rafmacalaba/datause-encoder-data huggingface dataset:
may be because they are already very sensitive to distance, as shown by the high average distance elasticity of -1.55.

Table 5 reports results for the goods split by the Rauch classifications. We find that organized exchange and reference priced goods have become more distance sensitive than differentiated goods. According to the liberal classification, nearly half of the goods trading on an organized exchange have become significantly more sensitive to distance. In addition, the average coefficient on distance from the trade growth regression, -0.26, is much larger in magnitude than the average coefficient on differentiated goods. This means that relatively more homogeneous industries have become distance sensitive and they have become distance sensitive to a greater extent than differentiated goods. This is consistent with the Rauch’s (1999) hypothesis that communications improvements that reduce transactions costs have had a greater impact on trade in differentiated goods than on trade in homogeneous goods.

# **VII. Conclusions**

We systematically examine bilateral trade data to look for evidence of a change in the effect of distance on trade. Results from regressions using aggregate bilateral data imply that distance has become more important since 1980. Using industry data, we find that compositional shifts do not explain the increase in the importance of distance. The increasing importance of distance is due to increasing distance sensitivity in about 25 percent of industries. In contrast, we find that only a handful of industries have become less sensitive to distance. We also find that homogeneous goods are more likely to display an increasing importance of distance than differentiated goods. One explanation 19
Douidich et al. (2015) estimate the relationship between consumption poverty and household characteristics using a household consumption survey and then use this model to predict consumption poverty into a series of annual labor force surveys. This approach enables users to leverage existing data sources. If one decides to collect new data for the estimation of poverty, one has the option of only collecting the covariates _x_ , since an estimator for _θ_ can be obtained from the secondary data source. Put differently, the covariate-only sample is typically part of data collection planning when _μ_ ˆ<sup>_DS_</sup> is used but this is not necessarily the case when _μ_ ˆ<sup>_NN,DS_</sup> is used.

ˆ We will not formally derive the precision of _μ_<sup>_NN,DS_</sup> here as this would require us to make assumptions about the dynamics of the model parameters. When a previous survey is used as in Douidich et al. (2015) for example, one would have to make an assumption about how the model has evolved over time (or assume that it is time-invariant), which is not required for _μ_ ˆ<sup>_DS_</sup> . In Section 4.1 we will however provide a brief discussion on the financial costs savings that may be expected when using the non-nested double sampling estimator (under some simplifying assumptions).

# **3 Cost efficient sampling**

To see whether a meaningful reduction in costs is achievable, we examine the trade-offs between financial costs and statistical precision analytically. A stylized yet informative financial cost function is used. For a measure of statistical precision we appeal to the analytic expression for the asymptotic variance. The advantage of studying this trade-off analytically is that it allows us to work out the conditions under which double-sampling may be expected to be most beneficial. And, similarly, under what conditions the benefits will be marginal.

Specifically, we consider the problem of minimizing financial costs given a statistical precision constraint and its dual problem of maximizing statistical precision under a given budget constraint. Formally, we make the following assumption:

**Assumption 15** _The cost of collecting only x for any additional household in a given cluster equals τ ∈_ (0 _,_ 1) _. The travel cost to visit an additional cluster is equal to c._

16
Policy Research Working Paper 9911

# **Abstract**

The Philippines was among the most infected countries in East Asia at the onset of the COVID-19 outbreak. This study analyzes how international trade on various margins was affected by the country’s own lockdown policies and those of trading partners. Using a monthly series of product-by-country data for the period from January 2019 to December 2020 and an event study design, the paper shows that domestic lockdown measures did not affect international trade but external lockdowns affected both ex- ports and imports. The introduction of lockdown measures by trading partners affected imports more than exports, leading to 7 and 56 percent monthly average drops in export and import values, respectively. Restrictions on internal movements and international travel controls in partner countries were responsible for the drop in exports. The slump in imports was because of workplace closure, stayat-home requirements, restrictions on internal movement, and international travel controls by trading partners of the Philip- pines. Intermediate goods were the key driver of the drop in imports following foreign lockdowns, reflecting supply disruptions in backward global value chain participation. At the same time, exports of intermediate goods were more resilient to the lockdown policies. Finally, both exports and imports were more affected at the extensive margin than the intensive margin, as lockdown measures hindered interactions among people, in turn reducing the potential of businesses to create new relationships and launch new products in foreign markets. Overall, diversified and geographically dispersed suppliers can help countries adjust better to future disruptions.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at amontfaucon@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
# **2.2 Measures of location and scale**

When μ is the mean and σ is the standard deviation of the normalized consumption distribution y, the outlier detection rule in equations (1) and (2) _itself_ is sensitive to the presence of outliers. Davies and Gather (1993) suggest the use of robust location and scale measures to contrast this problem. There is little dispute on the use of the _median_ instead of the mean: the median is simple to calculate, and, unlike the mean, provides a greater degree of resilience to the presence of outliers.<sup>5</sup> On the other hand, the choice of a robust scale estimator appears to be somewhat debated. Table 2 shows a selection of candidate estimators for the σ parameter in equations (1) and (2).

**Table 2. Robust scale estimators for the transformed variable** **_t_**

|**Estimator**|σ|**Source**|
|---|---|---|
|**IQR**|IQR= Q3 −Q1|1|
|**MAD**|MAD= 1.4826 × med|yi−med[y]||2|
|**S**|S= 1.1926 × medi{medj|yi−yj|}|3|
|**Q**|Q= 2.2219 × (|yi−yj|; i< j)(k)|4|

Note: Q3 and Q1 denote the 75<sup>th</sup> and 25<sup>th</sup> percentiles, respectively; t is the transformation of the target variable x. Sources: [1] any statistics textbook, [2] Hampel (1974); [3, 4] Rousseeuw and Croux (1993). The use of (k) as a subscript indicates that the data have been sorted in increasing order: given a sample of n observation, the k-th order statistic of the sample is its k-th smallest value.

An outlier detection algorithm based on a _z-score_ with the interquartile range (IQR) as the denominator has been experimented with in Dupriez (2007), for instance. Hampel (1974) suggested the use of the _median absolute deviation_ (MAD), defined in row 2 of Table 2: the MAD is the median value of the distance between the transformed expenditure of each household (yi) and the center of the distribution, as estimated by the median of the transformed distribution (med[y]). Under the assumption that y is close enough to the

> 5 The price for the robustness of the median is a loss of efficiency with respect to the mean (Rousseeuw and Hubert 2017: 2), but in large samples the main, if not the only, property that matters is their bias – efficiency is only relevant for small samples. Note that we are assuming unweighted estimators. The _weighted_ median does not, in fact, qualify as a robust statistic (Filzmoser, Gussenbauer and Templ 2016: 15).

8
|_VARIABLES_|Central Gov.|General Gov.|Public sector|Central Gov.|General Gov.|Public sector|
|---|---|---|---|---|---|---|
|_log GDP per capita_|0.302|0.204**|0.181**|0.551**|0.228**|0.182**|
||(0.225)|(0.102)|(0.082)|(0.241)|(0.107)|(0.080)|
|_log urbanization_|-0.786|0.133|-0.195|-0.780|0.145|-0.253|
||(0.709)|(0.229)|(0.209)|(0.727)|(0.238)|(0.228)|
|_Trade openness_|0.319**|0.007|0.016||||
||(0.125)|(0.051)|(0.055)||||
|_External risk_||||6.878**|1.065|-0.461|
|||||(2.647)|(1.283)|(2.171)|
|_EAP_|0.781|-0.614***|-0.700***|0.818|-0.630***|-0.669***|
||(0.567)|(0.137)|(0.171)|(0.605)|(0.135)|(0.153)|
|_SSA_|0.731|0.002|-0.351|1.178*|0.044|-0.322|
||(0.615)|(0.259)|(0.278)|(0.612)|(0.252)|(0.292)|
|_LAC_|0.701*|0.108|-0.249**|0.875**|0.103|-0.281**|
||(0.375)|(0.174)|(0.118)|(0.362)|(0.169)|(0.131)|
|_MENA_|1.899***|0.566***|0.401**|2.264***|0.572***|0.447**|
||(0.257)|(0.181)|(0.162)|(0.224)|(0.172)|(0.170)|
|_SA_||-0.289|-0.457||-0.189|-0.519*|
|||(0.331)|(0.296)||(0.376)|(0.297)|
|_ECA_|0.911***|0.266**|0.279***|1.028***|0.263**|0.289***|
||(0.189)|(0.121)|(0.088)|(0.153)|(0.109)|(0.082)|
|_Constant_|-3.674|-4.615***|-2.505***|-6.238*|-4.919***|-2.281***|
||(2.972)|(0.966)|(0.692)|(3.288)|(1.086)|(0.723)|
|_Observations_|634|953|949|623|926|908|
|_R-squared_<br>_Robust standard err_|0.289<br>_ors clustered b_|0.327<br>_y country in par_|0.513<br>_entheses_|0.285|0.315|0.519|

_Robust standard errors clustered by country in parentheses *** p<0.01, ** p<0.05, * p<0.1_ Finally, estimating the same regression but using a panel with country fixed effects, shows that external risk has a statistically significant positive sign in two of the three specifications (for central and general government), though the sign switches for the public sector aggregation; trade openness is not statistically significant (Table 6). Hence, controlling for unobserved time-invariant heterogeneity seems to be relevant to confirm the positive relationship between openness and public employment.

14
-8-

# The Present Study

This study has a twofold objective:

- a) substantive analysis of real output levels, PPPs, and labour productivity in Brazilian, Mexican and US manufacturing;

- b) a methodological survey of the analytical problems inherent in such an exercise for any group of countries, in order to facilitate the task of researchers who may wish to replicate our approach.

Thanks to the availability of computer technology we have been able to present our methodology and the underlying data in a more or less fully transparenit way. We also offer some shortcuts and guidelines not previously available, and we deliberately tried to reduce the ad hoc element which loomed rather large in previous studies.

The present study is part of a series of comparative industry of origin investigations in which we and our colleagues have been engaged. Other comparisons for the mantufacturing sector for India/USA (Van Ark, 1987) and Brazil/UK (Van *rk, 1988) are available, and others are underway for Japan, Korea, France and the Netherlands. A fourteen country comparison is available for agriculture by Van Ooststroom and Maddison (1985), and for mining in Brazil, Mexico and the USA by Houben (1988).

We used the benchmark year 1975 in order to facilitate comparison with the results of the third phase of the ICP, which is also based on that year. The basic sources for this study are the censuses of manufacturing of the individual countries. These provide information on quantities and gross value of output in considerable detail as well as information on employment and on value added and inputs at national prices. This material is used to derive PPPs for particular products, and relative output levels for the corresponding industries. These are aggregated to derive estimates of value added, labour productivity and purchasing power ratios for 17 branches of manufacturing.

We carried out the quantitative comparison across countries for gross output, and derived PPPs which we also used for inputs and value added in each industry. Although our PPPs are the same for gross output, input and value added in each industry, the quantity relation between countries which we have for value added is different from that for gross output, as the ratio for value added to gross output varies between countries (for details see chapter III).

The reasons for choosing these three countries for the present pilot proje'nt are as follows: 1) they are all big countries with better-than-average industrial statistics;

- 2) Brazil and Mexico are interesting because the ICP showed Mexico to have a higher per capita product than Brazil whereas an earlier "industry of origin" study (Maddison 1970) showed Brazil ahead of Mexico (see Maddison 1983);

- 3) the USA is interesting because it is the country with the highest real income and labour productivity levels, and generally serves as a benchmark in identifying the technological frontier.
**Table 8. Difference between true IOO and median estimated IOO in percentage terms, inequality enhanced scenario and following parameters from 2012 Egypt Labor Force Survey**

||||**Excluded**|**circumstanc**|**e**<br>**Father’s**|**Mother’s**|
|---|---|---|---|---|---|---|
|**Observed population**|**None**<br>**(1)**|**Gender**<br>**(2)**|**Urban**<br>**(3)**|**Region**<br>**(4)**|<br>**education**<br>**(5)**|<br>**education**<br>**(6)**|
||||True IO s|hare = 0.97|3||
|All|‐0.01|‐5.12|‐0.78|‐80.11|‐0.28|‐0.78|
|Top 1% truncated|‐0.17|‐5.55|‐0.98|‐82.04|‐0.45|‐0.98|
|Top5% truncated|‐1.05|‐7.90|‐2.10|‐86.11|‐1.41|‐2.11|
|<br>|||<br>True IO s|<br>hare = 0.86|<br>0||
|All|0.00|‐5.18|‐0.84|‐80.11|‐0.32|‐0.82|
|Top 1% truncated|‐1.01|‐6.36|‐1.80|‐81.69|‐1.28|‐1.81|
|Top5% truncated|‐5.84|‐12.60|‐6.87|‐85.03|‐6.18|‐6.88|
||||True IO s|hare = 0.53|5||
|All|0.00|‐4.88|‐0.50|‐80.01|0.02|‐0.48|
|Top 1% truncated|‐4.32|‐9.71|‐5.19|‐81.40|‐4.70|‐5.18|
|Top5% truncated|‐22.81|‐29.17|‐23.74|‐84.22|‐23.15|‐23.68|
||||True IO s|hare = 0.35|3||
|All|0.00|‐4.94|‐0.63|‐80.05|‐0.20|‐0.63|
|Top 1% truncated|‐7.66|‐6.36|‐1.80|‐81.69|‐1.28|‐1.81|
|Top5% truncated|‐32.88|‐12.60|‐6.87|‐85.03|‐6.18|‐6.88|
||||True IO s|hare = 0.26|7||
|All|0.00|‐4.81|‐0.42|‐79.97|0.00|‐0.52|
|Top 1% truncated|‐9.49|‐14.73|‐10.29|‐81.76|‐9.84|‐10.45|
|Top5% truncated|‐35.83|‐41.28|‐36.75|‐85.54|‐36.17|‐36.79|
||||True IO s|hare = 0.17|9||
|All|0.00|‐4.22|0.34|‐79.81|0.88|0.24|
|Top 1% truncated|‐10.71|‐16.04|‐11.57|‐81.87|‐11.02|‐11.69|
|Top5% truncated|‐36.96|‐41.88|‐37.51|‐85.75|‐37.54|‐37.57|

Notes: Results based on 1,000 simulations. Inequality enhanced scenario refers to data-generating process described by equation (4), modified as explained in section describing simulation with “real” parameters.

26
# **V.  Non-Farm Employment, Poverty, and Agricultural Wages:  A Regional Analysis**

We conclude the analysis in this paper by drawing on the considerable variation across NSS regions and over time to bring together the three strands of the analysis: poverty, agricultural labor and non-farm employment. We construct an NSS region-level panel dataset based the 1983, 1993/4, and 2004/5 rounds of the NSS surveys. These three rounds have generally been met with broad endorsement as to underlying data quality, reliability and comparability. They span a period of broadly 20 years and, given that there are on average some 60 regions that make up the major states in India, also reflect considerable spatial heterogeneity. Our panel data set is based largely on data derived from the NSS survey and aggregated to the NSS region level, but we have also appended information on region-level agricultural productivity to the dataset, built up from districtlevel indicators of annual production, acreage, and prices of over 40 major crops available from official publications.<sup>11</sup> The database brings together non-farm employment, poverty and agricultural wage outcomes at the region-level with data on non-farm wages, urban consumption levels, per-capita landholdings and average education outcomes.

# _Framework_

Our approach in this section is largely descriptive. We start with a regional analysis of the correlates of non-farm employment, asking to what extent the farm and non-farm sectors are to be seen as complements or as substitutes. We distinguish between types of non-farm employment (regular, casual and self-employment) as well as broad sectoral categories. We are interested in particular to see whether there are complementarities between non-farm employment growth and education outcomes.

We then turn to an analysis of the correlates of rural poverty and of rural agricultural wage rates. Our approach here builds on a framework developed in Datt and Ravallion (1998) in which a reduced-form specification for poverty is expressed as a

> 11 We are grateful to Himanshu for sharing his estimates of NSS region-level yields with us.  These estimates are based on annual district-level data on crop production and acreage reported by the Ministry of Agriculture.  Yields are measured as the value of total production per hectare of land devoted to over 40 main crops in the districts comprising an NSS region.  Triennium averages of production and acreage around the survey year are used to smooth out annual fluctuations.

15
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# **F Data sources and data management**

## **F.1 Data source details**

With the exception of the PTA database, all data used in this paper can be readily obtained from the CEPII, the World Bank, the UN Statistics Division, and the United Nations Conference on Trade and Development (UNCTAD). The PTAs used in this paper are listed in Appendix E, while information on the other series is summarized in Table 13.

<u>Table 13: Variables and their sources</u>

|Variable|Source|Available|Name in source data|
|---|---|---|---|
|Bilateral trade|COMTRADE|On-line or free software|Tradevalue (6-8 digit HS level)|
|Bilateral tarifs|TRAINS|On-line or free software|Tarif (6-8 digit HS level)|
|GDP|WDI|Subscription-based|GDP (current US$)|
|Population|WDI|Subscription-based|Population, total|
|Distance|CEPII|Free on-line|dist|
|Common language|CEPII|Free on-line|comlang<br>~~o~~f|
|Common border|CEPII|Free on-line|contig|
|Colonial link|CEPII|Free on-line|colony|

_Note:_ COMTRADE (Commodity Trade Statistics) is a trade database maintained by the UN Statistical Division. TRAINS (TRade Analysis and INformation System) is a trade and tariff database maintained by the United Nations Conference on Trade and Development (UNCTAD). WDI (World Development Indicators) is a comprehensive country-level statistical database maintained by the World Bank. CEPII is Centre d’Etudes Prospectives et d’Informations Internationales, Paris.

All data were collected by the author while at the World Bank, and therefore some additional steps may be required to collect the information in Table 13 for those researchers without access to World Bank software. The trade and tariff data were accessed through the WITS (World Integrated Trade Solution) software, which is a joined collaboration of the World Bank and UNCTAD. WITS is an integrated data query and numerical simulation package, and provides access to the following databases:

- The COMTRADE (Commodity Trade Statistics) database maintained by the UN Statistical Division.

- The TRAINS (TRade Analysis and INformation System) maintained by the UNCTAD.

- The IDB (Integrated Data Base) and CTS (Consolidated Tariff Schedules) databases maintained by the WTO.

75
Policy Research Working Paper 9319

# **Abstract**

This paper explores the dynamics of media chatter about economic reforms using text analysis from about a billion newspaper articles in 28 languages. The paper shows that the intensity of reform chatter increases during economic downturns. This increase is more significant in democracies.

Using instrumental variable techniques, the analysis finds the relationship to be causal. The paper also documents that reform chatter is followed by actual reforms, suggesting that democracies benefit from a “self-correcting” mechanism stemming from changing popular attitudes toward reform.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. 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://www.worldbank.org/prwp. The authors may be contacted at rarezki@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