rafmacalaba/datause-encoder-data huggingface dataset:
Skiba, 2004). The positive relationship between quality (and hence f.o.b. export prices) and distance that results from the endogenous quality choice of firms makes it possible to use the exporter f.o.b. unit value to help identify quality.

In particular, the heterogeneous firm trade model features a zero-cutoff-profit condition which determines the marginal exporter. As foreign demand increases, less efficient exporters enter the market, and they produce lower quality goods. This implies that quality and bilateral trade are negatively related from this supply-side equation. Combined with the positive association between trade and quality from the demand side, this makes it possible to obtain a sharper solution for quality than in the previous literature. The solution for quality is governed by cost, insurance and freight (c.i.f.) and f.o.b. prices, as well as model parameters (the elasticity of substitution, a Pareto productivity parameter, as well as a parameter governing non-homothetic demand). The fixed costs of exporting, which play an important role in the quality estimates through the supply side of the model, are captured by a general specification which depend on firm productivity and the size of the market, as well as bilateral gravity-like variables, such as language differences between the exporting and importing countries. Following Feenstra and Romalis (2014), we estimate these parameters using a gravity-type equation implied by their model (see Appendix A.1 for details). We use bilateral trade data at the HS 6-digit level in 2019.

# **4.2 Results**

We estimate the quality and quality-adjusted prices of Africa’s imports of digital goods, using detailed data on international trade flows and tariffs, and applying the Feenstra and Romalis (2014) methodology outlined above. We find that African nations tend to import relatively low quality, low price digital goods, as would be expected given their relatively low income per capita. Once quality differences are accounted for, digital goods in AfCFTA are sourced at broadly similar prices relative to higher-income nations, but face relatively higher tariffs, especially in low-income countries.

Panel A in Figure 3 plots average “impure”, or non-quality adjusted, unit values of imports of all countries (relative to the United States) and the corresponding income per capita. It reveals that these impure prices display a clear positive relationship with income per capita, and hence tend to be considerably lower in African nations than in richer countries from other continents. Panel B plots a similar graph relating the estimates for the average quality of digital goods with income per capita. We find again a clear and strong positive relationship between the quality of imported digital goods and income per capita, implying that African nations 9
Policy Research Working Paper 10139

# **Abstract**

This paper examines how formal firms have been impacted by and recovered from the pandemic, by drawing on two distinct but complementary data sources. This is the first attempt to use both survey and tax administrative data to measure the initial decline and subsequent recovery of firm sales and employment in a low- or lower-middle-income country. The findings of three rounds of follow-up surveys to a standard World Bank Enterprise Survey completed immediately prior to the pandemic are compared to information contained in the universe of value-added tax and personal income tax returns filled by firms during 2020 and the first half of 2021 in Zambia. Despite substantial differences in terms of the breadth and depth of these data sources, they show a very similar pattern. The sales of formal firms recovered from the pandemic far more strongly than their employment levels. By July 2021, both the survey and tax administrative data show that most firms experienced a complete recovery in sales, while levels of employment worsened over the course of the pandemic for many firms. Two key insights emerge from this analysis. First, formal firms appear to have adjusted their operations in a way that reduced their need for as much labor to achieve the same (or higher) level of sales. Second, if formal firms’ reduced reliance on labor persists, lower levels of formal employment in low- and middle-income countries may be a concerning consequence of the COVID-19 pandemic that lingers for years to come.

This paper is a product of the Poverty and Equity 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 choy@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
**16**

The shocks in table 3.4 are for the United States — the ‘epicentre’ of the crisis. But not all countries have been equally affected by the crisis. For example, durable manufacturing in Japan would be hit harder by the risk reappraisal given the collapse of their durable exports (dominated by cars) as a result of the combination of the global downturn and the appreciation of the Yen that resulted from the collapse in commodity prices and improvement in their terms of trade.

Also, Japan had their housing bubble a decade earlier than did the United States, so over the last few years they never experienced a property bubble as in America. So the shock to their economy from the bursting of the housing bubble would be less than for the United States. Therefore the shocks for equity risk, the housing bubble bursting and household risk are scaled off the United States. Taking the United States as 1 a series of weights for other sectors and economies appears in table 3.5.

# 3.5 **Weight for country and sector shocks**

||**_USA_**|**_JPN_**|**_GBR_**|**_DEU_**|**_EUR_**|**_CAN_**|**_AUS_**|**_OEC_**|**_CHI_**|**_IND_**|**_OAS_**|**_LAM_**|**_LDC_**|**_EEB_**|**_OPC_**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Equity risk by**<br>**sector**||||||||||||||||
|–energy|1|1|1|1|1|1|1|1|0.8|0.8|0.8|0.8|0.8|0.8|0.8|
|–mining|1|1|1|1|1|1.2|1.2|1|0.8|0.8|0.8|0.8|0.8|0.8|0.8|
|–agriculture|1|1|1|1|1|1|1|1|0.8|0.8|0.8|0.8|0.8|0.8|0.8|
|–durable||||||||||||||||
|manufacturing|1|1.2|1|1.2|1|1|1|1|0.8|0.8|0.8|0.8|0.8|0.8|0.8|
|–non durable||||||||||||||||
|manufacturing|1|1|1|1|1|1|1|1|0.8|0.8|0.8|0.8|0.8|0.8|0.8|
|–services|1|1|1.2|1|1|1|1|1|0.8|0.8|0.8|0.8|0.8|0.8|0.8|
|**Household risk**|1|0.3|1|0.5|1|0.5|0.5|0.5|0.5|0.5|0.5|0.5|0.5|0.5|0.5|
|**Household**||||||||||||||||
|**productivity**|1|0.1|1|0.5|1|0.5|0.5|0.5|0.5|0.5|0.5|0.5|0.5|0.5|0.5|

# **_Three main shocks capture the policy responses_**

On top of the above three financial crisis shocks there has been an unprecedented policy response comprising three more elements: 4. An easing of monetary policy to near zero official rates of interest in major developed economies.

5. An easing of fiscal policy across countries and large run-up in government deficits.

6. A rise in trade and financial protectionism.
simply indicate that market prices are close to insider’s beliefs about the fundamentals of a firm, rather than indicating low precision of managerial information. Nonetheless, we should expect better informed managers who are more confident about the quality of their information to trade more.

# **4.2. Price Informativeness**

To measure the amount of firm specific information contained in stock prices I use price nonsynchronicity. Specifically, I measure price informativeness for a firm as the share of its daily stock return variation that is firm-specific, defined as _PIit_ = 1 _− Rit_<sup>2,where</sup><sup>_R_</sup> _it_<sup>2is the</sup><sup>_R_2from the regression in year t of firm i’s daily</sup> returns on market and industry returns.<sup>17</sup> The idea, first suggested by Roll (1988), is that trading on firm-specific information makes stock returns less correlated in the cross-section and thereby increases the fraction of total volatility due to idiosyncratic shocks.<sup>18</sup> This measure is related to price informativeness in the model, in that increased informed trading volume should increase the idiosyncratic volatility of a firm’s stock price.

Firms are matched to their specific three digit SIC industry. I exclude firm-year observations with less than $10 million book value of equity or with less than 30 days of trading activity in a year. I used CRSP data to measure stock returns and Compustat to measure book values. I exclude firms in financial industries (SIC code 6000-6999) and utility industries (SIC code 4000-4999). The final sample consists of an unbalanced panel with 5,607 firms and 33,610 firm-year observations of uncertainty and price informativeness between 1990 and 2010. I detail the construction of all the variables in Table 1 and Table 2 presents summary statistics. To reduce the effect of outliers all variables are winsorized at 1% in each tail. Finally, I scale all variables by their standard deviation so that the estimated coefficients are directly informative about the economic significance of the effects.

# **4.3. Empirical Methodology**

To estimate the relationship between stock price informativeness and fundamental uncertainty, I consider the following baseline specification:

> 17The market index and industry indices are value-weighted averages excluding the firm in question. This exclusion prevents spurious correlations between firm and industry returns in industries that contain few firms.

> 18This measure has been used extensively in the literature studying feedback between prices and managerial decisions. See for example Durnev et al. (2004), Chen et al. (2007) and Foucault and Fresard (2013).

19
Figure 2 plots _t_ -statistics on _β_<sup>ˆ</sup> 3 from estimates of regression equation (3) with various community-level socioeconomic status variables substituted in place of sanitation, with and without a vector of controls _X_ , including the household’s own open defecation, as described above. Regressions take the following form: In all cases, the SES variables are community (survey PSU) averages, computed from the household recode, as in our estimated local open defecation variable. So, for example, open defecation is the fraction of households in the PSU which defecate in the open, radio is the fraction of households in the PSU which have a radio, and bottom fifth is the fraction of the PSU whom the DHS asset index sorts into the bottom fifth of their survey round. The one exception is GDP, which is a country-year level variable.

The dotted lines in figure 2 indicate the threshold for statistical significance. Figure 2 shows that only local open defecation robustly statistically significantly interacts with population density to predict infant mortality, with and without controls.<sup>9</sup> This specificity of the sanitation-density interaction increases our confidence that the result is indeed due to a greater effect of sanitation on height where population density is greater.

# **3.5 Extension: The shape of the sanitation-population density**

# **interaction**

For tractability, the regressions in section 3.2 assumed a linear association between population density and the sanitation-health gradient: each log-unit increase in population density

> 9A high fraction of the local area being in the bottom fifth of the country-year’s asset index statistically significantly interacts with population density _without_ controls, although not _with_ ; although it is beyond the scope of this paper, it is plausible that there is a special effect on health of poor people living densely close together. Note, however, that this cannot be an omitted variable in our results: our extended controls include indicators for the individual assets used to construct the asset index.

15
**Table C.34: Role model effects: Robustness to operator sorting**

**Panel A: Effects on income and decision-making power**

||(1)<br>Personal<br>income<br>(Taka)|(2)<br>Household<br>income<br>(Taka)|(3)<br>Decision-<br>making<br>(Index)|
|---|---|---|---|
|exposed|599_._23 ***|641_._20|0_._29***|
||(139_._32)|(608_._54)|(0_._08)|
|N|437|437|389|
|Non-exposed mean|9387_._40|19069_._09|_−_0_._12|
|Non-exposed SD|2809_._93|8054_._61|1_._13|

**Panel B: Effects on assignable goods expenditures and remittances**

||Women|Girls<br>(All outcomes as %|Men<br>in househol|Boys<br>d income)|Remittances|
|---|---|---|---|---|---|
|exposed|_−_1_._12|0_._46|_−_0_._35|0_._56|0_._89|
||(0_._71)|(0_._65)|(0_._44)|(0_._59)|(1_._50)|
|N|437|207|437|200|435|
|Non-exposed mean|7_._01|2_._63|3_._88|2_._93|9_._93|
|Non-exposed SD|7_._22|2_._71|5_._07|3_._65|14_._27|

Notes: Exposed is a dummy variable indicating whether the female sewing-line operator was working on lines to which the newly selected female supervisors were assigned to trial for the promotion program. The non-exposed group is a dummy variable indicating whether the female sewing-line operator was working on lines unrelated to the promotion program, and represents the comparison group for the exposed group. Standard errors clustered at the production line level in parentheses. Controls chosen using pdslasso from demographic controls and bargaining measures, their squares, and factory, enumerator and month dummies. The sample compares the subsample of exposed operators who joined the lines exposed to the new female supervisors before the start of the promotion program, comparing them to the previous sample of non-exposed operators. The estimation in Panel A, Column 2 excludes the household income variable from the set of possible controls. The sample in Panel B, Column 2 (Column 4) is limited to those respondent with a daughter (son), or a female (male) child in the household.

96
according to WITS/COMTRADE.<sup>18</sup> The countries with a broad manufacturing export base have high shares of manufacturing exports in merchandise exports, according to the _World Development Indicators_ across the period 1990-2010. In addition to comparator countries in each of these groups, the graphs below include a bar labeled ‘LAC countries’ showing the average across the LAC countries, a bar labeled ‘World Higher’ showing the average across higher income countries in the _Exporter Dynamics Database_ , and a bar labeled ‘World Lower’ showing the average across lower income countries in database.<sup>19</sup>

# **_4.1 Characteristics of Exporting Sectors and Exporters_**

Before examining the patterns of export entrepreneurship, it is worth documenting the diversity of characteristics across exporting sectors and exporters in the LAC region. Panels A and B of Figure 1 show the number of exporters in LAC and comparator countries in the two periods 2005-2007 and 2008-2009. The largest pools of exporters are found in the group of (large) broad manufacturing base countries Mexico and Brazil (close to or more than 20,000). While these two countries’ rates are lower than the comparators Spain and Turkey, they are actually higher than the average for higher income countries. Colombia is the LAC country with the largest number of exporters in the group of natural resource countries (in Panel B), though substantially smaller than those of richer South Africa and New Zealand. The smallest pool of exporters (about 1,243) is found in Nicaragua and in the other simple processing countries, though their pools of exporters are much larger than those of the comparator Cambodia. The average numbers of exporters in Panels A and B are quite similar suggesting that no major changes took place across the two periods.

Panels C and D of Figure 1 show average exports per firm in LAC and comparator countries in 2005-2007 and 2008-2009. Average exports per firm were the highest (above 7 million USD) in Brazil and Chile and the lowest in Guatemala and Nicaragua (about 1.3 million USD).<sup>20</sup> Chile and Peru, as well as Brazil and Mexico exhibited substantially higher average exports per firm than their comparators in the respective groups of natural resource countries and

> 18 While Mexico could be classified as a simple processing country due to the importance of the maquila sector for its economy, we chose to classify it as a country with a broad manufacturing base. 19 The countries entering the ‘World Higher’ average and ‘World Lower’ average are listed in the notes to the figures. 20 Note that the exporter size distributions are highly skewed in all countries, with a few very large exporters driving total exports, thus average exports per firm and median exports per firm differ tremendously as shown in Appendix A. However, considering the median Brazil still exhibits the largest exporters.

13
PRE Working Paper Series

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|WPS455|A Formal Estimation of the Effect<br>of the MFA on Clothing Exports<br>from LDCs|Junichi Goto|J'ne 1990|M. T. Sanchez<br>33731|
|WPS456|Improving the Supply and Use of<br>Essential Drugs in Sub-Saharan Afric|S. D. Foster<br>a|June 1990|Z. Vania<br>33664|
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|WPS458|Does Japanese Direct Foreign<br>Investment Promote Japanese Impo<br>from Developing Countries?|Kenji Takeuchi<br>rts|June 1990|J. Epps<br>33710|
|WPS459|Policies for Economic Development|Stanley Fischer<br>Vinod Thomas|June 1990|WDR Office<br>31393|
|WPS460|Does Food Aid Depress Food<br>Production? The Disincentive<br>Dilemma in the African Context|Victor Lavy|July 1990|A. Murphy<br>33750|
|WPS461|Labor Market Participation, Returns<br>to Education, and Male-Female<br>Wage Differences in Peru|Shahidur R. Khandker|July 1990|B. Smith<br>35108|
|WPS462|An Alternative View of Tax<br>Incidence Analysis for Developing<br>Countries|Anwar Shah<br>John Whalley|July 1990|A. Bhalla<br>37699|
|WPS463|Redefining Gove. nent's Role in<br>Agriculture in the Nineties|Odin Knudsen<br>John Nash|August 1990|K. Cabana<br>37946|
|WPS464|Does A Woman's Education Affect<br>Her Husband's Earnings? Results<br>for Israel in A Dual Labor Market|Shoshana Neuman<br>Adrian Ziderman|August 1990|V. Charles<br>33651|
Policy Research Working Paper 9304

# **Abstract**

This paper discusses and illustrates the analytical foundations of international comparisons (or benchmarking) for assessing a country’s potential for improvement along various dimensions of social and economic development. By providing a methodology for international benchmarking, discussing various alternatives and choices, and presenting a cross-country illustration, the paper can help practitioners be less arbitrary and more systematic in their approach to international comparisons, as well as more realistic in their expectations for a country’s improvement. The paper presents the stochastic frontier approach and applies it to estimate feasible frontiers or benchmarks for each variable, country, and year. It then interprets a country’s (one-sided) departure from the benchmark as inefficiency or potential for improvement. This contrasts with the literature that compares countries by looking at raw variables or indicators, without considering that countries differ in structural endowments that constrain the maximum performance that a country could achieve in a policy-relevant horizon. The Stochastic Frontier approach also improves upon the literature that uses regression residuals to measure performance. Regression residuals are hard to interpret as inefficiency, because they are mixed with noise and take positive and negative values. As an illustration, the paper uses a panel of 142 countries with yearly data for 2005–14 and considers a set of 10 development indicators. It finds that the potential for improvement does not follow a simple relationship with economic development, with some lower-income countries being closer to their own feasible frontier than more advanced countries are.

This paper is a product of the Development Research Group, Development Economics. 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 nloayza@worldbank.org, vnorambuena@worldbank.org, and kkar@binghamton.edu.

_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