rafmacalaba/datause-encoder-data huggingface dataset:
SME sample. The summary statistics also show that companies in Oceania and Africa show lower disclosure rates overall, while disclosure rates are similar across all regions for MNEs except Asia where companies tend to disclosure more sustainability information. The findings also seem to suggest that average disclosure rates are lower in lower‐income countries, although non‐OECD high‐income countries surprisingly show higher disclosure rates than OECD countries.

# **3.2 BASELINE REGRESSIONS AND CONTROLLING FOR MNE TYPE**

Table 11 shows the results of the baseline regressions, as specified in equation (2) in section 2.1. The dependent variable is the total disclosure rate in natural logarithms, _DR_total_ , defined as the average disclosure rate across all 54 indicators. GRI variables ( _DR_total, mne, large_ ) refer to the year 2015. All regressions control for country‐sector fixed effects. Standard errors are robust to heteroscedasticity.

MNE status, _mne_ , has no effect on the total disclosure rate (column 1). The full specification in column (3) reveals that this is driven by company size, as MNEs tend to be larger. Large companies with at least 250 employees ( _large_ ) show a significantly higher average disclosure rate compared to medium or small‐sized companies (columns 2 and 3).

In a next step, we interact the _mne_ dummy with a _private_ dummy to assess if the type of organization matters for the average disclosure rate (column 4). The regression follows the specification of equation (3) in section 2.1. MNE status now shows a negative and significant relationship with the average disclosure rate. The interaction term, however, indicates that private MNEs show a positive correlation, while non‐private multinationals (i.e. cooperative, nonprofit organization, partnership, public institution, state‐owned company, and subsidiary) show a negative correlation with the average disclosure rate.<sup>13</sup>

**Table 11: Baseline regressions and controlling for MNE type, OLS**

|Dependent variabl|e:_DR_totalisc_|||||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_mneisc_|0.0170||0.0017|‐0.1191***|‐0.0343|
|_largeisc_|(0.361)|0.1158***|(0.926)<br>0.1153***|(0.009)<br>0.1188***|(0.345)<br>0.1102***|
|||(0.000)|(0.000)|(0.000)|(0.000)|
|_mneisc*privateisc_||||0.1419***<br>(0.003)||
|_privateisc_||||‐0.0438*<br>(0.052)||
|_mneisc*listedisc_|||||0.0439<br>(0.289)|
|_listedisc_|||||0.0248<br>(0.216)|
|_constantisc_|0.6867***|0.5879***|0.5867***|0.6060***|0.6277***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Country‐sector FE|Yes|Yes|Yes|Yes|Yes|
|<br>R2|0.54|0.55|0.55|0.56|0.55|
|Observations|2,009|2,009|2,009|2,009|2,009|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). Note: GRI variables refer to 2015. All regressions control for country‐sector fixed effects. Standard errors are robust to heteroscedasticity.

> 13 The effect for private MNEs is given by the sum of the coefficients on _mneisc_ and _mneisc*privateisc_ , while the effect for non‐ private MNEs is given by the coefficient on _mneisc_ only.

15
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|VARIABLES|(1)|(2)|(3)<br>Height-for-a|(4)<br>ge z-scores|(5)|(6)|
|---|---|---|---|---|---|---|
||(0.591)|(0.585)|(0.584)|(0.582)|(0.590)|(0.582)|
|Ruvuma|-0.450|-0.469|-0.452|-0.438|-0.430|-0.465|
||(0.571)|(0.570)|(0.568)|(0.565)|(0.570)|(0.566)|
|Iringa|-0.697|-0.705|-0.699|-0.691|-0.674|-0.726|
||(0.569)|(0.569)|(0.567)|(0.565)|(0.572)|(0.566)|
|Mbeya|-0.172|-0.177|-0.174|-0.184|-0.150|-0.222|
||(0.589)|(0.589)|(0.588)|(0.586)|(0.591)|(0.588)|
|Singida|0.275|0.298|0.272|0.311|0.291|0.264|
||(0.598)|(0.602)|(0.598)|(0.595)|(0.603)|(0.598)|
|Tabora|<br>0.188|<br>0.179|<br>0.204|<br>0.192|<br>0.230|<br>0.175|
||(0.580)|(0.580)|(0.579)|(0.576)|(0.581)|(0.577)|
|Rukwa|<br>-0.300|<br>-0.306|<br>-0.302|<br>-0.291|<br>-0.282|<br>-0.317|
||(0.600)|(0.599)|(0.597)|(0.595)|(0.600)|(0.595)|
|Kigoma|-0.360|-0.372|-0.363|-0.359|-0.349|-0.402|
||(0.574)|(0.574)|(0.573)|(0.571)|(0.575)|(0.573)|
|Shinyanga|0.331|0.327|0.331|0.334|0.351|0.310|
||(0.577)|(0.578)|(0.576)|(0.574)|(0.579)|(0.576)|
|Kagera|<br>-0.438|<br>-0.459|<br>-0.438|<br>-0.450|<br>-0.421|<br>-0.471|
||(0.578)|(0.576)|(0.575)|(0.574)|(0.577)|(0.574)|
|Mwanza|<br>0.469|<br>0.451|<br>0.468|<br>0.470|<br>0.482|<br>0.427|
||(0.571)|(0.573)|(0.570)|(0.568)|(0.572)|(0.570)|
|Mara|0.070|0.051|0.067|0.077|0.081|-0.007|
||(0.597)|(0.598)|(0.598)|(0.596)|(0.599)|(0.600)|
|Manyara|0.671|0.665|0.674|0.674|0.692|0.622|
||(0.616)|(0.616)|(0.616)|(0.614)|(0.618)|(0.616)|
|Kusini ungunja|0.899|0.872|0.893|0.912|0.887|0.901|
||(0.933)|(0.934)|(0.935)|(0.956)|(0.928)|(0.947)|
|Mjini / Magharibi ungunja|-0.110|-0.111|-0.113|-0.094|-0.115|-0.136|
||(0.569)|(0.571)|(0.571)|(0.566)|(0.570)|(0.567)|
|Kaskazini pemba|-0.385|-0.399|-0.389|-0.371|-0.398|-0.396|
||(0.710)|(0.710)|(0.712)|(0.701)|(0.711)|(0.709)|
|Constant|-0.772|-0.772|-0.769|-0.761|-0.781|-0.689|
||(0.674)|(0.675)|(0.674)|(0.673)|(0.674)|(0.674)|
|Observations|1,086|1,086|1,084|1,086|1,084|1,084|
|R-squared|0.143|0.143|0.143|0.144|0.143|0.145|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in the total food expenditure, DDS is the household level dietary diversity score, PC expenditure is the per capita expenditure, and Food share is the share of food expenditure on total expenditure.

96
We next explore potential reasons for this. One natural explanation is that non-emergency social protection tools (in this case, the CCTs) have larger benefits in times of vulnerability. Another possibility is that a non-emergency social protection program might enhance the effectiveness of government programs rolled out in response to a crisis (in this setting, the SAP). In this section, we document evidence supporting both explanations.

# **4.3.1 Heterogeneity by Time Since CCT Receipt**

We begin by exploring the importance of the actual CCTs by examining heterogeneity based on how recently the 4Ps households received their regular CCT transfer. Due to variation in survey dates and CCT receipt dates, in survey wave 3 there are some households who report having received a 4Ps transfer in the last month and some whose last transfer was received before this. We divide the 4Ps households into two groups based on this response and estimate the following regression: where the two 4Ps transfer receipt categories are as described above.

Table 3: Severe Depression by 4Ps Transfer Receipt and Survey Wave

||(1)<br>Severe<br>Depression|(2)<br>Severe<br>Depression|(3)<br>Severe<br>Depression|(4)<br>Severe<br>Depression|
|---|---|---|---|---|
|Wave=3|0.33***|0.33***|0.33***|0.33***|
||(0.037)|(0.037)|(0.038)|(0.037)|
|4Ps who received last CCT before July 2020|-0.012<br>(0.0076)||||
|4Ps who received last CCT in July 2020|0.0086<br>(0.012)||||
|Wave=3 _×_ 4Ps who received last CCT before July 2020|-0.035|-0.031|-0.032|-0.026|
||(0.094)|(0.095)|(0.097)|(0.096)|
|Wave=3 _×_ 4Ps who received last CCT in July 2020|-0.12***|-0.12***|-0.11**|-0.11***|
||(0.041)|(0.041)|(0.042)|(0.041)|
|Observations|2906|2906|2906|858|
|Baseline Mean Outcome|0.01|0.01|0.01|0.02|
|Fixed Efects|None|Household|Household|Individual|
|Controls|None|None|Age/Gender|N/A|
|Sample|All|All|All|Panel<br>indiv.|

Notes: Standard errors (clustered at the municipality level) are in parentheses * p _<_ 0 _._ 1, ** p _<_ 0 _._ 05, *** p _<_ 0 _._ 01.

16
December 2018 were currently working, according to family members. Graduation from college in the relevant academic year (reported in table (5) column 4) is negatively associated with migration without any control variables included in the regression but is so strongly associated with province and urban locations that after controlling for these factors there is no association with households having a migrant member. The large majority of college graduates are concentrated in urban areas, and especially in Tashkent, where relatively few international migrants are from.

The survey data also allow for a reasonably clear profile of current migrants. More than 80 percent of migrants are men (varying between 80-89 percent depending on the source). Migrants are younger than the overall population with an average age of slightly over 33 years, in comparison to more than 38 years in the rest of the population in the baseline survey data. Adult migrants are substantially more likely to be unmarried than other adults (27 percent, in comparison to 22 percent of non-migrants). Nearly 60 percent of current migrants received a vocational degree as their highest level of education compared to about 45 percent among non-migrants. A similar share of migrants and non-migrants completed upper secondary (29 percent). However, only about 4 percent of migrants completed a bachelor’s degree or more, whereas about 11 percent of non-migrants had completed one. A remarkable difference between migrants and non-migrants is their higher propensity to speak Russian: more than 53 percent of migrants are described as Russian-speakers, while only 32 percent of the remaining population is.

The Russian Federation is by far the largest destination country for international migrants from Uzbekistan. Before 2015, regional cooperation agreements permitting visa-free entry and waiving certain registration requirements facilitated this migration (Ajwad et. al., 2014). However, changes in migration law in Russia beginning in 2015 has significantly reduced the ease with which migrants from outside of the Eurasian Union are able to legally migrate to Russia. Nonetheless, the country remains the dominant destination, accounting for more than 75 percent of current migrants abroad, followed by Kazakhstan at nearly 14 percent and Turkey at nearly 5 percent. In comparison to other Central Asian countries, this is a relatively diversified set of destination countries. This diversity is in part related to linguistic and cultural ties: Uzbek is a Turkic language, to an extent lowering the adjustment required to migrate to Turkey, and there is a large Korean community in Uzbekistan.

12
# **Table 3:  Regression Results:  Cameroon**

All variables in logs except the dummy variable for protected status Dependent variable: Log odds ratio, cumulative forest cleared %, 2014

||Rando|m Sample (10,00|0 obs.)|20% Ran|dom Sample|All Obse|rvations|
|---|---|---|---|---|---|---|---|
||IV<br>(1)|Bootstrap IV<br>(2)|Spatial IV<br>(3)|IV<br>(4)|Bootstrap IV<br>(5)|IV<br>(6)|Bootstrap IV<br>(7)|
|Distance from road|-0.097|-0.097|-0.094|-0.094|-0.094|-0.093|-0.093|
||(11.21)**|(6.64)**|(11.06)**|(85.35)**|(13.74)**|(188.37)**|(13.83)**|
|Distance to nearest|-0.374|-0.374|-0.345|-0.370|-0.370|-0.372|-0.372|
|urban center (DU)|(27.54)**|(20.84)**|(25.39)**|(223.72)**|(30.90)**|(501.44)**|(42.74)**|
|DU x Primary road<br>share|0.187<br>(29.47)**|0.187<br>(18.53)**|0.177<br>(28.08)**|0.192<br>(237.17)**|0.192<br>(34.52)**|0.192<br>(530.80)**|0.192<br>(38.13)**|
|Slope (Std. dev. of<br>elevation)|-0.499<br>(12.19)**|-0.499<br>(10.37)**|-0.515<br>(12.78)**|-0.551<br>(104.31)**|-0.551<br>(20.61)**|-0.543<br>(229.91)**|-0.543<br>(17.86)**|
|Slope x Elevation|0.061<br>(10.52)**|0.061<br>(8.20)**|0.063<br>(11.02)**|0.068<br>(90.44)**|0.068<br>(15.71)**|0.067<br>(199.75)**|0.067<br>(15.82)**|
|Protected area|-0.363<br>(12.65)**|-0.363<br>(8.72)**|-0.366<br>(12.94)**|-0.325<br>(89.78)**|-0.325<br>(16.53)**|-0.327<br>(201.42)**|-0.327<br>(16.25)**|
|Agricultural<br>opportunity value|0.388<br>(43.30)**|0.388<br>(31.24)**|0.375<br>(42.08)**|0.391<br>(342.24)**|0.391<br>(67.15)**|0.389<br>(761.91)**|0.389<br>(58.52)**|
|Constant|0.003|0.003|0.192|-0.007|-0.007|-0.006|-0.006|
||(0.04)|(0.03)|(2.74)**|(0.82)|(0.12)|(1.55)|(0.13)|
|Observations|10,000|10,000|10,000|645,901|645,901|3,229,507|3,229,507|
|R-squared|0.41|0.41||0.41|0.41|0.41|0.41|

Absolute value of t statistics in parentheses

* significant at 5%; ** significant at 1%

24
# **_Setting and Data_**

The potential for VCT to affect behaviors related to transition into adulthood is important when considering the social and economic environments facing young people in a poor country like Malawi. Education levels remain very low. According to the Malawi Demographic and Health Survey (DHS) from 2010, 16 percent of young men aged 25 to 29 had completed secondary school, and fewer than five percent had attended post-secondary school; among women in this age group, eight percent had completed secondary school and less than three percent had attended school beyond the secondary level (NSO and ICF Macro, 2011). Poverty remains high. There are few opportunities for non-farming employment and secondary education, age at first marriage remains young, and nearly everyone marries at least once. Median ages at first marriage for respondents age 25-49 are 17.8 for women and 22.5 for men.

Malawi has a generalized HIV epidemic; prevalence among 15-49 year old population is estimated to be 10.6 percent (NSO and ICF Macro, 2011) with a steep age gradient. The HIV prevalence among 15-17 year-old young women is 3.4 percent, while that of 18-19 year-olds is 5.7 percent. The prevalence is 1.3 percent among 15-19 year-old men, but increases to 4.6 percent among 23-24 year-olds and 6.9 percent for those 25-29. Prevalence rates among nevermarried women and men (our study sample at baseline) are about half of the rates among those ever married. Prevalence rates are much higher in urban Malawi than in rural areas like our study site.

This study uses data from the Marriage Transitions in Malawi (MTM) project, a panel survey conducted in 60 rural and semi-urban communities in the Salima district of central Malawi. The project was designed to understand socioeconomic patterns of young adults as they transition into adulthood and with an emphasis on HIV/AIDS. The connection between the two 7
Policy Research Working Paper 9924

# **Abstract**

After increasing for years and reaching high levels, Brazil’s subjective well-being deteriorated following the economic contraction in 2015. Using data from the Gallup World Poll for the 2010s, this paper identifies the factors that underpin Brazil’s subjective well-being and its change, paying special attention to heterogeneity across population groups. Having sufficient income, financial security, economic optimism, satisfaction with living standards and health services, social capital, tertiary education, and digital access are the main factors associated with subjective well-being. These factors matter to different extents along the income distribution and across generations and space. The decline in subjective well-being since 2015 was heterogeneous and more pronounced among men, rural residents, and the old. Economic expectations increased in importance as they assumed a greater role in people’s preferences, especially those of men, and more people grew pessimistic about the economic outlook. The decline in subjective well-being and the switch in voter support from one end of the political spectrum to the other in the 2018 general elections were both associated with the grievances triggered by the economic and leadership crisis of the mid2010s. These grievances signal an erosion in the support for the social contract in place since the 1990s and the need to renew it.

This paper is a product of the Office of the Chief Economist, 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://www.worldbank.org/prwp. The authors may be contacted at mburger@ese.eur.nl or eianchovichina@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
A summary of proposed topics for a World Health Survey tore questionnaire Table 2 and optional modules

|Core questionnaire|Optional modules|
|---|---|
|Mortality<br>- five year birth history<br>- orphanhood for women aged 15-49|Mortality- complete birth history<br>- cause specific moetality eg. tetanus, measles,<br>whooping cough, diarrhoea|
|Morbidity- 14 day recall of general morbidity<br>for all household<br>members<br>- 1 year recall of chronic illness for<br>adults|Morbidity- cause specific morbidity eg. ARI, diarrhoea|
|Use of services- use associated<br>with two<br>week recall of morbidity|Use of services- attitudes towards services,<br>drugstaken<br>- coverage of specific programmes eg. ORT,<br>vaccination,<br>malaria prophylaxis<br>- costs of services (time and money)|
|Nutrition- anthropometry<br>of all preschool<br>children<br>Explanatory<br>variables (age, sex,<br>education,<br>houshold possessions,<br>place of residence, length of<br>residence,marital<br>status etc)<br>Community variables|Nutrition- anthropometry<br>of adults and older children<br>- nutritional<br>deficiencies<br>eg. Vit A and iodine<br>Environmental<br>contamination<br>- water supply, sanitation,<br>housing<br>type (past and present)<br>- personal hygiene practices<br>Knowledge and attitudes|
|Nakivubo Channel Rehabilitation Project|Lance Morrell|
|---|---|
|Nakivubo Channel Rehabilitation Project|Lance Morrell|
|Nakivubo Channel Rehabilitation Project|Lance Morrell|
|Nakivubo Channel Rehabilitation Project|Lance Morrell|
|Nakivubo Channel Rehabilitation Project|Lance Morrell|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|Madhya Pradesh District PovertyInitiatives Project|Nathan M. Belete|
|District Upland Development and Conservation Project|Susan S. Shen|
|District Upland Development and Conservation Project|Susan S. Shen|
|District Upland Development and Conservation Project|Susan S. Shen|
|District Upland Development and Conservation Project|Susan S. Shen|
|District Upland Development and Conservation Project|Susan S. Shen|
|District Upland Development and Conservation Project|Susan S. Shen|
|District Upland Development and Conservation Project|Susan S. Shen|
|Structural Adjustment Loan Project|Ahmad Jamshidi|
|Structural Adjustment Loan Project|Ahmad Jamshidi|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|Second Water & Sanitation for Low Income Communities Project|Claudia Rokx|
|RURAL ACCESS PROJECT|Binyam Reja|
|RURAL ACCESS PROJECT|Binyam Reja|
|RURAL ACCESS PROJECT|Binyam Reja|
|RURAL ACCESS PROJECT|Binyam Reja|
|RURAL ACCESS PROJECT|Binyam Reja|
|RURAL ACCESS PROJECT|Binyam Reja|
|RURAL ACCESS PROJECT|Binyam Reja|
**Table 1:** Transitions among young (18-21 years old) between work statuses

||No work|Informal work|Formal work|Total|
|---|---|---|---|---|
|No work|78_._93|15_._43|5_._64|100|
|Informal work|26_._37|63_._07|10_._56|100|
|Formal work|15|14_._92|70_._07|100|
|Total|53_._06|29_._09|17_._85|100|

_Notes:_ Author’s calculations using panel data from 2018 and 2019 ENOE surveys. Table presents the average probability of maintaining or changing work status from one period to the next. The rows reflect the initial status, and the columns reflect the final status. Youth are followed for four quarters (3 month periods) between 2018 and 2019 in staggered cohorts.

the probability of transitioning to formal work from non-employment (5.6%) it is small in magnitude and less than the likelihood of transitioning from formal to informal employment (14.9%).

In sum, the data suggest that, similar to many other LMICs, including Argentina and Brazil (ILO, 2015), informal work is not a stepping stone to formal work for youth, and that the informally employed are prone to greater employment instability. These findings are also in line with recent evidence by Donovan et al. (2020) who show that across developing countries transitions between jobs are more common and are less likely to lead to wage gains than in developed economies. Overall, these results suggest that, similar to evidence from other settings, early career choices matter and have long-lasting effects on employment prospects (Oreopoulos et al. (2012)).

# **2.2 Formal vs. informal sector wage growth**

In addition to job stability, choices over different types of employment also affect earnings as different career paths offer different wage trajectories. Figure 1 uses social security data (IMSS) to plot the average wage growth for formally employed people in our study sample of school leavers. In order to test how much of the observed wage growth is driven by firm selection, we show wage increases separately for people who exit employment in a given month (light) versus those that retain their job (dark). The graph shows several interesting stylized facts. First, there is substantial wage growth at the beginning of school leavers’ careers. After six months, wages have increased by almost 25%. While the wage growth slows down slightly afterwards, we still see a large gain of 35% after 12 months and 50% after 18 months. (The average annual inflation rate over this period was below 5%.) Second, 8