rafmacalaba/datause-encoder-data huggingface dataset:
||(0.184)|(0.336)|(0.172)|(1.109)|(0.789)|(1.616)|(0.713)|(0.920)|
|---|---|---|---|---|---|---|---|---|
|Capital city Y:1 N:0|1.414|3.393|-0.030|0.478|-0.159|1.558|-0.008|0.612|
||(0.956)|(2.658)|(0.062)|(0.713)|(0.420)|(2.071)|(0.101)|(0.885)|
|Obstacles severity|-0.030|0.512|-0.001|-0.415|-0.140|0.319|0.588|-22.195*|
||(0.383)|(4.645)|(0.028)|(1.308)|(0.427)|(6.137)|(1.889)|(13.117)|
|Sole proprietorship Y:1 N:0|-4.510**|4.712**|-0.404|1.145|0.263|0.152|1.921|-2.113|
||(1.943)|(2.339)|(0.459)|(0.801)|(1.568)|(3.070)|(3.714)|(6.283)|
|Shareholding company (non-|1.209**|1.040|0.166|-1.026|0.180|-1.212|-1.042|-0.951|
|traded) or Partnership Y:1|(0.473)|(1.310)|(0.259)|(2.730)|(0.928)|(2.520)|(1.295)|(2.265)|
|N:0|||||||||
|R&D and New|0.084|-0.135|0.069|-0.033|-1.929|-4.445|0.918***|0.941**|
|product/process|(0.427)|(1.033)|(0.206)|(0.635)|(1.540)|(3.806)|(0.338)|(0.434)|
|Constant||-22.576||4.275||15.552||-38.293**|
|||(19.971)||(12.989)||(31.439)||(10.931)|
|Number of observations|1,087|1,087|2,695|2,695|726|726|444|444|
|||<br>**Panel B: M**|<br>**ean decompos**|<br>**ition estimates**|**above as a per**|**centage of th**|**e total gap**||
|Country-Industry|-19.0%|-16.4%|9.3%|33.3%|12.4%|-6.4%|8.6%|-19.7%|
|Labor productivity (logs)|22.9%|125.7%|4.5%|-79.5%|18.3%|-35.0%|7.2%|458.6%|
|Supply disruption Y:1 N:0|31.4%|17.6%|27.6%|24.1%|65.1%|21.2%|50.3%|306.0%|
|Overdraft Y:1 N:0|16.6%|17.6%|2.2%|4.3%|-1.2%|-5.6%|27.0%|-12.0%|
|Finance obstacle (0-4)|0.1%|18.1%|-0.1%|21.0%|6.5%|-7.4%|-18.0%|145.9%|
|Skills obstacle (0-4)|-0.8%|1.3%|-4.4%|-15.1%|-1.1%|-53.7%|21.0%|108.4%|
|Young firm (<=10 years) Y:1|0.7%|-12.2%|0.8%|8.7%|-0.3%|2.0%|-0.5%|15.8%|
|N:0|||||||||
|High share of women<br>workers Y:1 N:0|-0.3%|20.8%|-0.6%|23.6%|-0.2%|6.9%|10.5%|66.6%|
|Exports (proportion of sales)|9.2%|6.3%|-0.5%|3.8%|4.2%|4.3%|21.2%|1.7%|
|Foreign ownership<br>(proportion)|-1.3%|-5.6%|-1.7%|-0.4%|-19.5%|-4.1%|-19.7%|-0.6%|
|Tax rates are a major obstacle|-0.5%|-1.6%|2.3%|-13.5%|-0.5%|6.7%|-0.7%|-8.3%|
|Y:1 N:0|||||||||
|Government support Y:1 N:0|1.3%|-1.4%|-0.9%|13.7%|-5.9%|4.4%|-21.3%|-50.7%|

64
# **IV. SUSTAINABILITY OF HIGH GROWTH**

This section sets out the challenge of sustaining the high growth rates. As a starting point, Box 4 lays out the ―growth arithmetic‖ commonly used to ascertain the savings rates Sri Lanka would need to fulfill its growth aspirations. While this framework is helpful in assessing the savings effort needed from the public and private sectors to achieve economic growth targets, the arithmetic runs into trouble because it assumes a constant incremental capital-output ratio (ICOR), which is equivalent to assuming constant marginal returns to capital. In practice, with capital subject to diminishing marginal returns, the savings rate needed to support a target growth rate will rise over time for given total factor productivity, labor force and human capital growth rates. For the same reason, we would expect to see an increase in the ratio of capital to output over time. Therefore, a dynamic framework allowing for diminishing marginal returns to capital is needed to get a better sense of the medium-to-long term links between savings and growth.

**Box 4: Harrod-Domar Growth Arithmetic: Simple, but Incomplete**

Numerical calculations of the savings rate needed to support a target growth rate are commonly carried out using the Harrod-Domar equation. This equation can be obtained as follows: , where _g_ is the growth rate of real GDP, _Y_ , and _K_ is the physical capital stock. The first term in brackets on the right-hand side is the inverse of the incremental capital-output ratio (ICOR) while the second is the investment rate. The simplest version of the framework assumes a closed economy so that the investment rate equals the savings rate, _s_ . Therefore, given a target growth rate _g*_ , the required savings rate _s*_ based on the equation is the ICOR multiplied by _g*_ .

Given the Sri Lankan real GDP growth target of 8 percent and an ICOR of 4.5, the required investment rate would be 36 percent of GDP. If one assumes a sustainable current account deficit of 4 percent of GDP, then the required national savings rate s* would be 32 percent. Suppose the ICOR goes down to 4 as a result of growing productivity and efficiency as part of the peace dividend. Then s* would fall to 28 percent.

With national savings averaging 22 percent of GDP over the last decade, the increase in the savings rate would need to be 6 percent of GDP. Achieving the fiscal target of raising government savings from -2 percent to +1.5 percent of GDP would require an increase in the private savings rate of an additional 2.5 percent of GDP, which is eminently feasible.

While this appealing framework indicates that a target growth rate of 8 percent is readily within grasp, it is subject to pitfalls beyond the very short run. These are discussed in the text.

With this in mind, we present results below drawn from a dynamic simulation framework calibrated with parameters and data drawn from Sri Lanka‘s own past experience as well as that 18
In table S1.1 we report the main features of the ESPS and PSF consumption questionnaires. In both surveys the list of commodities is restricted to what has been included in the consumption aggregates computed for this paper. The PSF survey sample is small compared to that of the ESPS (1762 households versus 13559). For this reason, and since in Senegal the large majority of households are owners of their housing (more than 80% at the time of the survey, and more than 97% in rural areas), the PSF survey does not allow to compute imputed rents for owners in rural areas. Therefore, we decided not to include housing rents in our expenditure aggregates. Also excluded are income and direct taxes, together with gifts, construction, celebration that are not completely covered in PSF. We also exclude health expenditures, since we wish to exclude them from the analysis as interpersonal differences are likely to largely correspond to difference in needs. The PSF and ESPS surveys do not differ much in the level of disaggregation of the commodities included by the consumption questionnaire: 30 food and 29 non-food items are covered by ESPS, versus 28 food and 25 non-food in PSF. There are two major differences in the way food expenses are collected. First, while in ESPS the design of the food consumption questionnaire does not differ from that of non-food, in PSF the food section is first addressed to household members in charge of preparing meals. The respondents are asked about the amount of the daily expenditure (“dépense quotidienne”, DQ) that is spent for these meals. Field interviews that have been conducted before the survey showed that the DQ mainly covers expenses for fresh food, such as vegetables, fruits, fish and meat. Other food commodities, such as rice or oil for instance, are often bought directly by the head of the household, generally in bulks. The second part of the questionnaire covers these expenses. Later, cell heads are also asked to report any expenditure related to food and meals consumed outside the household by members of their cell. In ESPS, this question is addressed only to the household head. The second important 7
In middle-income countries, there has been a rapid expansion of door-to-door sales of highsweetened, low nutritional foods to poor people, expanding rates of obesity, diabetes, and undernourishment ( _New York Times_ , Sept. 19, 2017, p. A1).

A book titled _Nudge_ (Thaler and Sunstein, 2008) became “the spearhead of behavioral economics” ( _The Economist,_ March 24, 2012). The authors define a _nudge_ as a policy that changes behavior without substantially changing incentives or information. One way a nudge works is by changing how easy it is to choose a particular option and by giving immediate feedback that permits individuals to correct their mistakes. We use two examples to illustrate how a nudge can promote economic development.

# Example 1.  Simplifying the ballot process to increase the voice of the poor

There is often a high return to public investments in the poor, but relatively little is known about how to induce governments to increase such investments. The introduction of electronic voting technology in Brazil in 1998 showed that making it easier for voters to communicate their preferred choices greatly increased the voice of the poor by reducing the number of error-ridden votes, particularly by the less educated. The limited number of the devices available in 1998 meant that the electronic voting technology was introduced only in municipalities with more than a threshold number of voters, while the rest used paper ballots. Brazil requires all adult citizens to vote. Fujiwara (2015) identifies the impact of the machines by comparing outcomes for municipalities just above and just below the threshold. By reducing the number of error-ridden ballots that had to be thrown away, electronic balloting effectively enfranchised 11% of the voters. Political power in municipalities shifted to the Left. One of the things that legislators 17
**Wage Dynamics, Turnover, and Human Capital**

**Evidence from Adolescent Transition from School to Work in the Philippines**<sup>**1**</sup>

**Futoshi Yamauchi**<sup>**2**</sup>

**World Bank**

Keywords: Wage dynamics, Human capital, Transition from school to work, Philippines JEL classifications: I2, J3, O1

> 1 I thank Michael Alba, Luis Benveniste, Agnes Quisumbing, Yanyan Liu, Marites Tiongco, and Will Martin for useful comments. Special thanks to Yolanda Quijano, Irene Robles, and Ishidra Afunggol for insightful discussions. Supports from the Bureau of Elementary Education, the Research and Statistics Division, and Antique, Cebu, Ifugao, Iloilo, Leyte, Negros Oriental, Nueva Viscaya, Western Samar division offices of the Philippine Department of Education, the Office of Population Study of the University of San Carlos and the Japan International Cooperation Agency are acknowledged.

2 The World Bank, Development Research Group, 1818 H Street, NW, Washington, DC 20433; Email: fyamauchi@worldbank.org; Phone: +1-202-473-2065.
> **Mit-Demand at the Zero Frequency**

> **Inflation as Measure of Opportunity Cost** _VEYCM,_ **_STSM and_** _Merged Approaches*_

<!-- Start of picture text --> 9.40 -<br>9.35 -<br>9.30 -<br>9.25 -<br>9.20 -<br>9.15 -<br>9.10 -<br>9.05 -<br>9.00 -rr,- 1<br>oa ~ CN ~f. a, 'r ~ '.0 a, r- a, X o' o 0 0o _<br>0'. CS' 0' 0' 0' 0' 0' 0 0<br>_ -- - - - a , C_ C<br>-- - VECM and Merged STSM<br><!-- End of picture text -->

> *Using fitted valujes of cointegrating equation as VECM long-run component. Usiig stochastic trend of Ml in the STSM approach.

Similar results are obtained when the log of the inflation rate is used as the opportunity cost variable. The demand for real money balances is inelastic with respect to real GDP. Depending on the modeling approach used to estimate elasticities at the zero frequency, the elasticity with respect to real GDP is 0.86 or 0.24, with the STSM or VECM approaches, respectively. The elasticity of money demand with respect to the inflation rate is -0.31 and -. 48, using STSM and VECM approaches respectively.

The trend component of log real money demand under alternative approaches, is shown in the Figure "M1-I)emand at the Zero Frequency: Log Inflation as Measure of Opportunity Cost".

|Long-Run**ResponsivenessofMI-Demand**<br>|
|---|
|_InflationasOpportunityCostVariable_|
|_Elasticity_<br>_Elasticity_|
|**Approach**<br>**Real GDP**<br>**Inflation**|
|STSM<br>0.86<br>-0.31|
|VECMmdMerged<br>0.24<br>-0.48|

Money Demand in Venezuela, Mario A. Cuevas 24
low income neighborhoods, banks did so using credit scoring to evaluate ability and willingness to pay, and to charge higher rates of interest to clients that credit scores showed to be riskier. Prior to the 1990s, mortgage lending had been characterized by uniform pricing among individuals for a given loan type. During the 1990s, credit scoring gave lenders an objective means to price discriminate on the basis of credit risk. Consumer lending, and particularly mortgage lending, evolved to where many individuals that previously were denied credit were now granted credit, but at prices that varied with their credit scores (Avery, et al., 2007, Chomsisengphet and Pennington-Cross, 2006, Gramlich, 2007).

# **The role of subprime lending in raising the ownership rate is not clear and its**

**sustainability is questionable.** Data limitations make it difficult to determine how many first time homebuyers gained access to mortgage credit for the first time thanks to subprime lending. Jaffee (2008) estimates that subprime lending may have enabled 1.38 million first time home purchases nationwide between 2000 and 2007. This would be about 20 percent of the increase of 6.59 million owners over the same period or 1.8 percent of the 75.1 million overall owner-occupied units in the country in 2006.<sup>17</sup> To the degree that first time buyers subprime had high LTVs and now are in negative equity positions, or have loans with high debt-to-income ratios, and teaser rates that have adjusted radically higher, the addition to ownership is likely to prove short lived. The Center for Responsible Lending has estimated that over 2 million subprime loans will eventually end up in foreclosure. If this turns out to be the case, then the net effect of subprime lending on homeownership would be negative.

**The U.S. home ownership rate rose moderately between the 1980s and 2006, including the years of the subprime boom, then fell somewhat.** By the first quarter of 2008, the overall ownership rate had dropped to 67.8 percent from its 2005 peak of 69.1 percent, a drop of 1.44 million units, given the occupied stock of 110.8 million occupied units at the end of April, 2008 (Census, 2008).

**The current U.S. home ownership rate is among the higher of Western European countries with equally sophisticated mortgage finance systems.** Ownership rates in Western Europe range between 45 and 82 percent (Table 1). Ownership in many emerging markets ranges between 70 and 85 percent, reflecting in some cases weaker forms of tenure and lower quality units (South America), and in others massive and sometimes costless privatization of the formerly public housing stock (Eastern Europe).

# **<u>Table 1 Selected Home Ownership Rates</u>**

|**Country**|**Home Ownership Rate***|
|---|---|
|Germany|45|
|Denmark|49|

17 The 6.59 increase is the gross increase in the number of all types of owners, not just first-time owneroccupiers.

14
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006 for countries for which data was collected in the first phase of the project. For some of the countries, considered phase 2 countries like Sierra Leone, data collection began later. In the particular case of Sierra Leone, data is collected predominately for the 2007–2009 period. Most technical data presented are typically 2006 for the benchmarks, while financial data for comparator countries are averaged over the available period to smooth out the effect of short-term fluctuations.

Second, in order to make comparisons across countries, we had to standardize the indicators and analysis so that everything was done on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

In common with the rest of the continent, Western Africa‘s growth performance improved markedly in the 2000s. _._ The overall improvement in per capita growth rates has been estimated at 1.19 percentage points, of which 1.12 percent is attributable to better structural policies and 0.88 percent to improved infrastructure, with stabilization policies and external factors retarding growth by 0.81 percent.

Since the end of the 11-year civil war in 2002, Sierra Leone‘s economy expanded at annual average rate of close to 10 percent, led by agriculture, construction, services, and mining. Though the global economic crisis led to an almost 40 percent reduction in demand for diamonds—Sierra Leone‘s main export item—the economy continued to grow, albeit at a slower rate, and is expected to rebound (figure 1). Such strong performance is to a great extent a result of sound economic and fiscal policies, and good performance of the key sectors. In addition, recently discovered off-shore oil reserves<sup>1</sup> and iron deposits are likely to further boost the economy, once commercially explored.

> 1 Expectations about a boost in the real sector were heightened with news about the prospects of potential oil reserves in the deep-water offshore area within the Sierra Leone-Liberia basin. Anadarko Petroleum Corporation, a United States oil firm which made the discovery indicated that the new well - called Venus - is one of more than 30 identified prospects across 10 blocks offshore of Sierra Leone, Liberia, Ivory Coast and Ghana. This was positive news for the country going forward and has significant potentials to increase Sierra Leone‘s revenues (Bank of Sierra Leone, Annual Report 2009).

4
**Table A4:** Coefficient of determination ( _R_<sup>2</sup> ) by model and number of question for various coverage rates (approx. no. of questions in brackets)

|||PMT|Truncated|Earl|y stop|TrE|St|
|---|---|---|---|---|---|---|---|
|_10% _|_coverage_|||||||
|37.0||–|0.63 (37.0)|–||–||
|30.0||–|0.63 (30.0)|–||–||
|22.8||0.62 (22.8)|0.63 (23.0)|–||–||
|20.0||–|0.63 (20.0)|0.55|(20.5)|–||
|15.0||–|0.63 (15.0)|–||0.46|(12.1)|
|10.0||–|0.61 (10.0)|0.44|(10.5)|0.46|(10.3)|
|5.0||–|0.57 (5.0)|0.37|(5.1)|0.43|(4.9)|
|0.0||–|0.31 (0.0)|0.32|(0.8)|0.31|(0.1)|
|_20% _|_coverage_|||||||
|37.0||–|0.63 (37.0)|–||–||
|30.0||–|0.63 (30.0)|–||–||
|22.8||0.62 (22.8)|0.63 (23.0)|0.56|(23.0)|–||
|20.0||–|0.63 (20.0)|0.53|(19.8)|0.55|(19.1)|
|15.0||–|0.63 (15.0)|0.49|(15.3)|0.51|(14.8)|
|10.0||–|0.61 (10.0)|0.43|(9.8)|0.51|(10.0)|
|5.0||–|0.57 (5.0)|0.38|(5.2)|0.46|(5.0)|
|0.0||–|0.31 (0.0)|–||0.31|(0.1)|
|_30% _|_coverage_|||||||
|37.0||–|0.63 (37.0)|–||–||
|30.0||–|0.63 (30.0)|0.61|(29.7)|–||
|22.8||0.62 (22.8)|0.63 (23.0)|0.56|(23.2)|–||
|20.0||–|0.63 (20.0)|0.54|(20.2)|–||
|15.0||–|0.63 (15.0)|0.49|(14.9)|0.55|(15.0)|
|10.0||–|0.61 (10.0)|0.45|(10.2)|0.55|(9.8)|
|5.0||–|0.57 (5.0)|0.40|(4.7)|0.50|(5.0)|
|0.0||–|0.31 (0.0)|–||0.31|(0.1)|
|_40% _|_coverage_|||||||
|37.0||–|0.63 (37.0)|–||–||
|30.0||–|0.63 (30.0)|0.61|(29.5)|0.61|(27.2)|
|22.8||0.62 (22.8)|0.63 (23.0)|0.57|(23.2)|0.60|(22.6)|
|20.0||–|0.63 (20.0)|0.55|(20.3)|0.58|(20.1)|
|15.0||–|0.63 (15.0)|0.51|(14.7)|0.56|(15.0)|
|10.0||–|0.61 (10.0)|0.48|(10.3)|0.55|(10.1)|
|5.0||–|0.57 (5.0)|0.44|(5.6)|0.51|(5.0)|
|0.0||–|0.31 (0.0)|–||0.38|(0.5)|
|_50% _|_coverage_|||||||
|37.0||–|0.63 (37.0)|–||–||
|30.0||–|0.63 (30.0)|0.61|(30.5)|0.62|(30.2)|
|22.8||0.62 (22.8)|0.63 (23.0)<br>|0.58|(22.5)|0.60|(22.7)|
|20.0||–|0.63 (20.0)<br>40|0.57|(20.3)|0.60|(19.8)|
|15.0||–|0.63 (15.0)|0.54|(15.4)|0.60|(15.0)|
|10.0||–|0.61 (10.0)|0.49|(9.8)|0.56|(9.9)|
|5.0||–|0.57 (5.0)|0.45|(5.2)|0.51|(5.0)|
|0.0||–|0.31 (0.0)|–||0.39|(0.6)|
<!-- Start of picture text --> ee<br>©) onceistory—————————d@ ane, ron Credit Scoring Model<br>|<br>.<br>Probability of Default<br><!-- End of picture text -->

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For example, Panel B of figure 6 plots the correlation coefficient between parents’ and children’s schooling tabulated by Behrman, Birdsall, and Szekely (1999). It indicates that these correlations are much higher in Latin America (between .4 and .6) than in the US (.2). Similarly, Panel C of the same figure reports the elasticity of children’s income relative to their father computed by Grawe (2002) for a handful of countries reveling that for the countries for which we have information, this elasticity is larger among the Latin Americans than among the Europeans or the US.

**<u>Figure 6. Mobility and inequality in Latin America</u>**

<!-- Start of picture text --> Panel A. Mobility in Latin America Panel B. Educational Mobility<br>CHL<br>ARG PRYUSA<br>URY PAN<br>PER URY<br>MEX JAM<br>PRY CHLVEN<br>PAN DOM<br>VEN PER<br>DO HND<br>SLV COLCRI<br>HND BOL<br>COL ARG<br>CRI MEX<br>ECU<br>NIC BRA<br>ECU GTM<br>BOL NIC<br>BRA SLV<br>GTM<br>0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65<br>0.70 0.75 0.80 0.85 0.90 0.95<br>Education: Intergenerational correlation<br>Social mobility index<br>Panel C. Elasticity of son's income to father's Panel D. Mobility and income inequality<br>0.95<br>1.2<br>y = -0.4198x + 1.0514<br>1<br>0.90<br>0.8<br>0.6<br>0.85<br>0.4<br>0.2 0.80<br>0<br>0.75<br>0.4 0.45 0.5 0.55 0.6 0.65<br>Gini coefficient<br>Social Mobility Index<br>Ecuador Peru UK Malaysia US Nepal Pakistan Canada Germany<br><!-- End of picture text --> Source: Panel A is from Andersen (2001). Panel B is from Behrman, Gaviria and Szekely (2001). Panel C is from Grawe (2002). Panel D is own calculations.

One key question is whether there is any relation between this mobility index and the level of income inequality. In fact, there is evidence (see Aaberge et al, 2004) indicating that high income inequality tends to be associated with low income mobility, something that in turn would contribute to low social mobility and less equality of opportunities. We explore this element in Panel D of Figure 6.<sup>12</sup> It suggests that indeed there is a marked

> 12 Andersen (2001) presents a similar plot but finds a very modest correlation (-.12). The main difference between his and our calculations is that he uses adjusted gini indices to correct for lack of comparability, whereas ours are computed using comparable data and hence are not subject to adjustment.

10
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Uganda > Electricity investments > Macro results**

||||Reduction in<br>th bli|El|ectricity<br>It|Fi|I|
|---|---|---|---|---|---|---|---|
|Variable|Definition|Reference|oer puc<br>expenditure|VAT|mpor<br>duties|oregn<br>aid|ncome<br>tax|
|Ym|Aggregate household income|875,000.00|0.14|0.16|0.16|0.14|0.17|
|EV|Equivalent variation||0.14|0.16|0.16|0.14|–0.40|
|s|Wage|1.00|0.11|0.27|0.28|0.11|0.29|
|yg|Government income|163,000.00|0.18|2.98|2.98|0.18|2.98|
|g|Total government expenditure|103,000.00|–2.21|2.24|2.24|–2.21|2.24|
|ye|Firm income|113,000.00|0.26|0.06|0.06|0.26|0.05|
|It|Total private investment|105,000.00|0.35|0.16|–0.45|0.35|–0.19|
|GDP|GDP|834,000.00|0.18|0.18|0.18|0.18|0.18|
|e|Nominal exchange rate|1.00|0.07|0.05|–1.96|0.07|–0.12|

# **Uganda > Electricity investments > Sectoral results**

||||Reduction in|E|lectricity|||
|---|---|---|---|---|---|---|---|
|Variable|Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign<br>aid|Income<br>tax|
||Crop agriculture|270,000.00|0.05|–0.04|–0.03|0.05|–0.26|
||Export agriculture|112,000.00|0.20|–0.06|–0.09|0.20|–0.14|
|Va|Mining and gas|577.00|0.19|0.07|0.28|0.19|0.12|
|<br>Value added or|Industries|94,100.00|0.46|–0.17|–0.21|0.46|0.17|
|output|Construction|66,700.00|1.76|1.29|1.39|1.76|1.50|
||Private services|260,000.00|0.09|0.14|0.12|0.09|0.19|
||Public services|31,600.00|–2.15|1.88|2.01|–2.15|2.20|
||Crop agriculture|1.00|0.10|0.25|0.22|0.10|0.02|
||Export agriculture|1.00|0.12|0.15|0.11|0.12|–0.04|
||Mining and gas|1.34|0.02|0.20|1.65|0.02|–0.11|
|pq<br>Mkt i|Industries|1.10|–0.10|0.76|–0.02|–0.10|–0.19|
|are prces|Construction|1.01|0.31|0.73|0.36|0.31|0.27|
||Private Services|1.02|–0.21|0.35|0.07|–0.21|–0.06|
||Public services|1.00|–0.06|0.35|0.23|–0.06|0.04|
||Crop agriculture|1.00|0.19|0.19|0.21|0.19|–0.22|
||Export agriculture|1.00|0.37|–0.01|–0.07|0.37|–0.16|
|r<br>|Mining and gas|1.00|0.53|0.15|1.14|0.53|0.40|
|Rental rate of<br>capital|Industries|1.00|0.06|–1.13|–1.22|0.06|–0.40|
||Construction|1.00|2.48|1.84|2.01|2.48|2.22|
||Private services|1.00|–0.20|0.06|0.03|–0.20|0.15|

46