rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1 Introduction**

The aftermath of the 2008-09 Great Recession triggered renewed interest on the impact of fiscal policy changes on the economy. In response to the global recession, countries typically implemented a two-phase approach to fiscal policy. First, the sudden economic collapse led fiscal authorities in many advanced economies, including Canada, to introduce stimulus packages to revive their economies. Following the stimulus, fiscal authorities shifted gears, adopting adjustment plans to reduce public debt and ensure long-term fiscal sustainability and sustained growth.

Stimulus measures in Canada, introduced through Canada’s Economic Action Plan, were projected at 3.2 percent of GDP in the 2009 federal budget while the ARRA was estimated at 7 percent of GDP. As a result, the federal net debt-to-GDP ratio was expected to increase from 28.6 percent in the 2008-09 fiscal year to 32.1 percent by 2010-11. As the economy started to recover, the Canadian government introduced adjustment plans to tackle high deficits and increased public debt. The federal expenditures-to-GDP ratio, which rose from about 13 percent before the crisis to 16 percent in 2009-10, declined to 14 percent in 2011 and was projected in the 2012 budget to return to the pre-crisis level by 2014-15. The 2012 budget also projected that adjustment measures would bring the federal net debt-to-GDP ratio back to the pre-crisis level by 2014-15, while the consolidated net debt-to-GDP ratio (including federal, provincial and local administrations) was projected to fall to 36.3 percent of GDP (IMF Fiscal Monitor, April 2013).<sup>1</sup> Therefore, examining the size of the fiscal multiplier is important to informing the effectiveness of such policy measures in reviving or not economic activity.

While extensive analysis has been conducted on the U.S. economy, only a few studies (Perotti (2004), Corsetti and Müller (2006), Ravn et al. (2007) and Monacelli and Perotti (2010)) have investigated the impact of fiscal policy changes in Canada. One challenge when studying the
Kaplan, D. S., Piedra, E., & Seira, E. (2011). Entry regulation and business start-ups: Evidence from Mexico. Journal of Public Economics, 95(11), 1501-1515.

Karlan, D., Knight, R., & Udry, C. (2014). Consulting and Capital Experiments with Micro and Small Tailoring Enterprises in Ghana. Working Paper, October.

Leea, E. Y., & Cin, B. C. (2010). The effect of risk-sharing government subsidy on corporate R&D investment: Empirical evidence from Korea. Technological Forecasting & Social Change, 77, 881–890.

Lopez-Acevedo, G., & Tan, H. W. (2011).Impact Evaluation of Small and Medium Enterprise Programs in Latin America and the Caribbean. World Bank Publications, The World Bank, 2298, March.

Lopez-Acevedo, G., & Tan, H.W. (2005). Evaluating Training Programs for Small and Medium Enterprises Lessons from Mexico. World Bank Policy Research Working Paper 3760, November. Lopez-Acevedo, G., & Tinajero, M. (2010). Mexico: impact evaluation of SME programs using panel firm data. Policy Research Working Paper Series 5186, The World Bank.

Machado, L., Parreiras, M. A., & Peçanha, V. R. (2011). Avaliação de impacto do uso do Cartão BNDES sobre o emprego nas empresas de menor porte. Revista do BNDES 36, December.

Mano, Y., Iddrisu, A., Yoshino, Y., & Sonobe, T. (2012). How Can Micro and Small Enterprises in Sub-Saharan Africa Become More Productive? The Impacts of Experimental Basic Managerial Training. World Development, Elsevier, 40(3), 458-468.

Martincus, C. V., Carballo, J. and Garcia, P. M. (2014). Public Programmes to Promote Firm’s Exports in Developing Countries:
income of a country rises (Balassa, 1964). Choudri and Khan (2005) provide empirical evidence of the Balassa-Samuelson effect in developing countries.

The approach to estimating Balassa-Samuelson adjusted real exchange rates involves three steps.

First, using data from the WDI, we compute real exchange rates from 2000 to 2017 or 2018 (depending on availability of data) by dividing the purchasing power parity (PPP) conversion factor for each country by the official exchange rate to the US dollar for that country. The former is a measure of how much a basket of goods, which in the United States would cost one dollar, costs in the domestic currency of each country. Dividing the PPP conversion factor by the nominal exchange rate gives a measure of the domestic price level relative to the price level in the United States, computed at official exchange rates. A higher number denotes a more appreciated real exchange rate.

Second, to estimate the expected Balassa-Samuelson adjusted real exchange rates (henceforth BSA RER), we regress the log of the real exchange rate computed in step one on the log of per capita income in a cross-country regression involving 151 countries (countries with a population of less than half a million were omitted).<sup>4</sup> We run separate cross-country regressions for each of four sub-periods; 2000-04, 2005-09, 2010-14 and 2015-18. We obtained an intercept of between -2.2 and -2.7 for the four sub-periods and a coefficient on the log of GDP per capita of 0.18 to 0.21. The latter is similar to, although slightly lower than, the coefficient of 0.24 reported by Rodrik (2008). The coefficients are significant in each of the regressions. We then use these estimated coefficients to compute a fitted BSA RER for each country in each subperiod, which is an estimate of what the real exchange rate would be if the only determinant of it is the per capita income of the relevant country, relative to that of all other countries.
2010). From the voter’s perspective, online registration is convenient and easy, and it is an extra incentive for citizens to register in countries where voting is not mandatory (Pew Research Center 2014). Personal presence is not required to register to vote in India, and voter registration can be done online through the National Voter’s Service Portal.<sup>14</sup> In Ghana, personal presence is required, and online voter registration is not possible. In Chile and Vietnam, online voter registration is not possible since voter registration is done automatically based on a previous civil registry database.

Online voting is not permitted in any of the surveyed economies. Implementation of online voting processes can be hard in developing countries due to a lack of universal internet access (Thakur 2014). As a result, electors cast their votes either through manually marking of ballots or through electronic voting machines in India. Manually marking of ballots is the only form of casting a vote in Chile, Ghana, and Vietnam (ACE 2013).

## **3.2.5. Redress mechanisms**

Electors can request redress in case of inaccuracy or omission in the voter registration process in all surveyed countries. The process to seek redress in both Chile and India is relatively simple. Citizens can either change information online (in Chile) or in person at the local electoral office, bringing a document of identification and filing a form indicating voter information that needs to be changed. In Vietnam, voters can request corrections of mistakes in the register within 30 days of the posting of the electoral register at the electoral registry agency, and changes must be made within 5 days of filing a complaint.

Voters in Ghana can only have access to their personal data and request adjustments of any inaccuracies during an exhibition period in which voters can visit an electoral exhibition center and inform the exhibition officer about the corrections that need to be made. If the correction is minor and does not require printing a new card,
# **8. FINANCIAL INCLUSION BENCHMARK DASHBOARD –USER NOTES**

URL: http://dataviz.worldbank.org/t/ECA/views/tur_finance_equity_poverty7/Dshnew?:embed=y&:display_count=no#3

**_Financial Inclusion and Equity Benchmark Dashboard_**

**Access to finance allows for the smoothing of consumption, savings, management of money, and loans for purchases. Therefore it is important as an instrument against poverty and to boost shared prosperity. This dashboard explores the 2011 and 2014 FINDEX surveys to construct a panel of benchmarks accross countries to help decision makers better understand the specificities of several Financial Inclusion indicators in the context of Turkey**

## **<mark>Step 1. Select the Indicator and the Controls</mark>**

**Outcome Indicator  (Equity Adjusted Coverage Rate)**

Bank Account Ownership <!-- Start of picture text --> Turkey coverage: 57.601 Turkey coverage: 56.513<br>Turkey hoi: 43.953 Turkey hoi: 48.276<br>Turkey dindex: 15.969 Turkey dindex: 14.576<br><!-- End of picture text --> <!-- Start of picture text --> Control List<br>Controllist 2: age, education, gender, urban/rural, income quintiles<br>Take a quick look: Turkey's Bank Account Ownership<br>2011 2014<br>Turkey Turkey<br>57.60 56.51<br>43.95 48.28<br>15.97 14.58<br>Coverage Dissimilarity EACR Coverage Dissimilarity EACR<br><!-- End of picture text --> <!-- Start of picture text --> EACR Decomposition (Turkey as base country)<br>2011 2014<br>50<br>0<br>‐50<br>Three different sources
the true volumes, since a large portion of remittances are sent through unofficial channels.<sup>28</sup>

> 27 For instance, Mali and France initiated annual bilateral consultations at the ministerial level to discuss the integration of Malians who want to remain in France, co-management of migration flows, and cooperative development in emigration areas in Mali. Italy and Albania, on the other hand, have collaborated in a joint police program to combat smuggling and trafficking. (For more examples, see Martin _et al._ 2002) 28 Data collected by the central banks sometime do not distinguish remittances from other low-value transfers such as small-value trade payments. In a recent survey of the central banks (Irving _et al._ 2010)

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.2037|0.2030|0.1904|
|Bottom 50% income<br>share|26.43%|26.46%|27.16%|
|Middle<br>40%<br>income<br>share|44.33%|44.32%|43.93%|
|Top10%income share|29.23%|29.22%|28.90%|

_Source: Own simulations based on administrative tax data._ _*Sample includes wage earners and those who move out of formal employment (assumed to move to other employment opportunities in the formal or informal sector with zero earnings). This represents the lower bound of inequality reduction._ _**Sample restricted to wage earners, assuming all dismissed workers move to unemployment or inactivity._ _Note: The Gini coefficient and Theil Index are measured over labor income and do not include non-labor income sources._

**Overall, these policy simulations under scenarios 1 and 2 show the minimum wage increase does not result in substantial wage hikes among the formal employee population.** Nevertheless, noteworthy wage adjustments can be observed for those employees directly affected by the wage increase (individuals earning more than the 2021 minimum wage but less than the newly simulated minimum wage). However, it is crucial to note that a significant portion of this wage increase is counterbalanced by an income drop of those employees who lose their jobs after the minimum wage increase. Additionally, over the long term, the wage increase tends to be less pronounced than in the short term, primarily due to the heightened elasticity of labor demand.  This result could be sensitive to the assumption of non-compliance, as non-compliant firms could, in principle,

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based on data from the FECUs and the SAFP 6<br>Number of firms<br>Number of firms<br><!-- End of picture text --> The closed family owned structure of many firms may also be at the root of decisions not to go public. Many Chilean firms have a closed family-based structure, and many others belong to family-owned conglomerates. In fact, from a universe of about 6,500 large and mega large firms, only about 250 are listed. In addition, many listed firms have only a fraction of shares in the hands of the public (the free float of about 30 percent of all listed stocks is lower than 10 percent, and for nearly half of all listed stocks, the free float is lower than 30 percent). As a result, while there are 156 firms non eligible for AFP investments

- Net margin > 5%

- RoE > 8%

- RoA > 1.5%

6 Superintencia de AFPs
of Geographers_ , 106(5), pp.1135–1151.

- Field, E. et al., 2016. On Her Own Account: How Strengthening Women's Financial Control Affects Labor Supply and Gender Norms. _Working Paper_ .

- Gauri, V., Rahman, T. & Sen, I., 2019. Measuring Social Norms about Female Labor Force Participation in Jordan. _Policy Research Working Papers_ .

- Gindling, T.H. et al., 2019. Are Public Sector Workers in Developing Countries Overpaid? Evidence from a New Global Data Set. _Policy Research Working Papers No. 8754_ . World Bank, Washington DC.

- Jensen, R. & Oster, E., 2009. The Power of TV: Cable Television and Women's Status in India. _Quarterly Journal of Economics_ , 124(3), pp.1057–1094.

- Kurdistan Regional Government (2016). Social Protection Strategic Framework. A Time for Reform. Available at: https://us.gov.krd/media/1317/social-protection-strategic-framework.pdf

- Paluck, E.L. et al., 2010. Social Norms Marketing Aimed at Gender-Based Violence: A Literature Review and Critical Assessment. _International Rescue Committee_ .

- Mackie, G. et al., 2015. What Are Social Norms? How Are They Measured? _University of California San Diego Center on Global Justice_ .
prevalence of intercropping across most of the study countries suggest substantial opportunities for more holistic, landscape-level approaches in policy and other practical efforts seeking to reconcile biodiversity conservation, climate change mitigation, and poverty reduction goals. Given the geographic and socioeconomic diversity encompassed in our study countries, our results also suggest that such approaches may also be applicable in other countries across Africa as does a growing case study literature (e.g. Milder et al., 2014).

# 3.3 Contribution of Trees on Farms to Rural Livelihoods was Non‐negligible

Products harvested from trees on farms in the study countries were used mainly for self-consumption or sale (Figure 2). The relative mix of these uses varied among the study countries. In Uganda, fruit tree products were used primarily for self-consumption, whereas in Malawi such trees served solely as a source of cash income. That fruit trees were used significantly for self-consumption in countries like Uganda (and also Ethiopia) suggests that such trees may play an important role in household food security, as shown in a variety of contexts across Africa (Garrity et al., 2010). In the case of tree cash crops, production was mainly used for sale, as expected, though in Ethiopia a non-negligible share was also used for own consumption (linked to coffee consumption) and in Nigeria to other uses.

Turning to the contribution to total income, income from trees on farms contributed on average 6% of overall annual gross household income (i.e. among tree and non-tree growers alike). The income share averaged 7% in Nigeria, Tanzania and Uganda, and only 3% in Malawi (Table 4). We note that estimates for Malawi may be low because information on tree cash crops was not provided in LSMS-ISA survey data. For those households with trees on their farms the average contribution across the study countries was almost three times as much, i.e. 17% and about 32% as a share of agricultural gross income.<sup>12<
_log_ ( _sales/worker_ ) _i,t_ = _β_ 0 + _β_ 1 _ITi,t_ + _γxi,t_ + _αi_ + _ui,t_ (1) Where: _log_ ( _sales/worker_ ) _i,t_ =Logarithm of sales-per-worker for firm _i_ at time _t_ _ITi,t_ =IT use of firm _i_ at time _t_ _xi,t_ =Vector of control variables such as age, firm’s size and share of skills _αi_ =firm fixed effects In our estimation, because of data limitation<sup>3</sup> we use as performance indicator sales-per-worker, which has been widely used in the literature as a proxy for productivity , though an imperfect one (Haltiwanger et al., 1999; Bloom et al., 2012a).

Our equation is similar to what Stiroh (2002) and Bloom et al. (2012b) estimate. As it will be explained later, the main difference between our paper and these studies is the IT-use variables, which in their case is IT-stock-capital, while we use computers-per-worker.

Building on this equation, we then use different specifications in order to test whether the predictions of empirical models previously applied to developed countries hold for the case of Mexico, as well as to analyze the interaction between Chinese competition and IT use with firm-level data.

# **3.2 Instrumental variables**

Considering that IT is endogenous to firm performance, we use an instrumental variable approach where the first stage is defined as follows: Where: _ITi,j,s,t_ = IT use of firm _i_ from sector _j_ in state _s_ at time _t ITint,j_ =IT intensity of sector _j_ in the US _ITHHuses, t_ =Share of households with computers in state _s_ at time _t_

> 3We have no data available to estimate TFP because we lack detailed information on capital and intermediate inputs. The data we use are obtained from a firm survey which is focused on IT use and does not provide information physical non-IT related capital stocks

5
How can financial development successes and gaps relative to benchmark be explained? Is it due to demand effects, supply effects, or the lasting impact of past financial turbulence? To address these issues, we add policy controls to the regressions of financial development indicators (FDIs) and test the impact of credit crashes on financial development. More specifically:

- On the demand side, we add past output growth, as a proxy for the investment-related demand for loanable funds and equity (i.e., the supply of “bankable” projects).

- On the supply side, we use four enabling environment indicators (EEIs), credit information<sup>27</sup> , enforcement costs<sup>28</sup> , creditor rights<sup>29</sup> and property rights<sup>30</sup> .

- For financial turbulence, we define mild, strong and severe credit crashes as annual drops in private credit to GDP of 5 to 10 percent, 11to 20 percent and over 20 percent, respectively.<sup>31</sup> To eliminate the effect of recurrent volatility, we also include in the regressions the standard deviation of normalized private credit.

In all cases, we first filter these policy variables (effectively we also “benchmark” them) using the same set of controls as for FDIs. We then include the residuals as additional controls to explain financial development.

# **_b. Results_**

Table 2, which reports the results of the EEI benchmarking, provides revealing insights on how the underlying frictions that affect the development process are themselves affected by financial development. In particular, we find that the quality of the informational environment (as proxied by credit information) is _concave but subject to scale effects_ . In contrast, the quality of the contractual environment (creditor and property rights) is _convex but not subject to scale effects_ .

This suggests that informational frictions are of a mostly technological nature. Because of fixed costs, they are
point lower growth rate on average.<sup>7</sup> This is significantly different from zero, but there is still a wide variation (Figure 1), with a standard deviation of 4.0 percentage points, and a range from -20.7 to 8.1 points. Some of this is clearly due to measurement errors in both the surveys and national accounts. On top of the errors in both data sources, there are errors in matching survey periods to national accounting periods. Dropping just three (large-negative) outliers that may well reflect measurement errors in the surveys, the mean gap between growth rates falls to 0.7 points (s.e.=0.3).<sup>8</sup>

> 6 For the poverty measures reported here, two-thirds of the surveys used consumption, which is taken to be the preferred welfare metric. When consumption is not available, income is used instead.

> 7 These are simple un-weighted means across countries, for descriptive purposes. The poverty forecasts based on the World Bank’s current growth rate projections reported later were built up from the country level and are implicitly) weighted. These incorporate country specific adjustment factors for gaps

> 8 India has been prominent in recent discussions of the gap between survey data aggregates and national accounts aggregates (Ravallion, 2003; Deaton, 2005). Over the longest available spell between surveys for India (1977/78 to 2009/10) the annual growth rate in mean consumption from the surveys has been 1.4%, as compared to 2.0% from the private consumption component of the national accounts. This gap is in line with the average. But it is a sizable gap over such a long period.

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pre-ultrasound period. Standard errors are clustered by state; we have 25 states in our sample.<sup>28</sup> As discussed, we do not observe large differences in the socioeconomic characteristics of firstbornboy and firstborn-girl families (Table 2), so selection on firstborn sex is of limited concern. We nevertheless condition upon SES (education, wealth, caste, rurality); and we effectively difference between firstborn-girl and firstborn-boy families. In an extension, we also allow interactions of all covariates with child gender. To ensure that there remain no unobservables that are potentially correlated with firstborn sex and that influenced girl-outcomes differently from boy-outcomes in the post- relative to the pre-ultrasound era, we introduce mother fixed-effects in a variant of specification (1). While recognizing that the self-reported fertility preference variables are potentially endogenous (being asked during or after the fertility process), we also consider sensitivity of the estimates to conditioning upon mother’s stated desired fertility and desired sex composition of children. Although we show results with and without these endogenous controls, our main specification excludes them. We also show the robustness of our estimates to the inclusion of Female x State x Year FE.

# **4.2.2 Fertility**

We examine the impact of prenatal sex-detection technology on gender gaps in fertility in two ways. First, we test if ultrasound altered the male-bias in the hazard of birth in a given year for firstborngirl versus firstborn-boy mothers.<sup>29</sup> For this specification, we utilize a retrospective mother-year panel in which a woman enters the panel in her year of marriage and exits in the year of survey.<sup>30</sup> For mother _i_ from state _s_ of age _a_ in year _t_ , who has given birth to _b −_ 1 children by _t_ and whose last birth took place _r_ years ago, we estimate the following OLS regression: The outcome variable, _Birthit_ , equals one if the mother gives birth in year _t_ and is zero otherwise