rafmacalaba/datause-encoder-data huggingface dataset:
# **<u>Table A.1 Baseline Balance (Weighted)</u>**

||**Control**<br>**(Mean)**|**Arm 1**<br>**(Mean)**|**Arm 2**<br>**(Mean)**|**Arm 3**<br>**(Mean)**||**Nor**|**malized Mea**|**n Differen**|**ces**||
|---|---|---|---|---|---|---|---|---|---|---|
|Number of Gram Panchayats (GPs)|80|60|60|60|**Control-**<br>|**Control-**<br>|**Control-**<br>|**Arm 1-**<br>|**Arm 1-**<br>|**Arm 2-**<br>|
|Number of households|1,872|1,406|1,394|1,395|**Arm 1**|**Arm 2**|**Arm 3**|**Arm 2**|**Arm 3**|**Arm 3**|
|Tractor|.1168|.1138|.1584|.1087|.0067|-.0855|.0181|-.0922|.0114|.1036|
|Mobile phone|.9724|.9621|.9695|.9683|.0408|.012|.017|-.0289|-.0239|.005|
|Toilet|.7989|.7296|.7461|.7377|.1159|.0892|.1028|-.0266|-.013|.0135|
|**Education of the head of household**|||||||||||
|Secondary|.2316|.1919|.1968|.2199|.0687|.06|.0197|-.0086|-.0489|-.0403|
|College/graduate/postgraduate|.1061|.1087|.1077|.1034|-.006|-.0037|.0062|.0023|.0122|.0099|
|Illiterate|.317|.3504|.3513|.3377|-.0501|-.0514|-.0312|-.0013|.0189|.0202|
|**Primary occupation of the head of household**|||||||||||
|Cultivation|.1723|.1661|.2181|.1928|.0117|-.0817|-.0376|-.0934|-.0492|.0442|
|Agricultural labor|.08015|.1248|.1135|.08566|-.1043|-.0799|-.0141|.0245|.0903|.0659|
|Nonagricultural labor|.3154|.344|.3341|.3294|-.043|-.0283|-.0213|.0148|.0218|.007|
|Salaried work|.1526|.136|.121|.1186|.0335|.0651|.0704|.0316|.0369|.0054|
|Business|.0907|.0829|.06481|.09183|.0196|.0684|-.0028|.0489|-.0224|-.0712|
|**B. Village characteristics**|||||||||||
|SC/ST majority village|.4594|.4154|.4333|.5104|.0628|.0371|-.0722|-.0256|-.1353|-.1095|
|Village head w/college degree|.086|.1309|.08321|.0509|-.1023|.0071|.0985|.1093|.1986|.0915|
|Village head w/high school degree|.1368|.1351|.1786|.1663|.0033|-.0813|-.0583|-.0847|-.0616|.023|

41
**2**

How does one interpret the lack of an association between growth and expanded education? There is abundant and overwhelming evidence at the individua **l** evel from simple cross-tabulations as well as Mincerian type regressions that more educated individuals tend to have higher wages and incomes. It would seem to naturally follow this micro evidence that if more individuals were educated, average income should rise. In fact it does not. I discuss three possibilities for reconciling the macro and with the micro evidence. First, schooling may not actually raise cognitive skills or productivity but schooling may nevertheless raise the private wage because it serves as a signal to employers of some positive characteristic like ambition or innate ability. Second, expanding the supply of educated labor in the presence of stagnant demand for educated labor causes the rate of return of education to fall rapidly. The third possibility is that education does raise productivity, and that there is demand for this more productive educated labor, but that demand for educated labor comes from individually remunerative but socially wasteful or counter-productive activities so that while individual wages go up with education, aggregate output stagnates or even falls.

**I)** Mnre eduratinn _doeC_ **nnt** **_lead_ tn** mnre rapid grnwth

A) **Menagrinrg** eduraftinn **and** phygiral **rnpitIl** There are two sets of recently created cross national, time series data about the years of schooling of the labor force which use different methods to estimate educational attainment. Barro and Lee (1993) (B-L) estimate the educational attainment of the population aged _25_ and above using census data where available. They create a full panel of five yearly observations over the period 1960-85 for a large number of countries by filling in the missing data using enrollment rates. Nehru, Swanson and Dubey (1994) (NS-D) take a different approach, using a perpetual inventory method to cumulate enrollment rates into annual estimates of the stock of schooling of the labor force aged population, creating annual observations for 1960-1987.
that the gap between the business-as-usual off-farm employment share and the actual off-farm employment share was 6.8 percentage points, which means that it was affecting only 11.7 percent of those that would have been employed off-farm under the business-as-usual scenario as opposed to 12.5 percent in January. China’s second quarter 2009 GDP figures showed that the growth rate had stopped declining and that growth was picking up again; similarly, rural off-farm employment was also showing the initial signs of recovery. In other words, China’s off-farm labor market was already showing signs of recovery as early as the first and second quarters of 2009, when the global financial crisis was less than six months old.

As the economic recovery proceeded, national rural household surveys show that outmigration had picked up again by the end of 2009. According to the NBS rural household survey, 145 million migrant workers were employed outside their home villages for more than six months in 2009, which was a 2.6 percent increase over 2008 (NBS 2010). Estimates based on the RCRE household survey indicate that there were 147 million migrant workers by year end, or an increase of 6.8 percent over the previous year. As evident from Figures 4 and 5, the ratio of vacancy to job seeker also rose sharply between the second and third quarters of 2009, pointing toward an increase in labor demand relative to supply.

# **5. Wage Income in the Wake of the Financial Crisis**

From annual survey data, it is apparent that incomes fell in China in early 2009 but recovered later. Descriptive evidence from the CNRS survey shows that many workers who did not lose their jobs experienced a drop in earnings. According to the CNRS, the monthly earnings of the typical unskilled worker (who worked off-farm in both 2008 and 2009) employed in 2008 and the first four months of 2009 fell by 10.5 percent in early 2009.

By the end of 2009, however, the economic recovery was evident in wages as well. The nationally representative NBS and RCRE household surveys and the PBC firm survey show that 15
contemporaneous effects. This is the case for both labor market churn (via hiring and firing) and measures of wage dispersion.

**Table 2: Contemporaneous and lagged effects of internet access on municipality-level worker outcomes**

Municipality-year variation

|**Row**|**Outcome**|**InternetCoe**|**f.**<br>**lag Internet**<br>**Coef.**|**_N_**|**Adj. ****_R _**<sup>**_2_**</sup>|
|---|---|---|---|---|---|
|(1)|Total new hires|-0.024*|-0.102***|74170|0.921|
|(2)|Total separations|-0.036***|-0.087***|74170|0.923|
|(3)|Employment|-0.008|-0.005|74170|0.955|
|(4)|Average monthly wage|-0.024***|-0.027***|74170|0.802|
|(5)|SD (monthly wage)|-0.018**|-0.059***|74144|0.589|
|(6)|90-10 difference in ln(wage)|-0.001|-0.055***|74170|0.417|
|(7)|75-25 difference in ln(wage)|-0.005|-0.033***|74170|0.509|

Note: Estimates based on RAIS employee-employer records aggregated by municipality and year merged to internet access data from the School Census. Outcome measures listed in rows are in natural log. Interpretation of coefficients relate an increase in internet access from 0% to 100% in the municipality to the percent change in the outcome measure. Rows indicate outcome used in a common specification including unreported vectors of municipality and year fixed effects and a constant term. Standard errors clustered by municipality in parentheses. * p<.1, ** p<.05, *** p<.01.

# **_Employment and wages by 2-digit industry_**

Table 3 presents the coefficient estimates from this specification by industry. Among the five largest sectors by employment share, four have as-strong-or-stronger effects one year after a change in internet access; the only outlier is public administration, which does not sell in markets so its mission and activity levels are not likely to expand in response to lower marginal costs, and is more prone to have workers substituted for by ICT investments in the short run.

10
EUSILC and tax data (5% and over 20%, respectively<sup>17</sup> ), we focus only on full-time employees. The outcome of this comparison is presented in Table 5.

**In July 2020, a relatively large proportion of all full-time employees in the tax administration dataset earned a minimum wage or less compared to the EU-SILC.** In tax admin data, in July 2020, only a tiny About 24.7% fraction (2.7%) of all full-time employees earned less than 95% of the minimum wage. earn between 95% and 105% of the minimum wage. Thus, the share of minimum wage earners is at 27.4%. In the EU-SILC data, the share of minimum wage earners is significantly lower, at 11.95%. In the imputed EU-SILC, the share of minimum wage earners is between EU-SILC and tax data, which equals 23.04%.

**Results presented in Table 8 suggest that the share of minimum wage earners estimated based on tax data may be biased upwards.** However, even if the actual share of minimum wage earners is lower than in tax data, it is still among the highest in the European Union. The lower share of minimum wage earners in EU-SILC data and imputed EU-SILC data may also be explained by the fact that income is measured annually. Thus, some employees who earned minimum wage for part of the year are not in the minimum wage threshold for the whole year.

**Our estimates on the share of minimum wage earners in the survey data are close to estimates based on other surveys.** For example, Eurostat, based on EU-SILC 2018, estimated the share of minimum wage earners in Romania to be 13% for men and 18% for women. Based on the Structure of Earnings Survey 2018, Eurostat estimated the share of minimum wage earners to be 13%.<sup>18</sup>

> 17 The higher share of part-time workers in tax data may be driven by COVID-related reductions in working time, which are not recorded in the EU-SILC.

> 18 https://ec.europa.eu/eurostat/statistics-

<u>explained/index.php?ttle=File:Proporton_of_employees_earning_less_than_105_%25_of_the_minimum_wage,_2018_(</u>

> <u>%25).png</u>

30
|Regression<br>ofAnnual <br>Four Youth|**Poattiw.**<br>**Trainina**|Zarnin<br>**Prori**_|s**(Ir-ali**<br>.1969-1|ltrot<br>975|*on||
|---|---|---|---|---|---|---|
|**1969**<br>**1**|**970**<br>**19**|**71**<br>**1**|**972**<br>|**1973**<br>**1**|**974**|**1975**|
|Independent|||||||
|**Variables**s**|||||||
|TrainingPrograms|||||||
|APP<br>-873|-233|-49|-606|-1721|-482|-2441|
|(2.04)|(0.59)|(0.08)|(0.84)|(1.36)|(0.35)|(0.72)|
|IND<br>-40|459|1471|2406|1697|91|1876|
|(0.05)|(0.68)|(1.50)|(1.86)|(0.73)|(0.04)|(0.31)|
|TEEN<br>-1427|-3317|-2928|-4723|-573|-3483|-6272|
|(1.26)|(3.17)|(2.81)|(2.33)|(0.16)|(0.97)|(0.71)|
|VOC<br>-.|--|--|--|--|--|--|
|Training<br>Trades|||||||
|AUTO<br>-584|388|839|992|791|-650|-1023|
|(1.56)|(1.15)|(1.73)|(1.63)|(0.75)|(0.56)|(0.36)|
|ELECT<br>17|-44|46|371|468|1681|9558|
|(0.04)|(0.12)|(0.09)|(0.54)|(0.40)|(1.28)|(3.05)|
|MET<br>--|--|--|--|--|--|--|
|LEN<br>-417|-871|-424|-1057|34|29|1634|
|(1.25)|(2.83)|(0.93)|(1.84)|(0.03)|(0.03)|(0.61)|
|COMP<br>-1023|-824|-1439|-1659|-2578|-1488|-2168|
|(4.53)|(4.25)|(7.42)|(4.62)|(4.19)|(2.06)|(1.30)|
|Y8<br>-640|-180|-122|356|-570|-1297|536|
|(0.08)|(0.63)|(0.30)|(0.67)|(0.63)|(1.27)|(0.22)|
|IMM<br>82|-316|-455|-341|-358|-2087|-5142|
|(0.28)|(1.19)|(1.20)|(0.70)|(0.43)|(2.23)|(2.25)|
|Constant***<br>10342|12604|14477|19990|24248|31709|41616|
|R2<br>0.049|0.051|0.052|0.040|0.030|0.025|0.052|
|n<br>972|1049|975|972|859|910|611|

NB t **statistic is** shown in **parentheses**

- **The Israeli** lira **has been** superceded by the new shekel (1 **new shek-l** - 10.000 lirot)

** For definition of independent **variables, see** text.

***** The** constant **term** contains VOC, those born in 1947 **and** Israeli born. - 14 -
<!-- Start of picture text --> Alea! GOA 2007 § millions Growih index: 2008 = 700<br>“ “<br>an DevelopingH countries; Highilah-ineome countiesi "|World 350<br>60 i/ - 00<br>i 250<br>50) i<br>40 — _-- 200<br>|<br>“° —— —_ 180<br>a0 - 100<br>10 50<br>0 o<br>2005 2010 a1 aoe0 2025 2050<br>Sowrce: World Bank simulations using the Linkage model.<br>Note: Bars are meaaired on the lett asa; lines are measured on the nght ams.<br><!-- End of picture text -->

<!-- Start of picture text --> a a Oo Oo Oo<br><!-- End of picture text -->
# **INSURANCE**

_Insurance is a critical tool in managing risk, whether that risk relates to personal health or to one’s livelihood. Health problems can be catastrophic for someone with no health insurance. They can be especially so in developing economies, where government-provided safety nets and emergency room care are less common._ Seventeen percent of adults in developing economies report having paid for health insurance (in addition to national health insurance where applicable). This share ranges as low as 3 percent in Sub-Saharan Africa and 4–5 percent in Europe and Central Asia and South Asia (figure 4.1). The relatively high value for East Asia and the Pacific is driven by China, where 47 percent of adults report having personally paid for health insurance. With China excluded, the share of adults who report having purchased health insurance in East Asia and the Pacific drops to only 9 percent.

People who work in farming, forestry, or fishing are critically vulnerable to severe weather events. In recent years the microinsurance concept has become popular for managing weather-related risks, especially for those self-employed in agricultural industries in developing economies. The need to mitigate these risks has grown with mounting evidence on the effects of climate change on extreme variations in weather.

## **Purchasers of health insurance**

Adults paying personally for health insurance (%) <!-- Start of picture text --> 37<br>7<br>3 3 4 5<br>MIDDLE EAST SUB-SAHARAN EUROPE & SOUTH LATIN AMERICA EAST ASIA<br>& NORTH AFRICA AFRICA CENTRAL ASIA ASIA & CARIBBEAN & PACIFIC<br><!-- End of picture text --> _Source:_ Demirguc-Kunt and Klapper 2012.

Yet the vast majority of adults who work in farming, forestry, or fishing in developing economies do not report personally purchasing the kinds of insurance that could protect against weather-related risks.<sup>1</sup> Only 6 percent of those working in these industries report having purchased crop, rainfall, or livestock insurance in the past 12 months. In Europe and Central Asia only 4 percent report having done so (figure 4.2).

1. An important caveat is that it cannot be ascertained whether these adults chose not to purchase insurance or were not offered it.

###

## **Purchasers of agricultural insurance**

Adults in farming, forestry, or fishing purchasing crop, rainfall, or livestock insurance in the past year (%) <!-- Start of picture text --> 9<br>6 6 6 6<br>4<br>EUROPE & EAST ASIA MIDDLE EAST SOUTH SUB-SAHARAN LATIN AMERICA<br>CENTRAL ASIA & PACIFIC & NORTH AFRICA ASIA AFRICA & CARIBBEAN<br><!-- End of picture text --> _Source:_ Demirguc-Kunt and Klapper 2012.

**41**

MEASURING FINANCIAL INCLUSION
discrepancy in the measures of the stock of education relative to the enrollment rate. Whether or not the DHS are "better" is left for a different forum, but the fact that those from the DHS are transparently based on household surveys make these data particularly attractive.

_Wealth differences in enrollment_ The main advantage of using household surveys to carry out this analysis, however, is that various dimensions of inequality can be explored, and in particular wealth using the asset index approach. Gaps in educational enrollment and attainment across different wealth groups are large in almost all developing countries. Filmer and Pritchett (1999a), using a subset of the countries analyzed here, show that the difference in the median grade attained by 15 to 19 year olds from the richest and poorest households reaches as high as 10 years (India), and is commonly between 3 and 5 years in other countries.

Why would we expect to see wealth differences in education? A review of the elasticity between "income" and several educational outcomes can be found in Behrman and Knowles, (1997). As those author's discuss, a simplistic economic model where education is a pure investment, households are perfectly inter-generationally linked, credit markets are perfect and investment opportunities in education are equally distributed across households implies that investments in education will not be related to a family's present financial wealth. The assumptions of such models can break down on many fronts. Credit markets may not be perfect an*d the poor may have less access to it, there may be a large "consumption" component to education and wealthier households will therefore consume more of it. In addition, the opportunity costs of children's time spent in schooling, as well as the expected return to that schooling, may differ by household wealth leading to differential observed investment. 14

**14** For more discussion on these reasons for wealth difference **s** ee Filmer and Pritchett (1 999b). In particular, that paper argues that large cross-state variation within India in the magnitudes of wealth gaps cast doubt that credit constraints are a compelling reason for explaining wealth gaps.

15
Forest Carbon Partnership Facility (FCPF). The proposal was submitted in October 2010 and revised in 2011. The REDD+ Phase was launched in January 2013 with funding from the FCPF and the Royal Norwegian Government, and DFID (Moges and Tenkir 2014). The Proposal specifically notes that the key aspect of the enabling environment is to “reinforce and clarify the user rights of forest dependent people as this has been identified as a fundamental prerequisite for sustained protection of the forest.” Data were collected from 504 randomly selected households in 36 randomly selected villages in Amhara, Oromiya and Southern Nations, Nationalities and Peoples (SNNP) Regional States using in-person surveys. These regional states represent 80% of the population and over 70% of the land area of Ethiopia. The number of villages from each regional state included in the sample was stratified based on the forest cover of each state. As Oromia Regional State is the largest state and contains about half the forest area, 50% of respondents are from Oromia, with the remainder living in Amhara (22%) and SNNP (28%). Within each of the 36 surveyed communities 14 households participated in the survey, giving a total of 504 completed surveys. Data were collected in May and June 2013 by 20 enumerators and 5 supervisors who were trained on choice experiment surveys and had prior experience working with similar respondent groups.

# **_Household Characteristics_**

In this section we briefly overview respondent and household characteristics. All three regional states are multi-ethnic and respondents report membership in a total of 9 ethnic groups (one group per household). The most important ethnic groups are Oromo (39%), Amhara (25%) and Wolayta (14%). Slightly over half of respondents follow the Ethiopian Orthodox religion, 29% are Muslim and the remainder (17%) are non-Orthodox Christians. All Muslims in the 7
pass-through depends on the market power of Chinese firms (Berman et al. (2012)). Since the effects of Chinese competition on Indonesian firms ultimately depend on the extent of exchange rate pass through on prices, our results could be affected by the degree of Chinese firms’ market power in different product markets. A priori, the link between Chinese market share, exposure to Chinese competition, and yuan depreciation is unclear. We use the 2011 Chinese firm level data, the only year for which we have access to Chinese firm-level data, to compute the HHI share at the HS-6 level for Chinese firms who export to Japan. Similar to the competition index defined in 2 we define a measure of HHI that takes into account the market power of Chinese firms, defined below: where _EXig′_<sup>theimportanceofHS-6product</sup><sup>_g′_inthetotalexportsoftheHS-4</sup> _EXig_<sup>measures</sup> product _g_ exported to Japan by the Indonesian firm _i_ ; and _HHIg_<sup>_China′_</sup> is the sum of squares of firm-level market-share for HS-6 product _g_<sup>_′_</sup> sold in the Japanese market by Chinese firms.

We modify our main specification 1 by adding the interaction of the real exchange rate variation with the HHI measure defined above. The results from our main specification do not change: All the NTMs under consideration negatively affect firms’ export response. The positive and significant coefficient of the HHI measure interacted with exchange rate fluctuations in table 10 also provide suggestive evidence for the Berman et al. (2012) hypothesis that depreciations in Chinese exchange rate lead to a lower exchange rate pass-through for Chinese firms with higher market shares, as measured by the HHI, and thus lead to a lower fall in export values of Indonesian firms in markets with higher Chinese HHI. Even though interesting in its own right, since this is not a focus of our paper, we relegate further discussions of this to Appendix A3.3.

28
equation (8) below). Assumption 2 would be satisfied if the poverty change is mostly explained by the latter component. This can be expressed as where η _j_ is defined as μ _j_ + ε _j_ , j= 1, 2, for less cluttered notation.<sup>19</sup> Decomposition results are provided in Table 2, where seven different models are used. These models are built on a cumulative basis, with later models sequentially adding more variables to earlier models. The reason is that few common variables may exist between survey rounds in other settings—especially with surveys of different designs as will be discussed in the next section—thus using different models with different sets of control variables would provide a useful illustration.

Model 1 is the most parsimonious model and consists of household size, household heads’ age, age squared, gender, highest completed years of schooling, and a dummy variable indicating whether the head is Jordanian, and a dummy variable indicating urban residence. Model 2 adds to Model 1 the household demographics such as the shares of household members in the age ranges 0-14, 15-24, and 25-59 (with the reference group being those 60 years old and older). Model 3 adds to Model 2 employment variables, which include dummy variables indicating whether the head worked in the past week, whether the household has at least one female member working in the past week, whether the household has one member working as employer, whether the household has a member who is self-employed. These employment variables are commonly collected in most household surveys, and can provide a richer model than Model 1 while still keeping the model relatively parsimonious for most applications.

> 19 We estimate equation (8) for all households and then take the population averages rather than estimate this quantity at the means of _x_ . Similar to estimating the marginal effects in a probit model, the latter way of estimation may only capture a small fraction of the population (Wooldridge, 2010) and thus are not representative of the data.

22