rafmacalaba/datause-encoder-data huggingface dataset:
...xed e¤ects (�t), and other ...rm controls are included as before. As Table 7 shows, despite the declining cost share of imported materials and decreasing variety, processing ...rms’export variety is rising over time, particularly after 2002, one year after China joined the WTO.

In summary, our results suggest that the domestic content in Chinese processing exports is rising over time. The rise is mainly driven by ...rms actively substituting domestic for imported materials, but not rising production costs. Chinese exporters have been expanding their product scope while reducing imports, both at the intensive and extensive margins.<sup>40</sup>

# 5 Determinants of Firm DVAR

In the rest of the paper, we will focus on studying whether China’s trade and FDI liberalization since 2000 could explain its rising DVAR. We ...rst develop a simple model to guide our empirical exploration of the determinants of the rising ...rm DVAR. This model focuses on the time-series movement of ...rms’DVAR and thus the aggregate DVAR, and deliberately abstains from explaining the cross-sectional di¤erences in the DVAR.<sup>41</sup>

## 5.1 A Simple Model

Recall the accounting identity (4): 40There can be concerns that the regression results are di¤erent between the two processing trade regimes, as described in Section 3. To this end, we repeat all four regression analysis using the sample of importand-assembly (IA) and pure-assembly (PA) ...rms, respectively. As reported in Table A11 in the appendix, results remain robust and quatitatively identical to the results reported so far. This is not surprising given that 90% of the observations in our sample belong to the IA regime. It is assuring to see that ...rm DVAR is also increasing within PA exporters. The magnitude of the coe¢ cients on the year ...xed e¤ects are similar. Similar trends are also found using this sample for other dependent variables of interest, though the statistical signi...cance may sometimes be smaller due to the much smaller sample of PA ...rms.

> 41In the Appendix, we derive a model that features a Cobb-Douglas production function, and show how ...rm heterogeneity in price-cost margins may lead to a cross-sectional variation in ...rm DVAR.

24
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cooling and artificial lighting. For instance, high-quality flowers are grown year-round in the Bogota area in simple structures of wood and plastic. In the Medellin area, the same structures are used only to protect flowers from disease and heavy rainfall during the rainy season.

The ability to produce commercial-quality flowere year-round also mears that Colombian growers can continue to prod"_e during the winter months in the United States. These are the months during which demand in the United States is greatest (due to the large number of holidays), while environmental conditions ere least favovable for both home gardens and commercial growers.6 Two other factors made Colombia an ideal location for growing flowers. The country is abundantly endowed with naturally fertile land, which during the early 1970s was being used in low-value activities (Shypula 1981). More importantly, Colombia also had an abundance of low-skilled, largely female labor. This abundance meant that wage rates in Colombia were significantly lower than comparable rates in the U.S. This is illustrated by the data listed below.

||AVERAGE DAILY WAGE<br>Colombia|FOR PRODUCTION<br>Unite|WORKERS<br>d States|
|---|---|---|---|
||Agriculture|Horticulture<br>services|Private Non-<br>agricultural|
|1966|<br>$.82|$16.03|$19.80|
|1970|<br>.82|21.25|23.97|

Source: Urrutia (1985, 10), USDOL and CEA.

This wage difference translated into a sizeable cost advantage for Colombian growers. Using the cost data in table 1, the 1970 wage differential meant that Colvmbian production costs were 4! percent lower than U.S. costs. If the fuel cost savings are included, then Colombian production costs were 49 percent lower than U.S. costs. Moreover, the labor end fuel cost advantages were not offset by the high cost of sh.ipping flowers to the United States. Even after factoring in shipping costs, Colombian production costa were still 31 percent lower than U.S. costs.

Not surprisingly, these sizeable cost advantages meant that the profitability of selling Colombian flowers in the U.S. market at U.S. prices 7
Table 10. Average share of respondents conducting distinct activities together within a day (pairwise distribution), women

|||||S|martphone|sample|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Agri-<br>culture|Nonfarm<br>employ-<br>Ment|Obtaining<br>services/<br>resources<br>for HH|Cooking,<br>cleaning<br>in house|Care of<br>children,<br>elderly|Transport|School/<br>studying|Leisure|Comm.<br>Activitie<br>s|Personal<br>care|<br>Eating/<br>drinking|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Agriculture|(1)|0.03|||||||||||
|Nonfarm employment|(2)|0.01|0.01||||||||||
|Obtaining services/<br>resources for HH|(3)|0.01|0.03|0.04|||||||||
|Cooking, cleaning in house|(4)|0.04|0.05|0.14|0.02||||||||
|Care of children, elderly|(5)|0.03|0.03|0.07|0.17|0.01|||||||
|Transport|(6)|0.05|0.05|0.17|0.11|0.11|0.14||||||
|School/studying|(7)|0.00|0.00|0.00|0.00|0.00|0.01|0.00|||||
|Leisure|(8)|0.09|0.08|0.11|0.24|0.16|0.24|0.03|0.36||||
|Community Activities|(9)|0.00|0.00|0.00|0.01|0.01|0.01|0.00|0.02|0.00|||
|Personal care|(10)|0.01|0.01|0.01|0.07|0.04|0.05|0.00|0.11|0.01|0.02||
|Eating/drinking|(11)|0.02|0.04|0.02|0.18|0.09|0.04|0.00|0.28|0.00|0.07|<br>0.01|
|||||R|ecall sampl|e|||||||
|||Agri-<br>culture|Nonfarm<br>employ-<br>Ment|Obtaining<br>services/<br>resources<br>for HH|Cooking,<br>cleaning<br>in house|Care of<br>children,<br>elderly|Transport<br>|School/<br>studying|Leisure|Comm.<br>Activities|Personal<br>care|Eating/<br>drinking|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Agriculture|(1)|0.00|||||||||||
|Nonfarm employment|(2)|0.00|0.00||||||||||
|Obtaining services/<br>resources for HH|(3)|0.00|0.00|0.00|||||||||
|Cooking, cleaning in house|(4)|0.01|0.02|0.02|<br>0.00||||||||
|Care of children, elderly|(5)|0.03|0.04|0.08|<br>0.21|<br>0.00|||||||
|Transport|(6)|0.00|0.00|0.00|<br>0.00|<br>0.17|0.00||||||
|School/studying|(7)|0.00|0.00|0.00|<br>0.00|<br>0.00|0.00|0.00|||||
|Leisure|(8)|0.10|0.11|0.15|<br>0.47|<br>0.23|0.34|0.01|0.23||||
|Community Activities|(9)|0.00|0.00|0.00|<br>0.00|<br>0.01|0.00|0.00|0.01|0.00|||
|Personal care|(10)|0.00|0.00|0.00|<br>0.01|<br>0.04|0.00|0.00|0.06|0.00|0.00||
|Eating/drinking|(11)|0.00|0.01|0.00|<br>0.01|<br>0.16|0.00|0.00|0.45|0.00|0.00|0.00|

Notes: (1) Pooled sample of individuals across days 5, 8 and 10.

26
**Notes:** Robust standard errors in parentheses (clustered on country pairs). *** p<0.01, ** p<0.05, * p<0.1.  The data cover 1995-2018. Estimation in all columns include country-pair fixed effects and importer and exporter time period interaction terms estimated with Stata’s _ppmlhdfe_ command (Correia et al. 2019a, 2019b).  Estimation in all columns also include controls for the regional trade agreement, tariff preference margins, and the seven “deep” clauses shown in Table 4.  Estimation in column 3 also includes the two subcategories for category 1 shown in Table 1.

pg. 42
Table 7: Historical and Simulated “Best Practice” Weather-Related Losses 1970-2000

|RiskCategory|Historical|Best<br>Practice|Difference|%<br>Difference|
|---|---|---|---|---|
|Flood Deaths|153,079|91,541|61,538|40.2%|
|Floods Affected('000)|2,116,243|1,651,065|465,178|22.0%|
|Droughts Affected('000)|1,342,337|675,797|666,540|49.7%|

Table 8: Projected Net Female Primary and Secondary Enrollment Ratios by Region

|Region|Sub-Sa<br>Afr|haran<br>ica|East A<br>Pacific|sia and<br>Islands|Latin A<br>and Ca|merica<br>ribbean|Middle<br>North|East and<br>Africa|South|Asia|
|---|---|---|---|---|---|---|---|---|---|---|
|Year|P|S|P|S|P|S|P|S|P|S|
|2000|54.9|19.7|95.0|80.5|90.8|61.3|80.5|56.0|69.5|42.0|
|2010|68.7|28.5|96.9|86.5|93.3|73.1|89.1|67.4|81.3|60.5|
|2020|78.0|43.8|97.8|89.9|94.9|81.1|92.9|77.6|85.8|73.5|
|2030|86.0|59.4|98.2|93.0|96.0|85.8|94.9|84.7|88.8|81.9|
|2040|90.8|70.9|98.6|94.8|96.9|89.6|96.1|89.3|90.7|87.2|
|2050|93.5|78.0|98.8|96.2|97.4|92.1|96.9|92.1|92.4|90.8|

# Table 9:  Alternative Scenarios for India, 2000-2050

||Year||FloodDeat|hs|Af|fected by<br>(Millio|Floods<br>n)|Affe|cted by Dr<br>(Million)|oughts<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|||CSIRO|NCAR|Historical|CSIRO|NCAR|Historical|CSIRO|NCAR|Historical|
|2000||626.1|626.1|626.1|4.0|4.0|4.0|51.6|51.6|51.6|
|2050|Static|950.4|1,092.3|1,023.4|5.7|7.2|6.5|93.4|77.1|84.4|
|2050|Devel|461.0|461.0|1,023.4|2.8|3.6|6.5|75.7|62.5|84.4|
|Totals|||||||||||
|2000-50|Static|41,325.2|45,457.6|44,038.4|250.9|293.6|278.6|3,967.4|3,477.1|3,630.2|
|2000-50|Devel|27,768.1|30,269.3|44,038.4|176.7|204.1|278.6|3,362.6|2,956.4|3,630.2|

Table 10: Alternative Scenarios for Developing Countries, 2000-2050

||Year||FloodDeath|s|Aff|ected by F<br>(Million|loods<br>)|Aff|ected by Dro<br>(Million)|ughts|
|---|---|---|---|---|---|---|---|---|---|---|
|||CSIRO|NCAR|Historical|CSIRO|NCAR|Historical|CSIRO|NCAR|Historical|
|2000||5,520|5,520|5,520|16.4|16.4|16.4|142.7|142.7|142.7|
|2050|Static|10,861|11,018|10,871|24.6|27.1|25.5|262.2|241.6|250.5|
|2050|Devel|3,425|3,464|10,871|8.3|9.3|25.5|184.8|169.2|250.5|
|Totals|||||||||||
|2000-50|Static|419,849|427,755|423,185|1,054|1,140|1,102|10,690|10,116|10,304|
|2000-50|Devel|231,330|234,889|423,185|645|693|1,102|8,516|8,055|10,304|
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estimation of statistical precision, meaning that the estimated SEs obtained using the private NSO coordinates will arguably provide more accurate estimates of precision.

||BP (NSO)|BP (Public)|OLS (Public)|
|---|---|---|---|
|EA|0.155|0.165|0.186|
|TA|0.039|0.0.038|0.036|
|Region|0.0245|0.0199|0.011|
|National|0.0230|0.0178|0.0075|

Table 8: Mean standard errors (SEs) of poverty estimates from BP (NSO), BP (Public), and OLS for different levels of aggregation.

Provided that the spatial correlation structure is only partially destroyed by scrambling the coordinates, this explanation would predict that SEs obtained with public coordinates would be higher than SEs obtained using OLS which would fully ignore any spatial correlation structure in the errors, yet lower than SEs obtained with the private NSO coordinates (where the spatial correlation structure is fully accounted for). Fully ignoring the spatial correlation structure, as the OLS model does, would lead to a comparatively larger downward bias in SEs at more aggregate levels (as errors are – incorrectly assumed to average out) relative to SEs obtained with the spatial regression models. Both predictions are born out, as can be seen in Table 8 (which also includes estimates of SEs obtained with OLS). Standard statistics measuring the degree of spatial correlation found in the residuals are reported in Table 6, which confirm that the errors from the regression model with public coordinates exhibit a weaker spatial correlation structure.

# **5 Concluding remarks**

This study investigates whether an accurate poverty map could be obtained by combining remote sensing data that are available in the public domain with household consumption survey data. This approach to poverty mapping takes advantage of the fact that geo-referencing survey locations (i.e. Primary Sampling Units; PSUs) have become standard in recent years and that remote-sensing data from which predictors of poverty can be derived have become widely available. It should be noted, however, that 32
- b) <u>Real lending rates – In the same period, EMDE median real lending rates have declined by</u> almost half. However, median real rates have increased slightly in EAP and LAC (Figure 2C and 2D). While real rates dropped sharply during the height of the global financial crisis in 2008 due to a spike in inflation, they rebounded quickly and were largely stable since. Consistent with the pattern in nominal rates, real rates of poor performers (75<sup>th</sup> percentile) are high in AFR and LAC (>10%). In the decade 2007-2017, 45% of EMDEs in our sample experienced a decline in real lending rates.

-

- c) <u>Lending deposit interest rate spreads – The median interest spread declined by one-fifth.</u> Only MENA experienced a slight increase in spreads (Figure 2E, 2F, and 3B). EMDE median lending and deposit rates have fallen in tandem, consistent with a stable median spread. The most recent spreads for 2013-17 remain the highest in LAC and AFR, respectively. MENA is the only region where the median spread has slightly increased since 2003. In the decade 2007-2017, 60% of EMDEs saw their spreads decline.

- d) <u>Net interest margins (NIM) – Consistent with the trend in spreads, the median NIM in</u> EMDEs has declined by over one percentage point. Indeed, since the global financial crisis in 2007, 70% of countries experienced a fall in NIM in the wake of the global financial crisis as policy rates and government yields fell, private borrower risk and defaults increased, and banks increased their exposure to the sovereign. The rise in private sector risk premia did not keep pace with or was properly anticipated with the rise in realized defaults that drove the compression in NIMs. It is important to highlight that the NIM is impacted by holdings of interest-bearing government securities and non-performing assets which do no accrue interest income – this might distort the picture.

# **Box 2: Data and main variables**

Data from World Bank FinStats 2019, World Development Indicators and International Financial Statistics is used to form a panel of about 75 EMDEs for the period 2003-2017.

- _Lending rates_ are defined as the volume-weighted average interest rate charged by banks on short- to medium-term loans with fixed interest rates and with own funds to individuals and corporations. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. For example, riskier segments such as unsecured consumer lending,

10
4 lower-bound estimates of inequality of opportunity, either on their own, or in combination with consumption data from a (separate) household expenditure survey.

The first proposed method relies on information from the DHS exclusively, and uses a “wealth index” – constructed as the first principal component of the asset and housing quality indicators – as a composite measure of socioeconomic status (following Filmer and Pritchett, 2001). The second proposed method relies on additional information from a household income or expenditure survey, from which the correlations between consumption and various covariates common to both surveys can be inferred. These correlations are then used to impute consumption expenditures onto the DHS sample, following McKenzie (2005).<sup>4</sup> By construction, each of these methods gives rise to distributions with very different properties, requiring different inequality indices for analysis. For each case we derive suitable measures of inequality of opportunity, and estimate them both parametrically and non-parametrically, along the lines of Ferreira and Gignoux (2008).

Although the two approaches are quite distinct, they do ultimately rely, at least in part, on the joint distribution of asset, housing and amenities indicators in the DHS, and part of our contribution is to compare the ways in which that same underlying information gives rise to different measures, as a result of incorporating data from other sources, or applying different statistical procedures in the analysis.

We compare the two approaches in the context of an assessment of the degree and nature of inequality of economic opportunity among Turkish women, using Turkey’s Demographic and Health Survey (TDHS) and Household Budget Survey (HBS). Our estimates suggest that between one-quarter and one-third of the observed inequality among women in Turkey is due to unequal opportunities, depending on which method is used. We also propose and describe an “opportunity profile”, which reveals that opportunity deprivation is particularly pronounced in rural areas of the Eastern provinces, and among families headed by people with mothers with no formal schooling.<sup>5</sup>

> 4 This imputation method may be seen as a simplified version of the “poverty mapping” methodology of Elbers, Lanjouw and Lanjouw (2003).

> 5 Turkey is an interesting application not only because of the data configuration and of its interesting geographical and ethnic disparities, but also because it is a country with middling levels of income inequality (Aran et al. 2008 report a Gini coefficient of 0.31 for consumption per equivalent adult), but where people appear to be highly averse to inequality, and to attribute it to “social injustice”. 85% of
# **_1. Introduction_**

Although the region’s dependence on natural-resource exports is legendary, Latin America and the Caribbean (LAC) is composed of heterogeneous countries that differ in terms of size, trade structure and comparative advantage. Partly due to its comparative advantage in mining or agriculture, its history of economic thought is replete with pessimism about the region’s ability to develop (see, e.g., the literature reviewed by Lederman and Maloney 2007). However, the literature has remained silent with respect to the role of entrepreneurship as a driver of export growth in developing countries with diverse trade structures. This paper utilizes a new dataset on export transactions (collected from customs agencies) for a large set of LAC and comparator countries to assess the extent of “export entrepreneurship” during periods of fast export growth (2005-2007) and depressed external demand (2008-2009). Following the broader literature, “export entrepreneurship” is equated with the extensive margin of exports, namely the advent of new exporting firms, new export products and new export-market destinations. The paper provides a series of novel stylized facts on export entrepreneurship in the LAC region by focusing on dimensions of entry into export markets, entry into exporting of new products and entry into new destination markets by incumbent exporting firms. We identify within-regional differences and benchmark LAC’s export entrepreneurship relative to comparator countries in other regions, as well as cross-sectoral differences in export entrepreneurship.

We use novel exporter-level data from customs for 11 countries in the LAC region, including Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, and Peru from 2005 to 2009. The analyses consider two separate sub-periods: the steady export growth period of 2005-2007 and the period 2008-2009 characterized by the global financial crisis and the ensuing global recession. By examining the latter, this paper presents the first ever micro evidence on the effects of this major crisis on exporter dynamics in the LAC region. To benchmark the performance of the LAC countries, we selected comparators from around the world following a categorization of the LAC countries into three groups: (1) natural resource abundant countries (Chile, Colombia, Costa Rica, and Peru); (2) simple processing or assembly countries (Dominican Republic, El Salvador, Guatemala, and Nicaragua); and (3) countries with a broad manufacturing export base (Brazil and Mexico).<sup>5</sup> Moreover, we consider broad averages for upper-middle and high-income countries [henceforth

> 5 See Section 4 for details on the criteria used to classify countries in these three groups.

2
where superscript _i_ refers to a given observation, and superscript _n_ refers to the number of the row in the B matrix used in the estimation. As a result, for each observation (individual) we obtain the number of predictions equal to _n_ .

To obtain the estimate of prevalence at the district level (or any other level of aggregation we are interested in) we first calculate the mean value by district for each column of observations, denoted Pr<sup>n</sup> . Note, the calculation of the means takes into account the sample frame of the DHS sample (i.e., stratification and clustering). As a result, for each district we obtain the vector of _n_ such means (with _n_ =100 in our case). The mean calculated over those _n_ means will be our point estimate of predicted prevalence.<sup>21</sup>

# **3. Empirical Results**

## **3a. Regression results**

We first report and discuss the regression results which form the basis for predicting the HIV prevalence into the DHS 2000 and estimating prevalence rates at the district level. Table 4 presents the results based on the set of weights that make the distribution by age/gender for women in the sentinel survey reflect that for all women in the DHS. As mentioned earlier the estimated coefficients represent correlations, not impact parameters. Omitted variable bias is thus likely to afflict these models and as such the interpretation of estimated parameters is not terribly meaningful. Nonetheless, we offer a few comments on the estimated correlations based on the results reported in Table 4.

> 21  As mentioned earlier, a sense of the precision of our prevalence estimate can be obtained by calculating the standard deviation of those _n_ means calculated above.  This statistic will capture two important components of  the overall total prediction error: _idiosyncratic error_ and _model error_ . A further component, _computation error_ is attributable to the employment of a  bootstrap simulation approach. With the number of simulations ( _n_ ) equal to 100, the computation error is equal to σ /10, where σ is the standard deviation of the distribution of means. Thus by choosing a large enough number of simulations, the computation error can be set arbitrarily small.  By combining the idiosyncratic, model and computational errors we obtain the prediction error.  To this error one would still need to add the sampling error attributable to the DHS 2000 sampling design in order to obtain the final, total, predication error (see Elbers et al, 2003, and Elbers et al 2008, for further discussion).

12