation in environmental policy-making have also burgeoned. This paper uses data from the Brazilian Municipal Environmental Survey 2001 to examine the causal effect of municipio (county) level environmental institutions on perceptions about environmental problems in Brazil. Consistent with models of public choice, the analysis assumes that the existence of an environmental secretary or an environmental council is related to characteristics of the municipio population. To control for endogeneity – the possibility that the presence of environmental institutions merely signals constituents’ tastes rather than influences municipal actions – the authors construct a system of equations that identifies the causal impact of the institutions. Estimation via a trivariate probit model allows for correlation of unobserved determinants of problem perception, presence of an environmental secretary, and presence of an environmental council. The results suggest that the presence of environmental secretaries has a strong, highly significant, positive causal effect on environmental problem perception. Presence of local environmental councils with civic participation has a significant but weaker impact on environmental problem perception. The authors conclude that local environmental institutions indeed shape local environmental awareness and that participatory institutions can influence local government.

This paper—a product of the Development Research Group—is part of a larger effort in the department to understand the causes and consequences of land use change and to address climate change. This paper is a companion paper to the Policy Research Report, _At Loggerheads? Agricultural Expansion, Poverty Reduction and Environment in the Tropical Forests_ . Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at kchomitz@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
Bauwens, M., S. Compernolle, T. Stavrakou, J.-F. Müller, J. van Gent, H. Eskes, P. F. Levelt, et al. 2020. “Impact of Coronavirus Outbreak on NO2 Pollution Assessed Using TROPOMI and OMI Observations.” Geophysical Research Letters 47 (11): e2020GL087978. <u>https://doi.org/10/gg8vz9.</u> Bekbulat, Bujin, Joshua S. Apte, Dylan B. Millet, Allen L. Robinson, Kelley C. Wells, Albert A. Presto, and Julian D. Marshall. 2021. “Changes in Criteria Air Pollution Levels in the US before, during, and after Covid-19 Stay-at-Home Orders: Evidence from Regulatory Monitors.” Science of The Total Environment 769 (May): 144693. <u>https://doi.org/10/gmd6d2.</u>

- Carvalho, Monica, Danielle Bandeira de Mello Delgado, Karollyne Marques de Lima, Marianna de Camargo Cancela, Camila Alves dos Siqueira, and Dyego Leandro Bezerra de Souza. 2021. “Effects of the COVID-19 Pandemic on the Brazilian Electricity Consumption Patterns.” International Journal of Energy Research 45 (2): 3358–64. <u>https://doi.org/10.1002/er.5877.</u>

Chauhan, Akshansha, and Ramesh P. Singh. 2020. “Decline in PM2.5 Concentrations over Major Cities around the World Associated with COVID-19.” Environmental Research 187 (August): 109634. https://doi.org/10/ggwpmr.

Hale, Thomas, Anna Petherick, Toby Phillips, and Samuel Webster. 2020. “Variation in Government Responses to COVID-19.” Blavatnik School of Government Working Paper 31 (2020–11).

International Energy Agency (IEA) (2021). “COVID-19 Impact on Electricity, IEA, Paris https://www.iea.org/reports/covid-19-impact-on-electricity (Downloaded on June 18, 2022).

Jovanović, Saša, Slobodan Savić, Milorad Bojić, Zorica Djordjević, and Danijela Nikolić. 2015. “The Impact of the Mean Daily Air Temperature Change on Electricity Consumption.” Energy 88 (August): 604–9. https://doi.org/10.1016/j.energy.2015.06.001.

Le Quéré, Corinne, Robert B. Jackson, Matthew W. Jones, Adam JP Smith, Sam Abernethy, Robbie M. Andrew, Anthony J. De-Gol, David R. Willis, Yuli Shan, and Josep G. Canadell. 2020. “Temporary Reduction in Daily Global CO 2 Emissions during the COVID-19 Forced Confinement.” Nature Climate Change 10 (7): 647–53. https://doi.org/10/ggwr9w.

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# Box 4

# The Nature of Nontariff Protection for Processed Commodities

Table 5 shows that some Asian e:ountries' tariffs are higher than those in OECD markets and frequently incorporate greater escalation. However, primary and processed commodities may also face nontariff barriers in Asian trade. GATT records show that while NTBs are applied infrequently to commodity imports by Australia, Hong Kong, New Zealand and Singapore (records for the Philippines are not available), they are widely used in countries like Japan, Korea and Malaysia.

Share of prccessed commodity imports covered by nontariff barriers (9)

|Importing Country|Foods &<br>Feeds|Ores &<br>Metals|Agricultural<br>Materials|Energy<br>Products|
|---|---|---|---|---|
|Indonesia|18.3|6.2|0.4|0.0|
|Japan|'9.8|0.9|0.7|8.2|
|Rep. of Korea|18.4|0.0|22.2|85.4|
|Malaysia|23.2|22.3|36.5|52.5|
|<br>Thailand|11.9|1.3|2.6|22.2|

To illustrate this point, the above statistics show the percentage of tariff-line level processed commodities facing nontariff barriers in five Asian markets. In preparing these data, the 48 commodity processing chains wen aggregatd into four broad product groups. Processed foods are most often subject to NTBs - these - restrictions cover approximately 50 percent of Japan's imports but Korea, Malaysia and Thailand frequently apply nontariff measures to energy products (see Table 6 for information on the types of measures used in these markets).

Although information on the trade effects of nontariff barriers is limited, several studies suggest Asian NTBs often convey very high levels of nominal protection. For example, Ss-on and Anderson (1982) estimate that Japan's NTBs on primary and processed foods often have nominal equivalents over 100 percent, while ECAFE (United Nations, 1982) reached similar conclusions concerning nontariff protection in other Asian countries. The message from these studies is that nontariff barriers, as well as tariffs, must be addressed in any multilateral liberalization effort. At a minimum, the NTBs should be bound" to ensure that they cannot be tightened to offset the effects of any tariff cuts that might be achieved in negotiations.
# **Table 13: Correlates of Portfolio Debt Flows**

|||**Portfolio D**<br>|**ebt Inflows**<br>|||**Portfolio De**<br>|**bt Outflows**<br>||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Federal Funds Rate|-0.062***|-0.063***|-0.071***|-0.067***|0.001|0.009|0.006|-0.001|
||[3.50]|[3.40]|[3.99]|[3.63]|[0.08]|[0.76]|[0.62]|[0.09]|
|Log(VIX)|-0.590***|-0.584***|-0.641***|-0.664***|-0.453***|-0.483***|-0.446***|-0.441***|
||[9.54]|[8.54]|[10.13]|[8.90]|[3.65]|[3.83]|[3.65]|[3.54]|
|GDP growth|-0.002|-0.002|-0.005|-0.001|-0.006|-0.009|-0.006|-0.010*|
||[0.31]|[0.24]|[0.66]|[0.09]|[1.06]|[1.56]|[1.10]|[1.83]|
|Investment environment||-0.008||||0.051**|||
|||[0.27]||||[2.43]|||
|Chinn-Ito index of capital account openness|||0.019||||0.040||
||||[0.69]||||[1.29]||
|Bank assets, % of GDP||||0.002||||0.000|
|||||[0.90]||||[0.01]|
|Country fixed-effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|2,173|2,151|2,069|1,921|2,055|2,033|1,957|1,807|
|R-squared|0.047|0.046|0.060|0.062|0.021|0.025|0.023|0.021|
|# of countries|29|29|29|29|28|28|28|28|

_Notes:_ The dependent variable is Portfolio debt inflows as % of trend GDP in Columns 1-4 and portfolio debt outflows as % of trend GDP in Columns 5-8. The dependent variables are standardized by subtracting the country-specific mean and dividing by the country-specific standard deviation. The sample spans from 1990:Q1 to 2015:Q4. Robust t-statistics are reported in brackets. ***, **, * indicate significance at the 1%, 5% and 10% level.

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