rafmacalaba/datause-encoder-data huggingface dataset:
# **II. Why do satisfaction surveys merit study?**

Satisfaction surveys merit study for a number of reasons.<sup>2</sup> Even though perceptions of citizens are imperfect indicators of quality of services, satisfaction surveys have considerable appeal as a practical way of measuring the impact of governance reform and decentralization _._ While other tools have been developed to collect more objective information on service quality (e.g. facility surveys, public expenditure tracking surveys),<sup>3</sup> they are typically more time and labor intensive than satisfaction surveys. Thus the latter can be a quick and easy way for policymakers to measure the impact of governance reforms on government performance, particularly for sectors where measurement of service quality is not easy, _provided_ citizen satisfaction is closely correlated with the actual quality of services.

Even as satisfaction surveys are increasingly being used to measure the impact of governance reforms,<sup>4</sup> there is little consensus on whether citizens’ satisfaction reflects the actual quality of services satisfaction surveys. More research is therefore merited on the question of (i) _whether_ data from perception surveys are useful in measuring quality of services, and (ii) if there is information content, _how_ should such data be used and interpreted to measure quality of services.

Moreover, understanding what factors influence citizen satisfaction is crucial in order to evaluate the impact of decentralization. In the typical model linking decentralization to

> 2 See Amin et al (2008) for a more detailed discussion

> 3 Facility surveys are used to directly measure the quality of infrastructure and resources. An excellent example is found in Banerjee et al (2004) – their study of 100 villages in the Indian state of Rajasthan combined a household survey and a village census with a detailed facility survey of public and private health providers. Public expenditure tracking surveys (PETS) measure the efficiency of fund flow through different levels of government –see, for example, World Bank (2005) for results from PETS in the primary education sector in Cambodia.

4 Since the first citizen report card (CRC) initiative was adopted in 1994 in Bangalore, India similar initiatives have been adopted around the world including in Bangladesh, the Philippines, Sri Lanka and Vietnam (Paul, 1999).

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On The Governance Of Public Pension Fund Management

- Governors should be independent from political power and fit and proper for their role.

- The law of fiduciary duty should define the responsibilities of governors.

- Governors should be made accountable for the performance of the fund.

- Independent performance (investment, audit, actuarial, and other) evaluations should be conducted by external and independent entities on a regular basis.

- Outside experts should be used regularly in the definition and implementation of fund policies.

- Finally, internal controls should be established to avoid conflicts of interest.

Beyond this, proper guidelines are needed to implement these concepts and to develop a satisfactory list of governance elements for public pension fund management. In order to attain this objective, an exhaustive survey of governance practices in both developed and developing countries should be carried out. This should well exceed the preliminary information reported in the second part of appendix A. It is possible that the design of this exercise will be modified with time, given the endogenous nature of the guidelines one would like to develop. The starting point could be the list of governance elements reported in the first part of appendix A, which is the result of considerations made in this paper.

Research is clearly needed on several topics. There is a need to identify what governance elements are relevant for, and specific to, public pension plans that are not relevant for private pension funds. There is a need to identify what governance elements are linked with performance specifically in developing countries. There is a need to identify what good practices of governance can be used to improve the performance of public pension fund management in developing countries and how these can be implemented.

Finally, there is a need to establish a network of public pension fund managers responsible for the development of these guidelines.

# **REFERENCES**

- **[1] Ambachtseer, Keith P.** 2001. "A Framework for Public Pension Fund Management." Paper presented at the 2001 World Bank Public Pension Fund Management conference, www.worldbank.org/finance/.

- [2] **Ambachtseer, Keith P., and D. Don Ezra.** 1998. _Pension Fund Excellence: Creating Value for Stakeholders._ New York: John Wiley and Sons.

- [3] **Asher, Mukul.** 2001. "Managing National Provident Funds in Malaysia and Singapore." Paper presented at the 2001 World Bank Public Pension Fund Management conference, www.worldbank.orz/finance/.

- [4] **Bossone, Biagio, Patrick Honohan, and Millard Long.** 2001. _Policy for Small Financial Systems._ Financial Sector Discussion Paper 6. Washington, D.C.: World Bank.

- 22 -
Figure 3: Informal Expenditure Across Countries

# (a) Informal Budget Share (b) Informality Engel Curve Slope

<!-- Start of picture text --> BI CD<br>NE<br>BFTD TZBJ ST SN CG BI CD CL<br>MZ RW KMCM PG NE BJ ST CG<br>TD<br>BO TZ CM PG ZA CRBR<br>MA MZ BF RW KM BO CO<br>EC RSPE SZ UYMX<br>TN TN<br>DO<br>SZ DOCOME MXBR SN MA<br>PEME<br>CR CL EC<br>UY RS<br>ZA<br>5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>5<br>100<br>0<br>80<br>−5<br>60<br>−10<br>40<br>−15<br>Informal Consumption Share 20 Informal Consumption Slope<br>−20<br>0<br>−25<br><!-- End of picture text --> Panel A plots country-level informal budget shares on log per capita GDP. The average informal budget share is 52%. Panel B shows the slope of the informality Engel curves on log per capita GDP. The average slope is 9.8. The bars correspond to the 95% confidence interval of the slope coefficient. GDP per capita is in constant 2010 USD (Source: World Bank WDI).

40
The basic idea underlying the approach discussed here involves linking an extended functional distribution to a size distribution of income. Various socioeconomic groups are considered as representative households. Thus, we call this approach the _Extended representative household_ (ERH) approach to distinguish it from the standard _representative household_ (RH) approach, which limits the analysis of the distributional impact of shocks and policies to their effects on representative socioeconomic groups. There is one other way of implementing the ERH approach (discussed in Agénor, Chen, and Grimm 2004). The treatment of the size distribution in this case is analogous to the approach underlying PovStat. Indeed, it is assumed that, following a shock or the implementation of a policy, per capita consumption and disposable income for a household change at the same rate as the one predicted by the general equilibrium model for the socioeconomic group to which the household belongs.<sup>10</sup> Thus, the size distribution is represented directly by relevant unit records from a household survey. Following a shock or the implementation of a policy, a new distribution of income and consumption is generated by applying the predicted rates of change to the initial data. New measures of poverty and inequality can thus be computed and compared to baseline values.

This approach can only predict changes in between-group inequality because the income and consumption of all the households belonging to a same socioeconomic group are scaled (up or down) by the same factor. Another limitation of this approach noted by the authors is due to the fact that it does not account for changes in the structure of employment. Therefore, they propose an extension of the approach based on a reweighing procedure that reassigns population weights to households in a manner that is consistent with predicted changes in employment structure.

# **_4. Linking a Macroeconomic Framework to a Model of Income Distribution_**

To be sure, both the standard RH and the ERH approaches reveal the basic principles involved in linking a macroeconomic model to a representation of the distribution of economic welfare across individuals or households. In this section, we focus on some specific models (tools) that have been built more or less around these principles. We first describe the 123PRSP model, a three-layer and parsimonious framework designed to link macroeconomic policies to poverty outcomes via a two31
# **3.2 Breakdown by size**

To break down the number of firms by employment size, we develop a method that leverages complementary information from the WBES and census microdata from Ghana, Burkina Faso, Cameroon, and Rwanda. We also use a representative survey for Kenya. These data sources are combined in a framework that links the proportion of different firm sizes to key moments of the firm size distribution.

Using the five-worker threshold for micro firms, as per WBES size classification, figure 2 suggests that the proportion of micro firms will vary depending on the shape of the distribution. The economy of Cameron will have more micro firms (88%) than Ghana (80%) but fewer than Rwanda (93%). It is natural, then, that Ghana has a larger share of small firms (those employing between 5 and 20 workers),which is reflected in the larger grey shaded area between the reference point 5 and 20 in figure 2. In general, a firm size distribution that looks more like Ghana’s will yield a higher proportion of small firms than a firm size distribution that looks more like Rwanda.

Figure 2: Firm Size Distribution and Size Brackets: from establishment census data <!-- Start of picture text --> Firm Size Distribution and Size Brackets<br>Burkina Faso (2016) Cameroon (2009) Ghana (2014)<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>5 20 100 5 20 100 5 20 100<br>Average firm size Average firm size Average firm size<br>Rwanda (2014) Rwanda (2017) Rwanda (2020)<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>5 20 100 5 20 100 5 20 100<br>Average firm size Average firm size Average firm size<br>All firms Informal firms<br>Fraction of firms Fraction of firms Fraction of firms<br>Fraction of firms Fraction of firms Fraction of firms<br><!-- End of picture text --> _Source_ : Own calculations based on census data from Burkina Faso (2016), Cameroon (2009), Ghana (2014) and Rwanda (2014, 2017 and 2020). The x-axis reports log firm size to ease visibility. Informality definition according to each country’s criteria. Red lines are size thresholds of 5, 20, and 100 workers.

Since data on the whole firm size distribution is scarce, our strategy first defines a statistical 10
corresponds to the share of claims at _t_ = 1 for which the direct counterpart is investment projects. The “disconnected” part is claims held by banks on investment projects initiated by other banks.<sup>23</sup> The equilibrium level of connectedness is given by: It is easy to verify that this expression is decreasing in _λ_ .

Moreover, from equation (12), the aggregate amount of lending is increasing in _λ_ ( _∂I∂λd_<sup>_AGG_</sup> _>_ 0). Intuitively, securitization makes lenders less careful about investing in illiquid assets if they can be sold in case of need at time _t_ = 1.

Altunbas, Gambacorta and Marquez-Ibanez (2009), using detailed information on securitization activities for a sample of European banks, find both that securitization shelters banks’ loans from the effect of monetary policy and that it strengthens the capacity of banks to supply loans. Both results hold in the simple model we presented in this section.

# **4 Connectedness and monetary policy: Evidence**

This section presents some time series evidence to explore how _connectedness_ affects the responses of economic variables to monetary policy shocks. We follow both a structural VAR (SVAR) approach as well as a factor-augmented VAR (FAVAR).

**SVAR.** We adapt the approach of Boivin and Giannoni (2006) by including our measure of connectedness as an exogenous variable.<sup>24</sup> The model can be written as follows:

> 23Instead of “purchasing” the investment projects directly, we can think of the interbank market more realistically as a loan market, in which banks with tradable investment projects borrow against _t_ = 2 investment income. Here, “tradable” has the interpretation of “collateralizable”.

> 24While arguably also the connection might be an endogenous variable, the result obtained in figure 6 indicates how the movements in the connection are long-run smooth movements, and thus we believe it can be considered an exogenous when using business cycle frequency data. In addition, the connection is included with lag one.

16
source equally with the survey. Crucially, however, we note that the possibility of correlated errors among expert assessments does not imply that we should discard these data sources altogether. Rather, it means that we should continue to use them because even in the limiting case of perfectly correlated errors, collectively they still provide some information about unobserved governance.

This re-weighting of the survey is shown graphically in the top panel of Figure 3, which plots the weight of the survey in the aggregate indicator for alternative values of λ. When common prejudices play no role, the weight on the survey is equal to one-third. As we move to the opposite extreme where λ=1, i.e. where the error term is dominated by common prejudices, then the weight on the survey should be 0.5.<sup>17</sup> In Table 6 we provide a sense of how important shared prejudices are quantitatively in our dataset. For our expert assessments we consider our five major data sources provided by commercial risk rating agencies (DRI, EIU, MIG, PRS, and WMO). For our survey we take GCS, which is our only very large cross-country survey of firms. In the top panel we report the two correlations of interest. We first report R* which is the average pairwise correlation between these five expert assessments, for each of our six governance aggregates for the past four years. We also report R, which is the average pairwise correlation of each of these five expert assessments with the survey. In the bottom panel we report the estimates of λ implied by these correlations. The first thing to notice is that for four of our six indicators (PV, GE, RL and CC) R* and R are very similar to each other, and often R*<R implying that the expert assessments are on average actually more correlated with the survey than they are with each other. This pattern of R*<R is inconsistent with shared prejudices, and so for these we have no positive estimate of λ. And in many cases R* is only slightly larger than R, implying very small values of λ.

For two of the six aggregates, VA and RQ, we find that the expert assessments tend to be much more highly correlated with each other than with the survey, implying more substantial estimates of λ. We should however be a bit cautious in taking these

> 17 Clearly the importance of shared prejudices also matters for the precision of our estimates of governance.  In the extreme case where λ=1 the standard error would be the same as if we had only two instead of three sources.

23
|||Sales Per|Employee|||Prof|its||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|MScoreoverall|0.935***<br>(0.205)|0.234***<br>(0.090)|||0.845**<br>(0.379)|0.844**<br>(0.386)|||
|Log(Employees)||0.077***<br>(0.026)|0.079***<br>(0.027)|0.081***<br>(0.025)||—0.085<br>(0.083)|-—0.072<br>(0.082)|—0.077<br>(0.083)|
|Log(Cap/Employees)||0.043***<br>(0.011)|0.042***<br>(0.011)|0.043***<br>(0.011)||0.092**<br>(0.042)|0.093**<br>(0.041)|0.093**<br>(0.042)|
|Log( Mat/Employees)||0.563°**<br>(0.038)|0.565***<br>(0.038)|0.564***<br>(0.038)|||||
|DuighEdu||0.128°**<br>(0.034)|0.127°**<br>(0.035)|0.133***<br>(0.034)||—0.048<br>(0.148)|-—0.048<br>(0.148)|—0.031<br>(0.147)|
|MScore yonitoring|||0.182*<br>(0.095)||||0.453,<br>(0.374)||
|MScorer¢.7||||0.129*<br>(0.071)||||0.589*<br>(0.306)|
|R-Square|0.03|0.86|0.86|0.86|0.20|0.21|0.21|0.21|
|N|727|724|724|724|513|513|513|513|
|Region Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IndustryDummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Columns (1) — (4) use sales per employee of a firm as the dependent variable; columns (5) - (8) use profits of a firm as the dependent variable. “MScoreOyerall’ is the overall management score of a firm as calculated following Bloom et al. (2018). ‘MScoreyfonitoring’ is the data driven performance monitoring management score. ‘MScorer¢.7’ is the targets and incentives management score. ‘Employees’ is the total employees of a firm. ‘Cap/Employees’ is the capital per employee of a firm. We use fixed assets of a firm as an indicator for capital. ‘Mat/Employees’ is the per employee materials consumed by a firm. ‘DuHighEdu’ takes a value 1 if more than 20% workers of a firm have a higher education degree. Robust standard errors are in the parenthesis. Marginal effects are reported in case of columns (5) - (8). Intercepts included but not reported. *,**,*** denotes 10%, 5% and 1% level of significance, respectively.
## **(b) The Stability of Cocaine Supply: 2000-2005**

<!-- Start of picture text --> Cocaine<br>Price Increases in<br>Productivity<br>S1<br>Crop Erradication<br>Policies<br>Quantity<br><!-- End of picture text --> The next three subsections elaborate on possible sources of bias in the data, and draw attention to how the data on cocaine production and consumption should be read and analyzed carefully.

# **Possible biases on the supply side ́s estimates**

Although neither UNODC or ONDCP have any evidence of large-scale coca cultivation or cocaine production in countries other than the traditional three Andean countries, there exists some evidence concerning efforts undertaken by cocaine producing organizations and individuals to counteract the effects of anti-drug policies (such as aerial and manual eradication) in producer countries. For instance, peasants intermingle coca crops with legal crops to avoid the former from being detected by satellite imagery. By doing this, they avoid both monitoring and eradication. Also, they induce a bias in the figures of coca cultivation gathered by UNODC and ONDCP (and on their respective estimates of 22
<mark>percent of their workers using financial math on a daily basis. Those that use financial math the most are Stall and Market Salespersons and Motorcycle Drivers. Moderate math (fractions and percentages) is used even less often. The occupations that use it the most are Accountants and Software Developers. Advanced math (algebra, trigonometry, and regressions) is rarely used. The occupation that uses it the most is Secondary Education Teachers (though only 30 percent of workers report using it on a daily basis).</mark> <mark>Around one-quarter of occupations use</mark> **_<mark>digital skills</mark>_** <mark>frequently (</mark> **<mark>Figure 9</mark>** <mark>). While digital skills are more commonly required among more highly skilled workers, even low-skilled occupations sometimes require daily use of digital skills (</mark> **<mark>Figure 10i</mark>** <mark>). Conditional on using a computer or a smartphone at work, internet search, spreadsheets, social media, computer writing, and e-mail are the most commonly used individual skills (</mark> **<mark>Figure 11</mark>** <mark>).</mark>

**_<mark>Technical skills</mark>_** <mark>are not widely required (</mark> **<mark>Figure 9</mark>** <mark>). For most occupations (75 percent), few workers (at most a third) use technical skills like designing or adapting equipment or software, installing equipment or software, or maintaining and repairing equipment on a daily basis (</mark> **<mark>Figure 11</mark>** <mark>). Still, some occupations use technical skills such as monitoring equipment or quality control intensively. For example, two-thirds of Product Graders and Testers say they use quality control skills on a daily basis and nearly two-thirds of Welders and Flamecutters report monitoring equipment on a daily basis.</mark>

**Figure 9: Skill group requirements of the SDS’s 30 occupations**

_Share of occupations for which the skills group is in the top 3 skill groups (%)_ <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> Source: SDS.

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high informality. Some of these studies also find that women and young workers were disproportionately affected.

More recently, researchers have shifted the focus to other dimensions of the effects of Syrian refugee inflows into Turkey. Altindag et al. (2020) argue that refugee inflows had a positive impact on firm production, both in terms of the volume of production and the introduction of new product varieties. Akgunduz and Torun (2020) examine other channels by which Turkish workers and firms accommodated the inflows of Syrian refugees. The authors find that skilled native workers increased their specialization in complex tasks, moving away from manual tasks, and domestic companies took advantage of the increased abundance of labor by reducing capital intensity. As shown in earlier studies, both mechanisms contribute to mitigate the effects of immigration on the wages of the receiving country (Lewis (2005), Peri and Sparber (2009), Gonzalez and Ortega (2011) and Dustmann and Glitz (2015)).

In the last few years, some researchers have begun to analyze the economic effects of the exodus of Venezuelans on the surrounding countries but progress has been slow due to the difficulty of analyzing Venezuelan migrants equipped solely with governmentprovided data or the standard labor force surveys. The existing work has so far focused on the labor market effects in Colombia, the main receiver of Venezuelan migrants (e.g. Caruso et al. (2019) and Penaloza-Pacheco (2019)). To our knowledge, the only existing study concerning the labor market effects of Venezuelan migration to Ecuador is Olivieri et al. (2020). This paper relies on Ecuador’s labor force survey to document adverse effects of the inflows on the wages and employment quality of young, low-educated Ecuadoran workers in the main receiving areas within the country.

Relative to these studies, our paper provides the first analysis of the labor market conditions of Venezuelan migrants in a Latin American context on the basis of a survey specifically designed for this purpose. We also use this information to analyze policies that are tailored to the specific characteristics of Venezuelan migrants in Ecuador. In this sense, our paper is also related to Clemens et al. (2018). This paper analyzes the effects of granting formal labor market access to refugees, highlighting the potential for substantial economic gains for the host country but also the potential costs for certain groups of the host population, which will depend importantly on the characteristics of the migrants (e.g. education) as well as features of the host labor markets. In the US context, some recent studies have analyzed the economic effects, on wages and GDP, of providing legal status to undocumented workers (Edwards and Ortega (2017) and Ortega et al. (2019)).

3