rafmacalaba/data-use-mentions-extended huggingface dataset:
# 3.1 Errors of representation

## <u>Hypothesis 1: Household sample selection effects</u>

In the absence of universal phone ownership in our study countries, it is possible that phone survey samples overrepresent certain population groups such as better-off and urban households (Ambel et al. 2021; Brubaker et al. 2021) . Similarly, non-response can lead to selective attrition from the sample, potentially affecting its representativeness for the general population. Even though our sampling strategy (collecting phone numbers from reference contacts) and re-weighting approach has been found to mitigate these issues, it is conceivable that some sample selection bias at the household level remains (Ambel et al. 2021; Gourlay et al. 2021) . If selection into our sample (either through coverage bias in the list of households with phone numbers or through non-response) is correlated with vaccine uptake, estimated coverage will be biased. We thus formulate the first hypothesis for the divergence between survey and administrative vaccine coverage estimates.

**Hypothesis 1:** Phone surveys overrepresent population groups that are more likely to be vaccinated.

The COVID-19 vaccination module implemented as part of the Ethiopia Socioeconomic Survey (ESS 5) gives us the opportunity to test this hypothesis. This module collected information on vaccine uptake for a nationally representative sample of households of which our phone survey sample is a subset. While sample selection effects may affect the phone survey sample, they should be mostly absent in a fully nationally representative sample. Therefore, we can compare estimated vaccine uptake among the sample of phone survey households (interviewed during the ESS 5) to estimated vaccine uptake within the whole, general population sample contained in the ESS 5. We formulate the following prediction.

**Prediction 1:** Households included in the phone survey will display higher rates of vaccine uptake compared to the general population in a nationally representative sample.

Empirically, we run the following OLS regression.

Where _Vis_ is a dummy for whether individual _i_ part of sample _s_ has been vaccinated with at
in developing countries. However, to understand the broader universe of firms in the Africa context, we also need to incorporate informal businesses, given they represent a large share of the economy. There are very limited data available across African countries providing comprehensive measures of the formal and informal businesses that remain active.<sup>3</sup> An alternative to address the data limitation on firms is by using information on employer status. This approach allows the use of micro-level datasets (e.g., labor and household surveys) that are more widely available across Africa.

ILO defines employers as those workers who are self-employed and, in this capacity, have engaged one or more persons to work for them as employee(s). There is another group of selfemployed workers defined as own-account, who operates without hiring additional workers.

Our primary emphasis lies on the _employers_ category. This category is our definition of a “firm,” which we group between micro (with less than 5 workers, excluding own-account businesses), small (between 5 and 19 workers), medium (between 20 and 99 workers), and large (with 100 or more workers). In addition, we provide estimates of the number of own-account businesses, both formal and informal.

> 1Source: OECD Data.

> 2See for example “Enterprise Survey 2009-2017, Panel Data Liberia, 2009 - 2017”.

> 3WBED has recently incorporated information on the total number of registered and closed businesses, in addition to the number of new registrations. Comparison with external datasets suggests that business registry sources tend to provide robust estimations of the number of newly registered businesses, but they have more limitations when referring to the total number of active businesses.

4
# **3.2 Data**

For our empirical analysis we use a data set with balance sheet and profit and loss accounts of 3000 Bulgarian industrial firms for the period 1993 - 1996. We have included only those firms that reported for the entire period 1993 - 1996, which gives us a balanced panel of 1003 fims. The data set is taken from the AMADEUS data base and the data are presented in millions of US $.7 Because of the short time period, we used cross-section data for our analysis by averaging the variables in our data set. We did compute the average value of each variable for the full sample of 1003 companies over the period 1993 -1995. Due to the limited time period for which data are available we apply a cross section analysis on the averages of each variable. Estimating a dynamic investment model (hence taking the time series information into account) by using only three observations for each variable is misleading, since the adjustment process cannot be identified. Consequently, the coefficients we present refer to long-run average effects (Pesaran and Smith, 1994). This procedure of averaging along the time dimension is also used in similar studies in which the dataset covers only a few years (Lensink and Sterken, 1998 and Chirinko and Elston, 1997). We have excluded 1996 because of the severe financial crisis in this year, which distorted the data considerably.

All variables are thus expressed in US dollars. During the period 1993-1995, the dollar/leva rate fluctuated considerably (the leva depreciated by about 70%). Such changes in the nominal exchange rate tend to dominate the results. We, therefore, corrected the nominal variables for changes in the real US dollar/leva rate (see Annex 2). Annex I provides a list of all the variables. Net investment, I, is the dependent variable in

> 7 To be included in AMADEUS, a company must comply with one of the following size criteria: i., a turnover greater than EurolO million;
These econometric results are consistent with and support what was described in the previous figures and tables. Further, it is important to observe differences across services in terms of the importance of globalization on performance. Indeed, the results suggest that there are differential impacts of globalization on city performance, depending on the options city residents have for exit or choice. In the services where they can exit, such as water and electricity, then globalization has limited impact on city performance. In cases where they cannot exit, there seems to be more impact. The role of voice and participation, when you cannot exit seems to be critical, and significant, as shown by the significance of the variables such as bribery in utility and control of corruption (see Table 4 above).

Lee and Anas (1996) potently gave an illustration consistent with these results. They analyzed the decisions firms made to locate or not in cities in Nigeria and Indonesia, and also found that where public supply of electricity was unreliable, they could substitute by private generation capacity that they themselves installed. A sister paper (Kaufmann, Léautier, and Mastruzzi, 2004) provides a more detailed look at both the issue of technology and that of scale and city characteristics.

Country globalization (Kearney Globalization Index) also matters for city level performance. It is significant for all service variables except access to electricity, sewerage and telephone lines. What is interesting in the results in Table 4 is that city performance for network infrastructure (electricity grid, sewerage, telephone lines) is impacted by two aspects of governance (bribery in utility at the city level, control of corruption at the country level) and per capita income. Rich countries can afford to provide network infrastructure in cities. And better control of city and country level corruption leads to better performance of cities in providing access to services.

In summary, we find that there is an effect of city globalization on performance of cities using both the Taylor and the UN databases. While the strength of the effects appears
other hand, the altered terms could result in impact estimates being downwardly biased if they induce non-program schools to adapt to more effectively compete in the new local market environment created by the program such as by ratcheting up their investments in the quantity and quality of inputs and/or altering their fee structures to retain their existing student bodies and attract new students. Both types of effects could be present simultaneously; consequently, the direction of the net effect is theoretically ambiguous.

The second threat arises from anticipation of future treatment. Program entry is not a oneoff event. To date, there have five calls for applications over a three-year period. Given this pattern, it is conceivable that non-program schools interested in joining the program might alter their behavior in anticipation of a future call for applications and in an effort to increase the likelihood of program entry. These actions could, for example, take the form of non-program schools investing in more and better quality inputs and resources. Anticipation in this case would result in impact estimates being downwardly biased. In particular, behavioral changes due to anticipation might be most applicable to non-program schools who just failed to achieve the SLQAT pass rate cutoff in an earlier phase of entry (our marginal failers), as we would expect that the marginal costs of investments and efforts required for failers from a previous phase to gain program entry are likely to be decreasing as one approaches the cutoff from below.

While when the RD model is applied to the right data designs, it yields internally-valid estimates, the generalizability of these impact estimates is likely to be limited as they are in principle only valid for narrowly-defined subpopulations. In the case of the phase-4 SLQAT taker data, which are fitted using a sharp design, the RD impact estimates are valid for low-cost private schools that successfully applied to the program, cleared the physical inspection, and obtained pass rates near the SLQAT cutoff. Even if we generalize our estimated treatment effects 37
versions of the program have been tried since then, Trabajar I, II and III. In terms of design, Trabajar II (TII) and III are more similar to each other than either are to Trabajar I (TI). There were substantial design changes between Trabajar I and II. The inter-provincial allocation of spending was reformed, moving away from a largely political process to an explicit formula based on the estimated number of poor unemployed workers in each province. TII also put greater emphasis on creating assets of value to poor communities. Poverty measures were included in the center's budget allocation rules and in the selection criteria for sub-projects. The poverty focus was also made clearer to provincial administrators. TIII was very similar to its predecessor in design. The main difference was that greater emphasis was placed on the quality of sub-projects, to assure that the assets created were of value to the communities. The World Bank has supported TIl and TIII by loans (disbursed against the wage payments), and through technical support on program design, monitoring and evaluation. All results quoted for Tlll in this paper relate to the first 16 months of its operation, up to November 1999. (TIII is ongoing at the time of writing, and is scheduled to finish in mid 2000.) From the point of view of this paper, an important difference between the three versions of the program is in the level of funding. In Trabajar I, disbursements by the center (covering wages for participating workers) averaged $77 million per annum; for TIl this rose to $160 million per annum, and it then fell to $98 million per annum under TIII. As we will see, there were also differences in levels of funding between sub-periods.

Survey-based impact evaluation methods have been used to assess the gains to participating workers and their families from TIl and TIII. Propensity-score matching methods were used to construct a comparison group to surveyed Trabajar participants from an identical national sample survey implemented at the same time. Income gains were then
to crop cutting. Focusing on sorghum, EACI collected data on 3,569 plots of which 1,098 were subject to crop cutting.

We start this part of the analysis by comparing the data from ERIVaS and EACI. This provides clues regarding the potential modifications, if any, in the parameter estimates for the relationships between yields and inputs when we extend our analysis to a nationally-representative survey. Table 10 shows summary statistics for pure stand sorghum plots on in ERIVaS and EACI samples.<sup>17</sup> We see that the measures of yields and inputs are statistically different between the two surveys. The mean value in EACI is often larger, and as can be observed on Figure A2 in the Appendix, the amplitude is often greater for the inputs. The latter finding reflects the fact that EACI covers the entire country and captures more heterogenous farming practices and outcomes. This may in turn be beneficial for the use of ML techniques to derive imputed CC yields, since these algorithms work best with more observations and are more adapted to handle potential heterogeneity across units of observations.

**Table 10:** Sample summary statistics of ERIVaS and EACI 2017.

|Variable|Sample|Mean|Std.Dev|Min|Median|Max|Obs.|
|---|---|---|---|---|---|---|---|
|SR Yields|ERIVaS|636.86|822.26|0.00|400.00|8139.45|577|
||EACI 2017|536.70<br>***|874.18|0.00|317.89|9523.81|3237|
|CC Yields|ERIVaS|500.68|380.06|0.00|423.44|2472.50|577|
||EACI 2017|760.19<br>***|482.30|
10 10 the trade data, the country of origin corresponds to the place of origin of the good or service.<sup>6</sup> The indicators of protectionism can apply to the nationality of firms, goods and services, or both. For instance, the (lack of) national _treatment_ indicator applies to both firms and goods/services, while provisions for preferential treatment apply to firms. _Domestic sourcing_ obligations, on the other hand, refer to the nationality of goods and services and are consistent with the definition of nationality used in the trade data. The composite indicator includes rules that depend on the nationality of both firms and goods and services. By investigating also the impact of each type of protectionist measure, we can provide suggestive evidence on the role of the different nationality principles.

Because of these data limitations, our empirical strategy relies on simple cross-country regressions and gravity models of bilateral trade. Our estimates are interpreted as correlations suggesting the existence (or lack thereof) of a relationship between trade openness and the protectionism measures. Reverse causality and omitted variables can introduce biases that prevent us from interpreting the estimates as causal. Countries whose markets have been historically open to foreign firms might have designed their law in ways that accommodate such a trend, thus creating a link going from trade openness to the (low) protectionism level of the law. Furthermore, unobservable country characteristics (for instance, legal aspects that are not measured by the coding of national laws) that correlate with both the coding of the law and the trade flows can influence the estimates of the relationship of interests. For these reasons, the estimates found in the econometric analysis are suggestive of the relationship between trade openness and protectionism of the law, without indicating causal effects.

# IV. Results on the relationship between trade openness and protectionism in national laws on government procurement

We move beyond descriptive evidence and examine the relationship between protectionism in government procurement, as measured by a coding of the national law, and trade flows. We first estimate
Table C.2: Descriptive Statistics by Locations: Main Variables

||Aggregate|Location 1|Location 2|Location 3|Location 4|
|---|---|---|---|---|---|
|Child Care Choices:||||||
|Use: =1 if main form of care is a day care center|0.30|0.34|0.30|0.24|0.31|
|Price: Hourly fee for a 2 year old attending full-time|4.38|2.82|4.32|4.53|5.80|
|Quality: Average overall ITERS score|4.29|4.33|4.13|4.08|4.54|
|Use: =1 if main form of care is non-relative care|0.06|0.03|0.05|0.06|0.08|
|Use: =1 if main form of care is relative care|0.19|0.23|0.16|0.25|0.12|
|Child’s Cognitive Score:||||||
|Bailey Short Form Mental Ability Score|127.94|126.94|128.29|126.42|129.87|
|Parents’ Labor Market:||||||
|Mothers||||||
|=1 if working full-time for pay|0.28|0.29|0.31|0.25|0.28|
|=1 if working part-time for pay|0.21|0.27|0.14|0.22|0.18|
|=1 if not working for pay|0.52|0.44|0.54|0.53|0.54|
|Wage: Gross hourly wage|
. They identify the different migrants’ routes in Libya and their evolution across time, showing that migrants tend to move north, and that they sort into these routes according to their nationality.

Migrants in Libya travel in stages, turning to smugglers for the difficult legs of the journey.<sup>11</sup> Ethnographic research documents that the relationship between migrants and smugglers in Libya is more complex than usually thought (Sanchez, 2020). Identifying smugglers as criminals and migrants as victims oversimplify a complex and often symbiotic relationship (World Bank, 2023).<sup>12</sup> In the Libyan context, smugglers are often ordinary people, living in border areas along migration pathways, and in coastal towns and cities, who facilitate the movement of migrants through the country (Sanchez, 2020). Migrants survey data indicates that smugglers are in most of the case considered “travel agents” (59% of respondents) and only in few cases “criminals” (11%) (Murphy-Teixidor et al., 2020). Other studies emphasize that smugglers and migrants have a common interest that their interaction is successful. Moreover, the various roles of migrants (who sometimes contribute to the organization of the journey) blurs the boundary between smugglers and their clients (Achilli, 2021). Taken together, the evidence from these studies suggests that migrants do not perform a passive role in their journeys: they have agency and are essential actors of their mobility (Sanchez, 2020). Based on these arguments, migrants in Libya should be thought of as (most often) being able to choose when and where to move in controlling the Eastern part of the country.

> 11In a survey of 5,159 migrants conducted in Libya in 2019, 32% reported not using any smuggler, while 37% used one smuggler, and 31% used several smugglers along their journey (Murphy-Teixidor et al., 2020).

> 12Smuggling and human trafficking are two very distinct phenomena (Adesina, 2021
<u>Caveats:</u> Given the lack of a standardized official source on Chinese credit events, our tally of Chinese debt restructurings is likely to be incomplete. An omission that deserves particular attention is restructurings of recipient country SOEs and SPVs for which hardly any data is publicly available and which constitute and increasingly important share of China’s outbound lending (Horn et al. 2021; Malik et al. 2021). In the same way that off-balance sheet debt of these entities often remains “hidden” from official debt statistics, debt restructurings with these entities are also likely to go unreported. Our tally of credit events with the Chinese government and its state-owned entities therefore needs to be regarded as a lower bound.<sup>12</sup>

**Data on credit events with Paris Club creditors** : To add restructurings with the Paris Club, we use data from Cheng et al. (2019) until 2015 and update their database by using the Paris Club website and World Bank information on the implementation of the DSSI. As for Chinese creditors, we also exclude Paris Club reschedulings with DSSI eligible countries that are not considered to be “in debt distress” or “at high risk of debt distress” under the World Bank and IMF debt sustainability framework. To distinguish between Paris Club agreements that entailed only rescheduling and agreements that also implemented face value reductions, we additionally draw on the data set by Das et al. (2012), which we again update using the Paris Club website. Figure A3 shows our tally of Paris Club restructurings between 1970 and today.

**Data on debt restructurings with private creditors:** To identify restructurings with private external creditors (foreign bondholders and banks), we use the database by Asonuma and Trebesch (2016). Since 1975, their data identifies a total of 192 debt restructurings with 74 developing and emerging countries. To assess the scope of debt relief, we add information from Cruces and Trebesch (2013) and from the
Policy Research Working Paper 5902

# **Abstract**

Measurement of the middle class has recently come to the center of policy debate in middle-income countries as they search for the potential engines of growth and good governance. This debate assumes, first, that there is a meaningful definition of class, and second, that thresholds that define relatively homogeneous groups in terms of pre-determined sociological characteristics can be found empirically. This paper aims at proposing a view of the middle class based on vulnerability to poverty. Following this approach the paper exploits panel data to determine the amount of comparable income—associated with a low probability of falling into poverty—which could define the lower bound of the middle class. The paper looks at absolute thresholds, challenging the view that people above the poverty line are actually part of the middle class. The estimated lower threshold is used in cross-section surveys to quantify the size and the evolution of middle classes in Chile, Mexico, and Peru over the past two decades. The first relevant feature relates to the fact that the proposed thresholds lie around the 60th percentile of the distribution. The evidence also shows that the middle class has increased significantly in all three countries, suggesting that a higher number of households face lower probabilities of falling into poverty than they did in the 1990s. There is an important group of people, however, which cannot be defined as middle class from this perspective, but are not eligible for poverty programs according to traditional definitions of poverty.

This paper is a product of the Poverty, Equity and Gender Unit, Latin America and the Caribbean 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://econ.worldbank.org. The author may be contacted at lflopezcalva@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about