rafmacalaba/datause-encoder-data huggingface dataset:
zi = (zi1; zi2; :::; ziT ); one obtains the density of (yi1; yi2; :::; yiT ) given the initial conditions, yi0; and zi, by integrating out the ci. We refer to the relationship between the observed exogenous covariates and the unobserved heterogeneity in the ...rst method as one of “pure” random e¤ects because we assume ci to be independent of zi and yi0. While this method may provide a way to obtain consistent estimates of the model parameters, nonrandomness of the initial conditions requires a very strong and often implausible assumption of independence between the initial conditions and the unobserved e¤ects.

A second parametric approach involves treating the initial conditions as random and specifying the density for yi0 given (zi; ci). With this density, one can then obtain the joint distribution of all the outcomes, (yi0; yi1; yi2; :::; yiT ), conditional on unobserved heterogeneity, ci, and strictly exogenous observables, zi. The most important drawback of this approach, however, lies with the di¢ culty of specifying the density of yi0 given (zi; ci).<sup>4</sup> A third method, proposed by Heckman (1981), suggests approximating a density of the initial conditions, yi0; given (zi; ci) and specifying a density of the unobserved e¤ects given the strictly exogenous explanatory variables. The density of (yi0; yi1; yi2; :::; yiT ) given zi can then be obtained. While Heckman’s approach avoids the drawbacks of the second method, it is computationally challenging. Since both the second and the third methods explicitly specify a distribution of the unobserved heterogeneity conditional on strictly exogenous observables and a distribution of the initial conditions conditional on the unobserved e¤ects and the exogenous covariates, they can be classi...ed as random e¤ects models.

Finally, an approach proposed by Wooldridge (2005) recommends obtaining a joint distribution of (yi1; yi2; :::; yiT ) conditional on (yi0; zi) rather than a distribution of (yi0; yi1; yi2; :::; yiT ) conditional on zi as in Heckman’s approach. For this method to work, one must specify a density of ci given (yi0; zi):<sup>5</sup> This fourth approach is more ‡exible and requires fewer computational resources than Heckman’s technique. In this method, similar to Heckman’s approach, we call the relationship between the observed exogenous covariates and the unobserved heterogeneity a “correlated” random e¤ects relationship because we allow ci to be a linear function of zi and yi0:

> 4More details on this approach and potential drawbacks can be found in Wooldridge (2002), page 494. 5The speci...cation of this density in Wooldridge’s method is motivated by Chamberlain’s (1980) approach, which models the distribution of the unobserved e¤ect conditional on the strictly exogenous variables.

5
mean score on the national primary school leaving examination: perhaps as externally valid as a single “fuzzy” discontinuity could be. An expansion of secondary schooling that preserved the quality of secondary schools but reduced the minimum required score would be likely to bring about the effects I estimate on roughly 15 percent of the population near the discontinuity: the compliers. As governments (including Kenya’s) consider the expansion of secondary schooling against other policy options, this study should provide a useful guidepost for understanding the consequences of such an expansion, as long as the expansion does not substantially alter the characteristics of the schools.

The difference between the unambiguously positive human capital findings in this paper and the less cheery conclusions from other studies of education in Kenya suggest that increased school enrollment in Sub-Saharan Africa will have varying consequences, depending on how it is undertaken. The findings in this paper, and in other experimental and quasi-experimental papers, are contingent on the nature of the exogenous variation: the secondary school admission instrument I use, at the KCPE discontinuity, induces both a rise in secondary school completion, and a resulting delay in pregnancy among female compliers; a date-of-birth instrument in the US that also induces additional secondary education has no such effect, however, both because of the timing of the education effects and because of the underlying skills and preferences of compliers with the different instruments.

OLS and 2SLS do not always produce similarly signed effects in this analysis: cross-sectional analysis does not reveal the impact of secondary schooling on employment on this age, but in a causal framework, the pattern 33
both continuous at _DSi_ = _COS_ , it can be shown that for the case of a constant treatment effect, the average treatment effect can be identified by a simple comparison of the mean values of _Y_ between those individuals to the left (eligible) and to the right of the threshold score _COS_ (noneligible), i.e.,<sup>12</sup> In the less restrictive case where the treatment effect is allowed to be heterogeneous across individuals (i.e., β varies across individuals) then β _i_ identifies the local average treatment effect (LATE) for the subgroup of individuals at the threshold point COS.

In our application of the RDD estimator, we use only information on the outcome measures (school enrollment and work incidence), the discriminant scores, and the six qualifying threshold discriminant scores that vary from region to region. In order to obtain estimates of the unconditional means of the outcome measures of interest, denoted by _Y_ − and _Y_ + we use one-sided kernel regressions, which are given by the expressions where, ω _i_ denotes the indicator function _I_ ( _DSi_ ≤ _COS R_ ) , where _COS R_ is the region-specific _DSi_ − _COS R_ threshold value of the discriminate score, _K_ (<sup>_u_</sup> _i_ ) denotes the kernel and _ui_ = , _h_ sharp design is appropriate for the PROGRESA sample.

12 See Van der Klaauw (2002) and Hahn et al (2001) for more details. In the case of a sharp design, _E_ (ε | _B_ , _DS_ ) = _E_ (ε | _DS_ ). In other words, _DS_ will capture any correlation between B and ε since _DS_ is the only systematic determinant of treatment status. The treatment parameter β could thus also be consistently estimated by the equation _Yi_ = α + β _Ti_ + _c_ ( _DSi_ ) +ν _i_ where _c_ ( _DSi_ ) is a control function that is continuous in _DS_ and represents a specification for E[ε|DS].

10
Source: Bloomberg; Energy Information Administration; Haver Analytics; International Energy Agency; Thomson Reuters; World Bank.

Note: Oil price refers to Brent oil prices.

A. January 22, 2020 is the date the first human-to-human COVID-19 transmission was announced. Last observation is May 20, 2020. Data is from Bloomberg and U.S. Energy Information Administration.

B. “Base metals” is an unweighted average for aluminum, copper, lead, nickel, tin, and zinc. “Agriculture” shows an unweighted average for corn, rice, and wheat. “Oil price” refers to European Brent spot oil price. Figure shows the change in commodity prices between January 22, 2020, and April 21, 2020, which was the trough in Brent prices.

C.D. Figure shows the largest declines in oil prices since 1970. Dates on the horizontal axis indicate the date in which the decline occurred. Months with consecutive declines are omitted.

E. Days of demand represent the level of OECD oil inventories at the end of the quarter (government and industry) divided by average daily OECD oil demand. Last observation is 2020 Q1.

F. Last observation is May 15, 2020.

27
PHN Lending, FY86-90 Commitmcnt in US$ Millions (Numbcr of Projects)

|Region|<br>1*|FY86<br>2|3|1|Fv8T<br>2|3|<br>1|FY88<br>2|3|<br>1|F89<br>2|3|1|FM<br>2|3|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|AFRICA<br>ASIA<br>EKA<br>LAC|82<br>242<br>0<br>96|20X<br>582<br>OX<br>_23%_|(5)<br> (4)<br>(0)<br>(2)|31<br>0<br>13<br>10|57X<br>0X<br>242<br>192|(4)<br>(0)<br>(1)<br>(1)|121<br>74<br>0<br>109|402<br>242<br>OX<br>36X|(5)<br>(2)<br>(0)<br>(1)|81<br>290<br>80<br>99|15X<br>53X<br>142<br>182|(4)<br>(4)<br> (2)<br>(1)|232.7<br>192.5<br>119.0<br>389.2|25<br> 21X<br> <br>13X<br>412|(8)<br>(2)<br>(2)<br>(6)|
|TOTAL|420||(11)|54||(6)|304||(8)|550||(11)|933.4||(18)|

^1 - Lending in USS millions 2 - percent of PH1 lending 3 - number of projects
# The Data

Our data set builds on that of Rose (2004)<sup>21</sup> . Unless otherwise stated, the data are from that source; see our Table 1 for a concise description. Most of the variables from that source are standard and do not merit an elaborate description here (see Rose (2004) for complete details), except for the regress and in all our estimations: the log of _average_ bilateral trade flows. The direct source for this variable is the IMF’s “Direction of Trade” dataset. This variable is the average of all four potentially available trade flows between a country-pair (exports from _i_ to _j_ , imports into _j_ from _i_ , etc’). This implicitly imposes symmetry on the effects of the determinants of trade, which means that we are estimating the effect of a change in the regressors both on exports and imports<sup>22</sup> . This seems to be a plausible assumption for some regressors (e.g., distance), but not for all (e.g., tariffs vis-a-vis a trading partner). We deal with this issue below.

We focus our study on the 1984-1999 sample due to restrictions on the availability of data on institutional quality. Our measure of dissimilarity of institutional form is a dummy for different legal origins. This dummy is equal to 1 when a country-pair does not share the same legal origin and zero otherwise. Legal origins are from Djankov et al. (2003) and the CIA Factbook. Two of our measures of institutional quality are Bureaucratic Quality and Control of Corruption; they are taken from the commonly used International Country Risk Guide (ICRG) dataset, are available from 1984 and cover 135 countries for which we have trade data, although coverage is not complete in all years. Our third measure of institutional quality is Protection of Property Rights; it is taken from the Heritage Foundation dataset, is available from 1995 and

> 21 Available at Rose’s web site, http://faculty.haas.berkeley.edu/arose/ 22

> See Helpman, Melitz and Rubinstein (2005) for a framework that does not impose this restriction.

16
2 KENYA AND CHINA’S TRADE RELATIONSHIP <!-- Start of picture text --> 200<br>Mineral, petroleum oils<br>150 Plastics and polyethers<br>Rubber products, pneumatic tyres for buses and lorries<br>Leather, saddlery, handbags and suitcases<br>Cotton and woven fabrics of cotton<br>100 Woven fabrics of synthetic filament<br>Rubber of plastic footwear<br>Ceramic products<br>Television, image and sound recorders, telephone sets<br>50<br>Motorcycles and mopeds<br>0<br>Commodity<br>Import Value (USD Millions)<br><!-- End of picture text --> Figure 5: Kenya imports rubber footwear, tires, and fabrics from China (2012-2014) Source: UN Comtrade database 2015

# **2.3 China is still a small export market for Kenya**

As shown in Figure 6, Kenya only sends one percent of its exports to China, exporting US $63 million in 2012, US $48 in 2013, and US $70 million in 2014. Kenya exports little to China because it is an oil importer and relatively resource-scarce. With fewer natural resources, Kenya has been unable to take advantage of the commodity boom from China’s growth (Zafar 2007). What’s more, the growth does nothing for Kenya’s agricultural sector because it lacks a comparative advantage in China’s main food imports (wheat, corn, beef, soybeans), making it difficult for Kenya to increase its exports of agricultural products.

<!-- Start of picture text --> 6000<br>4000<br>China, P.R.: Mainland<br>World<br>2000<br>0<br>2012 2013 2014<br>Year<br>Exports (USD millions)<br><!-- End of picture text --> Figure 6: Kenyan exports to China small relative to total exports (2012-2014) Source: IMF World Economic Outlook Database 2014 7
The strong significance of the WASP variables in the concurrent regressions may have masked this effect, but in the lagged regressions it stands out as the only significant explanatory variable.

**Table 7a: Lagged Agricultural Value Added With Country and Year FE’s** Dependent <u>variable: Agricultural Value Added</u>

||(Precip)|(Temp)|(P, T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)<br>|(T,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|
|Precip_1|0.01<br>(0.00)**||0.01<br>(0.00)**|||||||
|Temp_1||-0.04|-0.06||-0.21||-0.03||-0.29|
|||(0.06)|(0.06)||(0.10)**||(0.09)||(0.17)*|
|WASP_minus1|(lag)|||||-0.80<br>(0.47)*|-0.79<br>(0.48)*|0.73<br>(1.04)|0.87<br>(1.04)|
|WASP_plus1(l|ag)|||0.49<br>(0.52)|0.40<br>(0.53)|||0.44<br>(1.18)|0.54<br>(1.18)|
|Observations|4286|4286|4286|2352|2352|2528|2528|1416|1416|
|Countries|171|171|171|166|166|169|169|125|125|
|R-squared|0.152|0.152|0.153|0.150|0.152|0.175|0.175|0.160|0.162|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1). The WASP_plus1_1, WASP_minus1_1 and Temp_1 variables are lagged by one year.

# **Table 7b: Lagged Agricultural Value Added with Country FE’s**

Dependent variable: Agricultural Value Added

||(Precip)|(Temp)|(P,T)|(W+1)|(T,W+1)|(W-1)|(T,W-1)<br> (|T,W-1,W+1)|(W-1,W+1)|
|---|---|---|---|---|---|---|---|---|---|
|Precip_1|0.01<br>(0.00)**||0.01<br>(0.00)*|**||||||
|Temp_1||-0.34<br>(0.06)*|-0.37<br>**(0.06)|***|-0.64<br>(0.09)*|**|-0.44<br>(0.10)***||-0.81<br>(0.16)***|
|WASP_minus1_|1|||||(0.51)|-0.72<br>-0.61<br>(0.51)|(1.10)|1.40<br>1.71<br>(1.09)|
|WASP_plus1_1||||0.35<br>(0.55)|0.13<br>(0.55)|||0.14<br>(1.25)|0.55<br>(1.24)|
|Observations|4286|4286|4286|2352|2352|2528|2528|1416|1416|
|Countries|171|171|171|166|166|169|169|125|125|
|R-squared|0.001|0.008|0.010|0.000|0.022|0.001|0.010|0.001|0.022|

Notes: Columns represent model specifications. Standard errors are clustered at the country level. Standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. P = Precipitation; T = Temperature; W+1 = WASP(+1); W-1 = WASP(-1). The WASP_plus1_1, WASP_minus1_1 and Temp_1 variables are lagged by one year.

20
# **3 A New Regional SAM for Italy**

Here, some novelties concerning the cited works for Italy are proposed. In particular, the IRENCGE-DF model is built on a regional SAM with the following characteristics:

- 20 sectors at Nace Rev.2 level;

- 20 commodities by converting through a bridging matrix the 12 COICOP and 10 COFOG commodities included in the national SAM<sup>3</sup> ;

- 20 Italian regions at Nuts-II level<sup>4</sup> ;

- 2 factors: capital and labor at the national and regional levels, respectively;

- 4 agents: households, enterprises, central government, and local governments;

- 10 tax categories: Social Security Contributions (SSC) from employers; property tax (IMU); Personal Income Tax (PIT) at the national, regional, and municipal level; taxes on production; taxes on products; business tax (IRAP); Corporate Income Tax (IRES); taxes on commodities; value added tax (IVA); excises; taxes on imports.

At the current version, the Italian regional SAM refers to the year 2016. However, from a computational perspective, one of the key features of IRENCGE-DF is the ability to update the SAM automatically through a cross-entropy approach (Robinson and ElSaid (2000)) with a Higher posterior density (HPD) estimator (Britz (2020)). The SAM estimation module is separate from the model code and is run independently. Users can introduce new information (e.g., split any accounts in SAM, update macroeconomic totals or the whole of the macro SAM), and the code balances the SAM automatically. The code also allows for incorporating GTAP data to split the activities. Regionalization of the SAM is done in a separate process and is combined of two components: regionalization and balancing. The first component prepares the proto-SAM using regional data, while the latter balances it. The second component is the standard SAM balancing algorithm cited above (see Section 3.10 for a further description of cross-entropy with HPD estimator).

Regional Input-Output coefficients are estimated using Augmented Flegg Location Quotients (Flegg et al. (1995); Flegg and Webber (1997); Flegg and Webber (2000)). We used the

> 3The national SAM was built in a previous project between the Italian Department of Finance, the World Bank, and the European Commission under the project "Improving the evaluation of VAT and excise tax policies in Italy."

> 4We consider the two autonomous provinces of Trento and Bolzano, which are counted separately in the NUTS-II regional classification, as a single region labeled Trentino Alto-Adige, to be consistent with the Italian regional framework.

6
total withdrawals, and net deposits into OIBM accounts in different time periods. The “preplanting” period, from March 2009 to October 2009, is the period when funds are accumulated from the previous season’s harvest in preparation for purchasing inputs for the 2009-2010 growing season. The “planting” period, from November 2009 to April 2010, includes the time of year when farmers purchase inputs and tend their crops prior to the 2010 harvest. This period includes the February to March 2010 “hungry” season when households may have depleted stocks of maize from the previous season’s harvest and have not yet harvested crops or received payments for the 2010 harvest. These data were obtained from OIBM administrative records.

Results from column 1 show that while none of the farmers in the control group transferred money via direct deposit into an OIBM account (since they were not offered direct deposit or account opening assistance), 16% of farmers in the ordinary account, no raffle treatment did transfer money. This percentage is somewhat larger at 21% for farmers in the commitment savings treatment without raffle. There are no statistically significant effects of either the public or private raffle on farmers assigned to either of the savings treatment conditions.

Figure 3 shows a histogram of commitment account release dates (when commitment account funds would be “unlocked” and transferred into ordinary accounts) that farmers chose during account opening. In accordance with their stated savings goals, 60% of farmers chose release dates in the months of October to December when most input purchases occur, immediately prior to or at the start of the planting season. Some farmers also chose to have access to the funds in January and February, during the lean or “hungry” season.

Turning to dependent variables related to deposits, both ordinary and commitment treatments led to higher total deposits as well as higher total withdrawals during the pre-planting period compared to the control group. Coefficients on both types of savings treatments are positive and statistically significantly different from zero for deposits (column 2), and negative and statistically significantly different from zero for withdrawals (column 3). The coefficient on the commitment (no raffle) treatment is virtually identical to the coefficient on the ordinary (no raffle) treatment.

To further explore the impact on deposits, we separately examine impacts on three different components of deposits in the pre-planting period: deposits into ordinary accounts (column 4), commitment accounts (column 5), and other accounts not set up by the project (column 6). It is clear that most of pre-planting deposits go into ordinary accounts, even among farmers in the commitment (no raffle) treatment. The relative sizes of the coefficients on the commitment (no raffle) treatment in columns 4 and 5 indicate that 89% of pre-planting deposits (MK 19,431.76 13
> - 23 -

power goneration ia adcquate to indLcate **the** range of fuels used ln the developLng world. Table 2.11 shown the **shares** for 1986.

The table shown that a very large number, especially of **the** smaller countrles, rely entirely on oll for thermal power generation. In addltlon there is a much more lLmLted range of fuel used for power generatLon than for Lndustry (for those countrLes where data is available on both). Thus, even when all primary fuels are used ln industry, often **one** or two **fuels are** not used for power. There lo clearly potentLal for substitutLon. The tendency to rely on one fuel may be due to strong oconomLes of **scale** ln the generatlng industry in some of the smaller countrles so that only wlth substantLal growth could a fuel mix evolve.

The same source glves data on fuel shares into power goneration going back to 1976. Thls date is before the second oil shock but will to a certaln extent not fully reflect the impact of the flrst oll shock, so that the changes, lf any, between 1976 and 1986 indLcate only part of the substLtutabliLty between fuels. Table 2.12 glves fuel shares Ln power generatlon for all the non O.E.C.D. countries covered by this U.N. source.

ThLs data for 1976 does not cover such a wlde range of countrLes as that for 1986 and also does not allow for biomass. Nevertheless the picture is very clear. Wlth the exceptlon of a handful of coal or gas produclng countries there was almost complete reliance on oll for thermal power generation. Where there were other fuels already used (e.g. AlgerLa, ArgentLna, Bangladesh, Brazil, etc.) the share of oll dld drop substantLally over the ten year period. Many of those countries completely dependent on oil in 1976 contlnued to be so a decade later despite the oil shocks whlch spanned thli perlod.

For studylng substLtutLon of fuels this appears to point to some lmportant flndings. In many countries, either because of their size or because of their distance from the production of competlng fuels, there is rather little scope for substLtutLon away from oil in thermal power goneratlon. If the shocks of the 1970. did not produce even a small degree of substitution, then lt is llkely that a large and expensive intervention would be needed to change the fuel