rafmacalaba/data-use-mentions-extended huggingface dataset:
are discarded from the NSS consumption survey. On average, the harmonized basket of goods accounts for about 96 percent of per capita consumption expenditure in the NSS-2011. Fifth, we standardize CPHS’ custom industry codes by constructing a concordance with the national industrial classification (NIC, 2008). Sixth, we discard the longitudinal properties of the CPHS by randomly selecting one wave out of a possible three waves in a year.<sup>13</sup> We adjust individual level sampling weights for non-response using an adjustment factor provided in the CPHS. This non-response adjusted weight, by design, addsup to the Census’ population projections for a given year. We choose not to rely on these individual weights as due to the passage of time -- the last available census is now a decade old -- population projections are likely to become imperfect. One of these imperfections stems from faster than expected fall in fertility rates in 2019 reported in the recent National Family and Health Survey round of 2019-21<sup>14</sup> . Instead, we reconstruct individual level survey weights by multiplying household level weights (provided in the CPHS survey) and the household size (observed in the household roster) for each round.<sup>15</sup> This approach allocates the same sampling weight to each household member and relies on the population distribution observed in the survey rather than the Census’ estimated population distribution.<sup>16</sup> Henceforward, we refer to these reconstructed weights as reported CPHS weights and implement a reweighting procedure (that produced adjusted weights) to achieve national representativeness.

# **2.5 Addressing differences in sampling design**

Comparisons of selected statistics obtained with the CPHS with those obtained with several nationally representative surveys identify key biases that raises concern about measurement of poverty and inequality using CPHS data with reported weights. For this reason, we undertake a systematic reweighting exercise with the objective to transform the CPHS into a nationally representative survey (and thereby correct for these biases)
of the RER is lower than that after appreciations.<sup>13</sup> We also conduct a more agnostic two-tailed test to check robustness.

We then test robustness of this result by using product-destination-year level data for Pakistan’s exports over the period 2003-2017. Product-destination-year level allows us to exploit additional sources of heterogeneity in exports to test the three hypotheses regarding the underlying causes of the asymmetric export response to changes in the RER. Also, because Pakistan’s export levels by product are substantially small in global markets, it is reasonable to assume exogeneity of world income and BRER (particularly considering the nominal exchange rate of the PKR to the dollar has been managed during the period).

Our baseline model using product-destination level data is specified as follows: l 3 3l l l 3<sup>l</sup> 3<sup>l</sup> ∗ (3) jjiitt iitt<sup>+ φφ</sup> iitt<sup>× dd</sup> iitt<sup>+ γγ</sup> iitt tt jj iitt ll = αα + ββ<sup>llll</sup> + θθ + μμ + εε where is export volumes of product to partner in year ; l l jjiitt iitt<sup>is the bilateral real</sup> exchange rate expressed (with ll l l jj ii)⁄ , which implies that a rise tt l l LL ii iitt<sup>= (NNl</sup> iitt<sup>× PP</sup> tt tt iitt means a decrease of Pakistani price-competitiveness)<sup>14</sup> ; PP is a dummy that takes value 1 if the iitt ∗ BRER movements correspond to a real depreciation and 0 otherwise; dd is the GDP of the trading iitt partner _i_ at time _t_ ; are time fixed-effects for ; are product fixed-effects at HS6 ll tt jj level; and is the error in the model. θθ tt= (1, ...
the passing of the FRLs (Table A3.3). In British Colombia and Nova Scotia, this decline reversed the trend of rising debt as share of GSDP in the pre-FRL period, with British Colombia experienced the largest turnaround. The other three provinces already had declining debt share of GSDP for the FRL. The debt/GSDP of Newfoundland and Labrador continued to decline in the post-FRL period at a faster speed, and of Alberta, Ontario and New Brunswick continued the reduction but at a slower pace.

In **Colombia** , the debt/GSDP ratio rose from 2 percent in 1996 (the year before the traffic light Law 358) to 3.5 percent in 2001. The ratio steadily declined to 1.5 percent by 2006 and stayed at this level since (Table A3.4, Figure A4.4).

In **Indian** states, the growth of debt /GSDP was slower in the post-FRL period than the pre-FRL period for 24 out of 26 states. Twenty one out of these 24 states had reversed the trend of increasing debt/GSDP in the pre-FRL period (Table A3.5).

From the above, FRL per se was not the pivotal moment for the turnaround of fiscal deterioration in Australia and Canada. In fact, legislating and regulating subnational debt was well underway before the enactment of various subnational FRLs. As noted before, the fiscal consolidation grew out of policy debates in Australia in the 1990s, before various states passing FRLs from 2000-2006. In Canada, many SNGs adopted balanced-budget and/or debt reduction legislation in the 1990s (Millar 1997).<sup>24</sup> The entire country was seriously undertaking fiscal corrections after rating downgrades.

> 23 For a country with its fiscal year ending during the calendar year, the debt data will cover up to June 2008.

> 24 Alberta, Saskatchewan, Manitoba, Quebec, New Brunswick, Nova Scotia, Northwest Territories, the Yukon from 1993-1996.
data sources to study the impact of PAL on child labor. First, we use information from the evaluation follow-up survey in order to study the attendance and labor outcomes of children age 12 and 13 who were enrolled in primary education. These children would have been eligible to take the ENLACE in 2005, if the test had been in place at the time.<sup>20</sup> We focus on five different outcomes: 1) whether the child only reports having attended school the week prior the interview; 2) whether she was both working and attending school; 3) whether she was only working; 4) whether she was neither working nor attending; 5) the number of hours of work (including zeros). Results are reported in columns 1 to 5 in Table 10. In the control group 84 percent of the children aged 12 to 13 in primary education,

> 20Because of the almost universal enrollment in primary education and the lack of any effect of PAL on the probability of attending a grade that is appropriate for student age, the results are unlikely to be driven by a selection effect. While 12 is the standard age for completing primary school in Mexico, over-age is quite common and we include 13 year old in order to improve statistical power. Results restricted to 12 year old are qualitatively similar.

18
total cultivated area was tenant operated, mostly (roughly two-thirds) under crop-sharing arrangements with the remainder under fi xed rent. Land and tenancy reforms have been attempted at various times, but they have been largely ineffectual, leaving tenant cultivators with little legal recourse in the event of eviction.<sup>4</sup> Land ownership, however, is clearly established in rural Pakistan and the risk of expropriation by the state or by powerful individuals is negligible.

Our empirical analysis draws upon a new nationally representative rural household survey. The Pakistan Rural Household Survey (PRHS), which was completed in early 2002,

> 4 Nabi (1986) reports data from a small 10 village survey in Pakistan’s Punjab in which 46 percent of share-tenants said that their landlords could evict them ‘easily’ and 32 percent said that they or their relatives had experienced eviction within the last two years. At the same time, however, most of these tenants had been leasing land of the same landlord for more than 10 years.

3
90 _γk_<sup>_n_,</sup><sup>_γ_</sup> _k_<sup>_l}k_) from Table III. Additionally, we use data on crop-specific fertilizer subsidy rates (</sup><sup>_{sk}k_)</sup> and crop-district-specific prices ( _{pik}i,k_ ), production ( _{Qik}i,k_ ), and cultivated area ( _{Lik}i,k_ ). And we use district-level annual wages ( _{wi_<sup>_m, w_</sup> _i_<sup>_e}i_), and numbers of mobile workers and farmers</sup> ( _{Ni_<sup>_m_,</sup><sup>_N F_</sup> _i_<sup>_}i_).Bilateral transportation costs (</sup><sup>_{τ A_</sup> _ni,k_<sup>_}n,i,k_,</sup><sup>_{τ M_</sup> _ni_<sup>_}n,i_) are computed using the coeffi-</sup> cients estimated in Section VI.A.2.

For the RoW, we use data on 2019 crop prices ( _{pRoW,k}k_ ), production ( _{QRoW,k}k_ ), and cultivated area ( _{LRoW,k}k_ ), as well as on the 2019 wage ( _wRoW_ ). We assume that the RoW has no estate workers ( _NRoW_<sup>_e_=0) and that its tea sector uses mobile workers instead, and we rely on the</sup> measures of the number of workers ( _NRoW_<sup>_m_) and farmers (</sup><sup>_N F_</sup> _RoW_<sup>) described in Appendix A.K. All</sup>
large amounts of cash. Likewise, other respondents in the study indicate that local businesses and street vendors often convert their cash to M-PESA at the end of the day for safekeeping. Additionally, mobile money empowers women by giving them an independent place to store and manage funds that is private and inaccessible to other family members.

Survey data examined by Aker and Mbiti (2010) show that current users of mobile money transfers in Kenya tend to be wealthier, better educated, urban and already banked, with most transfers occurring within urban areas and remittances being smaller but more frequent **.** They also develop the theory of the general mechanisms through which mobile phones remove constraints to social and economic progress. They assert that improved access and use of 5
capabilities or access to finance conditions pre-COVID-19. Results show that almost half of the firms increased the use of digital platforms in response to the pandemic, and about a quarter invested in new digital solutions. However, the response was not equal. Larger and more digitally ready (pre-pandemic) firms increased and invested more in digital solutions, increasing the digital divide. Inequality and concentration measures in online sales show that even if more firms are using digital channels, the gap remains due to a disproportionate increase in digital markets concentration by some firms.

The remainder of this paper is structured as follows. Section 2 describes the data and methodology used in the analysis. Section 3 provides some general descriptive statistics on the share of firms using digital technology in response to COVID-19 and analyze the main purpose for which the technology is being used for. Section 4 compares the gap in the use of and investment in digital technologies along three key dimensions: country, sector, and size of the firms. Section 5 analyzes the inequality across firms regarding online and offline sales, and remote and non-remote workers. Section 6 explores four potential factors associated with our findings: access to finance, regulations, infrastructure, and firm capabilities. The last section concludes.

# **2. Data and methodology**

The World Bank, through the Business Pulse Surveys (BPS) and the Enterprise Surveys (WBES), has collected data on more than 100,000 firms in more than 65 countries since the start of the COVID19 pandemic. This paper uses a harmonized dataset of both surveys, and in particular, the module on the digital response that firms’ managers implemented to counteract the negative impact brought by mobility restrictions and a change in consumption patterns (see Watanabe and Omori (2020)). In most of the paper, unless otherwise noted, the analysis restricts the sample to firms with 5 or more employees, which was a threshold used in most countries for which data is available.<sup>3</sup> Three main indicators
MDG 1.a Extreme poverty**|39(96)|17<br>67|
|**MDG 1.c Hunger**|19(60)|9<br>35|
|**MDG 2.a Primary education completion**|20(96)|9<br>40|
|**MDG 3.a Gender parity in primary education**|7(22)|4<br>14|
|**MDG 3.a Gender parity in secondary education**|16(52)|8<br>29|
|**MDG 4.a Child mortality under five**|23(59)|8<br>38|
|**MDG 5.a Maternal mortality**|32(80)|11<br>51|
|**MDG 7.c Access to safe drinking water**|25(76)|14<br>41|
|**MDG 7.c Access to sanitation**|27(50)|16<br>34|
|_Source:_Authors’ calculations based on data from<br>**Simple average**|the World Development In<br>23|dicators database<br>11<br>39|

_Source:_ Authors’ calculations based on data from the World Development Indicators database. _Note:_ A country is ―close to the target‖ if its distance to getting on target (that is, its gap of trajectory) is smaller than the average gap of all lagging countries. Otherwise, it is ―far from the target‖ (that is, its distance is greater than the average gap). Figures in parentheses indicate the range of variation (Maximum value – Minimum value) of countries off target, by MDG. Averages and numbers of countries cover only those with data--and that may vary by MDG.

8
The main findings of this paper of a high share of voluntary informal workers needs further exploration to better understand the drivers behind these results. Previous studies for developing countries with a high proportion of informality show that high voluntary informality is linked to low valuation of benefits of being formal and workers who prefer higher wages instead of lower wages with certain benefits (Anton 2012; Levy 2008; Maloney 2004; Maloney 1998). From the firm side, literature shows that frail governance, driven by rigid regulations, corruption and inadequate public services (Mbaye and Benjamin 2014; Dabla-Norris et al. 2008; Steel and Snodgrass 2008; Verick 2006; Schneider 2004; Djankov et al. 2002) and lagging economic growth (World Bank 2019; Loayza 2016; Calvés and Schoumaker 2004) are the key drivers of informality. The interlinkages between these factors result in informal firms having little incentives to formalize, thus preventing their employees from accessing the benefits of formalization.

The rest of the paper is structured in the following form. Section 2 describes the data. Section 3 presents stylized facts benchmarking informality in Senegal with respect to regional and global peers, as well as a brief discussion on a recent profile of labor informality in Senegal. Section 4 presents the methodology and results of the analysis on voluntary informality in Senegal, explores the presence of heterogeneous effects and provides some robustness tests. Section 5 concludes.

# **2. Data**

The analysis uses the 2018-19 Harmonized Survey of Household Living Conditions for Senegal (Enquête Harmonisée sur les Conditions de Vie des Ménages, EHCVM) which is nationally representative. The EHCVM was carried out by the West African Economic and Monetary Union<sup>1</sup> (WAEMU) Commission with the support of the World Bank in two collection waves of 3 months each to take seasonality into account. The first wave took place from mid-September to mid-December 2018. The second wave was implemented from April 11 to July 10, 2019. The sample design guarantees

> 1 The WAEMU region is composed of
country's position as the main external creditor, Japan now counts seven among the world's ten largest commercial banks. (The Banker, July 1987, p.<sup>91</sup> .) These have completely displaced US money center banks from their former dominant positions. In late 1986, Japanese banks held over $1 trillion in international assets, or about one-third of the world total. At that time, the sahre of US banks had dropped to only 20 percent of such assets. (World Economic Survey 1987, United Nations, 1987, p.83.)
# **3.10 New Zealand (NZL)**

In New Zealand, antidumping investigations are conducted by the Trade Remedies Group at the Ministry of Economic Development. The Trade Remedies Group publishes initiations and outcomes of antidumping cases in the official New Zealand Gazette as well as on the Ministry of Economic Development’s website. The source of the data was the Ministry’s website which was combined with some information provided directly by the Trade Remedies Group. Information about New Zealand’s antidumping investigative process can be found on the Ministry’s website via the Trade Remedies link.

In addition to the standard data described in section 2, the file for New Zealand contains additional data described in the table below.

**Table 3.10.1 : Additional Data from New Zealand Included in AD-NZL-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-N**|**ZL-Master :**||
|**U.**|**P_AD_DUTY**|The preliminary anti-dumping duty for all non-named foreign exporting<br>firms imposed|
|**V.**|**F_AD_DUTY**|The final anti-dumping duty for all non-named foreign exporting firms or<br>range of duties imposed|
|**W.**|**INIT_GP**|The government publication containing the case initiation|
|**X.**|**P_GP**|The government publication containing the preliminary case outcomes|
|**Y.**|**F_GP**|The government publication containing the final case outcomes|
|**Z.**|**NOTES**|Comments|
|**In AD-NZ**|**L-Foreign-Firms:**||
|**D.**|**F_AD_MARGIN**
As the mobile monitors were attached to motorcycle taxis, the location of the mobile measurements might be systematically related to time trends in pollution (e.g., taxi drivers might be more likely to drive through some specific city neighborhoods at the time of day when traffic, hence pollution, is highest or lowest). To address this potential concern of non-random spatial location of the mobile monitors across hours of the day and days of the year, we net out hour and date fixed effects using the readings from the stationary monitors.<sup>26</sup> We run the following regression using the readings from all our stationary monitors _k_ in order to recover hour _b_ and date _c_ where _ln_ ( _PM_ 2 _._ 5 _k,h,d_ ) is the log of the PM2.5 reading from monitor _k_ recorded on calendar date _d_ and hour _h_ . We include static monitor fixed effects _λk_ since we do not have a balanced panel. We then net out these time fixed effects from the readings of our mobile monitors. To do so, we compute the pollution residuals _em,h,d_ ˆ as the log of the raw measurements from our mobile monitors at GPS coordinates _m_ at time _h_ of date _d_ , net of the hour and calendar date fixed effects estimated from the stationary monitors: ˆ _em,h,d_ captures residual pollution variation across locations conditional on a particular hour of the day and a particular calendar date. As such, this allows us to document systematic _spatial_ variation in pollution within the city.

# **5.2 Grid Cell Approach**

To create the neighborhood-level measures of firm density, pollution and road size, we adopt a grid cell approach. The next administrative unit after sub-counties are parishes. Specifically, our 52 sub-counties comprise 179 sampled parishes. Following Ahlfeldt et al. (2015), Carozzi and Roth (2020) and Michaels et al. (2017), we split parishes in our sample into grid cells of 500 _m ×_
br>LIDCs LICs<br><!-- End of picture text --> Source: International Monetary Fund, World Bank.

A. LICs= Low-income countries. Figure shows median current account balance in percent of GDP. The sample represents a total of 30 low-income countries, of which 2 are oil exporters, 8 are metals exporters, and the remaining 20 are non-resource-intensive. It excludes Somalia, South Sudan, and Syria due to data restrictions.

B. The sample represents a total of 21 low-income countries, including 6 metals-exporting LICs, with current account deficits in 2016.

C. EMDEs= Emerging market and developing economies. Standard deviation represents the median standard deviation of foreign direct investment in percent of GDP from 2000 to 2017.

D. LIDCs=Low-income developing countries; LICs=Low-income countries. See Annex for details. Orange lines indicate interquartile range. Data is as of the last reported year, mostly 2016.

10
Policy Research Working Paper 5537

# **Abstract**

Recent empirical evidence highlights that access to basic financial services can make a substantial positive difference in improving poor people’s lives. Accordingly, financial sector reforms that promote financial inclusion are increasingly at the core of policymakers’ agendas. The Consultative Group to Assist the Poor and the World Bank Group, in response, launched the _Financial Access_ project, including a cross-country database on financial inclusion topics and an annual report to inform the policy debate. Using this database, this paper (i) counts the number of unbanked adults around the world at 56 percent, (ii) analyzes the state of access to deposit and loan services as well as the extent of retail networks, and (iii) discusses the state of financial inclusion mandates around the world.

This paper is a product of the Financial Access Team in Consultative Group to Assist the Poor, Financial and Private Sector Development. 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:// econ.worldbank.org. The author may be contacted at nmylenko@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
aggregating across population units (grids in this case). The two versions of implementing the EBP lead to the same estimates for a large number of Monte-Carlo iterations . To verify this, we compare poverty headcount and MSE estimates obtained using the traditional method with Monte-Carlo replications with those obtained by calculating the expected value approach for one focus country, and report the results in Appendix D. Estimates ll of the mean squared error (MSE) of the small area estimates are calculated using parametric LL= 100 bootstrap under model (1) (Gonzalez-Manteiga et al., 2007) as implemented in the Povmap package. MSE estimation adjusts for the fact that the population data we use contain only one observation per grid, while the actual population contains multiple households per grid. An empirical best predictor under the two-fold version of the nested error regression model is also available ( <mark>Marhuenda</mark> et al, 2017). The two-fold version of the EBP was used to produce official

> 7 The code is available on the development branch of the package at: https://github.com/SSA-Statistical-TeamProjects/povmap.