rafmacalaba/data-use-mentions-extended huggingface dataset:
EU Neighborhood Developing East Asia LAC<br>FDI Portfolio<br>Banking Official<br>Current Account Balance Foreign Exchange Reserves, change<br><!-- End of picture text --> Source: Authors’ calculations based on World Bank and IMF data (2011). Note: Period 1997-2008.

# **3. Estimation Methodology**

Against the background discussed above, the analytical framework of the growth model estimations in this paper builds on an influential paper by Sala-i-Martin et al. (2004) on the statistical significance of growth determinants. Their methodology is based on a Bayesian averaging of existing estimates and aims at narrowing the gap between the growth theories and the extensive empirical work of the past two decades. They examine the relationship between growth and a list of 67 explanatory variables (identified in the cross-country growth regressions from papers in refereed journals) using data for 88 countries during the period 1960-96. The selection of the variables is based on several criteria, such as: (i) sufficient time length availability of the published or computed variables (from 1960 onwards) and (ii) maximization of the number of countries with observations. The strength of the association between each 7
# **Table 4. Juhn, Murphy and Pierce (1993) decomposition**

|||**T**<br>|**Q**<br>|**P**<br>|**U**|
|---|---|---|---|---|---|
|**All**|mean|0.1013|-0.0156|0.1129|0.0040|
|**Teenagers**|mean|0.0559|-0.4840|0.5368|0.0031|
|**Young adults**|mean|0.1125|-0.1174|0.2243|0.0056|
|**Young old**|mean|0.1059|0.0188|0.0838|0.0033|

_Note: T = Total difference (All_extm-All_extw); Q = Contribution of differences in observable quantities; P = Contribution of differences in observable prices; U = Contribution of differences in unobservable quantities and prices._ _Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._

**Table 5. Quantity effects on the gender pay** **<u>gap based on JMP (1993)</u>**

|**Group of variables**|**All**|**Teenagers**|**Young**<br>**adults**|**Young**<br>**old**|
|---|---|---|---|---|
||mean|Mean|Mean|mean|
|**Working hours**|0.003177|-0.00072|0.002501|0.010033|
|**Education**|-0.00081
# 3.2.1 Macro stabilization variables

We include the log of _government consumption_ as a share of GDP, _lkg_ , calculated as the log of csh_g from the PWT 10.0. This variable is supposed to capture growthreducing effects through distortionary taxation (Afonso and Furceri 2010) or public debt issuance. The negative association with growth is motivated by the fact that we include the positive effects that government consumption may have on growth separately, for example, through spending on infrastructure. As our model describes long-run growth, it is also important not to conflate the short-term positive stimulus effect that increased government consumption can have during economic downturns.<sup>7</sup> _Inflation_ is measured as the log change of _v_c/q_c_ (household consumption in current national prices/household consumption in constant national 2017 prices) from the national accounts module of the PWT 10.0, which has greater availability than inflation data from the World Bank’s World Development Indicators (WDI).<sup>8</sup> The _real exchange rate_ , _lrer_ , is calculated as the log of the GDP price level (in PPP) over the nominal exchange rate: _pl_gdpo/xr_ , both taken from the PWT 10.0. Since _xr_ is measured as national currency/US$, an increase in _lrer_ reflects a real appreciation, which is expected to have a negative effect on output and growth through various channels (e.g., Rapetti 2019; Levy-Yeyati, Sturzenegger, and Gluzmann 2013).<sup>9</sup>

# 3.2.2 Financial variables

To measure countries’ _financial development_ , we use the log of domestic credit to the private sector as a percentage of GDP, _lcredit_ , taken from the WDI. The literature on financial development and its relationship to growth has grown extensively over the past decades (e.g., Buzzi and Russo 2019) and various studies and data sets on more detailed aspects have appeared (e.g., Svirydzenka 2016
Varghese 2012). And in Kenya, M-Shwari – a combined savings and loan product offered in partnership between CBA, a bank, and Safaricom, the mobile money operator behind M-Pesa – offers small, short-term loans to its customers based on their transaction and payments history with Safaricom and M-Pesa (Cook and McKay, 2015).

Incorporating payments data into credit files not only has benefits for potential borrowers but also helps financial institutions in better being able to assess credit risks and thus minimize their nonperforming loan (NPL) portfolio. Using the payments and transaction history of its customers with Safaricom to assess their credit worthiness and assign individual credit limits, CBA in Kenya was able to significantly reduce NPLs of its M-Shwari portfolio (Cook and McKay, 2015). While a reduction in nonperforming loans can contribute to overall financial sector stability, for most financial institutions loans extended to adults solely based on payments data will likely make up a very small share of their overall loan portfolio.

Finally, moving payments from cash into accounts might be especially valuable for women, who benefit from the greater confidentiality and control such payments offer. This is particularly true for payments women receive either as compensation for work, remittance payments, or government transfer payments. The arrival of a payment into an account is private information, allowing the recipient to at least temporarily conceal the payment from other household members or friends or relatives who may place inappropriate demands on the use of the money. A payment into an account thus gives women more control over their money and assets and makes it harder for family and friends to access these funds.

There is evidence from the earlier mentioned social cash transfer program in Niger, for instance, that the greater privacy and control of mobile transfers, compared to manual cash transfers, shifts intra-household decision making in favor of women who receive the transfers (Aker et al., 2013). And in Kenya, research has shown that the arrival of mobile money transfers has increased women’s economic empowerment in rural areas by making it easier
We also construct a transition matrix that relates the type of displacement to current occupational categories. Workers who experienced a plant closing and workers who were fired are equally likely to have a salaried job once they reenter the workforce. Table 3 shows that approximately 80 percent of those in either the plant closing or fired categories once they are employed again continue being salaried employees, and approximately 15 percent move into self-employment activities. Workers who voluntarily quit have slightly different transitions to those who were involuntarily separated (in either plant closing or fired categories). For example, 73.9 percent of the workers in the quit category are currently salaried workers, and 18.3 percent move into self-employment activities.

|**T**|**able 3: Type of**|**job displace**<br>|**ment and curren**<br>**Current em**<br>|**t employment stat**<br>**ployment status**<br>|**us**<br>|
|---|---|---|---|---|---|
|||**Employee**|**Employer**|**Self-employed **|**Unpaid worker**|
||**Plant**<br>**Closing**|79.9|3.0|15.6|1.5|
|**Type of job **|**Fired**|80.7|2.6|14.5|2.3|
|**displacement **|**Quit**|73.9|5.3|18.3|2.6|
||**business**<br>**Closed own**|58.5|8.5|29.9|3.2
_Tax Reform in Transition_ 21

# **_4. Conclusions_**

The analysis of the 1992 household budget survey indicates that Bulgaria can benefit from a much simpler and lower income tax structure than the one which prevailed in 1992. Two possible types of alternative income tax systems have been explored, a flat rate and a progressive, but simpler three-bracket rate system, and their implications for potential revenue and income distribution. The analysis suggests that each of these alternative systems would have been more efficient in terms of potential revenues, while increasing the protection to the poor through increasing the exemption level, i.e., revenue-enhancing and redistributive tax reform. The results are robust to the inclusion of the existing significant tax evasion.

The actual developments since 1992 have, first, moved Bulgaria even further away from the proposed simple income tax systems. In particular, the 1993 amendments to the 1992 personal income tax have complicated the system in two major ways. First, while the 1992 marginal income tax rates were not low by international standards, the rates implemented in the 1993 amendment were even higher, with top rate rising from 40 to 52 percent. Second, the number of tax brackets were increased from 7 to 10, with three additional brackets having higher rates of 44, 48, and 52 percent. These more complex and higher rates are almost certain to complicate administration and enforcement, and provide powerful additional incentives for tax evasion. Fortunately, the country began to move toward less nominal progressivity in its personal income tax structure, following the 1997 tax reform program.

But whichever alternative income tax structure is chosen, it should be clear from the above analysis that the 1992 system and, even more so, the 1993 amended income tax system has inferior characteristics to simpler and more efficient alternatives. Therefore, further efforts in improving the income tax system in Bulgaria must aim to raise the exemption level, set the rates as low as possible, and reduce the number of brackets and define them as broadly, yet realistically, as possible.
of budgets and because of elite capture problems, empowerment may not instrumentally serve the poor. However, in cases where large numbers of people (e.g. woman, ethnic minorities) are excluded from the public decision that affect their life, empowerment may be a valued outcome in its own right. Besides, the analytic division between the intrinsic and the instrumental approaches to empowerment is not definitive. In fact, both approaches to empowerment appear to be closely associated. Intrinsically disempowered individuals or groups are also likely to be instrumentally disempowered. Similarly, intrinsically empowered individuals or groups are also likely to be instrumentally empowered.

# **5. Conclusion: practical implications and further research**

The focus of this paper has been largely theoretical, but there are practical implications related to the fiscal determinants of empowerment. The paper suggests a number of ways in

> 22 Thus far, studies of participatory budgeting have not addressed this issue (perhaps for lack of disaggregated data at the subnational level), but without such analysis, it will be difficult to find out if the use of this mechanism benefits the poor. In Porto Alegre and Belo Horizonte in Brazil this problem is partially managed by using a system in which each region's share of total investment is weighted by its level of poverty and infrastructure needs. This guarantees a progressive distribution of resources (Souza 2001).

> 23 From a rational cost-benefit point of view this happens not only because the poor do not have an incentive to engage with government (as they bear all the costs but share the benefits equally), but also because those who participate (those who have positive net benefits of doing so) have interests sufficiently different from the (poor) majority.

25
variation (standard deviation/mean). In Uganda and Rwanda, we also analyze a panel data set to assess the autocorrelation in outcomes and find substantially higher autocorrelation in the AAA measures compared to traditional estimates.

Next, as a direct test against an accepted benchmark, we compare the AAA measures to two sources of administrative data: bank loan officer evaluations in Ghana, and assessments by professional business coaches in Uganda. Note that administrative data on business performance for small firms are rarely available, but we carefully matched a small sample of administrative records to contemporaneous measures collected through traditional methods and the AAA tool for the purposes of this research. Across both settings, we first confirm findings from previous research that traditional measures underreport both sales and profits, with differences in profits being statistically significant. By contrast, the magnitude of AAA sales is statistically indistinguishable from administrative sales in either setting. Administrative profits are higher than AAA profits in Ghana, but the two measures are statistically indistinguishable in Uganda. The results also show that AAA measures are significantly more correlated with the administrative data, especially for profits. Hence, the AAA measures more closely match our best estimate of “true” performance values from administrative data.

Finally, we explicitly test the validity of the methodology in a field experiment in Ghana where we randomly assigned half of a small firm sample the electronic AAA tool and the other half an identical paper version of the AAA tool. We find that both electronic and paper versions of the tool significantly outperform traditional measures but are not different from each other, which indicates that the improvement in accuracy is due to the innovation in method rather than simply better survey technology.

Ultimately, this research reconciles the recommendations of de Mel, McKenzie, and Woodruff (2009) for increased attention to survey design, with those of Fafchamps et al (2012) for identifying ways to leverage an electronic survey technology beyond _ex-post_ consistency checks. This research is also closely related to focused work on improving the accuracy of consumption expenditure surveys at the household level
ex ante evaluation of the reform using the 2013 fiscal records suggested that the tax reform would likely increase the effective tax burden for the top 1 percent of the income distribution by 6 percentage points (equivalent to an increase from 2.4 to 3.5 percent of the GDP, with 80 percent of the latter figure being paid by the top 0.1 percent), while for most of the population the tax burden is expected to remain roughly constant, making the tax system, eventually, more progressive (World Bank, 2016).

25
Bruine de Bruin, Wandi, Baruch Fischhoff, Susan G. Millstein and Bonnie L. Halpern‐Felsher (2000) “Verbal and Numerical Expressions of Probability. „It‟s a Fifty‐Fifty Chance‟,” _Organizational Behavior and Human Decision Processes_ 81: 115‐31.

Delavande, Adeline, Xavier Giné and David McKenzie (2011) “Measuring expectations in developing countries: A critical review and new evidence”, _Journal of Development Economics_ 94: 151‐63.

Dominitz, J. and Charles Manski (1997) “Using expectations data to study subjective income expectations”, _Journal of the American Statistical Association_ 92 (439), 855–867.

Groh, Matthew, Nandini Krishnan, David McKenzie and Tara Vishwanath (2015) “The impact of soft skills training on female youth employment: Evidence from a randomized experiment in Jordan”, Mimeo. World Bank Hirshleifer, Sarojini, David McKenzie, Rita Almeida, and Cristobal Ridao‐Cano (2015) “The Impact of Vocational Training for the Unemployed: Experimental Evidence from Turkey”, _Economic Journal_ , forthcoming.

Jensen, Robert (2009) “The perceived returns to education and the demand for schooling”, _Quarterly Journal of Economics_ 125 (2), 515–548.

Lewis, Randall and Justin Rao (2015) “The unfavorable economics of measuring the returns to advertising”, _Quarterly Journal of Economics_ 1941‐73.

Manski, Charles (2004) “Measuring expectations”, _Econometrica_ 72 (5), 1329–1376 McKenzie, David (2015) “Identifying and Spurring High‐Growth Entrepreneurship: Experimental Evidence from a Business Plan Competition”, _BREAD Working Paper no._ McKenzie, David (2011) “How can we learn whether firm policies are working in Africa? Challenges (and solutions?) for experiments and structural models”, _Journal of African Economics_ , 20(4): 600‐25 McKenzie, David, John Gibson, and Steven Stillman (2013) “A land of milk and honey with
of MTI||-0.128|0.092||
|with Script Change||(0.073)|(0.041)||
|�<br>_I_<sup>_MT I_</sup><br>_zy_<br>�||[0.085]|[0.031]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I−T_</sup><br>_zy_||-0.010|-0.003||
|||(0.008)|(0.002)||
|||[0.239]|[0.204]||
|_I_<sup>_F P E_</sup><br>_zy_<br>_× I_<sup>_MT I_</sup><br>_zy_||-0.001|-0.002||
|||(0.007)|(0.003)||
|||[0.930]|[0.342]||
|�<br>Years of Schoolingizy||||-0.365<br>(0.147)<br>[0.001]|
|First Stage F-Statistic||6.91||6.91|
|Number of Clusters|58|58|58|58|
|N|24,898|24,898|24,898|24,898|

_Source_ : Author’s analysis based on data from the Demographic and Health Survey (DHS) in years 2005, 2011, and 2016.

_Note_ : The dependent variable is either years of schooling in column 2 or number of births in the other three columns. � Years of Schooling _izy_ is the reported years of schooling from the data; Years of Schooling _izy_ is the
# **III. The Urban Unbanked in Mexico**

This section uses a survey of households in the Metropolitan Area of Mexico City to profile the unbanked and to explain why they are unbanked. In Mexico there has been no systematic national analysis of the extent to which people are inside and outside of the banking system. In 2002, the World Bank commissioned a survey in Mexico City Metropolitan Area. The survey, known as the _Encuesta Nacional de Servicios Financieros_ (ENDSFI) was conducted by _Instituto Nacional de Estadística, Geografía, e Informática_ (INEGI) as an addendum to the _Encuesta Nacional de Empleo Urbano_ . The survey sampled 1,500 households. The surveys were conducted in face-to-face interviews with an 80 percent response rate. For people using the survey data, INEGI supplies a set of weights to convert the sample responses into responses representative of 11.4 million adults living in the Metropolitan Area of Mexico City. All of the tables in this paper are based on the ENDSFI survey and use the weighted data.

One basic weakness of the survey is that it focuses only on the largest city of the country. The population in this city differs from that in most other urban environments and differs dramatically from that in the rural areas, where almost 30 percent of the population lives. Data from Mexico City, for example, could be misleading because it has the highest average income in the country, $14,180 U.S. dollars per capita in 2003 compared to a national average of $5,450.<sup>15</sup> Undoubtedly, due to this difference in average income and other factors, a national survey would find a larger percentage of the population to be unbanked compared to the percentage found in the capital city. In terms of comparison, the Mexican survey drew on the city with highest per capita earnings, while the two cities surveyed in the U.S. were considered below average for that country.

Table 12 presents an overview of the socioeconomic characteristics of the
# **3. Data and empirical strategy**

# Data

In order to assess the interventions’ impact, two rounds of survey data were collected (cp. Figure 2). A baseline survey took place in early 2016, immediately before the start of the 2016 main growing season. The endline survey was carried out in early 2018, i.e., before the start of the 2018 main growing season, among the same rural households. Data in both rounds were collected through tablet-based face-to-face interviews with the household head or spouse, using a structured questionnaire. The attrition rate was remarkably low, since 2,382 (98.6%) of the 2,416 baseline sample households could be re-interviewed during endline, and we cannot detect any non-random patterns in this.

The surveys collected data on household socio-demographic characteristics, income and assets, farming practices, agricultural production and exposure to agricultural extension. Data was elicited retrospectively for the last twelve months; thus, the agricultural data covers the preceding cropping season, i.e., the baseline survey refers to the 2015 and the endline survey to the 2017 season. Accordingly, the impact assessment measures adoption in the 2017 cropping season while extension activities were ongoing, and after farmers had been exposed to one full cycle of extension activities during the 2016 growing season.

The endline survey assessed most information in the exact same way as in the baseline to maintain comparability. Yet, for the adoption of ISFM practices we had to adapt the mode of measurement slightly, because the information collected at baseline was not detailed enough for the intended analyses. While at baseline, agricultural information was elicited at farm level, we obtained plot-level information in the endline, which allows to assess the integrated adoption of ISFM at the plot level. In addition, the endline survey contained measures to increase the reliability of the self-reported data. For compost, enumerators inspected and measured the compost pit or heap, and assessed detailed questions on the production process and quality of 10
Table 3: Difference Between Actual vs Predicted Employment Shares (based on Pareto distribution)

|||S|ize class|– Number|of worker|s|
|---|---|---|---|---|---|---|
||_κ_|1 – 4|5 – 9|10 – 19|20 – 99|100+|
|||Pane|l a - Nar|row size cl|asses||
|Burkina Faso (2015)|1.383|-0.013|0.150|-0.013|-0.066|0.014|
|Cameroon (2008)|1.149|0.021|0.012|0.015|-0.015|0.011|
|Ghana (2013)|1.336|0.007|0.098|-0.006|-0.053|0.023|
|Rwanda (2013)|1.299|0.047|0.012|-0.015|-0.017|0.039|
|||Pan|el b - Bro|ad size cla|sses||
|||1 –|9|10 –|49|50+|
|Burkina Faso (2015)|1.454|0.0|27|-0.0|52|0.044|
|Cameroon (2008)|1.143|0.0|15|-0.0|11|0.008|
|Ghana (2013)|1.366|0.0|28|-0.0|42|0.032|
|Rwanda (2013)|1.294|0.0|22|-0.0|24|0.019|

_Source:_ Establishment censuses obtained from the statistical agencies of the
structure of exports. On a gross value basis, 82 percent of exports in terms of gross value are in manufacturing (column B). However, when we focus on the activity content of exports on a sector basis (column C in Table 4), this drops to 53 percent for manufacturing, while the services share of total value added in exports rises from 17 percent to 43 percdent. This reflects forward linkages, by which we mean that while most Italian exports are in goods, the production of those goods requires a great deal of service inputs. As such, in terms of the activities where value added activities take place when producing for export, much of this is in services. Finally, the last column (column D) provides a breakdown of which export sectors actually contain domestic value added. Here, we are looking at the total value added content of exports, broken down by sector, e.g., the share of Italian value added contained in total exports that is embodied in exports of automobiles. Like the gross value basis in column B, manufacturing is the platform for most exports on a value added basis (column D). Basically,

> 30An Excel based version of the calculations for Tables 2, 3, and 4 is available on request, as well as GEMPACK code for replicating value added decompositions for countries in the GTAP database, and a RunGTAP version of the application reported in this section.

28