rafmacalaba/datause-encoder-data huggingface dataset:
# **_Child Development Outcomes_**

In Figure 6, we present the estimated impact of the ECED project on six child outcomes. Each child outcome has estimates for four groups as in Figure 5 based on ECED enrollment at baseline and household wealth. For the domains of social competence, language and cognitive development, and executive function (card sorting), we see a greater impact of the ECED project on children who were not enrolled at baseline or those who are from poor households when compared to all four-year-olds. For example, the impact of the ECED project on social competence is about 0.2 standard deviations larger for children who never enrolled and are from poor households than the impact for all four-year-old children. Children who never enrolled or are from poor households also have significant and considerable improvements as a result of the ECED project in communication and general knowledge, language tasks, and the draw-a-house task. Figure 7 presents two domains of the SDQ: conduct problems and pro-social behavior. As we mentioned earlier, negative scores in these domains indicate fewer difficulties and therefore improvements in child development. The ECED project has little impact on pro-social behavior for all four-year-old children but it greatly reduces pro-social behavior problems of children from poor households (-0.215 standard deviations). The reduction in pro-social behavior problems of poor children who were not enrolled at baseline is even larger (-0.287 standard deviations).

# **_Corroborating Estimation Results Using Randomized Assignment of Batches 1 and 3_**

In Figure 8, 9 and 10 we present the estimated impact of the ECED project on enrollment and child development outcomes using the randomization used to assign villages to batch 1 and batch 3. These estimates corroborate our difference-in-difference findings. We only include noticeable and statistically significant findings.<sup>13</sup> As we mentioned earlier, we estimate the difference between batches 1 and 3 controlling for household, caregiver and child characteristics at baseline and follow-up. At baseline, batch 1 had implemented the ECED project for six months and batch 3 had not started their program yet. At follow-up, batch 1 had implemented the ECED project for about 20 months and batch 3 had implemented the ECED project for about nine months. As a result, the estimated difference between the two batches at baseline indicates the estimated impact of six months of the ECED project and the estimate difference between the two batches at follow-up indicates the estimated impact of nine months versus 20 months of

> 13 All other regression estimates are available upon request.

16
||http://www.hcdn.gov.ar/leyes/buscarNormasXNumLey.jsp?id_norma=40<br>118.|
|---|---|
|February<br>5, 2010|On February 5, 2010, the government passed implementing legislation<br>that put into effect the Local Investment Incentives Regime for<br>Motorcycles and Motorcycle Parts Manufacturing, originally established<br>in 2009 by Law 26,457 and Decree 1857/2009.<br>Under the regime, local producers of motorcycles and certain motorcycle<br>parts will receive two types of benefits: (a) reductions of duties levied on<br>imported inputs, ranging from 20 to 60 percent of current import duty<br>rates, and (b) tax credits on purchases of locally produced inputs.<br>To be eligible for the benefits, applicants have to pledge investments of at<br>least US$1 million in real estate, facilities, machinery, and development<br>of local suppliers. Furthermore, to continue to be eligible for the benefit,<br>they have to gradually reduce the foreign content of their output, with a<br>view of reaching a 30 percent ceiling in foreign content by 2015.<br>_Source:_Global Trade Alert 2010a.|
|May 2010|At the 15th Trans-Andes Encuentro Empresarial, a Chilean spokesperson<br>commented that while Argentina is a large market for food, Argentine<br>government policies have been creating difficulties regarding the sale of<br>imported food items. Managers of supermarket chains, supermarkets, and<br>other retail stores have received verbal communication from the secretary<br>of commerce that they should stop importing, among other items, canned<br>corn and tomatoes from Brazil, Swiss chocolates, Greek peaches, and nuts<br>and several Chilean products that are processed in the local market.<br>The secretary of commerce has instructed the National Food Institute,<br>which formerly issued certificates of free movement of goods, to redirect<br>all requests for such certificates to the Secretariat of Commerce.<br>_Source:_Boeninger 2010.|
|January<br>21, 2011|<br>In a presentation announcing the regime for promotion of modems and<br>tablets in Tierra del Fuego, the Minister of Industry, stated, “Companies’<br>interest in manufacturing in Tierra del Fuego clearly indicates that we are<br>on the path to a national electronics industry. We will continue to promote<br>this strategic sector. We aim to replace imports of high-technology goods<br>and generate new jobs.”|
||<br>_Source:_Ministry of Industry 2011a.|
|February<br>22, 2011|<br>The Minister of Industry, said that “the changing economic and political<br>paradigm that began in 2003 continues to ensure historical rates of growth<br>with social inclusion.” She reiterated the importance of continuing to<br>preserve the domestic market for domestic production. “The import<br>substitution we are encouraging is a cornerstone for the growth of our<br>economy ... so we have extended the list of products on which import<br>licensing is applied.”<br>|
||_Source:_Ministry of Industry 2011c.|
|February<br>25, 2011|<br>The Ministers of Economy and Industry met with 300 entrepreneurs from<br>all sectors of the country, noting that the government “applies trade<br>administration as a way to guide import substitution and generate more|

14
**Table 1: Mean Sample Characteristics**

|**Variable**|Total|Union<br>Subsample|Nonunion<br>Subsample|
|---|---|---|---|
|Years of Schooling|8.4|9.7|7.9|
|Experience (years)|18.0|18.7|17.7|
|Hours Worked per Week|44.5|42.2|_45.5_|
|**Education Level**||||
|Prinary|0.35|0.28|0.38|
|Secondary incomplete|0.29|0.28|0.30|
|Secondary complete|0.14|0.20|0.12|
|Tertiary|0.16|0.23|0.13|
|Industrial**Sectors**||||
|Extraction|0.02|0.05|0.01|
|Manufacturing|0.23|0.21|0.24|
|Utilities|0.01|0.02|0.01|
|Construction|0.10|0.04|0.12|
|Commerce|0.14|0.05|0.18|
|Transportation|0.05|0.06|0.04|
|Financiai Services|0.02|0.03|0.02|
|Services|0.42|0.54|0.37|
|_Municipio_Indigenous Percent|2.90|3.01|2.86|
|Nortiern State Residence|0.23|0.19|0.24|
|Male|0.67|0.65|0.67|
|Union Membership|0.30|1.00|0.00|
|Log of Hourly Earnings|7.85|8.07|7.76|
|Actual Hour!y Farnings_(pesos)_|3,522.60|4,076.63|3,298.56|
|Number of Observations|9,954|2,954|7,000|

Source: INEGI (1989).

7
Table 4: Robustness analysis: unobserved e¤ects

|Regressand: ln(import)|I|
|---|---|
|Harmonization on intra-regional trade|0.4561***|
|(HAR)|(0.010)|
|Harmonization on imports from the ROW|0.0515***|
|(HAR_M)|(0.005)|
|Harmonization on exports to the ROW|0.6046***|
|(HAR_E)|(0.005)|
|MRAs with rules of origin on intra-regional trade|2.4154***|
|(MRA_RO)|(0.031)|
|MRAs with rules of origin on imports from the ROW|-0.7087***|
|(MRA_RO_M)|(0.037)|
|MRAs with rules of origin on exports to the ROW|0.1738***|
|(MRA_RO_E)|(0.036)|
|MRAs without rules of origin on intra-regional trade|0.3197***|
|(MRA_NRO)|(0.074)|
|MRAs without rules of origin on imports from the ROW|0.2512***|
|(MRA_NRO_M)|(0.037)|
|MRAs without rules of origin on exports to the ROW|1.1430***|
|(MRA_NRO_E)|(0.037)|
|RTA on intra-regional trade|0.0539***|
|(RTA)|(0.019)|
|RTA on imports from the ROW|-1.2349***|
|(RTA_M)|(0.012)|
|RTA on exports to the ROW|-1.2540**|
|(RTA_E)|(0.012)|
|Time-variant bilateral e¤ect (PX)|11.2570***|
|Number of observations|4160352|
|Log likelihood|-7834125|

25
incorporate FSD. Schools created after 1998 can apply as long as there is a lack of schools in their region. Public schools can also receive subsidies from regional or municipal governments.

The program was not introduced randomly. The first schools to change to the FSD format were rural and smaller institutions that already had a single schedule. As such, increased investment in infrastructure was not required. Furthermore, resources were focused on schools with populations of students who were at a greater socioeconomic risk.

Table 1 describes the FSD implementation process between 1998 and 2011 in the country’s 13 administrative regions.<sup>21</sup> In 1998, the regions with the highest rates of incorporation were IX and XI, with a percentage of FSD schools of 73% and 79%, respectively. In 2011, the regions with the highest rates were IX regions which reached 88% and 92% enrollment, respectively, which still represented the highest rates in the country.<sup>22</sup> FSD schools in the Metropolitan Region were 17% in 1998, which was the lowest rate in the country. Only 7% of the schools in the Metropolitan Region are of the rural type, which was first addressed by the reform. In contrast, 68% and 38% of the schools in the IX and XI regions are rural.<sup>23</sup> Finally, only six municipalities had not incorporated FSD by 1998. The last to

> 21. Chile was divided into 13 regions, which are the country's first-level administrative division. Each region is headed by an intendant, appointed by the President, and an indirectly-elected body known as regional board. Regions are divided into provinces (second-level administrative division), each headed by a governor (gobernador), appointed by the President. There are 54 provinces, in total. Provinces are further divided into municipalities (third and lowest level administrative division), which are governed by mayor.

> 22. This percentage is based on total enrollment in the region.

> 23. Ministry of Education 1998–2011.

11
16 preannounced daily devaluations at a rate which was systematically below the domestic-foreign inflation differential. Below I argue that the decline in private saving was in fact induced by such policy-induced real peso appreciation.

Table 1. Sources of the Current Account Deterioration Between 1983-87 and 1991 (in percentage points of GDP)

1. Increase in Public Saving

2. Increase in Private Saving 3. Net Increase in Domestic Saving (=1+2) 4. Net Increase in Investment 5. Change in Current Account Balance (=3-4) 5.4 -8.8 -3.4 3.5 -6.9 Notes. 1/ Aggregate investment in constant 1980 pesos. 3/ Public saving is defined as the sun of the inflation-adjusted economic baLance and public investment. 4/ Private saving is obtained residuaLly. 5/ 1991 figures based on the fotlowing estimates: a current account deficit equivalen **t** o 4.5% of GDP, a 12.2 rate of investment growth, a 4% rate of GDP growth, an operational fiscal surplus equivalen **t** o 3% GDP and a 5% public investment-GDP ratio.

Sources. "Indicadores", Banco de Mexico 1991 and own estimates.

The real peso appreciation has been a key factor behind the deterioration of Mexico's non-oil trade balance (regression 1 in Table 2); between 1987 and 1991 the current account balance turned from US$4 billion surplus into a US$11-12 billion deficit. From a macroeconomic point of view this was accounted for both by the favorable effect of a more appreciated peso on investment and by the adverse effect of a more appreciated peso on private saving (regressions 2 and 3 in Table 2). There is evidence that the positive impact on private investment of a real peso appreciation stems, by and large, from the implied reduction in the cost of capital goods (van Wijnbergen (1991) and Warner (1991)). Less is known about the negative impact of the real peso appreciation on private saving although there is empirical evidence for other countries that a real exchange rate appreciation (devaluation) is expansionary (contractionary), e.g., Edwards (1986). One possible interpretation is Diaz-Alejandro's (1965) hypothesis that devaluations are contractionary because it redistributes income from low savers to high savers. Another possibility is that an anticipated real peso appreciation leads to lower domestic real interest rates which, in turn, discourage saving. 5 S/ However, the impact of real interest rates on private saving was found to be statistically insignificant (see note of Table 2).
capacity is required. We test the difference in the strength of the population effect between high and low capacity dimensions in Column 3, where it is shown to be significant. In Columns 4 and 5, we replace the population variable with a set of dummies for small, medium and large countries (the latter are the omitted dummy). The difference in PEFA performance between high and low capacity indicators is most pronounced for small countries.

**Table 6: Analysis of PEFA Dimensions by Capacity Requirement**

||(1)<br>Low<br>capacity|(2)<br>High<br>capacity|(3)<br>All|(4)<br>Low capacity|(5)<br>High capacity|
|---|---|---|---|---|---|
|LGDPit (log income)|0.612***|0.697***|0.632***|0.404***|0.463***|
||_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.001)_|
|LPOPit (log population)|0.218***|0.285***|0.223***|||
||_(0.000)_|_(0.000)_|_(0.000)_|||
|AIDit|0.785*|1.318*|0.911**|-0.413|-0.136|
||_(0.072)_|_(0.063)_|_(0.050)_|_(0.365)_|_(0.797)_|
|HIGHCAPj|||-0.837**<br>_(0.020)_|||
|HIGHCAPj*LPOPit|||0.044*<br>_(0.055)_|||
|SMALLi||||-0.777***|-1.240***|
|||||_(0.003)_|_(0.000)_|
|MEDIUMi||||-0.478***|-0.827***|
|||||_(0.001)_|_(0.000)_|
|Time trend|YES|YES|YES|YES|YES|
|Regional dummies|YES|YES|YES|YES|YES|
|Number of observations|7,862|2,397|10,259|7,862|2,397|
|Number of clusters|151|151|151|151|151|
|Adj. R-Squared|0.023|0.031|0.025|0.021|0.031|

Notes: Dependent variable is discrete PEFA score. All regressions use Tobit estimation.

*** p<0.01, ** p<0.05, * p<0.1.  p-values in parentheses are based on robust and clustered standard errors. Constant is included but not reported.

These results suggest that smaller countries perform more poorly than other countries in their average PEFA scores partly because of a particularly strong “population penalty” against PEFA dimension that involve the application of technical capacity. While smaller populations seem to constrain performance against all PEFA dimensions, shortages of trained and skilled staff bite against those dimensions requiring the application of higher levels of capacity.

We find this pattern of particularly poor performance against dimensions involving higher levels of capacity is reproduced for small PICs. The lagging performance against high capacity dimensions for PICs on average is more pronounced than that for the average low or lower19
# **Comprehensive Wealth, Intangible Capital, and Development**

# **1. Introduction**

It has been understood since at least the time of Irving Fisher (Fisher 1906) that income is the return on wealth. But if we scale this idea up to the level of the national economy, we arrive at a puzzle. If we measure wealth only as produced capital, we see from the national balance sheet accounts of countries such as Canada that wealth is only a small multiple of gross national income, implying unrealistically high rates of return on wealth.

Table 1 shows Canadian figures for 2009. The value of produced capital is less than three times GNI, while net worth (the sum of produced capital, commercial land and net financial assets) is a bit less than four times GNI – the implicit rates of return on wealth are correspondingly high, 35.9% and 25.4%, respectively. Canadians appear to be very productive.

The ‘solution’ to this puzzle, of course, is that the national balance sheets of the system of national accounts (SNA) exclude many intangible<sup>1</sup> asset values, such as human capital and the value of social / institutional capital. Moreover, the Canadian balance sheets highlighted in Table 1 exclude the value of commercial natural resources.<sup>2</sup> Since a ‘normal’ rate of return on assets should be on the order of 5%, a comprehensive measure of national wealth should be on the order of 20 times national income.

Hamilton and Hartwick (2005) show how to estimate a comprehensive measure of national wealth for a competitive economy with constant returns to scale. For production _F_  _F_ ( _K_ , _L_ , _R_ ) with production factors _K_ (produced capital), labor _L_ , natural resource flow _R_ , and interest rate _r_ (equal to the marginal product of capital), comprehensive wealth is given by That is, comprehensive wealth can be measured either by adding up asset values _K_ , _H_ (human capital) and _S_ (natural resource stock)<sup>3</sup> or by measuring the present value of consumption _C_ along the competitive development path. The intuition behind expression (1) is that in the long run a country must consume within its possibilities, which are given by the sum of all its assets.

> 1 The SNA has precise definitions for intangible fixed and intangible non-produced assets, which include items such as mineral exploration expenditures and the value of patents. In this paper we use the term ‘intangible’ to include all non-physical, non-financial assets.

> 2 While SNA 1993 (United Nations 1993) requires the inclusion of the value of commercial natural resources in the balance sheet accounts, to date only Australia has published such accounts.

> 3 Note that _L_ and _R_ are flows of inputs, measured in worker-hours or barrels of oil, whereas _H_ and _S_ are asset values measured in dollars.

2
# Table **Al**

Regression Results: Whole Fund

> , **_0i_** I **_Rj,f + 2R,,+_** _P3RLA,_ **_+_** _4_<sup>_R,,_</sup><sup>**_+_**</sup> _fl5Rusl_ **_+i-,j_**

|**IndependentVariables**|**_All_**Sample|Non-Crisis|Crisis|
|---|---|---|---|
|**OwnReturn****_(a)_**<br>T-statistic|0.0021***<br>_5.34_|**0.0029*****<br>4.04|**_0.00150**_**<br>**2.76**|
|Own ReturnLagged**(3)**|**0.0029*****|0.00420**|0.0002|
|T-statistic|**3.15**|**_5.501_**|**0.138**|
|Latin AmericaReturn**(3)**|**0.0035*****|0.0021|0.0041***|
|T-statistic|**3.20**|1.49|**2.62**|
|LocalIndexReturn(04)|**0.0000**|**-0.0003**|**_0.0006_**|
|T-statistic|**-0.01**|**-032**|0.44|
|**USReturn**(a)<br>T-statistic|**-0.0065*****<br>-6.24|-0.0041*<br>**_-1.95_**|**-0.0096*****<br>**-4.54**|
|_Constant_|-0.0048|**-0.0086**|**0.0026**|
|T-statistic|-024|**_-030_**|**0.05**|
|Observations|4,842|**3,223**|**1,619**|
|AdjustedR-squared|**_0.05_**|**0.03**|**0.06**|

These results are "Whole Fund" **in** that they include no control for investor redemption effects as in equation **(7).** T-statistics are corrected for heteroskedasticity across funds. Full sample: April **1993** to January **1999. The** crisis portion of the sample is December 1994-June **1995,** July 1997-March **1998,** August 1998-October **1998,** and January **1999.** The non-crisis portion is the rest of the **sample.** For robustness, results in each **cell** are based only on observations within three standard deviations of the mean.

- Statistically Significant at the 10-percent level

** Statistically Significant at the 5-percent level

*** Statistically Significant **at** the 1-percent level

**32**
two data sets, i.e. the US IO1997 table with the EU-RoO index by Cadot et al. (2005). Using these databases, for each intermediate good we create two measures: a simple and a weighted average of the RoO index faced by the associated final goods. We call these measures the IO-RoO Index and the weighted IO-RoO Index respectively. For each intermediate, the simple average is constructed by considering all final goods to which this intermediate contributes. This measure, called IO-RoO, is thus computed as follows: where _j_ and _i_ refer to the intermediate and final good respectively, and _RoOij_ is the RoO index at the level of the final good, _i_ , associated to each intermediate, _j_ . In equation (1) we sum the RoO index, _RoOij_ , across all final goods into which an intermediate _j_ enters, and we divide it by the number of these IO relations, _ni_ . The drawback associated to our measure in equation (1) is that it gives all products using a specific intermediate the same importance. Therefore, to account for the heterogeneity in the use of the intermediate, we also construct a weighted measure where we exploit the direct input-output coefficient requirement which assigns a higher weight to final goods served most prominently by an intermediate good. This weighted measure, called _weighted_ IO-RoO, is then computed as: where _drij_ refers to the direct requirement coefficient.<sup>6</sup> This _weighted_ IO-RoO Index is our preferred measure. We believe that it better reflects the production process common across countries and is less tied to US specificity, since it provides a more accurate proxy of the production technology.<sup>7</sup> Table 1 presents descriptive statistics for the I-O relations according to the restrictiveness of the RoO index faced by final goods according to the sector of the intermediate goods and the resulting sector-level averages of our IO-RoO Indexes. Columns 1 to 7 present the share of intermediate goods hit by these sourcing restrictions.<sup>8</sup> For example, 5.9% of chemical intermediate goods contributes to a final good that faces a RoO Index = 1, with the vast majority of chemicals, 41%, contributing to the production process of a final good facing a RoO Index = 5. Finally, columns 9 and 10 of Table 1 provide the resulting simple and weighted IO-RoO Indexes when looking at the sector of the intermediate good using the 2-digit level of the HS.

To further stress the importance of considering the synthetic index rather than counting the IO relations, Tables 10 and 11 in Appendix A show descriptive statistics at the HS2 on the number of IO relationships and on the average RoO Index faced by intermediates entering

> 6The direct requirement matrix is taken from Conconi et al. (2018).

> 7A simple average will provide equal weights to final goods served by an intermediate good, even if an intermediate good may, for example, be used only for 0.1% of a final good.

> 8The total amount of input-output pairs (amount of intermediates entering in final goods) contained in our data set is 9 _,_ 499 _,_ 930.

9