rafmacalaba/data-use-mentions-extended huggingface dataset:
_Table 1. Main parameters for our idealized city_

|**Parameter**|**Symbol**|**Value**|**Obtention method and other relevant information**|
|---|---|---|---|
|Housing production<br>function scale|A|2.014|Calibrated based on data on the structure of Paris in 2008<br>(Viguié, Rozenberg and Hallegatte, 2014) using density data<br>from INSEE (2008) and rent data from CLAMEUR.<sup>2</sup>|
|Elasticity of housing<br>production with respect to<br>capital|b|0.64|Calibrated based on data on the structure of Paris in 2008<br>(Viguié, Rozenberg and Hallegatte 2014), using density data<br>from INSEE (2008) and rent data from CLAMEUR. The<br>calibrated value of 0.64 is very close to the value of 0.65 found<br>for France in Combes, Duranton and Gobillon (2021).|
|Share of household<br>income net of transport<br>costs spent on housing|β|0.3|Calibrated based on data on the structure of Paris in 2008<br>(Viguié, Rozenberg and Hallegatte 2014) using transport cost<br>data from Hourcade and Nadaud (2009) and Rouchaud and<br>Sauvant (2004), income data from Friggit (CGEDD, after<br>INSEE<sup>3</sup>), and real estate data from CLAMEUR.|
|Interest rate: cost of<br>capital|i|0.05|Assumed as a reasonable value. It is difficult to ascertain an<br>interest rate that would reflect the annual financial burden of<br>construction in an urban area that has developed over<br>centuries.|
|Depreciation rate
of the interim Taliban administration, emerging trends in self-reported welfare and food security make it reasonable to expect that poverty increased in the immediate aftermath and declined progressively afterward to the current level.

The rest of the paper continues as follows: Section 2 expands on the SWIFT-plus methodology, the imputation model, and its internal and external validity; Section 3 presents the poverty imputation result, an overview of welfare trends in the country since the regime change, and how they support poverty evolution trends.

Data We implement poverty imputation on the AWMS R3 using a SWIFT-plus module trained using 2019–20 IE-LFS data, the survey contains information for 18,139 households and has the official consumption vectors used for estimating the official poverty rates in the country. It was conducted between October 2019 and September 2020, matching the year’s four seasons and being representative at the quarterly and urban/rural levels. Households are considered poor if their expenditures are insufficient to satisfy 3
Gourdon, Marchat, Sharma, and Vishwanath (2011) apply the same type of quasi-experimental methods to the evaluation of FAMEX, a World Bank-supported export promotion program in Tunisia, which provided a mixture of counseling and matching grants to new exporters. The study exploits a customized firm-level survey to estimate the effects of FAMEX on the export performance of beneficiary firms at the intensive and extensive margins. Propensity-score matching DID estimates suggest a very large and statistically significant growth effect at the intensive margin: a 39% differential in terms of annual export growth compared to control firms over the 2004-2008 period. The treatment effect at the extensive margin – in terms of products and destinations – is both smaller quantitatively (a 5% growth differential in the count of products and destinations for program beneficiaries compared to control firms) and of marginal or no significance (at 10% confidence level for destinations and insignificant for products). In addition to the observed acceleration in export growth, Gourdon et al. find a significant boost to employment growth: a 10% annual differential for program beneficiaries, significant at the 5% confidence level. An original feature of their dataset is that it covers service firms in addition to manufacturing firms, and they find considerably stronger effects for the former. One potential issue with their data is that the survey was conducted ex-post (no baseline survey was conducted as IE was not part of the program design) so the data may suffer from recall bias. Preliminary results in Cadot, Fernandes, Gourdon, and Mattoo (2011) based on an alternative source of data (customs data) suggest a smaller and non-persistent treatment effect.

Jaud and Cadot (2011) also apply quasi-experimental methods to assess the impact of the E.U.funded pesticides initiative program (PIP) on the export performance of firms in Senegal's horticulture sector. Their results suggest that, while the program had no significant effect on exports of fresh fruit and vegetable pooled over all products and destinations, it had a positive effect when considering exports to the
# **1. Motivation**

Since the 16<sup>th</sup> century, banking has been at the heart of modern payment systems by allowing changes to a centralized ledger rather than physically exchanging assets (Bank of England 2014, p.263). The introduction of credit cards in the 1950s propelled the use of cashless payments now common across both advanced and developing economies.<sup>2</sup> In the 21<sup>st</sup> century, mobile wallets and digital finance have yielded widely recognized benefits in both economic performance and financial inclusion (Klapper et al 2014, Beck et al 2018). This chronology of innovations could be interpreted as there being stages in the adoption of payment technology, possibly implying that poor countries move from one stage of development to the next as the economy grows. Indeed, the idea that economies follow stages of development is a popular idea in the history of economic thought (see, for example, Rostow 1960). This note tests the hypothesis of the existence of stages of development in payments systems with simple econometric models relying on cross sections of international data. The key dependent variables are proxies of the incidence of use of the three stages of payment systems, based on publicly available survey data collected by the World Bank, namely the Global Findex database.<sup>3</sup> The main finding is that even though historically we can observe stages in the advent of various forms of payments, the data show that many poor countries have higher incidences of digital payments (per adult) than high-income economies, even though large segments of populations in poor countries remain under-served by traditional banking. We discuss the characteristics of different payment systems and estimate a series of Ordinary Least Square (OLS) models to study the relationship between the level of development (proxied by Gross Domestic Product, GDP, per capita) and the incidence of the three types of payments.

Ideally, to study transitions between stages of development (
- _Generator use:_ The alternative response to the absence of public electricity often sought by businesses is the use of generators. Therefore, at the firm level, self-generation may explain why some firms experience power outages and others do not. To capture this effect, we include a dummy variable in the covariates that takes 1 if the firm uses a generator, and 0 otherwise.

# **6.** **<u>Country-level covariates</u>**

- _Country-level variables:_ We control for the effect of several macroeconomic variables. These variables first serve as covariates, making treated and untreated companies comparable. Then they are introduced in the second stage to study their effect on dependent variables. These variables come from the World Bank and WDI databases: GDP per capita **(t-1)** , GDP growth **(t-1)** , electricity losses during transmission and transport **(t-1)** , and the rule of law growth **(t-1)** .

- 3.3. Statistics

Table 1 summarizes statistics for the variables described above. Regarding dependent variables, it appears that the bulk of SSA manufacturing firms’ sales is made on domestic markets. Overall, 90% of manufacturing company sales are within the domestic economy, with the remaining 10% being exported. This highlights the poor export performance of SSA manufacturing firms. In the case of the main variable of interest, only 19% of manufacturing companies declare not experiencing power outages, revealing the poor access to electricity by this group of firms in SSA. On average, the companies considered are 17 years old, the average status being a shareholding company with non-traded shares or shares traded privately, and only 12% of managers are women. The SSA manufacturing sector employed, on average 78 people and sold thirteen billion one hundred million products three years ago (US $). Most of the manufacturing firms in SSA consider access to transport a moderate obstacle. For firm-level covariates, we
Policy Research Working Paper 8926

# **Abstract**

This paper investigates the link between inequality and demand for redistribution by looking at how individuals form their perceptions of inequality. Most of the literature analyzing demand for redistribution has focused on _objective_ inequality, rather than _subjective_ perceptions of inequality. However, a model that links demand for redistribution to subjective inequality is needed, given that recent empirical research has shown a growing gap between subjective and objective inequality. Using data from the International Social Survey Programme survey, the paper focuses on explaining individuals’ formation of inequality perceptions using objective variables. The paper then studies the relationship between these perceptions and individuals’ demand for redistribution. The analysis finds that objective macro variables are associated with individuals’ perceptions of inequality, and that individual circumstances, some of which relate to self-interest, like age, educational attainment, and income, also play an important role. Perceptions of equality, in turn, are significatively correlated to demand for redistribution and seem to substitute for any effect of objective variables. This result suggests that contextual macro variables only affect individuals’ demand for redistribution through their perceptions of equality and do not have a direct effect.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbussolo@worldbank.org, ada.ferrer@iae.csic.es, a.giolbas@gmail.com, and itorre@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
sector j<sup>9</sup> . Therefore, _Backwardjt_ is a measure for foreign participation in the downstream industries of sector j. It is defined as The value of  _jk_ is taken from the 2002 input-output table<sup>10</sup> representing the proportion of sector j‘s production supplied to sector k. Finally, _Forwardjt_ is defined as the weighted share of output in upstream industries of sector j produced by firms with foreign capital participation. As Javorcik points out, since only intermediates sold in the domestic market are relevant to the study, goods produced by foreign affiliates for exports ( _Xit_ ) should be excluded. Thus, the following formula is applied: The value of  _jm_ is also taken from 2002 input-output table. Since _Horizontaljt_ already captures linkages between firms within a sector, inputs purchased within sector j are excluded from both _Backwardjt_ and _Forwardjt_ .

# **B. Data and Broad Trends**

The dataset employed in this paper was collected by the Chinese National Bureau of Statistics. The Statistical Bureau conducts an annual survey of industrial plants, which includes manufacturing firms as well as firms that produce and supply electricity, gas, and water. It is firm-level based, including all state-owned enterprises (SOEs), regardless of size, and non-state-owned firms (non-SOEs) with annual sales of more than 5 million yuan. We use a ten-year unbalanced panel dataset, from 1998 to 2007. The number of firms per year varies from a low of 162,033 in 1999 to a high of 336,768 in 2007. The sampling strategy is the same throughout the sample

> 9 For instance, both the furniture and apparel industries use leather to produce leather sofas and leather jackets. Suppose the leather processing industry sells 1/3 of its output to furniture producers and 2/3 of its output to jacket producers. If no multinationals produce furniture but half of all jacket production comes from foreign affiliates, the _Backward_ variable will be calculated as follows: 1/3*0
**Table 7 Different distributions for the inefficiency parameter** (λ)

|Variable|Obs|Mean|Std.Dev.|Min|Max|Coef.<br>Variation|
|---|---|---|---|---|---|---|
|Half‐Normal|130|0.872|0.131|0.449|0.981|0.150|
|Truncated‐Normal|130|0.835|0.142|0.420|0.992|0.170|
|Exponential|130|0.876|0.125|0.443|0.976|0.143|

# **5. Conclusion and Discussion**

The purpose of this paper is to assess the efficiency of public education expenditure to deliver good quality education, in the light of the learning‐adjusted years of schooling (LAYS). A stochastic frontier model is applied to data from 130 countries including 96 developing countries and 34 high‐income countries. Overall, the results show a positive and significant relationship between expenditure per school‐age individual and the LAYS, when countries’ contextual factors (including inefficiencies in use of public financial resources dedicated to the education sector) are controlled for. This relationship seems to hold after controlling for potential endogeneity issues.

The average efficiency is about 87 % overall and 84 % in developing countries. This illustrates that approximately 16 % of public resources (including education aid) dedicated to education in developing countries are wasted because of some inefficiencies in these countries. The efficiency level seems to be particularly low in low‐income countries (74%). Countries with the lowest spending levels seem to register the lowest LAYS and the lowest efficiency levels. This means that most developing countries are facing a double issue related to the “quantity” and the “quality” of public expenditure on education. Improving access to quality education
. Meanwhile, the neutral distribution method (NDG) uses the growth in real per capita consumption (with a pass-through) and applies it to every household. These methods keep inequality constant as they only shift the welfare distribution to the right (left) as positive (negative) growth occurs.

Non-neutral distribution methods use additional information to model changes in the welfare distribution. Quintile growth contribution (QGC) models build on the NDG approach but allow different income distribution quintiles to grow at different rates. For this, total growth equals the real GDP per capita, but it is distributed across the initial welfare quintiles, weighting it by their past contribution to growth, as informed by the previous Growth Incidence Curve (GIC). Finally, sectoral models recognize that GDP per capita growth differs across sectors of the economy and, therefore, does not translate into similar income growth rates across the income distribution. For instance, GDP growth led by labor-intensive sectors will have a more considerable impact on household welfare than economic growth prompted by sectors intensive on capital. Usual modeling approaches use the employment sector of the household member with the highest income and apply the growth rate of the three main economic sectors (agriculture, manufacturing, services) or more disaggregated data, such as eleven-sector models.

14
If we were using WITS to obtain information on tariffs and then estimate Equation (4), we would overstate the NTB effect of the trade agreement because the actually occurring tariff reduction cannot be identified due to the massive measurement error. Instead, the RTA dummy picks up both, the NTB effect and the tariff reduction. If tariff cuts are larger in deep trade agreements than in other ones, the measurement error creates an omitted variable problem that biases the NTB effect upwards: in the extreme, it could be possible that a positive and trade-creating NTB effect could be entirely due to tariff reductions. Therefore, it is crucial to use tariff data that adequately deals with the missing observations in the raw data.

The traditional approach to identifying the NTB effects of deep trade agreements does not control for time-varying unobservable country-pair-specific changes in bilateral trade costs such as general globalization trends that reduce the costs of international relative to domestic trade. As pointed out by Bergstrand et al. (2015), ignoring these forces overstates the effect of RTAs on trade. The resulting omitted variable bias is stronger for trade agreements that have been signed into effect in more recent years when these trends have been more prevalent. RTAs that entered into force more recently have a higher probability of being a deep trade agreement because the number of policy areas covered in trade deals increases with time: Hofmann et al. (2019) report that the average depth steadily increased starting from 1990 going from an average of 11 provisions for RTAs signed between 1990 and 1994 to 23 provisions for RTAs signed between 2010 and 2015.<sup>3</sup> Hence, not accounting for time trends in trade policy with the help of time-varying border dummies can lead to upwards-biased NTB effects for all RTAs, and the bias is larger for deep trade agreements than shallow ones. Following Yotov (2022), we add time-varying border dummies,<sup>L2015</sup> _t_ =2001 _Globijt_ , that equal one for international and zero for domestic trade flows to flexibly
# **2. The SIMPLES Program**<sup>**4**</sup>

The SIMPLES program was envisaged with the objective to simplify the tax system for SMEs. To be able to participate in the new program, microenterprises (self-employed) should have an annual revenue up to Brazilian real 120,000, while the annual revenue of small firms should be under Brazilian real 1.2 million.<sup>5</sup> The system combines six different federal taxes and social contributions into one single monthly-based rate: IRPJ (corporate income tax), PIS/PASEP (contribution to employee savings programs), CSLL (contribution on net profit), COFINS (contribution for financing the social security system), IPI (industrialized products tax), and employer social security contributions. Firms still had to pay other federal, state, and municipal taxes.

The reforms reduced the tax burden considerably. Rather than paying from 5 to 11 percent of gross revenues on taxes, under SIMPLES, micro firms would pay from 3 to 5 percent and small firms between 5.4 and 8.6 percent. In the beginning, only some sectors were covered by the new law. Eligible firms had to belong to one of the following sectors: retail trade, manufacturing, transportation, civil construction, and other services that do not require professionals with regulated occupations such as auditors, architects, dentists, engineers and others.

# **3. Reconciling the Fajnzylber el al. and Monteiro and Assunção Results**

A key difference between the two studies is the date considered for implementation of the program. According to Monteiro and Assunção (2012, p. 108): “ _Although we have only cross-sectional survey of firms in October 1997 [the Ecinf 1997], we introduce a time dimension in our analysis by considering firms created before and after the new legislation, which was December 1996_ .” Fajnzylber et al. considered November 1996 as cutoff instead. According to them (2011,
same parishes included in our sample using the Census.<sup>6</sup> A comparison of columns 1 and 2 indicates that, relative to the national average, households in our sample tend to be worse off: They have fewer rooms, and are less likely to have access to services such as piped water and flush toilets, are more likely to have dirt floors, and are less likely to have assets like a fridge, oven, or TV. Table 1 also suggests that households in our sample are generally worse off

> 5 The sample was constructed by randomly selecting “parishes”—the smallest administrative unit in Ecuador— within six regions in Ecuador (3 coastal and 3 in the highlands).  The sample contains 1/3 urban and 2/3 rural parishes. Within parishes, we randomly selected households that had pre-school children, no school-age children, and were eligible to be new entrants to the social transfer program. These children account for 82% of the sample. To this, we added another group of households that had pre-school children and were slightly too wealthy to be eligible for the program. These children account for 18% of the sample.  A small number of the wealthier households contain school-age siblings of the focal children. Our analyses are unaffected if this group is excluded. 6 The ECV is a household survey, and has the advantage of having more detailed information than the Census. However, it is not large enough to produce reliable statistics for the sub-sample of parishes we use.

5
# Figure 5. Macroprudential policy

# A. Macroprudential policy in EMDEs

<!-- Start of picture text --> Index<br>2013 2000 2007<br>3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>EMDEs ECA EAP LAC MNA SAR SSA<br><!-- End of picture text -->

# C. Macroprudential tools

# B. Macroprudential tools

<!-- Start of picture text --> Average number of instruments 2017<br>5 2000<br>2007<br>4<br>3<br>2<br>1<br>0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

# D. Macroprudential institutions in EMDEs

<!-- Start of picture text --> Percent Number of countries<br>0 20 40 60 80 100 70 2017 2016<br>Limits on foreign exchange positions, 60<br>banking sector 50<br>Capital conservation buffer 40<br>Limit on leverage ratio 30<br>20<br>Loan-to-value limits, hosueholds<br>10<br>FX limits: household credit 0<br>FX limits: corporate credit Macro- Housed in Macro-<br>Loan-to-value limits, hosueholds prudential Central Bank prudential<br>authority authorityʹs<br>Countercyclical capital buffer<br>powers<br><!-- End of picture text --> Source: Cerutti, Claessens, and Laeven (2017); IMF Annual Macroprudential Policy Survey; World Bank. Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa