rafmacalaba/datause-encoder-data huggingface dataset:
# References

Auer, R. A., C. Borio, and A. Filardo. 2017. “The Globalisation of Inflation: The Growing Importance of Global Value Chains.” BIS Working Paper 602, Bank for International Settlements, Basel.

Auer, R. A., A. Levchenko, and P. Sauré. 2017. “International Inflation Spillovers through Input Linkages.” NBER Working Paper 23246, National Bureau of Economic Research, Cambridge, MA.

Auer, R. A., and A. Mehrotra. 2014. “Trade Linkages and the Globalisation of Inflation in Asia and the Pacific.” Journal of International Money and Finance 49 (December): 129-51.

Baffes, J., M. A. Kose, F. Ohnsorge, and M. Stocker. 2015. “The Great Plunge in Oil Prices: Causes, Consequences, and Policy Responses.” Policy Research Note 15/01, World Bank, Washington, DC.

Bernanke, B. S. 2007. “Globalization and Monetary Policy.” Speech at the Fourth Economic Summit, Stanford Institute for Economic Policy Research, Stanford, CA.

Bianchi, F., and A. Civelli. 2015. “Globalization and Inflation: Evidence from a Time Varying VAR.” Review of Economic Dynamics 18 (2): 406-33.

Borio, C. E. V., and A. Filardo. 2007. “Globalisation and Inflation: New Cross-Country Evidence on the Global Determinants of Domestic Inflation.” BIS Working Paper 227, Bank for International Settlements, Basel.

Carney, M. 2015. “How Is Inflation Affected by Globalisation?” Remarks by the Governor of the Bank of England, World Economic Forum, Jackson Hole, WY, August 29.

———. 2017. “[De]Globalization and Inflation.” 2017. IMF Camdessus Central Banking Lecture, International Monetary Fund, Washington, DC.

Ciccarelli, M., and B. Mojon. 2010. “Global Inflation.” Review of Economics and Statistics 92 (3): 524-35.

Clarida, R., J. Gali, and M. Gertler. 2002. “A Simple Framework for International Monetary Policy Analysis.” Journal of Monetary Economics 49 (5): 879-904.

di Giovanni, J., and A. A. Levchenko. 2010. “Putting the Parts Together: Trade, Vertical Linkages, and Business Cycle Comovement.” American Economic Journal: Macroeconomics 2 (2): 95-124.

Draghi, M. 2015. “Global and Domestic Inflation.” Speech presented to Economic Club of New York, December 4.

Eickmeier, S., and K. Pijnenburg. 2013. “The Global Dimension of Inflation: Evidence from Factor-Augmented Phillips Curves.” Oxford Bulletin of Economics and Statistics 75 (1): 103-22.

Fang, W. S., S. Miller, and C. S. Lee. 2012. “Short- and Long-Run Differences in the Treatment Effects of Inflation Targeting on Developed and Developing Countries.” Department of Economics Working Paper 1003, University of Nevada, Las Vegas, NV.

14
compute the energy intensity in physical terms, the total energy consumed is divided by the sales of the firm if the total energy consumed is non-zero. The units of energy intensity in physical terms is BTU per US$.

# **A1.6 Treatment of Outliers**

We winsorize observations in the main sample by excluding the top and bottom 1 percentile on energy intensity (physical), energy intensity (expenditure), and output measures. We retain 19,521 observations, although not every firm reports energy expenditure, physical energy use, and output. The management and green economy survey modules were administered to a subset of the randomly-selected firms in the full sample. Constructing our data set thus required trading off its size against a requirement that all major variables be observed. We do not systematically drop firms with missing observations of key variables, but instead remain mindful that our regression samples differ in composition. For instance, in the winsorized sample, we observe energy intensity (physical) for 8,272 firms and energy intensity (value) for 14,713 firms. We observe at least six out of eight general management practices for 8,476 firms, and energy management practices for 6,178 firms. Firms that have non-missing values for all covariates are included in each regression. If we limit the sample to only firms that observe both measures of energy intensity plus output, the sign of the results remains mostly unchanged (not shown). However, firms that report both physical energy use and expenditures may differ systematically from those that report only one or the other. For instance, these firms may have better developed general management or energy management practices that support monitoring of input use. The composition of firms across countries and two-digit sectors before and after winsorization is similar.

# **A1.7 Descriptive Statistics**

The composition of firms across countries and two-digit sectors before and after winsorization is similar. Observations of services firms are generally missing capital stock and share of high-skilled labor. Using the World Bank classification (World Bank Data, 2020), only one country is classified as low-income (Tajikistan); we therefore group countries into three income categories: high, upper middle, and lower middle.

38
line for people with less schooling. However, schooling attainments depend on individual personality as well as opportunities. For example, there is evidence that more conscientious people acquire more schooling.<sup>29</sup> At the same time, more conscientious people tend to delay gratification in other aspects of their life, which may well mean that they report lower current subjective welfare at given current income than do others. Then a regression for subjective welfare ignoring the personality differences will tend to lead one to underestimate the required gradient in poverty lines needed to compensate for differences in schooling opportunities.

Even with similar personalities (or statistically ignorable differences) different people may apply different criteria for scaling their welfare. They have different ideas about what it means to be ―rich‖ or ―poor,‖ or what it means to be ―satisfied‖ or not with one’s life. Again latent heterogeneity in factors that are essentially irrelevant to welfare but influence responses to subjective welfare questions can cast doubt on the implied interpersonal comparisons of welfare from subjective data. If such heterogeneity was purely random then it would not invalidate inferences from such regressions (at least for linear models). Thus it has been argued in the literature that, while inter-personal welfare comparisons are invalidated by heterogeneous scales, the regressions are likely to be robust to such heterogeneity.<sup>30</sup> However, that claim is of doubtful veracity. As already noted, it is a well-established idea that people assess their welfare relative to some ―comparison group‖ such as neighbors or coworkers. This argument has emphasized relativist welfare comparisons. It has also been argued that reference groups play an important role in expectations formation.<sup>31</sup> It is a small step from these ideas to the proposition that survey respondents answer questions with reference to their immediate experiences and that this may well be highly localized in some relevant social or geographic dimensions. People will answer subjective questions relative to their personal ―frame-of-reference,‖ which depends on the respondent’s own knowledge and experience, and therefore is likely to vary systematically with the characteristics of that person, including objective measures of economic welfare.

> 29 Reported in Heckman (2011), using data for Germany.

> 30 For example, Frey and Stutzer (2002) note the possibility of heterogeneity in the scales used in selfreported welfare questions but claim that this does not invalidate regression models for such data.

> 31 For more recent discussions and evidence see Ravallion and Lokshin (2000) and Senik (2004).

21
and also is essentially uncorrelated with most of the distributional change measures, with the exception of the Gini in the long spells regression.

Overall, while most of the partial correlations documented in Tables 4 and 5 are not statistically significant, the qualitative pattern suggests that there may be some tradeoffs. Rule of law is positively correlated with growth but also with povertyincreasing shifts in relative incomes. The opposite is true for government consumption. In contrast trade is positively correlated with growth and with poverty-reducing shifts in relative incomes. In Table 6 we examine these possible tradeoffs in a slightly richer empirical specification, using the dataset of long spells. We begin by estimating a more fully-specified growth regression with initial income, and initial values of institutional quality, trade openness, and size of government as right-hand-side variables. Despite the likely noisiness of the data, it is possible to find plausible specifications in which some of the determinants of growth from the growth literature are also significantly correlated with growth in the household survey mean. The first column of Table 6 illustrates one such regression, which includes initial income, institutional quality, trade openness, and government consumption on the right-hand-side. Each of these variables enters with signs consistent with the broader growth literature. Initial income enters negatively, picking up convergence effects. Institutional quality and trade are both positively correlated with growth, and larger government size is associated with slower growth. I do not want to claim that these results are a robust feature of this particular dataset. However, the results are broadly consistent with the findings of the empirical growth literature, which uses per capita GDP growth rates for a much larger sample of countries, and so it seems reasonable to focus on this particular specification.

In the second column of Table 6, I show the same regression, but instead using the change in the Gini coefficient as the dependent variable. None of the correlates of growth are significantly correlated with changes in this summary statistic of inequality. It is however difficult to move from the results in these first two columns to conclusions about the effects on poverty, without making restrictive assumptions on the shape of income distributions. Since I have already constructed the growth and distribution components of changes in poverty, I can simply use these as dependent variables to investigate how these correlates of growth matter for changes in poverty. The remaining four columns of Table 6 do this for the headcount and for the Watts index. Given the 24
<!-- Start of picture text --> ro)<br>eog!<br>oO i<br>& !<br>L298Boo8Eg 1D U<br>gOo< !'<br>= a<br>Go3°o 1<br>os 1<br>£o Ui<br>3 ;<br>£ i<br>3g<br>Os a<br>gsoSD t)!<br>$ '<br>Ito a<br>f=}s '<br>2<br>0 50000 100000 150000 200000<br>Income<br>——— Baseline ===" 45° Line<br><!-- End of picture text --> <!-- Start of picture text --> So<br>=8<br>oOoN<br>Es<br>298Bdoa 7? °-<br>goooeo o* ° o<br>=e2 -*<br>oO> a o*<br>Be <i<br>&S Sa<br>58 Zor<br><8 ee<br>3<br>Ooo<br>o8DS£0<br>Z<br><x<br>lo}<br>0 50000 100000 150000 200000<br>Income<br>——— Baseline ====*= 45° Line<br><!-- End of picture text -->
# **1. Introduction**

Sustained export growth is a major economic goal for most developing and developed economies. The link between strong export performance and strong growth performance is obvious in the case of China in the last two decades, following on the evidence for other East Asian economies in earlier decades. Following a shift in the theoretical trade literature to models where firm heterogeneity plays a critical role in shaping trade flows such as those of Melitz (2003) or Bernard, Eaton, Jensen, and Kortum (2003), a burgeoning empirical trade literature has explored the increased availability of very disaggregated exporter-level datasets from customs agencies and several stylized facts have been uncovered for multiple countries.<sup>1</sup> For example, studies such as Eaton, Eslava, Kugler, and Tybout (2008) for Colombia and Amador and Opromolla (2013) for Portugal document a tremendous degree of churning in export markets, with a large proportion of exporters in a given year not having exported in the previous year.<sup>2</sup> Understanding the micro-economic dynamics behind episodes of strong export growth can help to identify the types of policies that can promote export growth.

In this paper we examine the microeconomics behind the dramatic export boom experienced by Turkey from 2002 to 2008 as well as the underlying forces for the strong contraction ensuing from the 2008-2009 global financial crisis and for the recovery thereafter. Turkish exports grew by 265% between 2002 and 2008, a very strong export performance compared to the average for peers in the same income group (Brazil, China, Mexico, and South Africa) whose exports grew by only 212%. Interestingly, Turkey was much more negatively affected by the global financial crisis than its peers since its aggregate export growth between 2002 and 2011 was 279% while that of peers at the same income level was 294%.<sup>3</sup> This was also a period when Turkey’s exports experienced a structural shift away from traditional textiles and clothing towards machinery and metals, as well as a movement across destination markets with the EU and EFTA losing ground to new markets in the Middle East and North Africa (MENA) as well as in Europe and Central Asia.

> 1 The datasets are sometimes referred to as ‘transactions-level’ trade datasets. See Bernard, Jensen, Redding, and Schott (2007, 2011) for reviews of the literature.

> 2 See also Iacovone and Javorcik (2008) for Mexico; Andersson, Lööf, and Johansson (2008) for Sweden, Freund and Pierola (2010) for Peru; Masso and Vahter (2011) for Estonia, De Lucio, Mínguez-Fuentes, Minondo, and Requena-Silvente (2011) for Spain, Fabling and Sanderson (2012) for New Zealand, Fernandes, Lederman, and Gutierrez-Rocha (2013) for a set of 11 Latin America countries, among others.

> 3 The growth rates are calculated for each country as 100* _X end year_ − _X_ 2002 _X_ 2002 where X designates exports.

2
- 28 -

schools, and the more positive attitudes toward schools held by students in private schools.

54. Other exRlanations for achievement differences. Even though the school type effect was nearly entirely explained by the social composition of the school, there remains significant between-school variance in achievement to be explained. Some of the differences in achievement may be due to other measured differences among the schools. The last step in our analysis involves exploring how differences among schools with respect to material and non-material inputs, orderly environment, academic emphasis, and school-level decision-making affect the average achievement and attitudes of students.

55. For this, we employ a feature of the HLM package called "exploratory analysis", which estimates slopes and standard errors of variables, presently not included in a model, as if they were included. We do this for each variable separately, and we report in Table 8 all variables for which the estimated t- statistic is greater than 1.65 (2-tailed p < .10). The model onto which these variables were added is the "contextual effects" model.
25 women having fewer children, (see Goldstein (1972) for an example, Simmons (1985) for a survey, and Singh and Casterline (1985) on the significance of substitutes for maternal attention). The opportunity costs and benefits for women of working clearly depend on the social structure of both household and kin, but these in turn depend partly on aspects of the work itself; hours, location, and accommodation arrangements, (see Section 4.3). In the production sector women tend to participate either when young, unmarried, and childless, as in SE Asia, or as married home workers as in Latin America and the Caribbean. In SE Asia, (and occasionally elsewhere), the preferred type of worker is female, under twenty-five, unmarried and childless, (Safa (1983), Arizpe and Aranda (1986), Wong (1986), Hein (1984), Pearson (1986b)). Here women's earnings primarily contribute to their parents' households, (Pearson (1986b p71). As a result, until recently, work before marriage has been associated with the postponement of marriage and so lower total fertility rather than lower marital fertility, (Wong (1986) p221, Leete (1987) p193). By contrast, in Latin America and the Caribbean, reproductive roles are more easily adaptable to wage labor and so it is low conjugal household income which drives women into work, (Pearson (1986b)). Production is organi4sed in a way which is more compatible with domestic labor, namely through subcontracting and outworking, (on Brazil see Saba (1983), and more generally Pearson (1986b) and Wong (1986)). This pattern is associated with a different kind of fertility decline. Rather than further increases in female age at first marriage, marital fertility
all findings and the ambiguity of effects within our conceptual framework suggest important caveats to changing caretaker choices about acute care through information policies.

**CHW effects on prevention.** While the results of this paper focus on care for acute illness, the CHW also promoted preventive care as part of AfH.<sup>26</sup> The 2014 data show that families who received CHW visits had better knowledge of the recipe for oral rehydration solution and higher self-reported use of water disinfection, although they are only weakly more likely to know the correct ages for exclusive breastfeeding, and an objective measure of water chlorine contents shows no effects. However, another objective measure (not related to diarrhea), whether a mosquito bed net was hung correctly, is 14 percentage points higher with CHW visits. Most knowledge and prevention measures essentially did not change in the subsidy-only group, as one might expect, and most also showed no significant difference between the CHW-only and the full-program groups. Together, these findings suggest that CHW visits and subsidies are effective in improving different aspects of health care for children, one primarily addressing preventive, the other curative care.

**Health outcome effects.** The effects we observe on diarrhea incidence support this interpretation. The data from 2014 show that the full program significantly reduces the number of days with diarrhea per child, equivalent to over 70 percent of the control-group average. While the effects in the other two treatment groups are not significant, they are negative as well. We do not reject that the full-program effect equals the sum of the CHW-only and subsidy-only effects.

It is notable that we see reduced diarrhea incidence in the 2014 data but no comparable incidence effects on diarrhea or other illness in 2013. Although the subsidy significantly reduces the number of days on which the child’s mother is “strongly concerned” and the length of time a given spell remains untreated (Appendix Table C.6, col. (1) and (4)), there are no significant effects of the interventions on number of illness spells or incidence of specific

> 26While the interventions were ongoing, Mali Health continued to provide additional training to CHWs. This might affect comparability of the CHW effects in 2013 and 2014.

32
# Panel A: Sorghum

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Panel B: Millet<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Burkina Faso Mali Niger Senegal Chad<br>Price (XOF/kilogram)<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20<br>Price (XOF/kilogram)<br>Jan-15 Apr-15 Jul-15 Oct-15 Jan-16 Apr-16 Jul-16 Oct-16 Jan-17 Apr-17 Jul-17 Oct-17 Jan-18 Apr-18 Jul-18 Oct-18 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20<br><!-- End of picture text --> Note: All prices are retail prices. Prices shown are national averages. Source: World Food Programme Vulnerability Analysis and Mapping Food Security Analysis tool and World Bank estimates.

in Annex 2). Second, responding earlier could reduce the overall number of households that need support. This could prevent major drops in food consumption, curtail the use of detrimental coping mechanisms, help households to plan their expenses, and even support investments in other incomegenerating activities to manage and cope with the effects of seasonality better. This echoes a growing literature pointing to the role of shock-responsive cash transfers to mitigate the impacts of climate-related shocks and the importance of the timing of the intervention (Gros, et al., 2019; Pople, Hill, Dercon, & Brunckhorst, 2021).

24
_5_ The economic recession of 1995 placed further constraints on public investment. As the economy began to recover from the recession in 1996 and 1997, public investment was expanded modestly in real terms (though not as a share of GDP). This suggests that the government has been, by and large, sticking to its strategy of relying mainly on private sector-led growth. The only aspect that departs somewhat from this strategy is the investment in the energy sector. Recognizing that this sector does not share the "public good" attributes of the social sectors, the Government had announced in 1995 a program of progressive divestiture and increased private sector participation in the "secondary" petrochemicals and electricity sub-sectors. Nevertheless, public investment in the energy sector now occupies an even higher share of the public investment budget than it did in the early 1 980s. As a share of GDP, however, it appears to have declined to half the level in the early 1980s, but these figures are misleading because they do not include the recently introduced "proyectos financiados".<sup>6</sup>

**Table 2: Sector Composition of Public Investment in Mexico**

(as % of total public investment)

|1982-1985|11.7|27.4|13.5|36.0|7.5|3.8|
|---|---|---|---|---|---|---|
|1986-1990|7.9|28.9|18.4|34.1|6.2|4.5|
|1991-1994|6.7|41.0|11.2|33.4|1.0|6.7|
|1995|5.1|36.8|12.0|40.3|0.9|4.9|
|1996|5.7|34.3|11.8|40.9||7.4|
|1997|6.6|33.6|9.6|44.4||5.9|
|1998B|7.2|35.0|9.4|42.7||5.6|

Note: All figures refer to executed federal expenditures, except for 1998, which refers to budgeted. Source: Presidencia de la Repuiblica, "Inforne de Gobierno, various issues, and "Proyecto de Presupuesto de Egresos de la Federaci6n para el Ejercicio Fiscal, 1998.

In 1997, the Mexican Government introduced a new modality for public investments, termed "proyectos financiados", which is based on "Build-OperateTransfer" or "Build-Lease-Transfer' principles. These investments are not explicitly included in the fiscal budget (although they do require Congressional authorization) and, therefore, are not captured by the figures in Table 2. "Proyectos financiados" consist of

> investment. Public investment in Construction is further subdivided into central government, social security and para-statal enterprises (all of which are captured in the figures in Table 2) and local government (comprising the state and municipal levels not captured in Table 2). No such subdivision is available for public investment in Equipment & Machinery, though it is conjectured that most public investment under this rubric takes place in the parastatal enterprises.

- 6It is important in this context to separate the issues of efficient spending versus sector management. Arguably, the energy sector could benefit from greater private sector participation. Being a productive sector, however, sectoral investments should be guided primarily by rates of the return in the sector, independent of whether it is the private or public sector that carries out the investment. That is, if rates of return are adequate, sector investments should be insulated from public spending cutbacks.