rafmacalaba/datause-encoder-data huggingface dataset:
also tracks the use of digital technologies to respond to the crisis: whether the business started to use or increased the use of digital platforms since the beginning of the pandemic; whether the business has invested in new equipment, software, or digital solutions since the beginning of the pandemic; whether the share of online sales in total sales increased during the 30 days prior to the interview; and whether the share of employees working from home increased during the 30 days prior to the interview.<sup>6</sup> Our preferred specifications combine these responses in a single binary indicator that equals 1 when the business reports at least one of the four digital adjustments and 0 if the business did not implement any digital response, but we present results for each digital adjustment separately in our robustness checks.

# **2.3 The severity of the COVID-19 shock**

Data for the FAT survey was collected in face-to-face interviews between August and December of 2019 in Cear ́a-Brazil; between August 2019 and February 2020 in Senegal; and between November 2019 and February 2020 in Vietnam. Data for the COV-BPS was collected in phone interviews in the early months of the COVID-19 pandemic: between the end of May and the end of July of 2020 in Cear ́a-Brazil; between the end of April and beginning of May 2020 in Senegal; and between mid-June and early July 2020 in Vietnam. Figure A2 in the appendix shows the exact collection period for the COV-BPS in each country with daily average mobility trends around transit stations, which we compute using the data collected by Google in their COVID-19 Community Mobility Reports ( _19_ ).<sup>7</sup> Mobility trends likely reflect both, lockdown restrictions and voluntary reductions in interactions out of fear of the virus, and help proxy for the severity of the COVID-19 shock on economic activity ( _1_ ). In Senegal, average mobility in the period covered in the COV-BPS reached the lowest level recorded for 2020; in Brazil, mobility around transit stations was increasing after a sharp reduction the first weeks of the pandemic; in Vietnam, mobility was almost back to pre-pandemic

> 6The question on digital platforms in the COV-BPS also includes the use of internet, online social media, and specialized apps. Appendix A describes the questions.

> 7In the figure we use average mobility during the 30 days before because our measure of performance in the COV-BPS (the reduction in sales) spans the 30 days prior to the date of the interview.

7
resources. However, the following conclusions can be drawn from preliminary observations from the case studies and the survey of dialogue participants:

- a. The roundtable dialogue can help improve mutual understanding among different stakeholders. As discussed before, communication between the citizens and the government and between the citizens and the polluters is not adequate in China, especially in the rural areas. People do not really have good understanding with each other, even though most of them thought they did.

- b. The roundtable dialogue provides a platform for all stakeholders to speak for themselves. Almost all resident representatives and some of the company managers appreciated the opportunity to speak for themselves. There is little chance for ordinary citizens to speak publicly on issues which affect their quality of daily lives, their health or their economic activities. For those companies which have good environmental performance, it is also a chance for them to show their neighbors what they have done to control their pollution. Even for some companies which cannot control pollution to a desired level, it is still an opportunity for them to explain the reasons why they did not get there and to get better understood. Environmental authorities in China do not have much chance to communicate their work to ordinary people, especially to those people who do not read newspapers or have no TV access.

- c. The roundtable dialogue provides a channel for different stakeholders to negotiate terms of action for the future. During the dialogues, many community representatives and company managers requested that government officials as well as company managers take action on some specific issues. To some of the requests, promises are given. But to some others, reasons are given for not being able to take actions. Of course, there are some requests which received no responses. There is evidence that most promises are kept at least partially and actions are taken. There is also evidence that different stakeholders work together to solve some issues collaboratively after the dialogues.

- d. The roundtable dialogue generates pressure on the bad performers and gives appreciation to good performers. Polluters need to participate from time to time in the roundtable dialogues and report to their neighbors what they have done in the past to improve the pollution situation. Those bad performers felt ashamed and got under scrutiny even outside the dialogue meetings. One of the common practices of those bad polluters in

20
As Figures 7, 8, and 9 illustrate, the domestic equity market is much more developed in relative terms than the banking sector or the bond market. It includes a private equity and venture capital industry for incubating firms, an IPO market, a liquid secondary market, stock market indexes and index funds, and equity derivatives.<sup>54</sup> There has also been policy activism to improve corporate governance, thus encouraging investment by minority shareholders.<sup>55</sup> The relative openness of equity markets has also improved liquidity. This has been a reinforcing process, since more liquidity has helped to attract more foreign investors, improving further the liquidity of the domestic market. Moreover, from 1995 onwards, the development of electronic trading in India improved the attractiveness of domestic trading compared to the trading of Indian equity abroad (through GDR/ADRs).

Finally, although the growth in the Indian equity market is impressive, Allen, Chakrabarti, De, Qian, and Qian (2006) raise some doubts about its overall contribution to capital formation. In particular, these authors highlight that equity issuance is a still a relatively minor source of investment finance, with bank lending and informal credit channels predominant, especially for smaller firms. In addition, these authors question the true effectiveness of shareholder protection, with corruption weakening the legal protection of investors in practice.

> 54 See Shah (1995), Shah and Thomas (1998), Fernandes and Shah (2001), and Dossani and Kenney (2002).

> 55 The Indian market’s level of corporate governance scores well in the ranking of the CLSA Asia-Pacific Markets and Asian Corporate Governance Association.

39
envelope calculation for the size of the targeting benefit relative to the enrollment benefit CCTs offer when parents under-value the return to schooling. This exercise is useful as it allows us to investigate whether our targeting benefit is comparable in magnitude to one of the standard benefits CCTs offer.

# _VI.A Size of the Targeting Benefit relative to the Exclusion Cost under Progresa Grants_

First, we estimate the size of the targeting benefit relative to the exclusion cost under the observed, average Progresa grants: _t_<sup> ̄</sup> _c_ = 6 _._ 86 and _t_<sup> ̄</sup> _u_ = 2 _._ 90 pesos, per-capita, perweek. To do so, we evaluate the following discretized version of Equation (4) for our sample of treated, eligible children in 1998 and 1999: where _N_ denotes our sample size; _S_ ( _yit, t_<sup> ̄</sup> _c, t_<sup> ̄</sup> _u_ ) denotes predicted enrollment for a household with parental income _yit_ under the average Progresa grants (where we predict _S_ ( _yit, t_<sup> ̄</sup> _c, t_<sup> ̄</sup> _u_ ) using our estimated coefficients from Equation (12)); _S_<sup> ̄</sup> = _N_<sup><u>1</u></sup> � _it_<sup>_S_(</sup><sup>_yit,_ ̄</sup><sup>_tc,_ ̄</sup><sup>_tu_)</sup> denotes average enrollment under average grants and is equal to 0.78; _γ_ is taken from Table VIII above, _γ_ = 1 _._ 05; and, finally, average income and price effects are taken from Table XV above, _I_<sup> ̄</sup> = 0 _._ 0037, _P_<sup> ̄</sup> = 0 _._ 010.

Our estimate for _EC_<sup>_<u>TB</u>_ispresentedincolumn(1)ofTableIXbelow.Wefindaratio</sup> equal to 0.88, indicating the targeting benefit is substantial relative to the exclusion cost under the average Progresa grants. Moreover, given this ratio is close to 1, this is suggestive that the current transfers are close to the optimal transfers (based on targeting arguments alone), although _t_<sup>_∗_</sup> _c_<sup>willbelowerthan</sup><sup>_t_ ̄</sup><sup>_c_and</sup><sup>_t∗_</sup> _u_<sup>willbehigherthan</sup><sup>_t_ ̄</sup><sup>_u_.Lastly,</sup> the size of the targeting benefit is robust to the magnitude of mothers’ earnings: _EC_<sup>_<u>TB</u>_stays</sup> between 0.87 and 0.88 as we vary mom’s earnings from 0% to 100% of father’s earnings or if we set mom’s earnings to equal 80% of average father’s earnings.<sup>33</sup>

> 33. There are two competing effects going on as we increase mom’s income share from 0% to 100%: 1) the variance of parental income is increasing which works against the targeting benefit; 2) curvature is increasing which works in favor of the targeting benefit. The reason increasing curvature works in favor of the targeting benefit is because in this setting, it turns out that the lowest consumption households are the enrolled households. Thus, as we increase curvature, we care more about transferring towards the enrolled households.

36
As expected, child’s age, mother’s height, household wealth, and mother’s education are significant determinants of a child’s nutritional status. We included the mother’s height to control for a child’s genetic endowment and the high significance of this coefficient would suggest that there is indeed an intergenerational effect. However, one would not expect the mother’s height to directly influence the development of a child, and hence the child’s nutritional status, which explains the small impact of this variable. The probability of a child being underweight or stunted increases significantly as the child gets older. Children between 12–23 months of age are at the highest risk of being malnourished and that risk remains very high as the child ages, particularly for stunting. The coefficient on the child’s gender is not significant in any of the regressions, suggesting there are no gender bias affecting the nutritional status of children in Ethiopia.<sup>14</sup> The impact of household wealth, as measured by the wealth index created, is to decrease the probability a child is underweight by about 4 percent and stunted by about 3 percent. We would generally expect a lower impact of current household wealth on stunting, as stunting is a measure of chronic malnutrition reflecting the impact of the households’ current as much as past wealth.

Our proxy for household food insecurity, whether the mother’s BMI is less than 18.5, is positive and significant in the equation for underweight, but not significant in the equation for stunting.<sup>15</sup> Since a low BMI is a reflection of current nutritional status, it may not be a surprise that this measure of household food insecurity only affects the probability a child is underweight. As an indicator of household food insecurity, the coefficient on the BMI variable suggests that children in a household where the mother has a low BMI are about 6 percent more likely to be underweight. Children living in a female headed household are also 3 to 4 percent also more likely to be malnourished. The coefficient on household size is not significant in any of the regressions. The number of children under 5 years of age, however, has an adverse impact on the probability of a child being stunted but not underweight. It increases the probability of a child being stunted by 2.5 percent.

> 14 Sahn and Stifel (2002) examine whether mother’s schooling has a higher impact on daughter’s than son’s nutrition and whether father’s education favors son’s nutrition using DHS data for 14 African countries. 15 The generalized Hausman test does not reject the consistency of the estimated model which includes the mother’s BMI.

20
With regard to fertilizer usage, we employ three different estimations: a probit model for fertilizer usage, a Tobit model for the amount of fertilizer application in kilograms, and an OLS model on non-zero values of fertilizer purchases (Tables 4, 5, and 6, respectively). Rwanda is excluded from these regressions since the EICV survey did not collect this information. For fertilizer use (Table 4), the coefficients on the recall variables are not significant, suggesting the lack of recall bias, which is consistent with fertilizer application being a decision of high salience for farmers. Even after introducing the interaction terms, again, we find little evidence of recall bias overall. A few exceptions are by gender of the head in Malawi. There are recall effects for fertilizer use on maize plots for female-headed households and on tobacco plots for male-headed households, with underestimation of use when the household is interviewed further from the harvest (whereas in Table 3 both male and female-headed households under-report hired labor for maize plots when interviewed further from the harvest date).

The level of fertilizer use is presented in Table 5. For maize in Kenya, although not significant, the results suggest that smaller landholders are more likely to under-report with recall duration. The result is the opposite for Malawi tobacco, where under-reporting is statistically significant for larger landholders; there is also more under-reporting among male-headed households. For maize in Malawi, overall fertilizer use levels decline with recall duration, although the results are not statistically significant. This recall decay is more concentrated among female farmers and is offset with the education of the head and smaller landholdings.

With respect to the value of fertilizer and the OLS results (Table 6), Kenyan farmers report larger values when interviewed further from harvest and the value is associated with smaller landholdings. Combined with the kilo quantity results in the previous table, this suggests that smaller landholders are over-estimating the price paid for fertilizer when interviewed further from the purchase itself. This is also the case for female heads growing maize in Malawi; they report higher expenditure on fertilizer when interviewed further from harvest (although a lower quantity is used in the previous table).

As a robustness check, we repeat the above analysis excluding the control variables (including only indicator variables for season and geographic area). Given that households are randomly assigned across interview months, we expect that including these control variables will not impact our results. This is what we find when we re-estimate Tables 1-6 excluding the controls.

15
# **Invisible or Mainstream? Disability in Surveys and Censuses in Low- and Middle-Income Countries**

**<mark>Sophie Mitra, Wei Chen, Justine Hervé, Sophia Pirozzi and Jaclyn Yap</mark>**

Originally published in the <u>Policy Research Working Paper Series on</u> _April 2021._ This version is updated on _May, 2021._ To obtain the originally published version, please email prwp@worldbank.org.

JEL codes: I30; I31; J11; J14; R20.

Keywords: Data; Censuses; Surveys; Disability; Functional Difficulties.

**Acknowledgments.** Wei Chen, Justine Hervé, Sophie Mitra, Sophia Pirozzi and Jaclyn Yap are affiliated with Fordham University. This paper is a product of the World Bank Development Data Group (DECDG)’s Inclusive Data and Statistics Project (P170339), led by Buyant Erdene Khaltarkhuu and Akiko Sagesaka. Wei Chen, Justine Hervé, Sophie Mitra and Jaclyn Yap were consultants at the World Bank for this project. The authors can be reached at mitra@fordham.edu.

This study <mark>received funding from the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) and from the Wellspring Philanthropic Fund. 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. We are grateful for insightful comments on an earlier draft from Jose Cuesta, Mari-Helena Koistinen, Mitchell Loeb, Daniel Mont and Deepti Raja.</mark>
5to coastal fisheries, disruptions to urban and industrial water supplies and increased damage due to fertilizer and pesticide runoffs. Costs of soil erosion on Java are estimated at about $400 million per year (Magrath and Arens, 1989). Of this, nearly 80 percent are due to on-site costs such as the siltation of irrigation systems and the loss of reservoir capacity.

13. A number of studies demonstrate the potential for reducing soil loss with improved farm technologies (Roche, 1986; Saragih, 1986). Javanese farmers are quick to adopt new farming systems, provided incentives are appropriate. The main constraint to adopting recommended upland farming practices is their cost (Gauchon, 1976; Bersten and Sinega, 1986; Tampubolon and Saragih, 1986). Apart from research programs to develop lower cost technologies for upland farming, efforts are also needed to improve the returns to upland farming.

# Forestry Management

14. In 1950, the area under forest in Indonesia was reported to be about 144 million ha. In 1990, the forest base was estimated at 109 million ha (see Table 2). Of the 1990 forest base, 62.2 million ha were considered to have management potential, consisting of 40.8 million ha of mixed hardwood production forests, 2.1 million ha of tidal forests and 19.3 million ha of conversion and unclassed forests. Of the total base of production forests, some 30.4 million ha have not yet been logged.

Table 2: AREA AND FUNCTIONAL CLASSIFICATION OF FOREST, 1950-90 (million hectares)

||1950|1990|
|---|---|---|
|Conservation forest|18.8|14.6|
|Protection forest|30.3|25.2|
|PLCoduCL<br>foreL<br>**St**|''.||
|Conversion forest|30.5/a|18.7|
|Unclassified|n.a.|4.0|
|Total|144.0|108.6|

la Includes unclassified.

Source: GOI/FAO (1990).

15. As noted, deforestation at the rate of 1.2 , :rcent per annum has happened in recent years, which is significantly more rapid than the long-term trend of 0.7 percent (1950-90). Even witn some improvement over the recent deforestation rate, about one-fourth of the natural forest cover remaining in 1990 could be lost bv the year 2030, most of it accounted for by loss of conversion forests and production forests (GOI/FAO, 1990). Accordingly, the annual allowable cut from natural forest is expected to fall from some 34
(5) Management practices are worse among informal businesses than their formal counterparts (consistent with Kanbur, 2017; Benhassine et al., 2018; Campos et al., 2018), with the components on record-keeping and planning appearing least prevalent.

(6) Analysis of business relations reveals limited integration of informal businesses into the rest of the economy (consistent with Lewis 1954; La Porta and Shleifer 2014; De Paula and Scheinkman 2007).

(7) Performance (as measured by sales per worker) of informal businesses is considerably worse than their formal counterparts (consistent with De Mel et al., 2008, 2009; Bardasi et al., 2011, La Porta and Shleifer 2014; Ulyssea 2020; Aga et al., 2021).

(8) Analysis of the reasons behind operating informally suggests that informal businesses mostly perceive no benefits, only costs associated with registering, with very few ever considering formalization (consistent with Maloney, 2004; De Mel et al., 2013; Benhassine et al., 2018; Campos et al., 2018).

By presenting broadly comparable business-level data across multiple cities and countries based on representative samples at the city level and collected through a rigorous sampling method, this paper fills a large gap in data availability. The World Bank's Enterprise Analysis unit is continuing the implementation of the Informal Sector Enterprise Surveys in additional cities and countries, making both—information about ongoing projects and the resulting data—publicly available through its website.<sup>4</sup> The ultimate purpose of the work of this unit is to enable and encourage further research into the informal sector, to better understand business environment characteristics and practices of a segment of the economy that remains largely unexplored.

# **2. The Data, Coverage, and Questionnaire**

We use the combined data from the Informal Sector Enterprise Surveys conducted by the World Bank Enterprise Analysis Unit in twenty-four cities across seven countries in between 2017 and 2022. The countries and cities covered are: nine cities in India (Hyderabad, Jaipur, Kochi, Ludhiana, Mumbai, Sehore, Surat, Tezpur, and Varanasi), four cities in Iraq (Baghdad, Basra, Najaf, Sulaymaniyah), two cities in Lao PDR (Vientiane, Pakse), three cities in Mozambique (Nampula, Beira, Maputo), two cities in Somalia (Bosaso, Mogadishu), three cities in Zambia (Lusaka, Kitwe, Ndola), and one city in Zimbabwe (Harare). While multiple useful definitions of

> 4 <u>https://www.enterprisesurveys.org.</u>

4
# **Table 5.2. OLS results**

_Dependent variable: Share of stages with unofficial payments in total (procedures No. 1, 2A, 2B,_ _<u>3A, 3B and 8)</u>_

||Model 5.2.1|Model 5.2.2|Model 5.2.3|Model 5.2.4|
|---|---|---|---|---|
|Constant|0.105<br>(0.217)|0.011<br>(0.218)|0.040<br>(0.229)|0.341**<br>(0.152)|
|Procedure 1||0.094**<br>(0.041)|0.064<br>(0.044)|0.015<br>(0.058)|
|Procedure 2A||0.065<br>(0.060)|0.107<br>(0.065)|-0.133<br>(0.083)|
|Procedure 2B||0.157**<br>(0.064)|0.118*<br>(0.068)|0.005<br>(0.090)|
|Procedure 3B||0.001<br>(0.054)|-0.020<br>(0.055)|0.006<br>(0.062)|
|Procedure 8|0.107**<br>(0.042)|0.165***<br>(0.050)|0.128**<br>(0.055)|0.110<br>(0.068)|
|Kaliningrad oblast|0.282<br>(0.221)|0.341<br>(0.219)|0.335<br>(0.216)|-0.192**<br>(0.090)|
|Saint-Petersburg|0.628**<br>(0.250)|0.659***<br>(0.247)|0.649***<br>(0.243)|0.024<br>(0.151)|
|Leningrad oblast|0.617**<br>(0.250)|0.648***<br>(0.247)|0.645***<br>(0.243)|0.169<br>(0.126)|
|Moscow|0.400*<br>(0.225)|0.422*<br>(0.223)|0.420*<br>(0.220)|-0.070<br>(0.099)|
|Moscow oblast|0.395*<br>(0.227)|0.428*<br>(0.224)|0.388*<br>(0.222)|-0.146<br>(0.118)|
|Sverdlovsk oblast|0.391*<br>(0.230)|0.415*<br>(0.228)|0.408*<br>(0.227)|-0.119<br>(0.206)|
|Tomsk oblast|0.031<br>(0.222)|0.060<br>(0.219)|0.062<br>(0.217)|-0.356***<br>(0.075)|
|Khabarovsk krai|0.075<br>(0.222)|0.120<br>(0.220)|0.112<br>(0.216)|-0.372***<br>(0.094)|
|Irkutsk oblast|0.333<br>(0.221)|0.365*<br>(0.218)|0.355<br>(0.217)|-0.013<br>(0.071)|
|Rostov oblast|0.449**<br>(0.220)|0.484**<br>0.218)|0.479**<br>(0.216)||
|Perm oblast|0.218<br>(0.225)|0.248<br>(0.223)|0.230<br>(0.219)|-0.328***<br>(0.124)|
|Novosibirsk oblast|0.077<br>(0.221)|0.084<br>(0.219)|0.077<br>(0.215)|-0.419***<br>(0.072)|
|Nizhny Novgorod oblast|0.263<br>(0.223)|0.327<br>(0.221)|0.309<br>(0.217)|-0.244*<br>(0.077)|
|Sakhalinsk oblast|0.058<br>(0.242)|0.082<br>(0.239)|0.075<br>(0.235)|-0.402<br>(0.125)|
|Ln Size of the object|||0.0002<br>(0.010)||
|Ln amount of the official payments||||0.020<br>(0.016)|
|Adj.R2|0.334|0.353|0.352|0.397|
|N|240|240|213|119|
|Mean of the dependent variable|0.38|0.38|0.37|0.37|

*, **, *** Significance level of 10%, 5%, and 1%, respectively

23