rafmacalaba/datause-encoder-data huggingface dataset:
is increasing as countries grow richer. But, the lowess line for the RCS poverty lines in Panel B does potentially indicate a very slight flattening of the fitted lowess line at the tail end of the RCS data.<sup>27</sup> This flattening of the line is the underlying motivation for the minimum at $1.25 (in 2005 PPP terms) of the Ravallion-Chen weakly relatively poverty line, but we argue that the lack of a statistically significant positive slope coefficient for the bottom quintile of the RCS sample (the bottom 15 countries) could also simply be an issue of power.

When we regress our more complete set of national poverty lines on household final consumption expenditure (HFCE) from national accounts (following RCS), we find no evidence that the slope of the fitted line goes to zero for poor countries. In our lowess regression (Figure 3, Panel B), the fitted line is everywhere upward sloping with no appearance of a flattening at the end of the distribution. Similarly, when we do a spline regression on the full sample of national poverty lines, both the log-log and the levels specifications indicate that the slopes of the fitted lines are positive and statistically significant over the first quintile. When the sample is restricted to the circa-2011 subsample (with reduction in sample size to approximately 1/7<sup>th</sup> of the full sample), both estimates are still positive, though only the log-log specification is statistically significant.<sup>28</sup> One reason for the difference in our results and previous analysis suggesting a nonincreasing relationship between national lines and income for the bottom quintile may simply be due to differences in country coverage. The sample of national poverty lines used in this paper

> 27 Using a Hansen (2000) threshold estimator, RCS  identify a break point for the bottom 15 countries (or about 20 percent of the sample) and flat portion of the schedule to the left of the break point. The lowess line is a more flexible approach and similarly provides some visual evidence that the correlation weakens. Klasen et al. (2016) find that the flat segment observed by RCS does not hold at 2011 PPPs when using the same specification as RCS, but it does hold for a log-log specification.

> 28 Spline regression results available from the authors upon request.

19
O.A.M.D.G.

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Table 7.6: Thematic Misallocation Measures

|||Num|erical data||||
|---|---|---|---|---|---|---|
|Theme|Lending|AAA(A)|AAA(L)||Lending|AAA(A)|
|Climate|45|89|142|AAA(A)|0.67||
|Pollution|22|82|239|AAA(L)|-0.32|0.45|
|Land|70|127|181||||
|Biodiversity|68|105|73||||
|Water|49|83|96||||
|Policies|38|35|48||||
|||<br>|Ranks<br>||||
|Theme|Lending|AAA(A)|AAA(L)||Lending|AAA(A)|
|Climate|4|3|3|AAA(A)|0.89||
|Pollution|6|5|1|AAA(L)|-0.09|0.26|
|Land|1|1|2||||
|Biodiversity|2|2|5||||
|Water|3|4|4||||
|Policies|5|6|6||||

# **8.  Future Opportunities for Thematic Lending**

To assess future opportunities, we assume that total environmental lending during the period 2004-2009 will be identical to lending during 1998-2003 ($9.2 billion). For each country, the optimal share of environmental lending by theme is proportional to the product of the country's thematic indicator and its OED success probability. To calculate optimal lending, we multiply each country's optimal thematic share by total thematic lending for the period 1998-2003. Then we add across themes to obtain total optimal lending for each country. Recognizing that the Bank may limit its lending to some countries for a variety of reasons, we derive a control factor from lending experience during the past six years. Across countries, the maximum ratio of environmental loans to total loans was 46%. As Figure 8.1 shows, most ratios were 10% or less.

23
Table 5: 2SLS for the Probability of Engaging in Formality Employment

||Form<br>Have boo|ality<br>k account|Form<br>Have|ality<br>licence|
|---|---|---|---|---|
|Sample|(1)<br>Public<br>Private<br>Self-employment|(2)<br>Private<br>Self-employment|(3)<br>Public<br>Private<br>Self-employment|(4)<br>Private<br>Self-employment|
|Ln Pop|-0.000|0.024<sup>_⇤⇤_</sup>|0.029<sup>_⇤⇤⇤_</sup>|0.051<sup>_⇤⇤⇤_</sup>|
||(0.010)|(0.011)|(0.010)|(0.013)|
|Addis Ababa|Yes|Yes|Yes|Yes|
|Control|Yes|Yes|Yes|Yes|
|Year|Yes|Yes|Yes|Yes|
|Mean|0.41|0.20|0.66|0.54|
|Underidentifcation|18.58|17.61|18.58|17.61|
|Weak identifcation|72.90|72.36|72.90|72.36|
|N|66,578|48,447|66,578|48,447|

Notes: Standard errors in parentheses,<sup>_⇤_</sup> _p < ._ 10 _,_<sup>_⇤⇤_</sup> _p < ._ 05 _,_<sup>_⇤⇤⇤_</sup> _p < ._ 01. Control variables are demographic: gender, age (ten years increment), marital status, and household size (and its squared term), and education: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, post-secondary, and adult education unemployment status. In general, well-educated workers attempt a transition to better-paid and/or formal jobs that are o↵ered more in larger towns. As described in World Bank (2016a), undertaking underpaid informal jobs during such a job seeking period could lower the chance of the worker’s finding a formal job later on. Workers who are aware of this tend choose to be unemployed until his or her job searching succeeds. However, the 2SLS estimation results imply that unemployment and town population size are associated only among married women.

# **Working Hours and Underemployment**

As discussed in Section 3, underemployment—rather than unemployment—is prevalent among lesseducated poor workers. Thus, it is useful to examine how working hours and underemployment among employed workers varies across towns with a di↵erent population size. Table 7 summarizes the estimation results of OLS and 2SLS on the log of minutes worked during the last month. The estimated elasticity of working hours to the town population size in column 2 is 6.2 percent. Distinguishing the e↵ects for working hours on private wage jobs (columns 4–6) and self-employment jobs (columns 7–9) shows that the elasticity is 6.1 percent among self-employed workers. This 17
# **III. Setting a Global Poverty Line with 2011 PPPs**

In order to estimate the level and trends in global poverty based on the various PPPs described above, it is necessary to first define the value of the global poverty line. For estimates based on the 2005 PPP data, we use the same $1.25 per person per day (in 2005 PPP units) as used by the World Bank (2014a, 2014b) and many other international organizations. However, for analysis of global extreme poverty using the 2011 PPPs, we need to define a poverty line denominated in 2011 PPPs comparable to the $1.25 line in 2005 PPPs. .

# **_A. Updating the Global Poverty Line – A Fixed Goal Line_**

One suggested method for updating the $1.25 poverty line is to simply update it by US inflation over time, so that the real value of the poverty line remains the same in the US. Between 2005 and 2011, there was about 15.2 percent inflation in the US, so adjusting the $1.25 poverty line by inflation results in a new poverty line of $1.44 in 2011 terms. Following this approach, the global poverty line would buy the same basket of goods in the US over time. To convert this into local currencies in 2011, we would simply apply the 2011 PPP conversion factor for private consumption from the ICP 2011. This is the method applied by Dykstra et al (2014) soon after the 2011 PPPs were first released and which led to a dramatic decrease in their estimated count of the extreme poor.

Despite reflecting the same real value in the US, the inflation-adjusted $1.44 line converted using the 2011 PPPs translates into a lower value than the $1.25 at 2005 PPPs, in the vast majority of national currencies.. The 2011 $1.44 line buys the same basket of goods in the US as the 2005 $1.25 line. However, the $1.44 line converted to national currencies at 2011 PPPs buys significantly fewer goods in most developing countries of the world than the $1.25 line 12
assessments. This line of research has revived the issue of the “micro-macro paradox.” In a famous paper, Mosley (1987) suggested that, while foreign aid seemed to be effective at the microeconomic level, its aggregate or macro impact was much more difficult to identify. Indeed, analyses of the impact of aid on growth based on crosscountry regressions produce mixed results but there is evidence that multilaterals and bilateral donors have successfully implemented individual projects. Thus the “micromacro debate” continues. The consensus among researchers is now that macro and crosscountry level results should be complemented by micro impact evaluation studies to yield a balanced assessment. Taking this approach, for example, Arndt, Jones, and Tarp (2010) find that aid has a positive and significant but moderate effect on growth.

Impact evaluation differs from the internal evaluation efforts carried out at the World Bank by various units, including QAG (quality at entry) and IEG (ex post evaluation). While the latter evaluate processes, assess whether projects have reached their intended objectives and document changes in specific outcomes, impact evaluations (carried out in the Research Department or other units of the Bank, or in universities around the world) evaluate the development impact of a project on its target population (e.g., on poverty or health outcomes) by comparing the observed outcomes to a counterfactual (i.e., what the situation would have been if the program had not been undertaken). Impact evaluations are a scientific means of establishing causal links between interventions and outcomes and constitute a key input to determining costeffective approaches to development. Aid would become much more effective if projects took into consideration the results of studies based on such rigorous methods.

IDA has gradually changed its approach to measurement from measuring inputs used for development projects to measuring outcomes, i.e., from public resources spent on inputs to outcome indicators (such as illiteracy rates, morbidity, infant mortality, school achievements, infrastructure characteristics, etc.). One reason is easily understandable: since governments do not spend money equally effectively, these numbers per se tell us nothing about government efficiency. Since the incentives to improve the quality and reduce the cost of public goods are stronger for private providers than for public ones, it is necessary to have data on both the quality and the quantity of output in order to compare public and private activities. Quality characteristics can be 50
12 decrease vulnerability due to consumption, despite a quadrupling of prices in real terms between 1999 and 2008. This, however, is in part due to historically low energy efficiency and high oil intensity of these countries at the beginning of the study period, providing large scope for improving efficiency.

Table 1: Distribution of production and consumption effects by income and region among net oil importers, 1999–2008 (percentage of countries in each category except for the median)

|**Category**<br>|||∆**P**<br>|||||∆<br>|**C**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|(number of<br>countries)|**Med**|**<**<br>**-5%**|**-5 to**<br>**0%**|<br>**0%**|**0–1%**|**Med**|**0–**<br>**2.5%**|**2.5–**<br>**5%**|**5–**<br>**7.5%**|**7.5–**<br>**10%**|**>10**<br>**%**|
|**Income**||||||||||||
|Low (29)|0.0|3|3|90|3|4.6|21|34|17|14|14|
|LM (34)|0.0|3|24|68|6|4.4|15|47|18|6|15|
|UM (33)|0.0|0|36|61|3|5.3|21|24|33|15|6|
|High (35)|0.0|0|60|40|0|2.3|57|26|9|0|9|
|**Region (exclu**|**ding high**|**-income c**|**ountries)**|||||||||
|EAP (13)|0.0|0|31|62|8|5.6|8|38|23|23|8|
|ECA (16)|0.0|0|44|38|19|1.9|63|31|0|0|6|
|LAC (21)|0.0|5|19|76|0|5.7|0|29|33|19|19|
|MENA (5)|0.0|0|20|80|0|6.8|0|20|40|20|20|
|SAR (7)|0.0|0|43|57|0|4.6|0|71|14|14|0|
|SSA(34)|0.0|3|6|91|0|4.6|21|35|26|6|12|

_Sources:_ Table A1.4 and author’s calculations _Notes_ : Oil import status in 2008. Med = median in percentage of GDP, LM = lower middle, UM = upper middle, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MENA = Middle East and North Africa, SSA = Sub-Saharan Africa. The median for ∆ P is equal to zero except among highincome countries, for which the median is -0.03.

Table 2: Distribution of production and consumption effects by income and region among net oil exporters, 1999–2008 (percentage of countries in each category except for the median)

|**Category**|||∆**P**<br>|||||∆<br>|**C**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|(number of<br>countries)|**Med**|**<**<br>**-20%**|**-20 to**<br>**-10%**|**-10 to**<br>**0%**|**>0%**|**Med**|**<0%**|**0–**<br>**2.5%**|**2.5–**<br>**5%**|**5–**<br>**10%**|**>10**<br>**%**|
|**Income**||||||||||||
|Low (2)|-28.6|50|0|50|0|1.1|0|100|0|0|0|
|LM (11)|-5.1|9|0|64|27|7.9|0|18|27|18|36|
|UM (16)|-12.0|19|38|31|13|3.7|31|6|31|25|6|
|High (10)|-7.2|20|20|50|10|2.9|10|30|40|10|10|
|**Region (exclu**|**ding high**|**-income c**|**ountries)**|||||||||
|EAP (3)|-4.8|0|0|67|33|8.3|0|0|33|33|33|
|ECA (4)|-6.9|25|25|0|50|-1.9|100|0|0|0|0|
|LAC (6)|-8.6|0|50|50|0|5.3|0|17|17|67|0|
|MENA (7)|-9.8|29|14|57|0|10.5|0|0|29|14|57|
|SSA(9)|-5.8|22|11|44|22|2.4|11|44|44|0|0|

_Sources:_ Table A1.4 and author’s calculations _Notes_ : Oil export status in 2008. No country in South Asia was a net oil exporter. Retention of three figures for medians exceeding 10 percent is for formatting purposes and is not intended to imply that the number of significant
the European Union, as well as in two candidate countries and in three countries of the European Free Trade Association (Iceland, Norway, and Switzerland). The survey draws on nationally representative samples; sampling rates in any country vary between 0.2 and 3.3 percent.

To assure comparability, the EU‐LFS uses output harmonization. This means that the European Union issues standards for the output of the LFS, including a list of variables and categories, minimum sampling precision, observation period, and periodicity, while the national statistical institutes across Europe are responsible for designing and conducting the survey. They must follow the standards and forward the results to Eurostat, which processes the information centrally. While some factors may make the survey less than perfectly comparable, this process of harmonization nonetheless makes it attractive for studying labor market dynamics.

This study makes use of the EU‐LFS microdata that are available in the period 1995–2013 to analyze the impact of the internet on the labor market. The list of countries and years that are covered is shown in Table A 1 in the Appendix.<sup>4</sup> All of the information is aggregated by sector of activity using the standardized NACE classification Rev. 1.1 at 1 digit.<sup>5</sup> Averages of all the variables of interest are computed for workers aged 25–64 in each sector of activity, except for those working in public administration, in extra‐territorial organizations and bodies, and households as employers, which are excluded from the sample.<sup>6</sup> Although the statistics are computed using harmonized variables, some outliers in the “working from home”

> 4 Data for Germany and the two candidate countries (Macedonia and Turkey) were not included in the 2014 release of the EU‐LFS microdata. Malta was excluded because data for this country are only available from 2009 on. 5 Unfortunately, the public version of this survey does not include a more disaggregated version by sector of economic activity.

> 6 No data on sector of activity are available for Poland in 1997, 1998, and 1998. These observations could therefore not be included in the estimations.

10
become self-employed when provided with a loan from a micro-credit scheme. Furthermore, while interactions between formal means of finance and labor origins are not observed to be significant, the averages in the table suggest that informal sector employees are much less likely to be granted access to formal finance.

In Table 6 we present estimates for three subsamples of individuals. First, in _Columns 1-2_ , we include only the sample of the 2001 household heads. Most findings persist, except for the gender and education differentials. Furthermore, more household heads appear to be initiating self-employment activities in RS. In addition, at the household head level, the effect of receiving overseas remittances is no longer significant, although the coefficient is still negative. However, transfers from NGOs continue to have a positive impact on self-employment in the sample of household heads. _Columns 3 and 4_ exclude farmers in our 2001 sample and find that all prior results are robust. In particular, we find that self-employed individuals continue to be significantly more likely to transition from the informal sector, which suggests that this result is not driven by possible employment diversification of farmers. Finally, _Columns 5 and 6_ restrict the analysis to the sample of individuals in paid employment in 2001, either formally or in the informal sector, from which almost 80% of the inflows to self-employment are derived. We see that all findings hold and are further reinforced in these specifications. Ceteris paribus, informal sector employees are more likely to enter self-employment compared to employees in paid employment. The inclusion of both past wealth and past employment status, together with an interaction term between the two terms render all three variables insignificant indicating a significant negative correlation between wealth and informal sector activity. With respect to sources of finance, examination of the subsample of individuals employed in 2001 reinforces the magnitude of the negative impact of remittances from abroad and the positive effect of charity/NGO money.

In Table 7 we show results from a multinomial probit analysis, where the dependent variable takes a value of 0 if the individual switches to self-employment with employees (NEWSE-EMP), a value of 1 if the individual switches to self-employment without employees (NEWSE-OA), and the value of 2 if the individual never switches to self-employment. Results are consistent with the last Tables. However, there are some interesting differences. For instance, only entrepreneurs with employees are significantly 16
methods as discussed in this paper, we would cautiously call for more attention to further developing these methods, and particularly validation studies to provide richer evidence on contexts where these methods may or may not work, or how well these methods work. Besides providing the typical (regional or national) capacity building exercises for staff at different national statistical agencies, a possibly useful way to proceed is perhaps some subsequent selective pairing of international experts with these staff, who can form small teams that provide further analysis with low costs. Another approach is to incorporate these imputation methods in existing popular software platforms with an accompanying guidebook (see, for example, Foster _et al._ (2013)) that can provide user-friendly and self-contained access to development practitioners.

36
30

**Table 2: External Factors and GDP Growth 1992-1999**

Sample: 1992:1 1999:3

|Variable<br>C|oefficient|t-Statistic||
|---|---|---|---|
|_A4Yl_|0.741|18.955 (*)||
|A4Y4|-0.446|-8.530 (*)||
|A4Y5|0.263|5.428 (*)||
|_A4TOT_|0.038|6.155 (*)||
|_A4TOT4_|0.040|5.235 (*)||
|USI|-0.005|-6.259 (*)||
|_CAI,_|0.0004|2.984 (*)||
|_A4SPD-3_|-0.010|-5.491 (*)||
|_ISO.4_|0.0004|4.222 (*)||
|**Fixed Effects**||||
|_Argentina_|0.029|||
|_Brazil_|0.024|||
|_Chile_|0.042|||
|_Colombia_|0.021|||
|_Ecuador_|0.021|||
|_Mexico_|0.029|||
|_Peru_|0.040|||
|_Venezuela_|0.020|||
|0.8<br>|00<br>Mean d|ependent var|0.032|
|R<sup>2 </sup>adjusted<br>0.7|83<br>S.D. de|pendent var|0.047|
|S.E. of regression<br>0.0|22<br>Sum sq|uared resid|0.092|
|Durbin-Watson<br>1.|839|||

(*) Significant at 1%.

# **Appendix 2**

# **Description of the GVAR methodology**

To give any economic interpretation to estimated VAR, traditional analysis revolved around the identification problem. Different analysts imposed various types of restrictions on the reduced form estimates of the VAR to recover the shocks of a structural model. Many altematives to restrict the variance-covariance matrix of the errors of the reduced form estimates exist (i.e. Sims(1986), Bemanke (1986), Blanchard-Quah (1989)), but the most common ones use a Cholesky decomposition and/or particular economic models to restrict this matrix<sup>2</sup> 8. All of the above seek to recover a primitive set of structural shocks that are assumed to be independent and serially uncorrelated Once these structural shocks are recovered, the impulse response functions can be interpreted in a meaningful way. The traditional impulse response analysis estimates the effects of a shock of size J affecting the system at time t on the system's state at time _t+n_ without any other shocks taking place during 28 Canova (1995) has an excellent overview of VAR analysis and the multiple ways researchers deal with the identification problem.