rafmacalaba/datause-encoder-data huggingface dataset:
background paper that tracked levels of community participation in Indonesia found that men and women who became unemployed between 2000 and 2007 were less likely to be involved in community activities (controlling for personal attributes). In some locations this result was observed in spite of a general increase in community activities, indicating that individual and household effects of unemployment can influence personal social outcomes, independent of social and community contexts (Giles et al. 2012). More qualitative evidence also suggests that job loss can lead to a breakdown of social associations. For example, a man who recently became unemployed in Serbia explained, “I automatically lost everything. I lost any freedom and power I had. Everything was lost.” (Petesch 2012).

The data from the WVS do not enable us to disentangle the specific factors that influence individual attitudes and levels of participation. In particular, the surveys do not record information on social contexts or parental background and we do not have information on people’s social attitude and activities in earlier periods of employment or education. However, in spite of these limitations, and keeping in mind the limitations of our measures of job status and quality in lower income countries, there are indications that levels of social and political activism vary quite considerably across individuals who differ with respect to their employment status or job quality.

**Figure 3. Links between employment status, job quality and perceived social status**

<!-- Start of picture text --> Unemployment and lower class status Job quality and upper class status<br>probit coefficients probit coefficients<br>OECD high middle low middle low inc. OECD high middle low middle low inc.<br>incom_grp incom_grp<br>1 min95/1 max95 1 estimate 1 min95/1 max95 1 estimate<br>.7 .2<br>.6<br>.5<br>.1<br>.4<br>.3<br>.2 0<br>.1<br>0<br>-.1 -.1<br><!-- End of picture text --> The vertical axis shows the probit coefficient and 95% confidence interval of individuals’ self-reported class status. In the case of the unemployed the outcome of interest is self-reported membership in the “lower class”. In the case of job quality the outcome is membership in the “upper or upper middle class”. The analysis controls for the same set of controls listed at the bottom of Table 2. Note that the graphs use different scales.

If we take people’s self-assessment of their “social class” as a summary measure for status-related social experiences, we see a comparatively clear association between social wellbeing and people’s employment status (Figure 3). In all sub-samples the unemployed are much more likely to place themselves in the lowest class. This finding is in fact quite robust across all the country categories and 21
# **Rising Food Prices and Household Welfare: Evidence from Brazil in 2008**

Francisco H. G. Ferreira, Anna Fruttero, Phillippe Leite, and Leonardo Lucchetti<sup>1</sup> September 27, 2011

**Keywords:** Food prices, welfare, poverty, inequality, price change incidence curve, Brazil. **JEL Codes: D31, I38, O15**

**Abstract:** Food price inflation in Brazil in the twelve months to June 2008 was 18 percent, while overall inflation was seven percent. Using spatially disaggregated monthly data on consumer prices and two different household surveys, we estimate the welfare consequences of these food price increases, and their distribution across households. Because Brazil is a large food producer, with a predominantly wage-earning agricultural labor force, our estimates include general equilibrium effects on market and transfer incomes, as well as the standard estimates of changes in consumer surplus. While the expenditure (or consumer surplus) effects were large, negative and markedly regressive everywhere, the market income effect was positive and progressive, particularly in rural areas. Because of this effect on the rural poor, and of the partial protection afforded by increases in two large social assistance benefits, the overall impact of higher food prices in Brazil was U-shaped, with middle-income groups suffering larger proportional losses than the very poor. Nevertheless, since Brazil is 80 percent urban, higher food prices still led to a greater incidence and depth of poverty at the national level.

> 1 Ferreira, Fruttero, and Leite are with the World Bank. Lucchetti is at the University of Illinois at UrbanaChampaign. We are grateful to Ricardo Paes de Barros, Tito Cordella, Ambar Narayan, Helena Ribe, and seminar participants at Georgetown University, the World Bank, the 31<sup>st</sup> Annual Meetings of the Brazilian Econometric Society, the 4<sup>th</sup> ECINEQ Meetings in Catania (Italy) and the IAMO Forum 2011 in Halle (Germany) for helpful comments on an earlier version. The usual disclaimer applies. The views expressed in this paper are those of the authors, and should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
preschool expansion. Measures of worker income and the number of workers enable us to test some of the mechanisms behind the identified impacts.

We also explore mechanisms utilizing Indonesia’s national labor force survey (SAKERNAS). SAKERNAS is a nationally representative survey of all working-age individuals conducted biannually in February and August. We use the August rounds of SAKERNAS data (for the 2007-14 period), which are representative at the district level, to construct indicators for employment in manufacturing, services, and wage employment for men and women, as well as an indicator for a change in employment during the 12 months prior to data collection.

Lastly, we employ data from the Indonesia Database for Policy and Economic Research (INDO-DAPOER) to conduct robustness checks. This data set was constructed by the World Bank from several governmental data sets and provides us with district-level economic and social indicators. We use total government per capita expenditure as a measure of fiscal development. To proxy socio-demographic dynamics, we take the district-level average scores on national exams of lower secondary and upper secondary students. Finally, we use the length of highways as a fraction of total road length to proxy quality of infrastructure. These variables enable us to test whether preschool expansion is correlated with other economic or social changes that may drive the observed impacts in firm productivity.

Table 1 provides summary statistics for outcome, treatment, and control variables from all the data sets.

# 3.2. Empirical strategy

Our identification of the impact of preschool availability on plant-level outcomes relates changes in performance of plants with varying degrees of exposure to changes in preschool availability. Specifically, we compare plants along two dimensions. The first is the degree of reliance on female workers. To that end we distinguish sectors (at a granular level of classification) with higher vs. lower share of female workers in the same district – hence exposed to the same changes in preschool density over time. The former should benefit more from any given level of preschool expansion, as we expect the absence of childcare services to primarily 9
respond. Carbon pricing could in this world act initially like an adverse aggregate supply shock creating involuntary unemployment (ruled out by assumption in the neoclassical case above). But there would also be a case for Keynesian deficit-financed spending, including on low-carbon plant and equipment, to increase employment.

# **3. Empirics:  Estimates of potential ‘green’ job creation**

A number of surveys have collated estimates of ‘job creation potential’ in green activities variously defined and discussed their methodological strengths and weaknesses, including GHK (2009), Fankhauser et al. (2008), GCN (2010) and the World Bank (undated). Kammen at al. (2004), Wei et al. (2010), and the World Bank (undated) have reviewed the literature on employment creation in the energy sector, including jobs in renewable energy. The estimates covered use a range of methods, reflecting the different definitions of green job creation discussed above. As GHK (2009) notes, “The estimates are not comparable due to:

- Geographical differences – EU, US, Member State, global;

- Sectoral differences – often focusing solely on individual sectors or sub-sectors;

- Gross and net effects;

- Inclusion / exclusion of whole value chain effects; and

- Differing assumptions concerning economic growth and the effect of existing business as usual policies.”

GHK tabulate results from 32 different studies; most used data from the EU or specific member states, seven used US data and four global data. No studies explicitly covered developing countries per se. Wei et al. (2010) cover 15 different studies, only one of which explicitly mentioned a developing country (Brazil). The World Bank review of energy and employment tabulates results from 33 studies, of which five focused on some aspect of energy in developing countries. Five out of the 15 studies covered by Wei et al, 14 out of the 32 studies covered by GHK and 25 out of the 33 studies covered by the World Bank are dated 2008 or later.

Given the heterogeneity of the studies, it is very difficult to draw broad conclusions. The impression given by many of the findings is that climate-change policies in general and renewable energy in particular can generate considerable extra employment. But many 13
# **Table 7. Purpose of Credit Correlations**

|||Co|nsumer<br>|||Co|mmercial<br>|||M|ortgage<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable name|World|HI|Developing|LAC|World|HI|Developing|LAC|World|HI|Developing|LAC|
|LN GDPPC|**0.34**|**0.23**|**0.34**|**0.28**|**0.41**|**0.38**|**0.46**||**0.37**|**0.3**|**0.33**|**0.18**|
|Inflation % Change|**-0.28**||**-0.27**|**-0.21**|**-0.28**||**-0.24**|**-0.24**|**-0.29**||**-0.33**|**-0.23**|
|Voice and Account.|**0.16**||**0.36**|**0.14**|**0.14**||||**0.41**|**0.22**|**0.43**|**0.32**|
|Political Stability|**0.23**||**0.35**||**0.18**||||**0.3**||**0.33**|**0.28**|
|Trade/GDP|**0.13**|**-0.31**|**0.19**|**0.52**|**0.15**|**-0.29**|**0.23**|**0.38**||**-0.2**||**0.52**|
|Dom. Deposits/GDP|**0.51**|**0.2**|**0.42**||**0.67**|**0.5**|**0.63**||**0.59**|**0.43**|**0.54**||
|Foreign Ownership|**-0.18**|**-0.29**|**0.24**|**-0.55**|**-0.19**|||**-0.41**||||**-0.35**|
|Gov. Ownership|**-0.17**||**-0.33**||||||**-0.15**|**-0.16**|||
|NPLs/Total Loans||||**-0.34**||||**-0.33**||||**-0.54**|
|3 Bank Asset Conc.|**0.11**||||**0.17**||||**0.26**||**0.19**|**0.34**|
|Strength Legal Rights|**0.21**||**0.24**||**0.13**||**-0.14**||**0.25**|**0.24**|**0.19**||
|Depth Credit Info.|**0.1**|**0.24**|**0.37**|**-0.59**||**0.17**|**0.37**|**-0.56**|**0.21**|**0.28**|**0.43**|**-0.75**|
|No. of observations|**714**|**261**|**248**|**205**|**714**|**261**|**248**|**205**|**633**|**214**|**214**|**205**|
|No. of countries|**88**|**31**|**36**|**21**|**88**|**31**|**36**|**21**|**84**|**29**|**34**|**21**|

_Note:_ Variables as percentage of GDP. Only significant correlations are shown.

34
- 19) Jentzsch, Nicola. 2012. “Implications of Mandatory Registration of Mobile Phone Users in Africa.” _Deutsches Institut für Wirtschaftsforschung_ , Disussion papers #1192.

- 20) Klonner, Stefan and Patrick Nolen. 2010 “Cell Phones and Rural Labor Markets: Evidence from South Africa.” Unpublished Manuscript.

- 21) Lee, D., and T. Lemieux. 2010. “Regression Discontinuity Designs in Economics.” _Journal of Economic Literature_ , 48: 281–355.

- 22) Martin, Aaron K., Rosamunde E. van Brakel and Daniel J. Bernhard. 2009.

- “Understanding Resistance to Digital Surveillance: Towards a Multi–disciplinary, Multi– actor Framework.” Surveillance and Society, Volume 6, Number 3, pp. 213–232, Accessed 21 January 2014.

- 23) Martin, Aaron K., and Kevin P. Donovan. 2014. “The Rise of African SIM Registration: The Emerging Dynamics of Regulatory Change.” First Monday, Volume 19, Number 2- 3, Accessed 29 September 2016.

- 24) Mbiti, Isaac and David Weil. 2013. “The Home Economics of E-Money: Velocity, Cash Management, and Discount Rates of M-Pesa Users.” _American Economic Review_ , 103(3): 369–74.

- 25) McCrary, J. 2008. “Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test.” _Journal of Econometrics_ , 142 (2): 698–714.

- 26) Southwood, Russell. 2011. “SIM registration in Africa: Subscribers numbers down but what about revenue and ARPU?” Balancing Act, Number 554. Accessed 29 January 2013.

- 27) Sridhar, Kala and Varadharajan Sridhar. “Telecommunications Infrastructure and Economic Growth: Evidence from Developing Countries.” National Institute of Public Finance and Policy (New Delhi, India) Working Paper No. 14, 2004.

- 28) Suri, Tavneet and William Jack. 2016. “The long-run poverty and gender impacts of mobile money.” Science, 354(6317): 1288-1292.

- 29) Tompsett Anna. 2013. “Bridges: Transport Infrastructure and Economic Geography on the Mississippi and Ohio 1960–2000.” Mimeo Columbia University.

- 30) The Economist. 2013. “Why does Kenya lead the World in Mobile Money?” Accessed July 27, 2016.

- 31) The Guardian. 2015. “Africa’s mobile phone penetration now 67%.” Accessed July 27, 2016.

- 32) Waverman, Leonard, Meloria Meschi and Melvyn Fuss. 2005. “The Impact of Telecoms on Economic Growth in Developing Countries.” Vodafone Policy Paper Series, 3.

- 33) Whitley, Edgar A., and Ian Hosein. 2005. “Policy discourse and data retention: The technology politics of surveillance in the United Kingdom.” Telecommunications Policy, Volume 29, Number 11:857–874. Accessed 21 January 2014.

- 34) World Bank. 2016. Republic of Niger, “Priorities for Ending Poverty and Boosting Shared Prosperity.” Systematic Country Diagnostic.

‐ 22 ‐
capital to small entrepreneurs in developing countries (de Mel, McKenzie and Woodruff 2008, McKenzie and Woodruff 2008). Were credit constraints to be relieved, it appears that many currently poor entrepreneurs could repay their loans. Exclusion of the poor thus constrains the entry of new firms and limits the Schumpeterian process of creative destruction (Beck, Demirguc-Kunt, and Martinez Peria, 2007; Klapper, Laeven, and Rajan, 2006).

Perhaps the major reason why financial sector breadth has gone under-studied is the difficulty in collecting data.<sup>3</sup> Whereas measures of financial depth can be derived from the balance sheets of financial institutions that already furnish this information to supervisors such as central banks, the same information is not readily available, and certainly not in a consistent form across countries, for financial sector breadth. Recent attempts to collect data on financial sector breadth have pushed beyond balance sheet information, using both demand- and supply-side approaches.

On the supply side, measures of the outreach of the financial sector often focus on the number of accounts of providers of financial services. For example, Beck, DemirgucKunt, and Martinez Peria (2007) collect information on the aggregate number of deposit and loan accounts from bank regulators in 99 countries. They also collect information on the number of bank branches and ATMs in each country as a proxy for physical access to financial services, even among those who do not actually use them. A limitation of those data is that they are derived only from information about banks, which, while important or even dominant providers of financial services in many countries, are not the full story. Honohan (2008) therefore combines the commercial bank accounts from Beck _et al._ with accounts at microfinance institutions (from Christen, Jayadeva, and Rosenberg, 2004)

> 3 See World Bank (2007) for a discussion.

3
**Figure 2. Sudan’s population pyramid 2014**

<!-- Start of picture text --> 90-94<br>80-84<br>70-74<br>60-64<br>50-54<br>40-44<br>30-34<br>20-24<br>10-14<br>0-4<br>-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2<br>Women/girls Boys/men<br><!-- End of picture text --> _Source:_ World Bank 2019b.

The size, growth, distribution, and structure of the Sudanese population directly affect its labor force. Sudan faces rapid population growth compared to many neighboring countries, and its total population in 2019 was estimated at 42.5 million persons according to the WDI. Sudan’s population is mostly young as a result of relatively high fertility (4.4 in 2018) and lower-than-expected life expectancy (less than 62 years). More than two in five (43 percent) Sudanese are below the age of 15 and only 4 percent are older than 64 (CBS 2017; World Bank 2020b). While fertility is expected to slow down from 5.3 in 2005 to 3.4 in 2030, the population is still anticipated to double during this period. Importantly, this growth will be accompanied by profound age structure change. Children below age 15 will increase to about 21.4 million in 2035 compared to 13.8 million in 2005. The workforce aged 20 to 64 is expected to reach 32 million in 2035 in contrast to 13.9 million in 2005; one half of the workforce will be youth (Republic of Sudan: National Population Council 2018). There are roughly equal numbers of males and females in the country’s urban and rural areas. However, there are a larger share of men above 50 years of age, whereas in the population aged 25–49, there are more women (Figure 2).

# **Characteristics of the labor market between 2009 and 2014**

Despite a significant increase in the number of jobs in Sudan, the labor force participation rate, at 53 percent of the working-age population, remains the lowest among the average rate of Sub-Saharan Africa, low-income and lower-middle-income countries.<sup>4</sup> The Sudanese economy added 2.5 million jobs between 2009 and 2014 while the working-age population increased by around 2.8 million and the active population by 2.6 million. As a result, there were measurable improvements for the working-age population between 2009 and 2014 with a more than 5 percentage point increase in the labor force participation, from 48 to 53 percent, and a decrease in the unemployment rate from 13 to 11 percent

> 4 Labor force participation rate is the proportion of the population aged 15–64 that is economically active: all people who supply labor for the production of goods and services during a specified period.

9
coefficients conform with the human capital theory. As expected, both years of schooling and experience have positive and significant effects on earnings, indicating that earnings increase with schooling and experience. The results are more or less same for the three surveys.

**Table 30: Basic Earnings function by years of schooling, in Ghana.**

|Variable|1987|1991|
|---|---|---|
|school|0.035|0.061|
||(7.04)|(13.3)|
|Age|0.11|0.08|
||(6.8)|(4.5)|
|Age-squared|-0.001|-0.007|
||(-5.2)|(-3.1)|
|Log hours worked|-0.24|-0.34|
||(-8.25)|(-7.6)|
|female|0.11|-0.06|
||(1.76)|(-1.04)|
|Other urban|0.09|-0.27|
||(1.4)|(-4.4)|
|Rural coastal|0.12|-0.12|
||(1.2)|(-1.33)|
|Rural forest|0.07|-0.22|
||(0.8)|(-3.03)|
|Rural Savannah|0.16|-0.14|
||(0.8)|(-1.26)|
|Constant|2.75|4.9|
||(8.40)|(10.9)|
|R-squared|0.24|0.29|
|Sample size|765|1021|

Earnings function using the dummy variables for the different levels of education are given in Table 31. The results corroborate the findings described earlier. Instead of using the years of schooling we use the highest attained level of education. However, we don't have information whether the individual has completed that level or not. This specification enables one to analyze the earnings premium associated with each level of education. Clearly, the earnings premium increases significantly with the level of education (Psacharapoulos, 1981). This is particularly the case for those with secondary and higher level education, and especially higher level.

43
than what we would expect in a random network. We compute the modularity of the weighted adjacency matrix at the sector-county level. We find that modularity in the revised network with informal firms increases by about 46% suggesting that the revised network exhibits a more pronounced group structure.<sup>27</sup> To further characterize this group structure, we apply a community detection algorithm to the trade flows between counties as per the revised network. As illustrated by Figure C3, the group structure now correlates strongly with Kenya’s geography, i.e., geographically proximate counties are now more likely to be clustered in the same group.

Next, we explore for which group of counties we underestimate the overall number of links the most. To do so, we regress the change in county-level outlinks between baseline and revised network on various county characteristics, including the aggregate level of formality, population, Gross County Product, and market access (Table C3). The results reveal that the baseline network, which relies exclusively on formal sector data, most significantly underestimates connectivity in smaller counties and those with high market access. Notably, after controlling for these other characteristics, the relationship between a county’s aggregate formality share and the change in outlinks is no longer statistically significant.

Finally, we find that incorporating informal firms also reshapes inter-sector trade patterns (Figure 9). Sectors with substantial informal activity like other services, retail, and wholesale, now gain prominence as buyers in the network. Manufacturing, wholesale, and mining experience the largest relative gains in new links.

# **5.4 Simulating the Effect of Economic Shocks**

As a next step, we ask how the newly predicted network that accounts for informal firms compares to the previous network in terms of its role in propagating domestic and international shocks. How does the predicted impact of the shock depend on whether we account for informality? Are sectors and regions with more informality more or less vulnerable to shocks than the administrative data would suggest? To answer these questions, we first simulate a series of domestic output shocks that reduce each firm type’s output and then analyse how it affects the output of all other types, both directly and indirectly, by propagating through the network over multiple time periods. Then, we simulate international supply shocks that affect firm types depending on their exposure to international markets. We discuss the results for both domestic and international shocks below.

> 27We compute this by running the Leiden algorithm multiple times and averaging the resulting modularity scores. See Traag et al. (2019) for details on the algorithm.

28