rafmacalaba/datause-encoder-data huggingface dataset:
of the International Labour Organisation endorsed core labor standards, as a result of their acceptance of the "Declaration on Fundamental Principles and Rights at Work". As for other ILO conventions, whether "core" labor standards are enforced in practice is a different issue.

**Table 2. Correlation between "Unexplained" Inequality and Labor Market Policies**

||_I_|_ncomeorcon_|_sumptionsha_|_rebyquintile_||_Giniindex_|
|---|---|---|---|---|---|---|
|_Labormarketindicator_|_Poorest_|**_2_**<sup>**_nd_**</sup>|_3d_|_4_|_Richest_||
|ILOconventionsratified|0.30*|0.26*|0.24*|0.23*|- 0.25*|- 0.26*|
|CoreILOconventions<br>ratified|0.03|0.01|0.03|0.06|- 0.03|- 0.01|
|Minimumwage|-0.22|-0.21|-0.20|-0.20|0.22|0.18|
|(%ofaverage)|||||||
|S.sec.contribution|0.31|0.29|0.27|0.21|-0.28|-0.29|
|(%ofwage)|||||||
|S.sec.revenue<br>(%ofGDP)|0.32*|0.33*|0.30*|0.26*|-0.31*|-0.33*|
|Daysofpaidmaternity<br>leave|0.22|0.18|0.12|0.09|- 0.06|- 0.05|
|Unionmembershiprate<br>(%)|0.14|0.14|0.16|0.18|-0.17|-0.10|
|Coverageofcoll.|0.24|0.33|0.48|0.60*|- 0.44|- 0.33|
|Bargaining(%)|||||||
|Numberofstrikesand<br>lock-outs|0.07|- 0.05|0.04|0.02|- 0.01|- 0.13|
|Generalgovt.|0.13|0.14|0.14|0.13|-0.12|-0.12|
|(%oflaborforce)|||||||
|Govt. wage<br>(%ofaverage wage)|0.08|0.06|0.03|0.03|0.04|- 0.06|

_Source:_ Based on work in progress by Rama and Ravallion (2001). The figures reported in the table are correlation coefficients between "unexplained" inequality indicators and labor market policies. Unexplained inequality is measured as the residual of a regression linking each mequality indicator with a large number of explanatory variables, none of which is directly related to the labor market. The number of observations varies from cell to cell depending on data availability. Significant correlation coefficients at the 10 percent level are indicated by an asterisk.

Taken together, these results suggest that pushing developing countries to adopt "core" labor standards, or higher minimum wages, or to expand public sector employment, may not do much to neutralize the impact of globalization on workers. The costs in terms of economic would be limited in the case of "core" labor standards and minimum wages, but they could be considerable in the case of public sector employment. The benefits in terms of reduced inequality would be small, if not negligible. For the international community, a sounder approach is to promote the adoption of social protection programs, like the compensation packages and income support programs for the unemployed that were reviewed in the previous section. The efficiency costs of social protection programs do not appear to be high. But programs of this sort have the potential to reduce inequality, hence to offset the effects of globalization on returns to education and job losses. If the main obstacle to the implementation of appropriate responses to globalization is lack of information, the international community should play a more 29
**COVID-19 containment and economic support policies.** Policies that aim to restrict community mobility to stem the spread of COVID-19 have also impeded economic activity and revenue mobilization which puts pressure on public, private, and financial balance sheets. Many countries have adopted fiscal stimulus measures to stimulate the economy. Some of these measures directly support the financial sector through guarantees and may therefore necessitate or complement a financial sector policy response. The data are taken from the Oxford Government Policy Response Tracker (see Appendix A for more detail).

**Economic development.** The level of economic development can be interpreted as a broad proxy of available economic resources and buffers to respond to COVID-19, as well as differences in economic structure, financial development, and institutional frameworks. We expect countries with higher economic development to be more active in their policy response as highlighted by Benmelech and Tzur-Ilan (2020). The notable exception could be the policy response on the _Payments Systems_ category. As more developed economies have more developed payment systems,<sup>13</sup> additional policy measures in this category could be less needed. Therefore, the observed correlation between economic development and policy response might be negative.  We use GDP per capita (expressed in current international dollars converted by purchasing power parity (PPP) as per the end of the year 2019 as a measure of economic development. The data are taken from the World Bank’s World Development Indicators.

**Macro-financial fundamentals.** On the one hand, the current account, fiscal deficits, and high levels of public and private debt may limit the capacity to mount an effective policy response. On the other hand, external and debt sustainability vulnerabilities as well as crowding out effects could amplify the economic shock of the COVID-19 prompting authorities to respond faster and at a larger scale. We use four indicators: the 2019 current account balance from the IMF World Economic Outlook (April 2020); the 2019 general government net lending (borrowing) taken from the IMF World Economic Outlook (April 2020);  and the 2018 total private-sector debt and total public sector debt as a percentage of GDP sourced through the World Bank and IMF Global Debt databases.

**Banking sector characteristics.** The initial state of the banking sector may also influence the reaction function of financial sector authorities. We explore the role of the following variables: regulatory Tier 1 capital to risk-weighted assets (IMF Financial Soundness Indicators, end-2019), ii) non-performing loans as a percent of total gross loans (IMF Financial Soundness Indicators, end-2019), iii)  domestic credit to the

> 13 https://www.bis.org/statistics/payment_stats/commentary2011.htm.

18
(dNWc), this allows us to determine<sup>the distributed profits and</sup> losses (P&Lc) as the residual variable<sup>in the current account.</sup> Profits and losses contain all the<sup>items other than interest</sup> payments and receipts such as operating<sup>costs, exceptional revenues</sup> and costs, etc. These profits and losses<sup>are transferred as factor</sup> incoide to the budget. The bottom<sup>part of Table A1.5 presents the</sup> current account of the Central Bank.

The construction of the accounts of<sup>the domestic banking</sup> system follow exactly the same procedure.<sup>Table A1.6 presents the</sup> stock balance sheets for 1987 and 1988,<sup>the capital account and the</sup> current account for 1988. The existence<sup>of financial SEEs (deposit</sup> money banks and development finance institutions<sup>owned by the</sup> public sector) forces us to adjust<sup>the balance sheet of the</sup> domestic banking system in order to<sup>inc'lude explicitly some</sup> transactions between the budget and these<sup>financial institutions.</sup> Henca, the stock balance sheet contains<sup>paid in capital (Kbd) and</sup> real assets (Kd). Since we do not have<sup>information on the stocks</sup> of these two variables, they are obtained<sup>as the accumulation of</sup> flow data which we obtain from fi5cal data. Paid in capital is the accumulatLon of equity transfers<sup>from</sup> the ceritral Government (KTbd), while real assets correspond<sup>withthe</sup> accumulation of purchases of existing real estate by<sup>the financial SEEs (KTdp).'</sup> 4 The fiscal data report new fixed investment of the financial SEEs. This item Jc classified<sup>under the investment of</sup> the budget to maintain the total figure<sup>for public investment in</sup> the public sector. The corresponding offsetting item is<sup>the</sup> reduction in capital transfers from the budget<sup>to the banking</sup> system.

21
# **XXX** INTRODUCTON

Figure 15 Assumed Total Fertiliy Trend, Less Developed Countries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> 3.8-<br>37<br>3.6 -2<br>N,<br>3.4<br>3.3<br>3.2 -<br>31.11<br>3.0 -IU<br>2.9<br>2.8<br>2.7<br>2.6 .18<br>2.5 -18<br>2.4<br>2.3<br>1059 96-51252 635 22.8 031 2015.20 2O202<br><!-- End of picture text --> Figure 17 Assumed Life Expectancy Trend, Less Developed Countries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> 10<br>69<br>W!EIl8S 2010 199s95 n 1995BX00 2015O20 2Dal: alOrOS 2005s10<br>68<br>66-<br>65<br>63<br>I ~~~~~U.N.<br>62<br>61<br>60-<br>59<br>¶M9059 1920.5 199526 2WO-8 2005. t 2010.-6 2015.20 2020:<br><!-- End of picture text --> Figure 16 Assumed Total Fertility Trend, More Developed dountries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> 2.0<br>02<br>2 W<br>11292<br>1<br>1<br>1M965 19929 19920D3 2OW(5 2005.02 201015 20`1520 MD-0:<br><!-- End of picture text --> Figure 18 Assumed Life Expectancy Trend, More Developed Countries, and U.N. and Previous World Bank Trends <!-- Start of picture text --> 73<br>1985B90 95 1b2000 2alO-05 2110S10 XtIS 20 2'U.N.<br>76<br>75-<br>74-<br>~~~~~~~~~~~~~~~~~~~~~~~73<br>19r5-90 199295 1925209 2 03)-OS 2005-10 209D-1i 2015.20 W&20232<br><!-- End of picture text -->
Historically, mangroves have shown considerable resilience to fluctuations in sea level rise (Alongi, 2009; Erwin 2009; Gilman et al. 2006). However, their adaptation to future sea level rise (SLR) depends on their success in landward progression and is conditioned by the availability of adequate and suitable space for expansion/migration, continued supply of sediment and nutrients from fresh‐water inflows, and a rate of sea level rise that is not greater than the rate at which mangroves can migrate (Ellison and Stoddart 1991; Semeniuk 1994; UNEP 1994; McLeod and Salm 2006; Lange et al. 2010).<sup>12</sup> The ability of mangroves to migrate landward, in turn, is determined by local conditions, such as topography (e.g., steep slopes) and, perhaps more importantly, infrastructure (e.g., roads, agricultural fields, dikes, urbanization, seawalls and shipping channels). If inland migration or growth cannot occur fast enough to compensate for the rise in sea level, then mangrove areas will become progressively smaller with each successive generation and may perish.

Understanding the impact of SLR on mangroves must take into account factors that affect the ecological balance of the ecosystem, such as the history of sea levels in regard to development of coastal gradients, relative geomorphic and sedimentologic homogeneity of the coast, coastal processes including tidal range and its stability, density of mangroves, availability of fresh water and sediment, and salinity of soil and groundwater (Belperio 1993; Semeniuk 1994; Blasco et al. 1996; Kumura et al. 2010).

In order to estimate the impact of SLR on mangroves and the potential for adaptation, we use the wetland migratory potential (WMP) characteristic in the DIVA database from the DINAS‐ COAST project (Vafeidis et al. 2008). WMP indicates the potential for wetlands, including mangroves, to migrate landward in response to a 1‐meter rise in sea level. The migratory potential is based on a few geophysical characteristics of the coastline: coastal type, topography, tidal range, and other information when available (e.g., whether mangroves are

> 12 Mangroves have adapted special aerial roots, support roots, and buttresses to live in muddy, shifting, and saline conditions. Mangroves produce peat from decaying litter fall and root growth and by trapping sediment in water. The process of building peat helps mangroves keep up with sea level rise. Mangroves can expand their range despite sea level rise if the rate of sediment accretion is sufficient to keep up with the sea level rise.

13
11

# Table 1

Characteristics of Household Groups Identified in the Cte d'lvoire Social Policy Model

|Household<br>Types|Population<br>Share|Poverty"<br> <br>index<br>(%)|Per capita<br>income<br>(CFA)|Savings<br>as %<br>of total<br>income|Income<br>tax<br>as % of<br>total<br>income|Transfer<br>payments<br>to other<br>households<br>(as % of<br>total<br>income)|Transfer<br>income<br>received<br>from<br> <br>other<br>house-<br>holds<br>(as % of<br>total<br>income)|
|---|---|---|---|---|---|---|---|
|Food croppers in<br>Savannah (MSAV)|11.03|63.68|132,972|0.33|0.60|0.0|24.00|
|Export croppers<br>(MEXP)|20.28|44.04|173,722|0.57|_0.50_|15.60|0.00|
|Small businesses<br>(MIND)|21.48|46.81|187,600|0.91|1.00|0.00|3.50|
|Other food croppers<br>(MVIVA)|12.77|34.46|202,176|0.37|_0.55_|32.75|0.00|
|Inactive (MINAC)|15.10|37.07|220,629|0.75|2.81|0.00|67.86|
|Govemment employees<br>(MADP)|11.80|8.69|423,085|0.89|5.86|11.24|0.00|
|Households in formal<br>sector (MFOR)|7.60|1.79|622,807|0.80|2.48|16.96|0.00|

a Percentage of households in the group below the poverty line, based on a poverty line of CFA 140,000 (or 50 percent of mean income for 1986).

Final demands for each of the household groups are generated by maximizing a nested constant elasticity of substitution (CES) utility function subject to the household budget constraint. Figure 2 provides a schematic representation of the nesting structure used in the household utility function. In the top level of the nest, the utility function is specified
ethnic minorities in an ordered probit, if everyone had the same probability of completing primary school, but women and ethnic minorities were not able to complete secondary school. In other words, a particular group may be more likely to fall into the “completed primary school” because they are moving up from the “less than complete primary school” category or because they are not able to move up to the “completed secondary school” category.<sup>29</sup>

**Table 4. Primary School Completion Regression Results - Ordered Probit**

_Marginal effects, using Male Reference Language Speakers as reference group_

||Bolivia(2001)|Mexico(2010)|Peru(2007)|Senegal(2002)|Sierra Leone(2004)|
|---|---|---|---|---|---|
|Age|-.000 ***|-.003 ***|.001 ***|.001 ***|-.003 ***|
|Rural resident|-.059 ***|-.084 ***|.000|-.202 ***|-.232 ***|
|Female|-.008 ***|-.010 ***|.011 ***|-.067 ***|-.135 ***|
|Minority|-.039 ***|-.089 ***|.014 ***|-.012***|-.041 ***|
|Female * Minority|-.041 ***|-.036 ***|.013 ***|-.022 ***|.018 ***|
|Obs.|320,603|5,612,740|1,289,699|327,958|171,963|

Note: Regressions include individuals aged 25 and above. ***p<0.01, **p<0.05, *p<0.10.

Collapsing the ordered school completion variable into two separate binary variables for primary school and secondary school allows us to explore these two explanations through probit regressions (full regression results are provided in Appendix C). The three positive coefficients for Peru in the ordered probit regression become negative in the probit model, suggesting that women and ethnic minorities may be facing barriers in progressing to secondary school. In particular, ethnic minority women are 32 percentage points less likely to complete primary school than males in the reference group (Table 5), but 38 percentage points less likely to complete secondary school compared to the same reference group (Table 6). In other words, ethnic minority women are more likely to remain in the “completed primary school” category than move up to the next rung. In contrast, the intersectionality variable in probit regressions for

> 29 There can be other explanations, including the possibility that the language variable used for categorizing individuals into “reference” and “minority” groups may not capture the relevant differences in terms of ethnicity or other factors that may be associated with the observed educational gaps.

20
**Table 1. Fiscal Balances, Tax Revenues, and Expenditures**

|Dependent variable:|Fiscal balance/GDP<br>(%)|Total revenues/GDP<br>(%)|Total<br>expenditures/GDP<br>(%)|
|---|---|---|---|
|Constant|-7.34***<br>(-8.04)|-7.06***<br>(-4.79)|-0.099<br>(-0.06)|
|Dummy fuel > 25%|4.74***|9.45***|5.05***|
|exports|(10.52)|(14.87)|(9.07)|
|Dummy ores and|0.384|0.404|0.359|
|metals > 10%<br>exports|(1.30)|(0.83)|(0.71)|
|Hard peg dummy|1.43***|-2.69***|-4.15***|
||(2.96)|(-4.53)|(-8.17)|
|Political stability|0.0620***|0.469***|0.412***|
|(0 to 100)|(4.66)|(20.72)|(17.64)|
|Time trend|0.0185|0.315***|0.278***|
|(=0 in 2000)|(0.74)|(7.05)|(5.91)|
|n|1,378|1,392|1,378|
|R-squared|0.134|0.299|0.231|
|RMSE|4.75|8.03|7.97|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2012 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

There is a question of whether these results are unduly influenced by outliers. Robust regression is a technique that minimizes the weighted sum of squares of residuals, with observations weighted between zero and one based on Cook’s distance statistic, which measures the effect on the fitted values of deleting a particular observation. Outliers tend to have low (sometimes even zero) 7
girls of mothers with no high-school education by 0.2 standard deviation; if this effect were exclusively the result of attending a CPBA, then the effect of CPBA attendance would be 0.83 standard deviation because 24 percent of these children attended a CPBA. Effect sizes may also be understated by the exclusion of 5-year-olds, as 5-year-olds at the time of the TEHCI survey would have had the most exposure to CPBA in 2017 and preceding years. Nevertheless, even with the low participation rate in CPBAs, just the intent-to-treat can have non-trivial effects on children with less educated mothers.

For boys of mothers with high-school education, the intervention reduced participation in preschool and had a small negative effect on some domains of school readiness. It is not clear why these two effects emerge. One hypothesis is that parents may have viewed CPBA as an alternative to preschool, and the reduction in preschool participation reduced school readiness. Preschools generally charge fees for participation while CPBAs did not. For boys of mothers without high-school education, the intervention increased preschool participation.

# <u>4. Effect of the CLRW intervention</u>

The CLRW intervention was expected to improve 1<sup>st</sup> and 2<sup>nd</sup> grade students’ reading skills by changing teaching techniques and exposing students to a new pedagogic approach. The randomized assignment of schools allowed us to identify the effect of the intervention on teaching practices measured in classroom observations and on reading outcomes. Self-selection could occur if students switched schools in response to the intervention; however, no evidence of this was reported. We found that the CLRW intervention had a positive effect on the prevalence of teaching practices promoted by the intervention and a positive effect on students’ reading abilities. Effect sizes tended to be higher for those exposed to two years of the intervention rather than just one.

# _Data_

Two sources of data were used to evaluate the effects of the CLRW intervention. The first was based on classroom observations that were conducted approximately halfway through the 2016 21
**<u>Table C.12. Descriptive statistics of estimation sample, Tunisia</u>**

|||**2005-2010**|||**2010-2015**|||**2015-2021**||
|---|---|---|---|---|---|---|---|---|---|
||**2005**|**2010**|**diff**|**2010**|**2015**|**diff**|**2015**|**2021**|**diff**|
|L f it ti|7.23|7.58|0.4***|7.55|7.98|0.4***|7.96|8.36|0.4***|
|og o per capa consumpon|(0.01)|(0.01)|(0.0)|(0.01)|(0.00)|(0.0)|(0.01)|(0.01)|(0.0)|
|Hd`|43.55|46.72|3.2***|44.24|47.23|3.0***|44.32|48.40|4.1***|
|eas age|(0.09)|(0.09)|(0.1)|(0.09)|(0.06)|(0.1)|(0.06)|(0.08)|(0.1)|
|Hd i fl|0.12|0.11|-0.0*|0.11|0.11|0.0|0.10|0.12|0.0***|
|ea s emae|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Hd' hiht dti ll i i|0.04|0.03|-0.0***|0.03|0.46|0.4***|0.44|0.44|0.0|
|eas ges eucaon eve s prmary|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Hd' hiht dti ll i d|0.14|0.11|-0.0***|0.12|0.30|0.2***|0.32|0.32|0.0|
|eas ges eucaon eve s seconary|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Hd' hiht dti ll i tti|0.08|0.09|0.0|0.09|0.11|0.0***|0.12|0.12|0.0|
|eas ges eucaon eve s erary|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|
|Ub|0.65|0.66|0.0|0.65|0.62|-0.0***|0.61|0.62|0.0**|
|ran area|(0.01)|(0.01)|(0.0)|(0.01)|(0.00)|(0.0)|(0.00)|(0.00)|(0.0)|

Note: ***, **, and * denote statistical significance at the 0.01, 0.05, and 0.10 levels. Standard errors are in parentheses. The data are unweighted. Household heads' ages are restricted to between 25 and 55 for the first survey round and adjusted accordingly for the second survey round. The reference groups are household with no primary education and living in rural areas.

90
# (d) Total Business Revenue

# (e) Business income used to pay household expenses

We randomly assigned some firms to receive books, and others as control firms. Firms receiving books were asked to record daily amounts in each of the five columns, and were given clocks or umbrellas as small incentives for participating in the bookkeeping.

In Kandy we randomly allocated firms into three groups, each of 60 firms. The first group was re-interviewed in early October and asked about September sales, expenses and profits. The second and third groups were given the ledger books. The second group were then given weekly interviews, and the third group were given interviews every two days. Compliance with the bookkeeping was extremely high, and 71 percent of firms said they planned to continue maintaining the books after the experiment was over. Unannounced visits to the firms in November 2005, one month after we had thanked them for participating in the experiment, found 52 percent were still keeping the books of their own volition.

In the SLMS we randomly allocated half of the firms to receive the books when the second round of the survey was collected. Firms were then given new books during the third and fourth round surveys, in sum receiving books for 9 months. This is much longer than the typical expenditure diary, which asks for two-weeks, or at most one month’s expenditure. In contrast to the Kandy experiment, in which data were collected from the books weekly or at two-day intervals, in the SLMS book data were collected alongside the regular survey, every three months. The increased time between giving out the books and checking that they were being maintained, along with the longer time for which they were given, resulted in less compliance with the book treatment. 68 percent of firms given books had recorded data in them for the first month (July 2005), falling to 53 percent by the third month for which books were given. The first round of data collection from the books then increased use of the books in October 2005, up to 69 percent, with use falling to 57 percent by December 2005. The January interview did not lead to any 17