rafmacalaba/datause-encoder-data huggingface dataset:
where **b** ={e,q*,c*,k} and **E** is an _m-_ by- _a_ vector of uniformly distributed random numbers that determine the realizations of the shocks that move individuals in and out of the social security ( **E** is fix across simulations). The vector **C** b can then be used to compute the probability that at age _a_ , an individual characterized by **M** d(.|  ,ξ) would be in a given state _e_ . From **C** b one can also derive the distribution of other output variables of interest. We keep track of six: (i) the present value of capital accumulations at age 55; (ii) contribution densities; (iii) the average value of the pension at retirement; (iv) the present value of contributory pensions paid; (iv) the present value of explicit subsidies paid through the pension system; (v) the present value of unemployment insurance benefits; and (vi) the present value of FGTS payments.

# **4  Strategy to sample the joint distribution of model parameters**

There are various ways to estimate the joint distribution of model parameters, which as usual are constrained by the type of data available and computational power. The ideal, in terms of data, would be to use individual records on career histories (see, for instance, Jiménez-Martin and Sánchez-Martín, 2006). For each individual in the sample (which determines ξ) and for a given  , **C** b(a,m|Md(.|  ,ξ), **Е** ) would then be used to calculate the likelihood of observing his/her career path (taking wages as given) and the distribution of assets at a given age(s). The vector  would be estimated to maximize the likelihood of the data set. The vector  could also be estimated for different subgroups characterized, for instance, by level of education and gender. Unfortunately, at the time of writing, individual records are still not available. But in addition, estimating in this way would be computationally very intensive. Indeed, when all the policies are “on” solving the model for a given  and ξ takes around 2.5 hours.<sup>9</sup> Furthermore, we are not interested in a “point estimate” of  but rather on a joint distribution that allows us to explore policy impacts across a large range of possible behaviors. Otherwise, one would be assuming that preferences are more or less the same across individuals (and that preferences on various dimensions are independent) and then addressing a limited range of uncertainty (on this point see Pizer, 1996).

In this first application we have opted instead for a Bayesian method to sample the ex-post distribution of model parameters. In the absence of individual records we use a pseudo panel of age-cohorts derived from the _PNAD_ household survey to construct a targeted distribution by state

> 9 We work with a server with 8 processors that operate in parallel.  So, individuals could be arranged in 8 groups according to their life-time earnings (which is capture by ξ).  In this case, each iteration would take around 2.5 hours.

13
Finally, regarding inflation, Nicaragua and Costa Rica followed different paths (Figure 2c). For six consecutive years, from 1976 to 1982, Costa Rica faced an increase in its annual inflation rate, going in that period from 3% to 90%. Nicaragua, on the other hand, although it experienced a major surge in 1978, during the onset of the Revolution, experienced for most of the period between 1973 and 1983 declines in its inflation.

<!-- Start of picture text --> Figure 2a. Nicaragua & Costa Rica ‐ Government consumption<br>20<br>16<br>12<br>8<br>4<br>0<br>1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983<br>‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br>Nicaragua Costa Rica Onset of conflict<br>Figure 2b. Nicaragua & Costa Rica ‐ Public debt<br>250<br>200<br>150<br>100<br>50<br>0<br>1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983<br>‐5 ‐4 ‐3 ‐2 ‐1 0 1 2 3 4 5<br>Nicaragua Costa Rica Onset of conflict<br>(as % of GDP, in PPPs)<br>Share of Gov. Consumption<br>Public debt (as % of GDP)<br><!-- End of picture text --> 9
Groh, Matthew, David McKenzie, Nour Shammout, and Tara Vishwanath (2015) “Testing the importance of search frictions and matching through a randomized experiment in Jordan”, _IZA Journal of Labor Economics_ , 4:7, 2015 Hardy, Morgan and Jamie McCasland (2015) “Are Small Firms Labor Constrained? Experimental Evidence from Ghana.” Mimeo.

Imbens, Guido and Donald Rubin (2015) _Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction._ Cambridge University Press, New York, NY.

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Levinsohn, James, Neil Rankin, Gareth Roberts. and Volker Schöer (2014) “Wage subsidies to address youth unemployment in South Africa: Evidence from a randomised control trial”, Mimeo.

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30
to the total benefit, as yielded by the model, will then yield an estimate of the total benefit that has a substantial empirical anchoring.

The object of this paper is to estimate the _relative_ size of the contribution of reduced transport costs, in connection with the all-India rural roads program known as _Pradhan Mantri Gram Sadak Yojana_ (PMGSY), a very large scheme that will eventually cover some 170,000 habitations, take at least 20 years to implement and cost in excess of US$40 billion (World Bank, 2010). The unified framework employed for this purpose is the overlapping generations (OLG) model of Bell, Bruhns and Gersbach (2006), substantially extended so as to cover transport costs and morbidity. Important elements of the empirical basis for this application are supplied by an analysis of a survey of – – some 240 households drawn from 30 villages lying in the upland and poor region of Orissa (Bell and van Dillen, 2012) and two large surveys in Bangladesh (Khandker _et al_ ., 2009). There is also an indirect check, in the form of completely independent estimates of the so-called value of a statistical life provided by Lui _et al._ (1997) and Simon _et al._ (1999).

The plan of the paper is as follows. The model is set out and analyzed in Section 2, the essential difficulty with valuing the benefits arising from effects other than those of lower transport costs being addressed in Section 2.3. The numerical set-up follows in Section 3, which is divided up into subsections dealing with functional forms, parameter values and calibration under perfect foresight. This is the basis for the exact welfare analysis in Section 4, treating in sequence the benchmark of ‘no-road’, the world with the road and the contribution of lower transport costs to the whole resulting benefit. The conclusions are drawn together in Section 5.

# **2 The Model**

The basis is the model of Bell, Bruhns and Gersbach (2006), which deals with human capital formation and growth when there is premature adult mortality. To summarize, an extended family comprises three overlapping generations, with all surviving adults caring for all related children in each period, which stretches over a generation.<sup>1</sup> The young adults are assumed to decide how current resources are to be allocated between consumption and the children’s education. The level of current resources available to

> 1This arrangement is admittedly a rather idealized description of the social structure even in Kenya, let alone in rural India, but the pooling of the risks of premature mortality among the adults greatly simplifies the analysis.

3
industries to ensure maximum compatibility across the two datasets. Around 8.5 percent of the plants in our sample switch industry over the period of analysis. To avoid potential endogeneity of industry choice to robots’ adoption, we drop the observations after a plant switches industry. However, we obtain very similar results if we do not drop such observations, or adopt alternative strategies.<sup>10</sup> We keep only plant-year cells with non-missing observations for all the dependent variables involved in the analysis. Our final plant level (unbalanced) panel includes 22,288 plants operating in thirteen 2-digit manufacturing industries between 2008 and 2015, for a total of 55,417 observations.<sup>11</sup> Table 2 presents summary statistics for the variables used in the analysis.

# **2.4 Empirical Specification**

We use equation (1) for the plant level analysis using the following model: where _Yf,i,t_ is an outcome of plant _f_ in industry _i_ at time _t_ ; _ηf_ are plant fixed effects; _ui,t_ are industry-year effects defined using the same classification as IFR robots’ import data; _If_ is a plant-specific innovation intensity index (measured in the 2006 census year); and _θt_ are time effects.

The plant fixed effects should absorb the impact of unobserved time-invariant characteristics correlated with the available opportunities for automation i.e. plants’ 2006 share of secondary education and the outcome variables. That may be the case for instance as foreign owned plants are usually more productive and have a larger share of routine labor.

> 10The first alternative strategy is assigning robots to plants based on the industry in which it operates the year of the first available information. The second strategy is dropping altogether all switching plants.

> 11In principle, we could run the plant level analysis starting in 2007. However, representative data by regency and industry, which are discussed in Section 4.3, are only available from 2008. Therefore, to be consistent across levels of analysis, we start from 2008. Plant level results for the period 2007-2015 are very similar, which is not surprising given that robots’ adoption started to take off in 2009. This is discussed in Section **??** .

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# 2012; Hyde and Marinov, 2014; Borzyskowski, 2013).

More generally, a number of political scientists argue that bad governance, abuse of power, and grievances stemming from political suppression are the principal causes of civil conflict (Schock, 1966; Hegre et al., 2001; Henderson and Singer, 2000; Tucker, 2007).<sup>1</sup> While there is strong evidence of a positive association between elections and civil conflict (Weidmann and Callen, 2013), this association could potentially be due to cultural, historical, or institutional factors. Nevertheless, international donors spend substantial amounts on election monitoring and related programs in an effort to dampen civil conflict and its attendant ills (Kelley, 2008).

Understanding whether and how elections can lead to violence is thus an important issue relevant to development policy. Civil conflict is a major impediment to development and poverty-reduction (World Bank, 2012). Its effects include reductions in economic growth (Abadie and Gardeazabal, 2003), educational attainment (Leon, 2012), height-for-age Z- scores (Akresh et al., 2012), and birth weight (Mansour and Rees, 2012). According to the World Bank (2012), “[p]overty reduction in countries affected by major violence is on average nearly a percentage point slower per year than in countries not affected by violence” (p. 59). The enormous potential gains from ending or preventing civil conflict have led development researchers and practitioners to renew their focus on better understanding its causes and developing prescriptions for effectively addressing it.<sup>2</sup> This study examines whether elections narrowly won by incumbents are associated with post-election violence in an existing civil conflict between government security forces and insurgents.<sup>3</sup> The analysis is based on data from the 2007 Philippine mayoral elections, the

> 1For instance, Tucker (2007) argued that election fraud galvanized the public, triggered protests, and eventually led to the so-called “colored revolutions” in Eastern Europe.

> 2See Blattman and Miguel (2010) for a review of the extensive literature on civil conflict.

> 3The fact that this study examines organized violence in an already existing conflict sets it apart from most previous papers in the literature on the relationship between electoral manipulation and violence, which tend to focus on less organized forms of violence such as riots and protests.

2
alike. Furthermore, while ODA financing provides annual financing estimates, some financing will be capital expenditures with long term benefits. As such, the benefits of ODA allocations may be realized over many years, but categorized as financing in just one year.

# (ii)

education, primary data collection was conducted for this report. This was achieved through extensive engagement with government, non-government, and international organizations working in the 25 largest refugee hosting LICs and MICs. These efforts yielded estimates on host government financing for refugee education in 20 of the largest 25 hosting countries, which combined hosted 77 percent of all school-age refugees in LICs and MICs in 2021. Given the deficiencies in the recording of such information at country level, as outlined in the methodology, assumptions were required to estimate host government financing for the majority of estimates produced. To ensure the validity of these estimates, officials from international organizations, host country governments, and/or non-government organizations (NGOs) either verified or worked with the authors to produce this studies estimates.

examined, there is little commonality in the methods used to estimate host government financing for refugee education by country. Rather, approaches were adapted depending on data availability.

# (iii)

OECD Creditor Reporting System (CRS), the grant aggregator database _Candid_ , and a survey of International Education Funders Group (IEFG) members conducted for this paper. The OECD’s CRS forms the basis of the accompanying dataset’s philanthropic financing data. This information is then supplemented by additional project data recorded in the grant aggregator database _Candid,_ and primary data collected through the September 2023 World Bank and IEFG survey of IEFG members on refugee education (IEFG and World Bank, 2023). These data were then triangulated to produce estimates of the philanthropic sector’s financing for refugee education in LICs and MICs. While this paper uses the latest available information to estimate philanthropic financing for refugee education, given the lack of transparency in this sector, these figures are likely to underestimate the extent of philanthropic financing for refugee education.

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Data on household consumption and detailed price data for the COICOP two-digit group for 19 countries in EMDE ECA is our primary sample of analysis.<sup>6</sup> This sample is complemented by data on 25 countries in the European Union and European Economic Area.<sup>7</sup> For these countries, we have detailed price data and aggregate household consumption statistics by quintile of the consumption distribution.

# **5 Inflation dispersion and consumption patterns in ECA countries**

Differences along two dimensions explain differences in COL indexes across households: variation in prices across goods and services and variation in consumption patterns across households. In periods of low inflation, adjustment of prices of individual goods and services occurs infrequently; the dispersion of relative prices over time is, therefore, low. When inflation increases, however, price changes become more frequent, and the relative price dispersion expands over time <u>(Alvarez et al., 2019). Median annual inflation in Europe jumped from 2 percent in</u> January 2021 to around 12 percent in October 2022 (Figure 1) – values the region had not seen in more than two decades. This high inflation could lead to larger price dispersion in the region.

# _5.1 Heterogeneity of inflation across countries_

A simple way to measure price dispersion across the 12 broad COICOP groups is to calculate the root mean square deviation (RMSD) in price changes with respect to the mean.<sup>8</sup> <u>Figure 2 presents</u> the RMSD for the 47 countries from which the data is available and where price changes correspond to the 12-month variation by December 2022.

The country with the widest dispersion in price changes across consumption groups is the Netherlands, where the RMSD in price changes was almost 22 percentage points in 2022, but this was driven by a substantial increase in the education category, which differed from a more

> 6 The countries are Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Georgia, Kazakhstan, Kosovo, the Kyrgyz Republic, Montenegro, Moldova, North Macedonia, Poland, Romania, Serbia, Türkiye, and Uzbekistan.

> 7 The countries are Austria, Belgium, Cyprus, Czechia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Norway, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom.

> consumption component, ∆p̅̅̅̅ is the mean change in prices for all consumption components, and _m_ is the number of components. The change in prices is measured as the percentage point variation in prices between a given month and the same month the previous year.

9
and Measurement, which are part of the Instruments field of technology. On the other hand, there is no Russian comparative advantage in chemistry sub-fields such as Micro-structural and Nano-technology or Furniture, Games and Consumer Goods. As well, there is no RPA in Instruments sub-fields such as Optics and Control. Finally, none of the 8 sub-fields of Electrical engineering Russia show a RPA. However some sub-fields of Electrical engineering are part of government priorities such as Computer Technology, Telecommunications and Digital Communications. (The complete RPA/RTA is in Annex II.)

**Figure 5: Russia’s RTA in Sub-fields of Technology, 2002–07**

<!-- Start of picture text --> 7 6.3<br>6<br>5<br>4<br>3.2<br>3<br>2<br>1<br>2.4 2.2 2.1 1.9 1.9 1.7 1.6 1.5 1.4 1.4 1.3 1.2 1.2 1.2 1.0 1.0 0.9 0.8 0.8 0.8 0.6 0.5 0.4 0.4 0.3 0.3 0.2<br>0<br><!-- End of picture text --> Source: Authors’ elaboration using WIPO 2011 data (http://www.wipo.int/ipstats/en/statistics/patents/).

Contrary to more advanced economies where the business sector plays a leading role, the bulk of R&D expenditures in Russia is funded by the public sector (66.5 percent in 2009) (Figure 6). This share has continuously increased in the last few years, while that of both private and foreign sources has decreased to 27 percent and 6 percent respectively, probably as a result of the global financial crisis. It is estimated that the number of small, innovative enterprises fell by around half. Many of them were working under contract to large firms that had outsourced R&D activities and cut their R&D spending in the wake of the recession (Gaidar Institute 2010).

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38

**Table 4b**

# **Base regression**

_(measure of current account reversals: ED5)_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||probit|probit|probit|probit|linear|linear|linear|
|CA<br>RES|-8.910<br>[0.773]***<br>0.304|-11.592<br>[1.040]***<br>-1.107|-11.621<br>[1.095]***<br>-0.947|-11.594<br>[1.074]***<br>-1.014|-2.028<br>[0.147]***<br>-0.074|-2.034<br>[0.147]***<br>-0.219|-2.036<br>[0.149]***<br>-0.216|
|EXTD|[0.486]<br>-0.028|[0.872]<br>0.303|[0.844]<br>0.291|[0.893]<br>0.228|[0.130]<br>0.056|[0.208]<br>0.055|[0.208]<br>0.063|
||[0.083]|[0.135]**|[0.150]*|[0.162]|[0.028]*|[0.028]*|[0.044]|
|SHORTD|-0.095|0.108|0.321|0.427|-0.055|-0.054|-0.054|
||[0.446]|[0.631]|[0.669]|[0.703]|[0.090]|[0.090]|[0.090]|
|CREGRO|0.006|0.010|0.014|0.013|0.002|0.002|0.002|
|PPPGDP|[0.016]<br>0.056|[0.016]<br>0.158|[0.017]<br>0.151|[0.018]<br>0.113|[0.003]<br>0.011|[0.003]<br>0.009|[0.003]<br>0.010|
||[0.019]***|[0.091]*|[0.104]|[0.114]|[0.016]|[0.016]|[0.016]|
|OPEN||||0.513|0.102|0.116|0.116|
|||||[0.467]|[0.067]|[0.070]*|[0.070]*|
|OT||||0.431|0.019|0.026|0.020|
|||||[1.406]|[0.276]|[0.275]|[0.277]|
|CONCD||||-0.165|-0.045|-0.041|-0.039|
|||||[0.662]|[0.082]|[0.082]|[0.082]|
|RES^2<br>EXTD^2||||||0.196<br>[0.235]|0.196<br>[0.235]<br>-0.002<br>[0.009]|
|Country|NO|YES|YES|YES|YES|YES|YES|
|fixed effects||||||||
|Year|NO|NO|YES|YES|YES|YES|YES|
|dummies||||||||
|Observation|1904|1904|1904|1890|1890|1890|1890|
|s||||||||
|R-squared|0.19|0.26|0.29|0.29|0.26|0.26|0.26|

_Notes_ : the dependent variable (ED5) takes the value 1 if a reduction of a current account deficit of at least 5 percentage points of GDP occurs at time t, and zero otherwise. Robust standard errors in brackets. All explanatory variables are described in Table A1 and lagged one period. For the probit models we report the pseudo – R<sup>2</sup> . * significant at 10%; ** significant at 5%; *** significant at 1%
Table 5: Analysis of classroom quality, teacher, and structural characteristics on children’s social <u>competence</u>

||no|DV: Social|competence|(Raw score)<br>w/ teacher||
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.150***|0.096*|0.083|0.070|0.111*|
||(0.051)|(0.053)|(0.052)|(0.052)|(0.063)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||0.825**<br>(0.335)|0.826**<br>(0.336)|0.808**<br>(0.340)|
|% of teachers w/ post secondary edu.|||1.103***<br>(0.347)|1.086***<br>(0.351)|1.073***<br>(0.356)|
|Mean years of teaching|||-0.005|-0.006|-0.007|
||||(0.009)|(0.009)|(0.009)|
|% of teachers w/ no ECED experience|||0.089<br>(0.127)|0.067<br>(0.126)|0.055<br>(0.126)|
|% of teachers w/ no training|||-0.125<br>(0.155)|-0.110<br>(0.156)|-0.111<br>(0.156)|
|**Structural characteristics**||||||
|Student-to-staff ratio||||0.001<br>(0.009)|0.002<br>(0.009)|
|Hours of operation per week||||0.028*<br>(0.016)|0.028*<br>(0.015)|
|Constant|6.317***|3.084***|2.347***|2.257***|2.188***|
||(0.154)|(0.420)|(0.540)|(0.556)|(0.565)|
|Observations|7,957|7,957|7,957|7,957|7,957|
|R-squared|0.007|0.134|0.141|0.144|0.141|
|No. ofclusters (centers)|565|565|565|565|565|
|First-stage F-statistic of excluded<br>instrument|||||646.386|
|**Control included:**||||||
|Child characteristics|N|Y|Y|Y|Y|
|Kindergarten dummy|N|Y|Y|Y|Y|
|District dummy|N|Y|Y|Y|Y|

Note: Robust standard errors clustered at the center level in parentheses. Child characteristics include age, agesquared, gender, whether child has special needs (reported by teacher), and mother's highest education level. Kindergarten dummy is 1 for kindergartens or Islamic kindergartens and 0 for non-project playgroups or project playgroups. District dummies are included for the 8 districts where centers and children are located. Models 1-4 are OLS regressions. Model 5 is a two-stage least squares model to correct for measurement error in the ECERS-R. The F-statistic of the first stage is reported.

* p<0.1, ** p<0.05, *** p<0.01

22