rafmacalaba/datause-encoder-data huggingface dataset:
_Amostra de Domicílios_ , PNAD), which is fielded quasi-annually by the National Census Bureau ( _Instituto Brasileiro de Geografia e Estatística_ , IBGE). The PNAD is fielded every year in September, except in Census years and in 1994. We also excluded 1986 from our sample, since poverty statistics in that year are not comparable with other years.19 This left sixteen surveys: every year between 1985 and 2004, except for 1986, 1991, 1994 and 2000. The PNAD is nationally representative and representative at the level of each state, except for the rural areas of the Northern Region (minus the state of Tocantins), which roughly corresponds to the Amazon rainforest. The excluded rural areas accounted for 2.3% of Brazil’s population in the 2000 Census. Sample sizes ranged from 291,000 to 525,000 individuals over the period.

Although there are a number of caveats about the precision with which rural and informal sector incomes are captured through the PNAD questionnaire, recent comparisons between PNAD income and consumption expenditure distributions from other surveys (such as the POF and the PPV) suggest that the income distribution from the PNAD contains some noise, but substantial signal as well; see Ferreira et al. (2003) for further discussion.

Poverty measures were constructed using total household per capita income as the individual welfare indicator.<sup>20</sup> Incomes were deflated spatially using the Ferreira et al. (2003) regional price index, and temporally using the Corseuil and Foguel (2002) price deflator series from the national consumer price index (INPC/IPC) for the PNAD reference months. Incomes are expressed in Brazilian Reais (R$) in September 1996 prices.

We constructed two poverty measures from the PNAD surveys, namely the headcount index (H) and the poverty gap index (PG). The headcount index is the percentage of the population living in households with income per person below the poverty line. The poverty gap index gives the mean distance below the poverty line as a proportion of that line (when the mean is taken over the whole population, counting the non-poor as having a zero gap).

> 19 In the aftermath of a price freeze in early 1986 (the “Cruzado Plan”), shortages and rationing became widespread during August–November 1986. As a result, the monetary incomes deflated by official prices, which are reported in the PNAD 1986, overstate real purchasing power and welfare. While GDP grew 7.5% in 1986, the mean household income in the PNAD was 46% higher in 1986 than in 1985, implying a 40% decline in poverty. This was largely reversed by 1987, after rationing was abandoned. See Ferreira et al. (forthcoming) for a more detailed discussion. 20 The use of per capita income is a common but fairly extreme assumption about equivalence scales. While a poverty profile can be sensitive to this assumption (Lanjouw and Ravallion, 1995), the aggregate dynamics of poverty in Brazil are reasonably robust to variation in the equivalence scale (see Ferreira and Litchfield, 2000).

10
Table 4: Digitalization

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Conditional|Cont. initial<br>digital|Conditional|Cont. initial<br>digital|Conditional|Cont. initial<br>digital|Conditional|Cont. initial<br>digital|
|||||**Pane**|**l A**||||
||S|tarted usingor|increased digi|tal||Has onli|ne sales||
|Informality|-0.292**|-0.215**|||-0.242**|-0.166**|||
||(0.0194)|(0.0226)|||(0.0251)|(0.0323)|||
|Necessity|||-0.111**|-0.0667**|||-0.0657**|-0.0267|
||||(0.0145)|(0.0154)|||(0.0163)|(0.0182)|
|De Soto|||-0.0979**|-0.0592**|||-0.0591**|-0.0255*|
||||(0.0124)|(0.0130)|||(0.0136)|(0.0154)|
|Parasite|||-0.112**|-0.0602**|||-0.116**|-0.0792**|
||||(0.0125)|(0.0132)|||(0.0149)|(0.0169)|
|Fully formal|||_Baseline_|_Baseline_|||_Baseline_|_Baseline_|
|Initial digital readiness score||0.0981**||0.102**||0.0600**||0.0617**|
|||(0.00408)||(0.00407)||(0.00515)||(0.00514)|
|Observations|14037|11870|14324|12138|14124|11969|14296|12122|
|||||**Pane**|**l B**||||
||I|nvested in digi|tal technologie|s|Share o|f employees w|orkingremote|ly (%)|
|Informality|-0.240**|-0.230**|||-3.819**|-4.523**|||
||(0.0203)|(0.0256)|||(1.235)|(1.490)|||
|Necessity|||-0.0875**|-0.0744**|||-2.247**|-2.471**|
||||(0.0134)|(0.0146)|||(0.980)|(1.077)|
|De Soto|||-0.0950**|-0.0799**|||-3.027**|-3.960**|
||||(0.0114)|(0.0124)|||(0.822)|(0.912)|
|Parasite|||-0.121**|-0.0863**|||-1.253|-0.938|
||||(0.0119)|(0.0130)|||(0.843)|(0.937)|
|Fully formal|||_Baseline_|_Baseline_|||_Baseline_|_Baseline_|
|Initial digital readiness score||0.0571**||0.0591**||0.0806||0.166|
|||(0.00397)||(0.00394)||(0.276)||(0.277)|
|Observations|14214|12047|14484|12298|19033|16869|19432|17249|
|Panel controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Other controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Initial productivity|No|Yes|No|Yes|No|Yes|No|Yes|
|Change in sales|No|No|No|No|Yes|Yes|Yes|Yes|

**N** otes: All continuous variables are winsorized at the 1% and 99%. Robust standard errors in parentheses

* p<0.10, ** p<0.05, ** p<0.01 Source: Authors’ calculations using data from the World Bank Business Pulse Survey (BPS).
# **Vietnam**

Table 7a: Returns to levels of education - Employed for wages

||1992|2006|Change *<br>2006-1992|
|---|---|---|---|
|_Basic controls only_||||
|- Primary|-0.019|0.094|_0.113_|
|- Junior Sec./primary|-0.172|0.055|_0.227_|
|- Senior Sec./ primary|-0.175|0.210|_0.385_|
|- Tertiary/primary|0.00|0.648|_0.648_|
|_Basic controls + Industry dummies_||||
|- Primary|-0.018|0.080|_0.098_|
|- Junior Sec./primary|-0.167|0.039|_0.206_|
|- Senior Sec./ primary|-0.123|0.195|_0.318_|
|- Tertiary/primary|0.151|0.638|_0.487_|

||1992|2006|Change *<br>2006-1992|
|---|---|---|---|
|_Basic controls only_||||
|- High school general/ primary|-0.040|0.147|_0.187_|
|- High school vocational/ primary|-0.222|0.211|_0.433_|
|- Tertiary/primary|0.023|0.648|_0.625_|
|_Basic controls + Industry dummies_||||
|- High school general/ primary|0.001|0.142|_0.141_|
|- High school vocational/ primary|-0.167|0.187|_0.354_|
|- Tertiary/primary|0.135|0.634|_0.499_|

* Changes rather than percentage changes are given.

Table 7b: Returns to skill over time – Employed for wages

||1992|2006|Change *<br>2006-1992|
|---|---|---|---|
|_Basic controls only_||||
|- Lower Sec. and above/ primary and below|-0.206|0.242|_0.448_|
|- Upper Sec. and above/ Low sec. and below|-0.100|0.414|_0.514_|
|- At least some tertiary/Secondary and below|0.146|0.545|_0.399_|
|_Basic controls + Industry dummies_||||
|- Lower Sec. and above/ primary and below|-0.082|0.170|_0.252_|
|- Upper Sec. and above/ Low sec. and below|0.030|0.276|_0.246_|
|- At least some tertiary/Secondary and below|0.239|0.516|_0.277_|

***** Changes rather than percentage changes are given.

16
# **Table 2. GNP per capita and grants as a share of exports for three groups of countries,**

# **1984-88 and 1989-93**

||**_GNP per ca_**|**_pita_**_(USS)_|**_Grants as a sh_**<br>**_earnings_**|**_are_**_of_**_Export_**<br>**_(percent)_**|
|---|---|---|---|---|
|Country Group|**_1984-88_**<br>**_average (*)_**|**_1989-93_**<br>**_average (*)_**|**_1984-88_**<br>**_average(*)_**|**_1989-93_**<br>**_average(*_**|
|Low group (16 countries)|277|320|66|126|
|Middle group (9 countries)|480|452|59|95|
|High group(7 countries)|433|460|21|40|
|Total|371|390|54|98|

(*) Simple average. Average number for a country has been taken for each five year period, then simple average was calculated using the average of each country.

Source: OECD/DAC; World Bank, IMF.

**_(ii)_** _Access to other sources offinance._ From a different perspective; tGtal grants represent a much larger share net long-term debt flows for the low group than they do for the high group, meaning that countries in the high group have not only received larger aid flows (grants) but also larger net debt flows (Table 3). In other words lack of access to other sources of finance (in particular, new lending) does not seem to have been a criteria used to allocate grants and ODA debt relief in recent years.

However, more important is the fact that for the high group grants as a share of net long-term debt flows tripled between 1984-88 and 1989-93, while this ratio suffered a slight decline for the low group. The 200 percent increase in this ratio for the high group, from 0.43 to 1.29, while grants increased by 157 percent, implies that net long-term debt flows are decreasing in absolute tenns for this group of countries. Thus, if the dynamic trend-the drying up of other financial resources-has been a rationale for the allocation of grants and debt relief, it appears that the relative allocation between the high and low groups has been well implemented. This criteria fails to explain the relative allocation of grants and ODA debt relief between the high and middle groups, however. Indeed, the last two columns of Table 3 show that the middle group is suffering more from such drying up than the high group, yet the middle group received less grants and ODA debt relief than the high group.

Table 3. Ratio of grants to net financial flows **and debt for three groups of countries**

|||(pe|rcent)||||
|---|---|---|---|---|---|---|
||**_Grants as_**_shar_|_e of total_**_debt_**|**_Debt as a sh_**|**_are of GDP_**|**_Grants a_**<br>**_net finan_**|**_s share of_**<br>**_cial flows_**|
|**Country group**|1984-88<br>average (*)|1989-93<br>average (*)|**1984-88**<br>**average (*)**|1989-93<br>**average(*)**|**1984-88**<br>**average (*)**|**1989-93**<br>**average(*)**|
|**Low group (16 countries)**|13|12|84|117|213|204|
|**Middle group**(9)**countries**|4|6|133|161|46|205|
|**High group**(7)**countries**|4|7|109|135|43|129|
|**Total**|8|9|**104**|134|129|188|

(*) Simple average. Average number for a country has been taken for each five year period, then simple average was calculated using the average of each country.

Source: OECD/DAC, World Bank, IMF.

5
Table 3 gives the participation rate, demand rate and the rationing rate by rural household quintiles defined on household consumption per person from the survey.<sup>13</sup> As expected, we see that demand for work on MGNREGS declines with consumption per person. Richer households are less likely to want to do this work, although there is demand even amongst the richest quintile in rural areas. Consistently with the incidence of expressed demand, we also see that the proportion of households who have obtained job cards declines with consumption per person. But notice that the demand rate is higher than the proportion with job cards; there are many households who express demand for work who have not obtained job cards.

Across India as a whole, the rationing rate tends to rise with consumption per person. The local-level processes of deciding who gets work amongst those who want it entails that poorer households are less likely to be rationed, although the difference is modest. Thus we see that the participation rate declines with household consumption per person even more steeply in proportionate terms than the demand rate.<sup>14</sup> By contrast the absolute gap between the participation rates for the poorest quintile and the richest is attenuated by rationing, given that its overall incidence (the excess demand rate) falls with consumption (Table 3).

Quintile averages lose a lot of detail. Figure 4 provides a finer representation of the data using the household-level data from the NSS. Panel (a) gives the non-parametric regression function—a locally smoothed scatter plot—of the participation rate against consumption per person, with the latter converted into ranks and normalized to be between 0 and 100. Thus the horizontal axis gives the percentile of the consumption distribution.<sup>15</sup> The lower panel (b) gives the rationing rate. (The demand rate shows a similar pattern to panel (a), though at higher levels given the rationing.) In both cases we also give the regression functions with controls for state effects, adding dummy variables for the state and using the PLREG procedure of Lokshin (2006).

> Jha et al.. 13 Household quintiles were drawn after correcting per capita consumption for cost-of-living differences across states using the price deflators implicit in the Tendulkar poverty lines. 14 The participation rate for the richest quintile is a lower proportion of that for the poorest than is the case for the corresponding demand rates. 15 Alternatively one can just use (say) log consumption per person. However, given the uneven spread of the data across levels of consumption, this can be deceptive. Using percentiles instead assures a uniform distribution of the data.

9
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# **References**

- Abay, Kibrom A., Guush Berhane, John Hoddinott, and Kibrom Tafere. 2021a. COVID-19 and Food Security in Ethiopia: Do Social Protection Programs Protect? _Economic Development and Cultural Change._ <u>https://doi.org/10.1086/715831.</u>

- <mark>Abay, Kibrom A., Mulubrhan Amare, Luca Tiberti, and Kwaw S. Andam. 2021b. COVID-19-induced disruptions of school feeding services exacerbate food insecurity in Nigeria.</mark> _<mark>The Journal of Nutrition</mark>_ <mark>151, no. 8 (2021): 2245-2254.</mark>

- <mark>Aiken,</mark> Emily, Suzanne <mark>Bellue,</mark> Dean Karlan, Chris Udry <mark>, and</mark> Joshua E. Blumenstock <mark>. 2022. Machine learning and phone data can improve targeting of humanitarian aid.</mark> _<mark>Nature</mark>_ <mark>603: 864–870.</mark>

- Almenfi, Mohamed, Breton, Melvin; Dale, Pamela; Gentilini, Ugo; Pick, Alexendar; Richardson, Dominic. 2020. Where is the Money Coming From? Ten Stylized Facts on Financing Social Protection Responses to COVID-19. Social Protection and Jobs Policy and Technical Note; No. 23. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/34802

- Alloush, Mo,  Jeffrey R. Bloem, Jonathan G. Malacarne, 2022. Social protection amid a crisis: new evidence from South Africa’s older person’s grant. Working Paper.

- Amare, Mulubrhan, Kibrom A. Abay, Luca Tiberti, and Jordan Chamberlin. 2021. Impacts of COVID-19 on food security: Panel data evidence from Nigeria. _Food Policy_ 101(May): 102099.

- Andam, Kwaw S.; Edeh, Hyacinth; Oboh, Victor; Pauw, Karl; and Thurlow, James. 2020. _Estimating the economic costs of COVID-19 in Nigeria._ NSSP Working Paper 63. Washington, DC: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/p15738coll2.133846

- Andersson, Camilla, Alemu Mekonnen, and Jesper Stage. 2011. Impacts of the Productive Safety Net Program in Ethiopia on Livestock and Tree Holdings of Rural Households. _Journal of Development Economics_ 94 (1): 119–26.

- Ardington, Cally, Anne Case, and Victoria Hosegood. 2009. Labor Supply Responses to Large Social Transfers: Longitudinal Evidence from South Africa. _American Economic Journal Applied Economics_ 1 (1): 22–48.

- Arndt, C., Davies, R., Gabriel, S., Harris, L., Makrelov, K., Robinson, S., Levy, S., Simbanegavi, W., van Seventer, D., Anderson, L., 2020. Covid-19 lockdowns, income distribution, and food security: an analysis for South Africa. _Global Food Secur_ ity, 26, 100410 <u>https://doi.org/10.1016/j.gfs.2020.100410.</u>

- Baird, Sarah J., Craig T. McIntosh, and Berk Özler. 2011. Cash or Condition? Evidence from a Cash Transfer Experiment. _Quarterly Journal of Economics_ 126 (4): 1709–53.

- Banerjee, Abhijit, Michael Faye, Alan Krueger, Paul Niehaus, Tavneet Suri. 2020. Effects of a Universal Basic Income during the pandemic. Working Paper. ~

- <u>https://econweb.ucsd.edu/ pniehaus/papers/ubi_covid.pdf</u>

32
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|——.—_<br>Be Re<br><!-- End of picture text -->
Figure 3. Percentage of primary school teachers who joined prior to 2020 projected to still be teaching in 2030 (Base scenario: PTR=25 or lower by 2040, with high and low 2020 teacher stock scenarios) <!-- Start of picture text --> 100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Percent<br><!-- End of picture text -->

# Source: Authors’ projections.

Note: Blue: East Asian countries; Yellow: Pacific Island countries. Myanmar is not presented in this figure because it is difficult to project its future situation due to its current situation and data availability. The vertical line in each country bar chart represents the range of simulation results by different scenarios: the top of the bar is the high teacher stock scenario; the bottom is the low teacher stock scenario (see robustness checks in Box 1).

How could the simulations be strengthened?

As with any model, the projections are only as good as the data available and the assumptions made. This section discusses a number of factors that may affect the results. Specifically, the weakest point of our model is the data and assumption we make on attrition rates, a weakness we have tried to address with robustness checks. Teacher attrition rates are not readily available for our countries of interest: only 8 of the 22 MICs have reported data to UIS for at least one year between 2015 and 2019. However, the data gaps are particularly acute for the Pacific Island countries: Solomon Islands is the only Pacific Island country (out of a total of 12) where data are available between 2015 and 2019. Overall, data availability is better in East Asian countries. However, even when data are reported, the attrition rates fluctuate substantially year by year. For instance, primary teacher attrition rate for Malaysia were 6.7% in 2015, 3.1% in 2016, 2.2% in 2017, no data in 2018, and 3.5% in 2019 (WB Edstats Database).

On the one hand, the projections presented here likely _underestimate_ the proportion of 2030 teachers who have already been recruited, for a number of different reasons. For the countries with dwindling student numbers, these countries will likely struggle to reduce the number of teaching positions (at the rate our 7
# **5. Empirical investigation of the factors related to nominal and real wage rigidity**

# **_5.1. Estimation of the multinomial logit model_**

This section presents the results of the regression analysis on the relationships between real and nominal wage rigidity vs various firm-level and institutional characteristics. We start by examining firm-level characteristics, and move next to study the impact of labour market institutions. As our firms fall into one of three categories – those subject to downward nominal wage rigidity, those subject to downward real wage rigidity and those with flexible wages – we use multinomial logit estimation methods. All the regression specifications presented below include fixed effects based on firm size, country and sector. The inclusion of the fixed effects enables us to control in a cross-sectional context for the variation in relevant omitted variables that can influence the likelihood of a firm being subject to nominal or real wage rigidity. They will account for differences in the survey design across countries, differences in the business cycle during the time the interviews took place, etc.<sup>12</sup> The multinomial logit model is valid if the independence of irrelevant alternatives (IIA) assumption holds. The IIA assumption means that adding or excluding categories for the dependent variable does not affect the odds among the remaining outcomes. We use two tests of the IIA assumption, as described by Hausman and McFadden (1984) and Small and Hsiao (1985). In the baseline regression, both tests support the IIA assumption. This also holds for almost all the other regression specifications. There is no regression specification for which this assumption is unambiguously rejected. We conclude on the basis of the IIA tests that multinomial logit is a valid estimation method given the structure of the data in the current study. Note that two countries covered by the original WDN survey – Germany and the Netherlands – are left out of the regression analysis, because their national surveys do not include the questions related to wage indexation.

# **_5.2. Estimation results – firm characteristics_**

We begin by examining the effects of a range of firm characteristics on nominal and real wage rigidity. The results of the multinomial logit estimation are shown in Table 4. The first

> 12 The period covered by the survey relates mainly to the growing phase of the business cycle. Therefore, firms’ answers are likely to be biased towards reporting fewer wage freezes and wage cuts as compared to the situation of economic downturn, which gives us potentially less variation in the data. The extent to which the cyclical position affects the interaction between wage rigidities and such factors as firm characteristics, competition and labour market institutions is a-priori unclear and represents an interesting field of future research. Examination of firms’ reactions to the current economic and financial crisis is the subject of a follow-up survey and is beyond the scope of the present study.

18
# **Table AB.2 Empirical evidence of the impact of resource abundance on local living** **<u>standards</u>**

|**Paper**|**Explanatory**<br>**variable**|**Outcome variable**|**Sign**|**Country, data**<br>**Level**|**Identification**<br>**strategy**|
|---|---|---|---|---|---|
|Allcott &|Whether the county<br>produces any oil or|Income growth rate,<br>wages|+|||
|Keniston<br>(2014)|gas in any year after<br>1969|Manufacturing<br>employment and output|+|U.S., county level|Difference-in-<br>differences|
|||Factorproductivity|0|||
||Gold mine<br>production|Household income|+|P||
|Aragon and<br>Rud (2014)|Mining transfer|Municipality revenue and<br>expenditure|+|eru,<br>household<br>level|Difference-in-<br>differences|
|||Household income|0|||
|Black et al.<br>(2005)|Whether the county<br>is the coal boom<br>county|Employment and wages|+|U.S., county level|Instrumental variables|
|Caselli &||Local government<br>revenues|+|||
|<br>Michaels<br>(2013)|Oil output|Local government<br>spending|+|Brazil,<br>municipality<br>level|Instrumental<br>variables|
|||Localpublic service|0|||
|||Household income|0|||
|Fleming &<br>Measham<br>(2014)|Indicator of having<br>a coal seam gas<br>operation|Income growth,<br>employment|+|Australia,<br>individual<br>level|Cross-sectional OLS|
|Jacobsen<br>(2013)|Whether the county<br>is the oil and gas<br>boom county|Nominal income, wages,<br>employment and<br>population|+|U.S., county level|Fixed effect panel<br>|
|||Manufacturing<br>employment|0||model|
|||Service sector<br>employment<br>Agriculture employment|+<br>-|||
|Kotsadam &<br>|Mine openings|Women - service sector<br>employment|+|Sub-Saharan||
|Tolonen<br>(2014)||Men - skilled manual jobs<br>employment|+|Africa,<br>individual level|Difference-in-<br>differences|
||Mine closins|Women - agriculture<br>employment|0|||
||g|Men - agriculture<br>employment|+|||

54