rafmacalaba/data-use-mentions-extended huggingface dataset:
# **IV. DB and “favored” and “disfavored” firms in the ES**

The ES data reveal that, for any given country or question, there are enormous reported differences _across_ firms. When firms are asked about time to obtain an operating license, construction permit, or clear goods through customs the responses not only do not cluster tightly around the DB average but do not cluster around the ES average. A significant fraction of firms report that (their version of) regulatory compliance takes essentially not time at all, while others report significant delays. So there is an important question of how the _levels_ of the DB indicators are associated with the _dispersion_ and entire _distribution_ of firm level responses. One notion might be that an increase in the DB time from say 20 days to 50 days would just shift the entire distribution up, so that all firms would have roughly X additional days. However that is not at all what the data suggest. Rather the data suggest that the ―favored‖ firms are affected roughly not at all by increases in the DB figure while, at low levels the dispersion increases as the firms who report relatively long delays report longer delays as the DB value goes up, but only to a point, after which there is roughly no effect on the distribution of responses at all.

The figures 2a, 3a and 4a show the scatter plots for each country of the 10<sup>th</sup> and the 90<sup>th</sup> percentile of the firm responses plotted against the DB for imports, business licenses, and construction permits respectively to show how the dispersion of ES responses evolves with DB.

One common pattern is clearest for construction permits (Figure 3a). When the DB value moves from its lowest value of 77 days (for Lesotho) to the (third) highest value of 471 days in Ukraine the predicted 10<sup>th</sup> percentile (using the two spline specification) increases from 4.
regulations appears to be the one which robustly stands out as significant. This latter result remains robust when using our alternative preferred TFP measures of Hsieh and Klenow. (See Table A10.) The estimated coefficients of Table 2 and Table A10 imply that for an average EU country, a reduction in overall services regulations to a targeted average level of the three most deregulated European economies means an increase in the level of firm productivity performance of about 2.8 percent. When breaking down the overall regulatory restrictions into entry and conduct barriers, a similar reduction in services regulation would imply an increase in the average productivity level of the firm by, respectively, 1.39 percent and 1.59 percent. Note that these numbers represent an average of the coefficients found in our preferred TFP specifications from Ackerberg and Hsieh and Klenow. In a comparable scenario in which one takes the highest coefficient outcome in Table A10, the results would imply a productivity level increase of the firm by 5.34 percent when reducing overall barriers; a 2.83 percent increase when reducing entry barriers; and a 3.38 percent increase when reducing conduct regulatory barriers. Table A11 in the annex provides more detail on these computations while Table A12 shows TFP impact results by country.

Following our discussion on using common versus country‐specific input‐output coefficients, we repeat our analysis using the EU‐wide input‐output tables. The results are presented in Table 3 and show that much less significant coefficient results are obtained. Nonetheless, they substantiate our results found in the previous table in the sense that conduct regulations are negatively significant for TFP levels using the Ackerberg specification. Results from Table 3 confirm the negative effect of conduct barriers in services on downstream TFP when entered separately (albeit weakly) and is reinforced when entered with the other regulatory services linkage variables. Other coefficient outcomes remain entirely insignificant as part of this specification. In column (6), the regulatory services linkage index for FDI now receives a negative and significant sign as well when
# **A Demand-Side View of Mobile Internet Adoption in the Global South**<sup>**1**</sup>

Rong Chen Keywords: Internet adoption, mobile broadband, developing economy, digital gap. JEL codes: O12, I30, L96, O55.

1 This research was undertaken as a Background Paper prepared for Chapter 5 of the World Development Report 2021 _Data for Better Lives_ . The author would like to thank the following colleagues from the World Bank Group for their review and valuable feedback on the paper: Mark Williams (Practice Manager, Digital Development Global Practice), Carlos Rodriguez Castelan (Lead Economist, Poverty and Equity Global Practice) and Takaaki Masaki (Economist, Poverty and Equity Global Practice). The author is also grateful to participants to the InfraXchange seminar: A Demand Side View of Mobile Internet Adoption in the Global South, on November 19<sup>th</sup> , 2020 for their comments and suggestions. The author appreciates RIA (Research ICT Africa), LIRNEasia (Learning Initiative for Network Economies in Asia), and DIRSI (el Diálogo Regional sobre la Sociedad de la Información / Regional Dialogue on the Information Society) for sharing survey data to support the analyses.
connections, compared with just 8 percent of households in informal settlements. And in Accra, Ghana, more than two-thirds of the poorest households share a toilet or latrine with more than 10 other households (World Bank, Water Supply and the MDGs, 2003). Such disparities have clear effects on health outcomes: in urban Kenya the under-five mortality rate averages 84 per 1,000 (and 62 in Nairobi), while in the country’s two largest slums it averages 187 (Kibera) and 254 (Embakasi; Africa Population and Health Research Center 2002).

# _Quality of services_

Although data on access are more meaningful when combined with data on service quality, most access data are not adjusted for quality. Access data usually include individuals with all-day access as well as individuals with access for just a few hours a day. But quality matters. A recent study of seven Latin American countries suggests that because of poor quality, the effectiveness of public infrastructure in the region is only about 74 percent of that in industrial countries (Roja, 2003). According to the same study, the long-run cost of this underperformance is equivalent to about 40 percent of real per capita income. Thus, raising infrastructure effectiveness to industrial country levels would reduce the per-capita income difference between Latin America and the United States from ten-fold to about seven-fold.

Despite growing recognition of the economic and social significance of service quality, there is no indicator of the combined dimensions of quality. So, in the short run, the only option is to rely on partial indicators. In some cases, for example, quality must be inferred from associated health indicators. Although this approach is not satisfactory, analysis 13
years, the proportion of children coming from urban areas exceeded the one of those coming from rural ones, albeit maintaining a certain level of symmetry in the population.

Finally, children speaking Spanish are more than 90 percent in all years and it is quite unusual for a child to have an educated head, but it is relatively more common that this is the father instead of the mother.

The data presented in Table 2 shows the proportions of children enrolled in primary public school, attending it and carrying out working activities. It is clear that the percentage of enrolled children has been increasing over the considered years. However, the attendance proportions do not show any clear

> 14 MECOVI is a regional program that aims at standardizing household surveys in the Latin American and Caribbean region, funded by the World Bank, the Inter-American Development Bank, and the United Nations.

> 15 A slightly wider age interval has been used to allow for children that go to school one year before or one year later the traditional age of entry.

> 16 The percentage of those enrolled in private schools is on average below 10 percent.

12
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- Pape, U., and J. Mistiaen (2015) ‘Measuring household consumption and poverty in 60 minutes: The Mogadishu high frequency survey.’ mimeo, The World Bank

- Pettersson, H., and B. Sisouphanthong (2005) ‘Cost model for an income and expenditure survey.’ In ‘Household Sample Surveys in Developing and Transition Countries,’ vol. ST/ESA/STAT/SER.F/96 of _Series F_ (Department of Economic and Social Affairs, United Nations Statistics Division) studies in methods 13, pp. 267–277
What then are the implications of these findings for developing countries? It is interesting to start this discussion by noting that the empirical testing of the altemative models of investment in developing countries have been confined to accelerator, neoclassical, and the cash flow models. The Q theory of investment has not been in this mix given the rather exacting data requirements for the computation of the Q ratio. Overall, the evidence for the developing countries have favored the cash flow theory of investment. 2 While there exists no current research that directly distinguishes between managerial and information-theoretic approaches that underpin the cash flow theory of investment for developing countries, there are some interesting pointers. For instance, based on a detailed comparison of U. S. and Indian firms, Samuel (1996) has shown that apriori, information and agency problems are likely to be less severe for Indian firms compared to U. S. firms, given that the Indian financial system is predominantly a bank-oriented one compared to the U. S. stock marketoriented system. Further, the analysis of the financing of project costs for existing and new Indian firms suggested that internal finance and debentures played a much greater role in project financing for existing firms, while external equity and loans from All-India Financial Institutions' played a much greater role for new companies. Since agency costs are likely to 23 See Athey and Laumas (1994) for India, Harris et al. (1994) for Indonesia, Jaramillo et al. (1993a, 1993b) for Ecuador, Nabi (1989) for Pakistan, and Tybout (1983) for Colombia. Also, Bilsborrow (1977) found support for accelerator and cash flow theories using panel data for manufacturing firms in Colombia.

24 There are three All-India Development Banks: Industrial Development Bank of India (IDBI), Industrial Finance Corporation of India (IFCI), and Industrial Credit and Investment Corporation of India (ICICI). In addition to IFCI, ICICI, and IDBI, Industrial Reconstruction Bank of India (IRBI) also provides long-term finance to Indian
services, access to early warning systems, sanitation, and building and infrastructure quality are also playing a key role in determining disasters’ impacts, and has been included in other estimates, but is left for future inclusion here.

# Inability to cope with losses

The first dimension of inability to cope is not having income to manage the impact of shocks. The aim of this measure is to identify individuals that have incomes that are too low to be able to meet basic needs should a shock to incomes occur. The motivation for this measure comes from work that has set vulnerability lines for identifying those at risk to falling into poverty. Many of the early estimates on measuring vulnerability to poverty utilized panel data that had repeated observations of welfare outcomes on the same individual and considered households to be vulnerable if the probability that their consumption was below a poverty line was high (Hoddinott and Quisumbing, 2010). In an extension of this work, vulnerability lines were defined—the level of consumption or income that an individual would need in order to have a low probability of falling into poverty (López-Calva and Ortiz-Juarez, 2014). LópezCalva and Ortiz-Juarez (2014) proposed using a 10% probability of becoming poor to identify those vulnerable to poverty. Using this approach they calculated that the vulnerability threshold for Chile, Mexico, and Peru was about 2.5 times the poverty line. Similar analysis in Indonesia found that the vulnerability threshold is 1.5 times the poverty line (Jellema et al., 2017; World Bank, 2019). This work is informative, but it is important to note that this approach takes into account both idiosyncratic and covariate shocks, whereas here we focus only on one type of covariate shock, extreme weather events.

This approach—of setting a vulnerability line at some multiple of the poverty line—has been used in World Bank regional reports and poverty assessments in East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC)
question includes an evaluation of corruption (Question J). We present the exact wording of this questions and potential responses in the Appendix.

**Table 2: Evaluations of Impact of Corruption on Business Activity**

||Number|Percent|
|---|---|---|
|Verynegative|37|2.61%|
|Moderatelynegative|370|26.11%|
|No impact|865|61.04%|
|Moderately positive|127|8.96%|
|Very positive|18|1.27%|
|Total|1,417|100%|

As presented in Table 2, the majority of firms respond that corruption has no impact on their business. Unfortunately, the collection of the survey data doesn’t allow us to differentiate “No impact” from the responses providing nonresponses and are thus coded as no impact. The sizeable minority of firms claiming corruption has a moderately negative impact (26.11%) is important, and has clear policy relevance. But do these levels of corruption distort investment decisions and ultimately have a negative impact on economic performance?

Unfortunately, these two corruption questions presented may suffer from a number of problems. The first question asks a general subjective assessment of the level of corruption, while the second asks a specific question on how corruption affects the business environment. Broad questions on corruption may not inform us about firm experiences with corruption, with firm specific question on experiences with corruption may lead managers to use nonresponse and false responses to minimize any legal or political repercussions (Jensen, Li, and Rahman 2010).

Our approach is to utilize a series of questions based on a “coin flip” method of randomization of questions, often called a randomized response technique (RRT) that we 10
What aggregate trends such as those shown in Figure 1 and Figure 2 obscure is the considerable variation that exists amongst EM debt markets. To illustrate this, it is useful to compare the total amount of domestic and international government securities outstanding at a given time. Figure 3 does so for the BIS sample of 22 EMs as of the end of 2011. We see that there are many differences amongst these countries in the relative amounts outstanding of domestic versus international bonds, and in the size of domestic sovereign bond markets.

The increase in the stock of EM debt has been accompanied by an increase in foreign demand. EM debt is now firmly institutionalized as a mainstream asset class<sup>2</sup> , as demonstrated by the rise in the global financial centers of investment funds with EM bond-specific asset management mandates, and sizable EM-dedicated capital markets sales, trading and research operations in global investment banks.

**Figure 3** : **Outstanding government securities of 22 EMs as of end-2011**

<!-- Start of picture text --> 70 International<br>Line of<br>bonds (USD<br>60 bn) equality PL<br>50<br>AR TR MX BR<br>40<br>RU<br>30<br>LB PN<br>HU<br>20 ID<br>SI SK PE CO<br>10 ZA<br>HR CN<br>CL MY<br>0 PK TH<br>1 10 100 1000 10000<br>Domesic bonds (USD bn) - log scale<br><!-- End of picture text -->

**Source: BIS**

**Note: A log scale for the x-axis has been used to improve the display (the value of domestic bonds outstanding increases exponentially with every unit increase along the x-axis).**

Yet despite their strong growth, emerging local currency bond markets remain small relative to those of
sanitation (taken from WDI); other gender specific laws in the WBL database including whether or not a married woman can choose where to live in the same way as a man does, whether or not the government supports childcare, whether or not law mandates break time for nursing mothers, whether or not non-pregnant and non-nursing women can work the same night hours as men and do the same jobs as men, whether or not parents are entitled to flexible time schedules, whether or not the law mandates equivalent positions to women on their return from maternity leave, and the number of days of maternity leave (paid and unpaid) mandated by the law (henceforth, _Other WBL laws_ ).

# **3. Empirical results**

OLS regression results are provided in Table 1. These results show a large positive relationship between paternity leave and the share of women workers. The relationship is statistically significant at the 1 percent level and this holds irrespective of the controls in place. Quantitatively, without any other controls, the estimated coefficient value of paternity leave equals 0.068 (column 1). This implies an increase of 6.8 percentage points in the share of women workers for a typical firm (against the mean of 32 percent) associated with mandating of the law on paternity leave. This positive relationship becomes only stronger when we control for the variables discussed above (columns 2 to 8). For instance, with all the controls discussed above included in the specification, the estimated coefficient value of parental leave law equals 0.074 (column 8). A few points should be noted. First, controlling for _Other WBL laws_ hardly changes our results (column 7 vs. column 8). This raises our confidence that our paternity leave variable is not a proxy for other gender specific differences in the laws. Second, controlling for overall economic development does cause the estimated coefficient value of the paternity leave variable to decline but the decline is not 5
employment growth, whose characteristics typically affect results and make the choice of indicators key to any analysis. Growth can be defined in terms of employment, sales and net assets. Each of these indicators is geared to a different aspect of firm growth. The choice of the growth indicator – in terms of employees, sales and net assets – can lead to quite different results (Heshmati 2001), since expansion processes may be rather different.<sup>5</sup> This paper focuses on employment, and uses two indicators which are commonly found in firm growth literature. First, a relative growth indicator puts the extent of employment growth in relation to the firm size in the base year. Second, a synthetic indicator suggested by Birch (1981, 1987) controls for bias towards firm size.

Since the choice of the indicator affects the analysis of the relationship between size and growth, and qualitatively does not change the results, we will for the country and industry analysis focus on relative growth rates. In the sections on size and growth, and the effect of location and growth, we will also consider the Birch indicator.

Let Ei,t denote the number of permanent, full time individuals working for the interviewed establishment i in the survey year t, and Ei,t-3 the number of permanent, full time individuals working for the establishment i three years ago. Formally, the basic growth indicators are: 4 It is unclear if Afghanistan is representative, since a total population of firms (e.g. census data) is unavailable. Pakistan is not representative, because enumerators could not enter certain regions due to security concerns. In addition, in India only Andhra Pradesh, Delhi, Gujarat Maharashtra and Tamil Nadu were covered.

5 Among the three indicators, employment generation seems to be best suited for cross-country comparisons, as employment data is less influenced by other aspects, such as volatility or different legislation leading to different reporting behavior. Moreover, employment generation is of strong political interest.

5
a benchmark without market failures are generally small. However there are exceptions, indicating that careful analysis is needed of the operation of a given market in a country.<sup>17</sup>

# **1.2  Natural resource markets**

As is true for agricultural markets, governments have a long history of intervening in markets for natural resources, both renewables and non-renewables. Contrary to policies that affect agriculture – where import protection has tended to dominate – in the case of natural resources the focus has been much more on export restrictions. Protection rates on imported nonagricultural commodities tend to be relatively low because many of these commodities are intermediate inputs, for which powerful user industries apply pressure against high rates of protection. On the export side, numerous countries have intervened on export markets in an effort either to raise export prices or to support specific industries. Export restrictions are sometimes also justified on environmental grounds, although any measures targeted efficiently to environmental problems would generally target production or consumption, rather than exports. Data reported in WTO (2010) suggest that it is mostly developing countries implement export restrictions. Export taxes on natural resources account for about one-third of all export taxes imposed—some 11 percent of world trade in natural resources is covered by such taxes, with timber, iron, copper, pearls and gemstones being among the most frequently affected.<sup>18</sup> Although export restrictions are more frequently observed than import protection, the underlying motivations have been very similar, either exploiting market power (terms of trade) and or political economy/industrial policy—an effort to subsidize/tax certain industries or activities. In cases where a country does not have the ability to affect its terms of trade, the objective underlying the use of export restrictions is frequently to subsidize the domestic processing or another downstream industry by providing it with access at less than world market prices. Such support to specific industries may also be granted through direct subsidies or tax concessions. Many energy producers subsidize domestic consumption