rafmacalaba/datause-encoder-data huggingface dataset:
for the panel respondents in the 2016 sample, where we observe results similar to those for the 2015 full sample.

Panel C presents the summary statistics for the total 2016 sample as well as for those with and without an written contract, separately. Column 7 shows that the average age of respondents is 37. Note that 18% of women and 15% of men are above the age thresholds at which beginning participation could not lead to full pension benefits at retirement. Men comprise 57% of the respondents, and the average years of schooling of respondents is nearly 9 years and 75% of the sample are married, and have 1.16 children. Nearly all respondents, 99.6%, were working in the previous year, with 36% self-employed, and _hukou_ policy permitting, 64% would prefer to stay in the city permanently. When comparing those with a contract (Column 8) to those without (Column 9), it is clear that migrants in the formal sector (with a written contract) are younger, more educated, more likely to be men, less likely to be married and have fewer children. Employees with a contract are more likely to work in large firms (with more than 100 workers) than those without a contract. In addition more than 60% of workers with a contract work in firms that are state/collectively owned, domestic private, or foreign/joint venture firms. The corresponding share for those without contracts is only 19%.

# **4 Results**

## **4.1 Intention-to-Treat Estimates**

As the information intervention was randomly assigned, we use an ‘assignment to treatment’ indicator to estimate the intention-to-treat (ITT) effect. Table 2 presents the OLS estimation of equation (1) in Panel A, and it is evident that, after controlling for base year health insurance and pension participation, the average effect of assignment to the information intervention is zero (model 1), and the effect does not change as base period individual controls and firm-level controls are added in models (2) and (3), respectively. As the RUMiC survey is sampled through workplaces, standard errors in all models are clustered at the workplace of the household head.

**Heterogeneity of Treatment with Contract Status.** This average zero impact, however, masks considerable heterogeneity in the effect of the information intervention on individuals with and without written employment contracts. To examine this difference, Panel B of Table 2 presents the OLS estimates of equation (2). The bottom line of the panel shows the treatment effect on individuals without contracts by adding the coefficient on treatment ( _β_ ) to that on the interaction between treatment and the indicator for not having a contract

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# FIGURE 9

ANNUAL UELOCITY OF CIRCULATION OF Ml <!-- Start of picture text --> 3 5 404 ....... . . . . . ... .I . , ... .. . . . .. . . .. ..<br>25 .<br>I s . . . . . . . . . . . . . . . . . . . . . . . . .<br>1960 1965 1970 1975 1980 1985<br><!-- End of picture text --> TABLE 9

# **ANNOAL MONEY DEMAND FUNCTION**

LV = aO + al.INF LV Ln(GDP/M1) V Annual Velocity of Circulation of MI. GDP= Annual Nominal GDP M1= Average of Monthly holdings of Nominal<sup>Ml.</sup> INF= Equivalent monthly inflation rate for<sup>the year.</sup> D77= Financial reform dummy (1 from<sup>1977-88, 0 otherwise)</sup> SAMPLE= 1960-1988 METHOD: Two-Stage Least Squares INSTRUMENTS: INF(-1) INF(-2) LV(-1) D77 LV = 1.9090 + 4.5484 INF + 0.67087<sup>D77</sup> (49.49) (5.83) (9.89) MA(1) = 0.925 (T-value=3.98) R2 ADJ.= 0.96 D-W.Stat.= 1.85 F= 206 33
Figure 12. Poverty and structural reforms <!-- Start of picture text --> A. Poverty rates, by strength of institutions<br>Percent of population Percent of total poor<br>40 Poverty Rate Share of total poor (RHS) 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Lowest Highest<br>Regulatory quality<br><!-- End of picture text --> <!-- Start of picture text --> B. Poverty rates, by financial inclusion<br>Percent of population Percent of total poor<br>40 Poverty Rate Share of total poor (RHS) 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Lowest Highest<br>Frequency of account ownership<br><!-- End of picture text --> <!-- Start of picture text --> C. Poverty rates, by Doing Business ranking D. Poverty rates, by trade openness<br>Percent of population Percent of total poor Percent of population Percent of total poor<br>40 Poverty Rate Share of total poor (RHS) 40 40 Poverty Rate Share of total poor (RHS) 40<br>30 30 30 30<br>20 20 20 20<br>10 10 10 10<br>0 0 0 0<br>Lowest Highest Lowest Highest<br>Ease of Doing Business Trade across borders<br><!-- End of picture text --> Source: World Bank Note: The poverty rate is an unweighted average in each group. Share of population is the cumulative total. Based on poverty data for 2015.

A “Highest” indicates quartile of EMDEs with the strongest regulatory quality (based on data for year with latest poverty data). “Lowest” indicates quartile of EMDEs with the weakest regulatory quality. The back data for regulatory quality has been taken from the Worldwide Governance Indicators. The data is for 2017.

B. “Highest” indicates quartile of EMDEs with the highest share of account ownership at a financial institution (greater than 59 percent) in 2017. “Lowest” indicates quartile of EMDEs with the lowest shares (less than 21 percent). India is excluded from the “Best” category.

C. “Highest” indicates quartile of EMDEs with the highest 2019 Ease of Doing Business score (above 67.5). “Lowest” indicates quartile of EMDEs with the lowest 2019 Ease of Doing Business score (below 51.6).

D. “Highest” indicates quartile of EMDEs with the highest 2019 Trade Across Borders score (above 78.1) in the Ease of Doing Business survey. “Lowest” indicates quartile of EMDEs with the lowest 2019 Trade Across Borders score (below 57.1).
and empirically.

Assuming exogenous education policies, the new literature on the brain drain asserts that high-skill migration entails bene...cial feedback e¤ects for the source countries. Authors such as Vidal (1998), Mountford (1999) and Stark and Wang (2002) argue that migration prospects may foster education investments in developing countries, thus making it possible for a brain drain to be bene...cial to the source country. Survey data and macroeconometric studies con...rm that possibility. Endogenizing education subsidies, our theoretical analysis reveals that a positive emigration can be desirable if, and only if, tax distortions are su¢ ciently high, i.e. if the local government is unable to decentralize the ...rst best solution with non distortive tax-transfers. Conversely, when tax distortions are low, impeding skilled emigration would be socially better. The optimal emigration rate is zero. Hence, resorting to the brain drain must be considered as a second-best policy option that re‡ects the inability of the government to costlessly use domestic instruments.

Some recent studies such as Beine et al. (2001, 2007a) provide empirical support for the bene...cial brain drain hypothesis, abstracting from various indirect e¤ects on skill premia, ...scal policies, etc. In particular, they disregard possible "crowding-out" ...scal e¤ects on public subsidies. Investigating the empirical relationship between education subsidies and migration prospects, we obtain a signi...cant and negative relationship in a cross-section of 108 countries. Skilled migration and public subsidies are empirical substitutes. We then revisit the country speci...c e¤ects of skilled migration on human capital by considering public subsidies as endogenous. Our counterfactual experiment consists in setting the skilled emigration rate to the unskilled rate. We then compute the hypothetical human capital change between 1990 and 2000. The endogeneity of public subsidies strongly reduces the relative number of losers, increases the size of the losses and reduces the size of the gains in other 25
population shares by age and education subgroups are computed and household sampling weights are rescaled according to the demographic and educational changes above (larger box in the middle of Figure 1). In a second step, the demographic changes will impact overall labor supply by age and skill groups. These changes are incorporated into the CGE model to simulate overall economic growth, growth in relative incomes by education groups and sector reallocation of labor (link between the middle and bottom rectangles). Finally, the results of the CGE are passed-on to the re-weighted household survey (bottom link in Figure 1).

**Figure 1 GIDD methodological framework**

<!-- Start of picture text --> Population Projection by Education Projection<br>Age Groups ( Semi- Exogenous )<br>( Exogenous )<br>Household Survey<br>( new sampling weights<br>by age and education )<br>CGE - LINKAGE<br>( Growth, New Wages,<br>Sectoral Reallocation )<br>Simulated Distribution<br><!-- End of picture text --> In reality these changes take place simultaneously, but in the GIDD’s simplified framework they are accommodated in a sequential fashion. In the first step, total population in each country is expanded until it reaches the World Bank’s projections for 2030. The structure of the population is also changed; for example, as fertility rates decrease and life expectancy increases, older age cohorts will become larger in many countries. To accommodate these changes in the survey data, larger weights have been assigned to older people than those assigned to younger individuals.<sup>3</sup> In the next step,

> 3 Actually weights are not changed for each single individual but for whole households. Therefore, in the example in the text, households whose heads are older are assigned larger weights than households with

5
**Figure C.  Changing Expenditure Shares in Percent of GDP**

<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Rural Subnational Gov't Total<br>1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br>percent GDP<br><!-- End of picture text --> Source: Calculated from Compendium of Local Fiscal Statistics.

While the TSS saved the Chinese fiscal system by reversing the fiscal decline, its impact on rural public finance was more adverse. Some salient trends are presented in Table 10 to show the changing status of rural public finance. From the mid-1980s, under decentralization both revenue and expenditure shares were increasing for the rural sector and the two were closely balanced. After the central government recaptured a majority share of revenues in the TSS reform in 1994, the trends were reversed. The county and township governments’ share of revenues fell sharply, from 30 percent in 1992 to only 17 and 18 percent in 2004 and 2005. On the expenditure side their share fell only moderately, from 31 percent in 1992 to to 28 percent in 1998 and 29 percent in 2002. The result of these divergent trends was the growth of a vertical fiscal gap for these local levels, to 8 percent in 1998 and 14 percent in 2005 (or, in GDP terms, to 3 percent).

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### Lagged dependent variables

Lagged dependent variables also exploit variation in outcomes of the same child observed over time. However, identifying assumption is different: to establish causal relationship between maternal work and child’s development in the lagged dependent variables framework, one needs to assume that maternal decision to work is as good as random, conditional on child’s past development outcomes (not unobserved time-invariant characteristics). This assumption is likely to be violated if maternal decisions to work also respond to unobserved environmental variables, which may also affect children’s outcomes such as employment of other household members.

Notably, fixed effects and lagged dependent variables can be thought of as bounding the causal effect of interest; and several studies included in this review include both FE and LDV. Consequently, we included studies that rely on lagged dependent variables when combined with child FE.

# 2. Samples

Our review covers 10 countries: 7 high income (Australia, Chile, Denmark, Italy, Norway, UK and US), and 3 lower-middle income (the Arab Republic of Egypt, India and Indonesia). Only four countries are nonWestern: Chile, Egypt, India and Indonesia. Table A1 presents the distribution of the number of papers by country and method.

#### <mark>Table A1 <title>?></mark>

|Country|Number of<br>||Number ofpape<br>|rs bymethod<br>||
|---|---|---|---|---|---|
||studies|IV|Child FE & LDV|Mother FE|Mixed methods|
|Australia|1||||1|
|Chile|1||||1|
|Denmark|1|1||||
|Egypt,Arab Rep.|1|1||||
|India|2|1|||1|
|Indonesia|1|1||||
|Italy|1|1||||
|Norway|1|||1||
|United Kingdom|3||1|1|1|
|United States|14|4|4|3|3|
|Total|26|9|5|5|7|

The papers rely on a wide range of data, including survey and administrative data. A significant share of surveys are nationally representative, some focus on low-income populations. Some studies focus on a specific subsample – due to methodological demands (e.g., children with younger siblings only). <mark>Table AX <table 1?></mark> presents the details for each study.

# 3. Scope of the measures

## Maternal employment

There was a broad spectrum of how maternal employment was operationalized. 6 studies used exclusively a binary variable indicating whether the mother is employed and 3 studies differentiate between parttime and full-time employment with full-time usually defined as more than 35 hours per week ~~.~~ 13
appear to be the end of the story.<sup>In fact, after the peak of</sup> 195% monthly inflation rate in July 1989,<sup>inflation goes back to</sup> the one-digit monthly level until December<sup>1989. Then a new</sup> hyperinflation starts, which reaches<sup>a peak of 95% in March 1990</sup> and apparently ends in April 1990 with<sup>a monthly rate ot "only"</sup> 11.5%. The monthly inflation rates since<sup>1982 can be seen in</sup> Table lb.

If one were to give a quick characterization<sup>of government</sup> action in Argentina it would be that<sup>government has</sup> systematically tended to increase<sup>spending and to run deficits.</sup> It is well known that governments<sup>do not need to run fiscal</sup> surpluses all of the time, and much<sup>less in the context of a</sup> growing economy. The fact of the matter<sup>is, however, that the</sup> Argentine economy has been stagnant in<sup>the last two decades and</sup> the government has run fiscal deficits for every<sup>year at least</sup> since 1961. In fact, the government has run<sup>a primary deficit</sup> (not including any interest payments) for every<sup>year in our</sup> sample dating back to 1961. This means<sup>that for every year of the</sup> last 28 years, after paying for the current<sup>and capital spending</sup> the government has not had any genuine<sup>resource left to service</sup> any interest on its debt (internal or external;<sup>this issue has</sup> been raised by Dornbusch and de Pablo<sup>(1987) and by</sup> Rodriguez(1989) in the context of explaining<sup>the fiscal nature of</sup> the country's apparent inability to serve<sup>her foreign debt that</sup> is mostly public). In onsequence the government<sup>has resorted</sup> systematically to issu-ing money and<sup>interest earning debt. This</sup> deficit policy resulted in a systematic<sup>tendency of the economy</sup> to run high inflation and high real interest rates<sup>as a</sup> consequence of the pressure in the credit<sup>markets of the</sup> incremental government borrowing.

For an economy that does not grow and faces<sup>a positive</sup> real rate of interest, running a positive<sup>primary deficit implies</sup> an ever growing stock of Public debt<sup>in relation to GDP. Of</sup> course, in an ex-post sense real government<sup>debt did not grow</sup> continuously because every once in a<sup>while the existing stock of</sup> debt has been melted down by outbursts<sup>of inflation in excess of</sup> nominal interest rates fueled by large<sup>devaluations in the face</sup> of foreign exchange crisis (on the relation<sup>between deficits and</sup> devaluations, captured by increases in<sup>the level or in the rate</sup> of change of the exchange rate, see Calvo(1981),<sup>Fernandez (1989)</sup> and Rodriguez(1978)) 3
# **7.3  Pathways through which maternal social participation might affect child nutritional status**

Identifying the pathways through which maternal social capital affects child nutritional status is a policy relevant question. There is a large literature examining how maternal education affects child health but little investigation of the pathways through which maternal social capital improves child nutritional status, apart from some theoretical speculations.

As discussed above, three main pathways might be identified: the support pathway, the information pathway and the empowerment pathway. Because of data limitations only the support pathways can be investigated for this empirical exercise. Specifically, the YL contains data about the support received in the past 12 months. Support is defined as “ _any emotional help, economic help or assistance_ ” received from the group the mother is a member of ( _internal support_ )<sup>36</sup> or more generically from family, friends, neighbours, community leaders, religious leaders, politicians, NGOs, government officials, or any other individuals in the community ( _external support_ ).<sup>37</sup> Being a member is the condition required to receive internal support; conversely membership is not required to receive external support.

_Table 7 Internal and external support received by member mothers._

||_Mean_<br>**N**|_Std. Dev._<br>**on-membe**|_N_<br>**r**|_Mean_<br>**M**|_Std. Dev. _<br>**ember**|_N_|**t-test**<br>_pvalue_|
|---|---|---|---|---|---|---|---|
|Received support (either<br>internal or external)|0.66|(0.012)|1,021|0.85|(0.018)|344|(0.000)|
|Received external support|0.66|(0.012)|1,021|0.78|(0.000)|317||
|**Who provided mothers**<br>**with support?**||||||||
|(a) Only external support|1|(0.000)|1021|0.36|(0.026)|125||
|(b) Only internal support|.|.|.|0.08|(0.015)|27||
|Note:Standard error in par<br>(c) Both internal and<br>external support|.<br>enthises,|.<br> T-test for|.<br>differen|0.56<br>ces in m|(0.070) <br> ean.|192||

Table 7 shows there are positive associations between maternal social capital and receiving 36 The original question is: “For each group the caregiver has been an active member of in the last 12 months: Establish if they received any emotional or economic help or assistance in knowing or doing things (advice/tips)”.

37 The original question is: “The caregiver has received any emotional or economic help or assistance in knowing or doing things (advice) from the people on the list in the last 12 months (family members, neighbours, friend who are not neighbours, community leaders, religious leaders, politician, government officials, charitable organization or others)”.

24
variable approach similar to Acemoglu and Restrepo (2017) and Dauth et al. (2017).

# **4.1 Instrumental Variable**

This approach instruments _ETRi,t_ with robots’ adoption in the same industry in other countries. The idea underlying the instrument is that the use of industrial robots is induced by improvements in technology (or a reduction of their price) increasing their profitability for adopters. As such, these are trends that are largely unrelated to the specific market conditions prevailing in Indonesian industries. Similarly to Acemoglu and Restrepo (2017), we focus on countries that are ahead Indonesia in terms of robots’ adoption. To that end we use data from OECD countries, and we first match IFR data with 2-digit industry employment figures from the OECD Structural Analysis Database (STAN).<sup>27</sup> Then for each 2-digit industry-year pairs we compute the number of imported robots per thousand workers averaged across OECD countries. To construct the instruments at various levels of aggregation, we simply replace the density of robot imports from Indonesia with this OECD average for each industry-year pair.

If technological trends drove robot adoption, we would expect that the industries with higher exposure of robots should be broadly the same across countries, even for countries with different levels of economic development. The strong correlation between 2007-2015 changes in _ETRi,t_ between Indonesia and OECD countries is consistent with the notion that Indonesia’s automation across industries is driven by technological factors (Figure 5).<sup>28</sup> An endogeneity concern relates to the possible relation between robots’ adoption,

> 27 We are not able to construct employment for Wood and furniture, and Installation and repairs industries. Those are available in IFR data but not in STAN, which does not report a sufficiently disaggregated breakdown of employment for these 2 industries. As a result, the number of available 2-digit industries drops to twelve.

> 28As the change for Motor vehicles and Plastic and rubbers is much larger than for the other industries, the figure uses a log-scale to ease readability. The log-scale allows to include only nine of the twelve available industries as Textile and Paper did not experience any robots’ adoption in Indonesia and the average change in robots per thousand workers was negative in Other transport equipment for the OECD. Figure A1 in the Appendix shows that the positive relationship holds also when using a normal scale, which allows to include Textile, Paper and Other transport equipment while it excludes Motor vehicles and Plastic and rubbers.

26
# **The Cases of Rice and Wheat**

The two food commodities that have received the most attention because of price surges are the key staples of wheat and rice. The length of their international price spikes around 1974 were broadly similar to those around 2008, but the height of the spike – particularly for rice – was greater in 1974. The recent price rises were more gradual except in the final months, so we consider an extra year in the lead-up to the 2008 spike.

Estimates of the Ti’s are available for all key rice and wheat countries, in the form of nominal assistance coefficients (NACs), from three sources. Anderson and Valenzuela (2008) provide them to 2004 for developing countries and to 2007 for high-income countries (summarized in Anderson 2009). They are similarly available for high-income countries for 2008 in OECD (2010). For developing countries, Anderson and Nelgen (2010b) provide estimates based on FAO and World Bank data on producer and border prices, respectively, for 2005 to 2008. The most-recent developing country estimates are less reliable than the NAC estimates in Anderson and Valenzuela (2008), for several reasons. One is that the coverage is not as extensive, because domestic prices are not available for some countries. Another is that the FAO’s producer prices and World Bank international prices are not always as reliable as previously-used domestic and border prices from national statistical agencies. The FAO producer prices in US current dollars (FAO 2010) were converted into an index set at 100 for 2004, and the 2004 US dollar prices in Anderson and Valenzuela (2008) updated using the changes in these indexes through 2008. Likewise, the Thailand 5 percent broken rice and Canadian wheat prices from the World Bank were converted to indexes set at 100 for 2004, and the 2004 border prices in Anderson and Valenzuela (2008) updated using changes in those indexes through 2008.

These NAC estimates are reported in Table 1 for the two upward price spike periods. For each of the regions shown, as well as for the world as a whole, the patterns are strikingly similar: falls in the NAC as the international price rose. The proportional changes in NACs in the first half of each spike differ across products and 9