rafmacalaba/datause-encoder-data huggingface dataset:
that produce reliable results when data from the census can be used or when the focus is on a poverty index with an exogenous poverty line. Table 1 reports the direct estimates of three inequality indices calculated from the 2000 and 2007 HBSs. The standard error reported in brackets is computed with a bootstrap estimator based on 500 iterations.

Table 1: Estimated Indicators by Sample Unit

|Id|20|00|20|07|
|---|---|---|---|---|
|nex|Rural|Urban|Rural|Urban|
|Gini|0.349|0.408|0.353|0.430|
||(0.006)|(0.005)|(0.006)|(0.006)|
|Theil|0.240|0.325|0.228|0.361|
||(0.017)|(0.016)|(0.012)|(0.015)|
|HCR|0.161|0.209|0.177|0.194|
||(0.005)|(0.004)|(0.007)|(0.006)|

_Source_ : Authors’ calculations based on Moroccan HBS.

Before moving to regression techniques, it is necessary to identify the variables that could be used as regressors. To do this, we must bear in mind that a bias could also arise from differences between questions and answers in the surveys.<sup>3</sup> To better understand the SSIT results, the trends of dependent and independent variables should be examined to predict trends for our indicators. The sample mean expenditure per capita in 2000 was 1 _,_ 752 _._ 96$ for rural areas and 3 _,_ 514 _._ 9$ for urban areas, which increased to 2 _,_ 265 _._ 9$ (+29%) and to 4 _,_ 053$ (+15%) respectively in 2007. The bulk of the dependent variables seem to have constant values over the years, with the notable exception of electricity. In rural areas, the proportion of families with electricity increased from 0.35 to 0.70.

The first model tested was the log-linear regression over the split data set. The results (see table [A.1]) are consistent with those presented by Douidich et al. (2016). Illiteracy and a low level of education have a negative impact on expenditure, while a higher number of rooms per capita has a positive impact, all other things being equal.

3Once the structure of questions and answers has been checked, we suggest also performing a graphical analysis of PDF and checking position and variability parameters, see Betti et al. (2018). We do not report this analysis for all the variables since the comparability has already been checked by Douidich et al. (2016) and we use the same set of variables. It is, however, important to underline that since 2015, Morocco has officially administered 12 regions. Previously, there were 16 regions. From that moment the new regions and the oldest ones are not directly comparable. In the interest of future comparability, we decided to use 14 regions, which was also decided by the Moroccan statistics department that unified the regions that had been merged. For more on this point, we recommend the official survey site. The only other variable that needs an explanation is the “low level of education,” with a value of 0 “up to the 1st cycle" and 1 for "over the 2nd cycle.

7
Policy Research Working Paper 10172

# **Abstract**

Quantifying the impact of the COVID-19 pandemic on poverty in Africa has been as difficult as predicting the path of the pandemic, mainly due to data limitations. The advent of new data sources, including national accounts and phone survey data, provides an opportunity for a thorough reassessment of the impact of the pandemic and the subsequent expansion of social protection systems on the evolution of poverty in Africa. This paper combines per capita gross domestic product growth from national accounts with data from High-Frequency Phone Surveys for several countries to estimate the net impact of the pandemic on poverty. It finds that the pandemic increased poverty in Africa by 1.5–1.7 percentage points in 2020, relatively smaller than early estimates and projections. The paper also finds that countries affected by fragility, conflict, and violence experienced the greatest increases in poverty, about 2.1 percentage points in 2020. Furthermore, the paper assesses and synthesizes empirical evidence on the role that social protection systems played in mitigating the adverse impact of the COVID-19 crisis in Africa. It reviews social protection responses in various African countries, mainly focusing on the impact of these programs and effectiveness of targeting systems. Although the evidence base on the protective role of social protection programs during the pandemic remains scarce, the paper highlights important findings on the impacts of these programs while also uncovering some vulnerabilities in social protection programming in Africa. Finally, the paper draws important lessons related to the delivery, targeting, and impact of various social protection programs launched in Africa in response to the pandemic.

This paper is a product of the Development Data Group, Development Economics. 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 nyonzan@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 and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**_La Prensa Grafica_ Survey Answers**

|Date|Being satisfed<br>conditioned on<br>support of ARENA|Being satisfed<br>conditioned on<br>support of FMLN|% answering<br>“satisfed” or “very<br>satisfed”|
|---|---|---|---|
||party|party||
|Jan 2011|18.8|44.1|30.0|
|May 2011|33.8|57.7|43.2|
|Aug 2011|44.2|50.5|44.9|
|May 2012|42.1|57.8|50.2|
|Aug 2012|52.7|76.9|66.0|
|Sep 2013|55.0|71.3|64.3|

Table 10: Selected questions from the _La Prensa Grafica_ survey as reported in Calvo et al. [2014]. All numbers are percentages. We observe similar timings in the shifts in the topics being discussed on Twitter with respect to the gas subsidy. Source: Calvo et al. [2014]

# **Results: Sentiment**

|Date|Count|Negative|Neutral|Positive|
|---|---|---|---|---|
|January 2011|275|44.4|55.6|0.0|
|April 2011|863|59.4|40.6|0.0|
|May 2011|310|63.9|36.1|0.0|
|August 2011|118|64.4|35.6|0.0|
|May 2012|570|37.4|62.6|0.0|
|August 2012|168|43.5|56.5|0.0|
|September 2013|680|19.0|81.0|0.0|

Table 11: The classification results for the sentiment of all the tweets. Source: Author’s calculations.

25
Policy Research Working Paper 9192

# **Abstract**

As cities grow and spatially expand, agricultural land is converted into residential land. In many developing countries, especially in Sub-Saharan Africa, this process is accompanied by a change in land tenure, whereby plots held under traditional customary arrangements are sold to new urban residents, possibly with formal property rights. This paper studies joint land-use and land-tenure conversion in an urban economics model in which intermediaries purchase agricultural land from customary owners and attempt to transform it into residential plots with statutory property rights. The spatial equilibrium includes a mix of land uses and rights where statutory and non-statutory residential plots coexist with customary land that is mainly used for agriculture. Because customary ownership is subject to uncertainty (because of tenure insecurity), the conversion process includes a potential information asymmetry between customary owners and intermediaries. The analysis shows that a market failure may emerge whereby some customary owners prefer to continue farming their land rather than participate in the urban residential land market, which results in a city that is too small. Empirical analysis using Malian data validates the key features of the model captured by land price gradients, as well as the ranking and the variance of land prices, and is suggestive of the presence of information asymmetry.

This paper is a product of the Development Research Group, Development Economics. 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 pierre.picard@uni.lu and hselod@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 and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
_Acknowledgements:_ We thank the National Opinion Research Center (NORC) at the University of Chicago—in particular, Renee Hendley, Kate Hobson, David Kordus, Elise Le, Aparna Ramakrishnan, Alex Rigaux, Bhanuj Soni, and Stacy Stonich—for their survey design and data collection assistance. We also owe a debt of gratitude to the 6,731 development policymakers and practitioners from 126 countries who participated in the _2014 Reform Efforts Survey_ . Were it not for their willingness to graciously share of their experiences and insights, this study would not have been possible. Finally, this study was made possible through generous financial support from the Development Research Group of the World Bank, the John Templeton Foundation, the Smith Richardson Foundation, and the William and Flora Hewlett Foundation.
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# **4.5 Exports and Economic Diversification**

**This section presents an overview of the export structure and behavior in recent years, since it adds important information on Togo’s stage of economic transformation and growth prospects.** Export growth of 5.6% per year explains almost half of the increase in aggregate supply between 2000 and 2007 (see Table 1)<sup>24</sup> . The ability of Togo to sustain the higher growth rates observed between 2000 and2007 therefore hinges on its capacity to sustain a growing exporting sector.

**Growth of exports has been accompanied by diversification of the export basket, as shown by the decrease in the Herfindhal‐Hirschmann index of export diversification (** Figure 14 **)**<sup>**25**</sup> **.** Togo is actually the second most diversified country within the WAEMU, something that is owed to a substitution of traditional products such as cotton, coffee and phosphates in favor of an array of new emerging products (Table 4).

**Figure 14: Non‐Oil Herfindahl Index for Togo, 1980‐2006 (LHS), and Compared to WAEMU Countries, 2006 (RHS).**

<!-- Start of picture text --> 0.4 0.9<br>0.8<br>0.35<br>0.7<br>0.3 0.6<br>0.25 0.5<br>0.4<br>0.2<br>0.3<br>0.15 0.2<br>0.1 0.1<br>0<br>0.05<br>0<br>Scale 0‐1 (1=no diverisifcation)<br>Scale 0‐1 (Lower = Higher Diversification)<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> _Source:_ Export diversification data, PRMED, World Bank.

**Table 4: Export Value of Selected Products (% of total exports), Togo, 1980‐2006.**

<!-- Start of picture text --> product code 1980 1985 1990 1995 2000 2005 2006<br>71 Coffee and coffee substitutes 11.0% 14.3% 6.0% 12.0% 5.6%<br>72 Cocoa 13.9% 7.7% 12.0% 22.0%<br>223 Oils seeds and oleaginous fruit, wh 2.4%<br>263 Cotton 12.6% 25.0% 26.7% 22.7% 10.5% 7.2%<br>271 Fertilizers,crude 46.7% 51.2% 52.1% 28.9% 18.1% 10.1% 14.1%<br>333 Petrol.oils,crude,& c.o.obtain.from 9.8%<br>334 Petroleum products,refined 18.5% 5.0% 2.5% 18.1% 13.6% 15.3%<br>661 Lime,cement,and fabricated construc 8.5% 20.3% 12.2%<br>667 Pearls,precious& semi‐prec.stones,u 2.3% 8.2%<br>Grand Total 92.4% 90.8% 88.0% 85.6% 72.9% 66.5% 70.7%<br><!-- End of picture text --> _Source:_ Export diversification data, PRMED, World Bank.

**The shift of the export basket away from classic to new, emerging products has been accompanied by an increase of the income value of such basket in Togo, as defined by EXPY**<sup>**26**</sup> **‐ an indicator of export sophistication of a country (Hausmann and Klinger, 2006)** . Higher EXPY, authors hypothesize, allows countries to move more easily in the “product space” towards their “technology frontier”. Togo is one of the WAEMU countries that has shown one of the faster increases in the income value of the exportable basket (Figure 15).

> 24 Notwithstanding, imports of goods and services have also increased, at a pace of 4.0% per year.

> 25 Explained in Appendix 2.

> 26 Explained in Appendix 2.

19
neighbor’s TV, or store your perishables at a neighbor’s house which has a refrigerator. The village externality can also operate through domestic production possibilities. The domestic production activity need not be physically located within the household in question but might reside instead with a friend or neighbor’s dwelling. Thus a household still without electricity can benefit by using the electric sewing machine (say) of a neighbor who has acquired an electric connection, possibly with some compensation to the neighbor. This is another example of an asymmetric external effect. We will not be able to disentangle the various “internal” effects identified above, but we will be able to separate out the external effect.

Village-level external effects can also arise through status-seeking behavior. Having electricity in one’s home in a typical Indian village is conspicuous, and conveys a sense of status. Those without electricity (for some exogenous reason) may then respond by changing their own consumption behavior, spending more on other status-conveying goods to compensate; Frank (1997) discusses such behavior and points to supportive evidence. These goods would clearly need to also be conspicuous goods, rather than (say) food consumed within the home. Spending on celebrations and festivals is a plausible example. The welfare implications of such behavior are unclear. As Frank (1997) argues, such social effects on consumption behavior may have little or no lasting effect on welfare, because everyone keeps trying to keep up their relative status in a race that leaves everyone spending too much on such goods. Rao (2001) describes the importance of status-related spending in South Indian villages, though Rao describes more positive roles that such spending can play in building social capital, which can be interpreted as a welfare enhancing role. We will look for signs of spending shifts in response to both household and village-level electrification.

# **4. Setting and data**

India’s early, post-independence, economic plans had given priority to the use of electricity for urban industry, in an effort to develop capital-intensive domestic production. As a result, efforts to bring electricity to rural households were delayed until the 1970s. Based on India’s National Sample Surveys, only around 18 percent of rural households had an electricity connection for household use in 1982, while close to 70 percent did so 20 years later, although access continues to vary widely across regions. The expansion of the grid was initially influenced by population density (households in large cities were among the first to be 14
# **I. Introduction and motivation**

A large social science literature suggests that the larger and more established the middle class, the better off society is, or will be (Easterly 2001; Banerjee and Duflo 2008). This suggestion is not new: Aristotle argued, in _Politics_ , that the middle class serves as a mediator between the rich and the poor, preserving values conducive to stability and prosperity. Among academics but also in the public discourse, a narrative of increasing inequality and a crisis of the middle class has emerged (Fukuyama 2012; Besharov and others 2016; Sitaraman 2018; Bussolo and others 2018). Remarkably, the most common way in which the crisis of the middle class has been represented is by its shrinking.

This paper examines the _health_ of the middle class by using on a novel definition of the middle class. In this new definition, introduced by López-Calva and Ortiz-Juarez (2014), being in the middle class requires absence, or a low risk, of vulnerability to poverty. The risk of experiencing poverty –defined as having income levels below a given poverty line– is what makes an individual _vulnerable_ or insecure in an economic sense. Since economic security is a hallmark of the middle class, being vulnerable means not being in the middle class. So rather than focusing on what happens to the size of the middle class, this paper investigates changes in the risk of vulnerability to poverty. In essence, this vulnerability is assessed by estimating a level of household income associated with a low probability (defined as less than 4 percent over four years) of falling into poverty. By using European panel data covering 22 countries during the period from the 2008 global financial crisis to just before the onset of the COVID-19 pandemic, this paper assesses, for the first time, whether the crisis of the middle class manifested itself as an increase of the risk of falling into poverty or, equivalently, as an increase of the minimum income needed to be (almost) immune from this risk.

Our results show that to maintain this low risk between 2008 and 2018, household incomes had to increase, on average, by about 11 percent in real terms. This value, however, is an average of a heterogeneous picture at the country level: in many countries, the income associated with this low probability of falling into poverty has increased substantially – above 20 percent – while in some countries, the associated level of income has decreased. Interestingly, the increase in the level of the vulnerability threshold is related to an increase in the magnitude of the shocks which households have been facing between the two periods. The average income shock experienced by a household that fell into poverty in the most recent period is about 21 percent larger than the shock in the earlier period. To _insure_ against these larger shocks, the minimum income needed to maintain the middle class status had to increase.

2
assigns the value "0" to flexible exchange rate regimes and "1" to fixed exchange rate regimes.<sup>16</sup> The coefficient associated with the exchange rate regime implies that countries with fixed exchange rates accumulate more reserves, which is consistent with Bianchi and Sosa-Padilla (2020). Finally, the coefficient associated with sovereign spreads suggests a negative correlation between reserves and spreads, which is inconsistent with the model.<sup>17</sup> Overall, and to the best of my knowledge, this is the first paper that documents a positive correlation between reserve accumulation and central bank independence.

Table 1: Panel Regressions

|||Dependent vari|able: _log_(_A/y_)||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**CBI**|**2**_._**36**<sup>_∗∗_</sup>|**2**_._**38**<sup>_∗∗_</sup>|**2**_._**37**<sup>_∗∗_</sup>|**3**_._**45**<sup>_∗∗_</sup>|
||(**0**_._**96**)|(**0**_._**92**)|(**0**_._**90**)|(**0**_._**94**)|
|_log_(ˆ_y_)|_−_0_._95|_−_1_._41<sup>_∗∗_</sup>|_−_1_._42<sup>_∗∗_</sup>|_−_0_._65<sup>_∗∗_</sup>|
||(0_._64)|(0_._59)|(0_._58)|(0_._23)|
|_log_(_B/y_)|_−_0_._24|_−_0_._18|_−_0_._17|0_._21|
||(0_._30)|(0_._28)|(0_._27)|(0_._17)|
|infation||_−_0_._20<sup>_∗∗_</sup>|_−_0_._20<sup>_∗∗_</sup>|_−_0_._13<sup>_∗∗_</sup>|
|||(0_._08)|(0_._09)|(0_._05)|
|fx regime|||0_._05|0_._35<sup>_∗_</sup>|
||||(0_._15)|(0_._19)|
|spreads||||_−_0_._47<sup>_∗∗_</sup>|
|||||(0_._16)|
|Number of countries|11|11|11|9|
|Observations|359|359|359|148|
|_R_<sup>2</sup>|0_._47|0_._51|0_._51|0_._61|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1; _p <_ 0 _._ 05; _p <_ 0 _._ 01 a monetary framework that rationalizes the negative correlation between reserve accumulation and inflation rates. 16I use the fine classification codes from Ilzetzki, Reinhart, and Rogoff (2017).

17Subsection 5.2 discusses the main reason for this discrepancy between the model and the data.

9
# Figure 2 (a): Philippines Skill Premiums – basic controls

<!-- Start of picture text --> Philippines: Skill Premium and Skilled Workforce - Basic controls only<br>0.8<br>0.7<br>0.6 Skilled-1: At least<br> Secondary<br>0.5<br>Skilled-2: Tertiary and<br>0.4 above<br>0.3 Skill premium-1<br>0.2<br>Skill premium-2<br>0.1<br>0<br>1988 1991 1994 1997 2001 2004 2006<br>Year<br>Figure 2 (b): Philippines Skill Premiums – industry controls<br>Philippines: Skill Premium and Skilled Workforce - With Industry<br>controls<br>0.7<br>0.6<br>Skilled-1: At least<br>0.5<br>Secondary<br>0.4 Skilled-2: Tertiary and<br>above<br>0.3<br>Skill premium-1<br>0.2<br>Skill premium-2<br>0.1<br>0<br>1988 1991 1994 1997 2001 2004 2006<br>Year<br>workforce<br>Skill premium and proportion of skilled<br>workforce<br>Skill premium and proportion of skilled<br><!-- End of picture text --> Figure 2 (b): Philippines Skill Premiums – industry controls 23