rafmacalaba/data-use-mentions-extended huggingface dataset:
unlikely to adjust the area of land in response to changes in tax rates. We therefore assume that landholders bear the full burden and behavioral responses are not expected.

The tax incidence of the rural land use fee and agricultural income tax is measured at two different levels. First, we calculate tax incidence as the household payments of land use fee and agricultural income tax as a proportion of annual nominal total household expenditure.<sup>14</sup> Second, we impute individual taxes by assigning household tax payments to the individual in proportion to the individual’s share of household land.<sup>15</sup> Individual tax incidence is derived by dividing the individual’s imputed tax payments by per capita expenditure.

Our empirical approach has the following limitations. First, the analysis is a first-order approximation of tax incidence and does not consider behavioral responses—although, as noted, area-based land taxes are unlikely to cause such responses. Second, although we assume that the self-reported tax payments recorded in the survey are accurate, there could in fact be a recall or reporting error on the part of respondents. For this reason, we conduct a sensitivity analysis by estimating the tax incidence using tax liabilities estimated from land area and the regional tax schedules in section 4.4b. Lastly, we examine self-reported individual landholdings and the associated tax burdens, but our analysis does not investigate intra-household gender relations.

# **4. Results**

# **4.1 Household and Individual Characteristics**

Table 3 shows demographic and other characteristics of all rural households by household type. (See Appendix 4 for characteristics of individual male and female respondents and Appendix 5 for household characteristics disaggregated by household head marital status.) Statistically significant differences (p<0.05) are reflected in bold (1) between male- and female-headed households, and (2) between female-only and dual-adult households. Female-only households are a subset of the female-headed households: just over half of female-headed households (57 percent)
data on the voting records of U.S. congresspersons to provide persuasive evidence that congresspersons with female children tend to vote in favor of reproductive rights issues, such as teen access to contraceptives. Furthermore, Washington (2008) argues for broader results: congresspersons support a range of measures that favor women, such as flexibility for working families and tax-free education. Oswald and Powdthavee (2010) model the idea that daughters increase parents’ left-wing tendencies. Their model incorporates the presence of pay discrimination and the fact that women derive greater utility from public goods than men do. In this scenario, women prefer a larger supply of the public good and a greater tax rate on income because their marginal utility from the former is relatively high, and the tax penalty they face as a result of the latter is relatively low. Because men have female children, however, they shift their political stance and become more sympathetic to the female desire for a steeper income tax schedule and a larger amount of the public good, so they become more left wing. Similarly, a mother with many sons becomes sympathetic to the male case for lower taxes and a smaller supply of public goods and becomes more right wing. Their theory is supported by German and U.K. data.

Although it seems that the gender of offspring affects an individual’s attitudes, previous evidence has only examined political views. It would be interesting to investigate whether the gender of offspring has implications for practices linked to the labor market, such as employers’ hiring decisions and wage-setting structures.

Overall, previous evidence indicates that men’s behaviors and attitudes toward women are likely to be affected by a range of factors (i.e., technological progress, media exposure, wage gaps, sex ratio, and offspring’s gender). Given the considerable influence that men exert on the economic development of women, these factors should be considered in the design of public policies intended to improve women’s well-being.

# **<<A>>
) - survey(2015) Peru: census(1993) - survey(2015) Uruguay: census(1985) - survey(2015)<br>25<br>15 25<br>20<br>10 20<br>15<br>10 5 15<br>5 0 10<br>.2 .4 .6 .8 .2 .4 .6 .8 .6 .7 .8 .9<br>Share of population<br>Rural Urban 95% CI<br>Medican income 2011 PPP US$ a day<br><!-- End of picture text -->

**Source:** Own elaboration based on IPUMS data and national household survey.

22
exports|11.6|1.6|0.0|1.5|1.0|2.4|1.3|**1.1**|1.9|10.8|
|**FDI Output**|30.0|**26.8**|0.0|0.7|0.3|0.4|0.3|0.4|0.3|25.9|
|**Factor Earnings**|||||||||||
|<br>Skilled labor|2.2|0.9|0.0|0.5|0.1|0.2|0.0|0.3|0.2|1.0|
|Unskilled labor|3.9|0.7|0.0|0.2|0.5|-0.1|1.3|0.0|0.7|2.6|
|Capital|3.3|1.1|0.0|0.4|0.3|0.4|0.3|0.2|0.5|2.0|
|**Diagnostics***|||||||||||
|Feenstra Ratio Summary|100.84%|100.26%|100.00%|100.09%|100.08%|100.10%|100.09%|100.05%|100.14%|100.00%|
|Terms of trade|100.01%|99.96%|100.00%|99.61%|100.31%|99.52%|100.38%|99.69%|100.47%|100.36%|

*****
tences into tokens or individual words based on text delimiters like spaces and commas. Tokenization is an important step in preparing data to be input into models because it converts text into a machine readable format. Stemming is the process of reducing a word to its base version. During the stemming process, for example, “talking”, “talks”, and “talked” will be reduced to “talk”. Stemming ensures the accurate count for words within each document.

Stop words–which are common words such as prepositions (before, an, above) and determiners (the, a)–and names were removed from the tokenized text using a custom lexicon made up of words from earnings call reports. Further, we removed words with fewer than three letters. These pre-processing steps ensure that various sentimental and uncertainty variables (section 2.1) from the earning call transcripts can be accurately calculated.

It is worth noting a key issue about the text-based approach employed in this study. The conversations in earnings calls may reflect corporate managers’ viewpoints which may include error and bias. However, the nature of earning calls forces its content to be reliable and accurate because financial figures are disclosed at the time of the call, and possible biases may be addressed immediately by other participants in the earning calls. Furthermore, because they are repeated every quarter, consistency in reporting is critical for the company’s credibility, encouraging an unbiased conversation about the company’s performance and broader economic developments in these calls. Finally, the SBVAR model in section 2.3, which predicts shocks to demand and supply under structural assumptions without any reference to earning calls, produces findings that are broadly consistent with the text-based sentiment series.

# **3.2 Macroeconomic data for SBVAR and LPM models**

The SBVAR model is estimated using real GDP, consumer price inflation, central bank policy rates, nominal effective exchange rates, and oil prices (table 1). The variables are 10
dS/m)|0.06|0.10|***|
|N|840|840||

Notes: Two-sided p-values reported: *** p<0.01, ** p<0.05, * p<0.1

# **4. Empirical Approach**

Various approaches to agricultural productivity are used in the agricultural literature, depending on research objectives and data availability. Average measures of productivity, including partial and total factor analysis, can be used to create a single statistic but the methods require high quality crop price data for the monetization of reported production that are often hard to come by in rural agricultural contexts with thin markets. Alternatively, marginal productivity analysis can be conducted with more direct policy-related takeaways. Cobb Douglas functions, and variations of Cobb Douglas, are commonly used (Deininger et al., 2007; Sherlund et al., 2002). The limitation of a simple linear production function in this context is that it assumes all farmers to be performing at optimal levels, without explaining the deviations between the observed and attainable (predicted) output levels. In order to allow for the analysis of the heterogeneity in production potential conditional on crop-specific suitability, one of the main objectives of the paper, we use stochastic frontier analysis, which allows for a better understanding of the aforementioned deviations.

9
and Drug Administration (FDA) granted initial licenses to garment manufacturers to produce masks. This policy was accompanied by a certification process, ensuring that all formally traded masks met FDA filtration standards. Licensed mask manufacturing also received economic incentives to engage in mask production, including a Value Added Tax (VAT) exemption. To isolate exogenous variation in exposure to the policy-induced mask manufacturing, we make the observation that sub-Districts are more likely to source masks from the same sub-Districts from which they source non-mask textiles, yet non-mask textile sourcing does not predict sub-District characteristics nor pre-licensing changes in sub-District outcomes. We use this observation to construct a shift-share (Goldsmith-Pinkham et al., 2020) measure of exposure to licensed mask manufacturing, based on purchases by destination sub-Districts of non-mask textiles from origin sub-Districts with high and low mask manufacturing intensity. Our identification rests on the assumption that destination sub-Districts’ sourcing of non-mask textiles is exogenous—destination sub-Districts that disproportionately source non-mask textiles from high and low mask manufacturing intensity sub-Districts would have experienced the same post-licensing changes in outcomes if there was no domestic mask manufacturing. We construct high-frequency panel data on inter-sub-District product trade, including prices and sales of masks, using the universe of timestamped transactions made through electronic billing machine software from November 2019 through August 2020. For each transaction we observe firm identifiers, product information, prices, and quantities, for sales to both final consumers and to firms. We produce four key results.

First, the increased supply of masks generated by average exposure to licensed domestic mask manufacturing persistently reduced mask prices by 8.8%. This decrease offset 13% of a large increase in mask prices in Rwanda that followed the start of the COVID-19 pandemic. These impacts on prices persisted to the end of our 2
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The recent experience of the Morupule coal-fired generation project, half of which has been funded with private resources, illustrates Botswana’s considerable to raise private finance for other areas of infrastructure; such as power.

**Figure 17. Botswana’s pattern of capital investment in infrastructure differs from that of comparator countries**

<!-- Start of picture text --> Investment in infrastructure sectors as percentage of GDP, by source<br>Botswana<br>1.8<br>1.6<br>1.4 MIC<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Public ODA Non-OECD Private<br>Source : Derived from Briceño-Garmendia, Smits, and Foster (2009).<br>Note: Private investment includes self-financing by households. ODA = official development assistance; OECD = Organisation for Economic<br>Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and<br>sanitation; MIC = middle-income country.<br>Figure 18. Many African countries capture more private investment than Botswana<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% of GDP<br>% GDP<br><!-- End of picture text --> _Source:_ Private Participation in Infrastructure Database (2009). _Note:_ GDP = gross domestic product; ICT = information and communication technology.

# Efficiency gap

As of 2008, around $68 million of additional resources were lost each year by inefficiencies of different kinds
# **Table A1:**

**Estimating the death toll and measuring the downward bias in using the sample based approach, by comparing it to the reconstruction approach. Sources: Siblings' mortality data. Cambodia Demographic and Health Survey, 2000 and Heuveline 2001(b)**

||**Population**<br>**in 1970**|**size**<br> <br>|**Death probab**<br>**1970-1974**|**ilities**<br><br>|**Deaths**<br>**1970-197**|**4**|**Remainin**<br>**in 1975**|**g alive**|**Death probab**<br>**1975-1979**|**ilities**<br>|**Deaths**<br>**1975-1979**||**Deaths**<br>**1970-1979**||**Excess de**<br>**1970-1980**|**aths**|**Ratio sampl**<br>**vs. reconstr**|**e**<br>**uction**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**From Heuv**|**eline**<br>|**From Table 1**|**(**|**1)*(3)**<br>|*
that is most consistent with the base survey, a more recent base survey, or more disaggregated CPI deflators than the annual data. We also find that the predictive performance of the imputation models stabilizes at the minimum sample size of around 1,000 observations for both the base survey and the target survey.

If similar evidence is obtained in other contexts, this can suggest a promising cost-saving approach with two options for survey design. The first option is to impute from an existing base survey (if such base survey exists) into a new target, non-consumption survey. The second option is to field a survey consisting of a smaller sample (preferably with at least around 1,000 households) with a full consumption questionnaire and a separate sample (of an equal or larger size) with partial or no consumption data collection. We can subsequently build an imputation model using the smaller sample with full consumption data to impute into the larger sample with partial or no consumption data. The tradeoff between the two options is that the first option requires zero cost with collecting new consumption data. Consequently, the first option is less expensive but may result in somewhat reduced imputation accuracy as discussed earlier, while the opposite holds for the second option. Yet, both options are still relatively less expensive than the traditional approach of implementing a full household consumption survey to obtain updated poverty estimates.

27
Figure 4. Private <u>Credit to GDP Ratio (1995-2009), MENA vs Non-MENA</u> <!-- Start of picture text --> MLT<br>ISR<br>JOR LBN<br>TUN KWT<br>BHR<br>MAR EGY<br>ARE<br>DJI OMNSAU QAT<br>IRN<br>LBY<br>YEM IRQ SYR DZA<br>7 8 9 10 11<br>Log GDP per capita (2005,USD)<br>Source: WDI<br>200<br>150<br>100<br>privatecredit<br>50<br>0<br><!-- End of picture text -->

# **4.4. SECTOR-LEVEL DEPENDENCIES**

Two measures of sector-level dependencies are used: external finance dependence, and asset tangibility.

Measures of an industry’s need for external finance are taken from Rajan and Zingales (1998). External finance dependence is calculated as the fraction of capital expenditures not financed with cash flow from the firm’s own operations, using data from U.S. firms in the 1980s. The United States is a good example to isolate industrial financing needs since markets are relatively frictionless especially among large companies. For example, industries requiring a lot of R&D such as drugs and pharmaceuticals require a lot more financing than say pottery. By treating external finance dependence as similar across all countries, one also avoids the problem of entangling country financial sector effects into industry-specific measure.

An industry’s level of asset tangibility is derived in Braun (2003) using U.S. Compustat data from 1986-1995. The most tangible industries include metals and chemicals, less tangible industries include apparel and chinaware.

Figure 5 illustrates the average external finance dependency and asset tangibility by country averaged over the period 1995-2009. The two sector dependency indicators are trade weighted by sector using BACI export values from this same time period. Figure
. The table reports OLS estimates. The unit of observation is a household. This table relies on the second definition of demobilized ex-combatants ( _D_<sup>_CNDD_</sup> _i_<sup>+2</sup> and _Di_<sup>_FNL_2</sup> )): cross-checked self-reports and matches. Odd-numbered columns do not include control variables or fixed effects; the remaining columns include both. We report the _p_ -values of F-tests assessing whether the coefficients associated with CNDD+ and FNL variables are of similar size but of opposite sign. Controls include past violence, whether the household head changed, and the head’s sex, age, matrimonial status, education, and occupation. Outliers are excluded (as explained in Appendix B.1). Sampling weights are accounted for. Clustered-robust standard errors are in parentheses. * _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 This finding is consistent with the theoretical framework, since at the time of the interviews, the demobilized FNLs were in the process of receiving the equivalent of 18-months’ salary in four cash installments. In a context of poorly functioning financial savings institutions,<sup>26</sup> none of the FNL households reported saving part of their allowance in financial assets. Instead, a large proportion of ex-FNLs reported buying consumption goods (46%), buying clothes (15%) and paying medical bills (8%). This observation is consistent with previous qualitative evidence from Burundi as well. Uvin (2007) showed that some ex-combatants, depending on the region they returned to, faced immediate needs such as building houses, paying medical bills, and buy-

> 26Our community data reveals the absence of formal financial institutions in 84% of the collines surveyed. Informal financial institutions are also scarce, with 87.5% of the collines lacking such associations.

21
simply equal to the monetary size of the transfer. However, calculating the willingness to pay for other types of policies is more difficult; see Hendren and Sprung-Keyser (2020) for examples on how to do this.

> 35 A recent paper by Athey et al. (2019) shows a way to construct ‘surrogate indices’ for the long-term outcome of interest using experimental or observational data, so that interventions can be evaluated in the shorter-run to form a good idea about their long-term effectiveness. We discuss this approach in Section 5.

57
Policy Research Working Paper 9665

# **Abstract**

During the COVID-19 pandemic, consumers were encouraged to use contactless payments. An important policy question is whether merchants with contactless payment technology are more resilient to an external health shock than those without. Using a matched difference-in-differences setting on unique merchant card-sales transaction data, this study finds that merchants with contactless payment technology increase their card-sales amount (count) on average by 8.3 percent (10.2 percent) compared with merchants without this technology. It also finds evidence that accepting contactless payment during an epidemic shock helps merchants attract more new consumers. Digital payment technology continues to support sales growth, especially for small businesses and new entrepreneurs.

This paper is a product of the Sustainable Development Global Practice Group. 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 author may be contacted at ycamara1@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
of financial institutions. The reason for this outcome is the process by which new financial markets acquire information on borrowers. Initially, when financial markets are deregulated, banks have very limited information on specific borrowers. In addition, even if banks have the necessary informnation they might not have the skills to use it efficiently. Thus, banks lend small amounts to many borrowers (i.e., credit rationing occurs as in Stiglitz and Weiss (1981)). As a result, investment is low and inefficient, projects with high productivity get the same amount of credit as projects with low productivity. As time passes, information on specific projects (borrowers) is revealed and banks update their credit accordingly. They increase credit to projects with high productivity and decrease credit to projects with low productivity. If the economy is transparent (i.e., there is no uncertainty regarding the true outcome of the projects) the learning process is fast and efficient in the sense that the new information is transparent and certain, the banking system is solid. If, on the other hand, transparency is poor in the sense that the new information is non-transparent and uncertain, the learning process may lead to inefficient and unsustainable allocation of credit. Even though banks adjust their belief rationally, updating their belief based on their priors and the new information, a series of temporary shocks or non-transparent expansionary policy would increase banks' exposure and vulnerability.

The analysis has several policy and empirical implications. The main policy implications are as following. First, a more transparent macro economic data and government policy, such as an increase in the transparency of fiscal, monetary and trade, would decrease the probability of a financial crisis, especially following financial liberalization. Second, policy makers should be very alert about credit expansions (and banks' behavior) during periods of high economic activity, especially if they posses bad information not available to 'See Campos, Ed, Root, Hilton L (1994) 2