rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2 Sustainable Debt for Different Velocities and real interest rates** gP = .l0; gy = 0.03; -d = 0.02

|**(1/V)**|**1/20**|**1/15**|**1/10**|**1/5**|
|---|---|---|---|---|
|**r**|||||
|.05|_.50_|.54|.60|.80|
|.10|.25|.27|.30|.40|
|.15|.16|.18|.20|.27|
|.20|.12|.13|.15|.20|

Table 1 and 2 call attention to the importance of income velocity of base money. Table 1 shows the importance and inflation effect of the **_level_** of income velocity of money. Table 2 shows impact of velocity on sustainable debt given other policy variables.

For two countries having a similar surplus of about 2 percent of GDP and yearly inflation and growth of about 10 and 3 percent, according to table 2, if one of the countries experienced a base velocity of 10 and the other 5, the former's sustainable debt would be 60 percent of GDP, while the latter would represent 80 percent.

The higher the demand for base money (the lower its velocity), the higher the "sustainable" real debt for given interest rates. For example (according to row 2 of Table 2), for a real interest rate of 10 percent per year, the sustainable debt would rise from 25 percent to 40 percent of GDP, as velocity decreases from 20 to 5 per year.

Given velocity, the columns in Table 2 show a significant reaction of the equilibrium of debt to changes in real interest rates. For example, if interest rates are reduced by 5 percentage points (from 15 to 10 percent) and velocity is 10, the sustainable debt would consequently increase 10 percentage points of GDP (from .20 to .30). For the same interest rate change (same percentage points change) the increment in sustainable debt is larger, the lower the income velocity of money.

To make the calculation of sustainable debt, it is necessary to consider "reasonable" values of parameters. A one or two year "blip" makes no sense when applying these values to the equations. Consider, for example, the case of Chile in 1995, with an income velocity of base money of 3.3, a primary surplus close to 2 percent of GDP, an inflation of about 8 percent, and an interest rate of 10 percent per year. Its sustainable debt should have been approximately 453 percent of GDP. The same would apply to other countries with atypical figures, for example the Mexican 1995 economic statistics.

14
# **Table 8. Guatemala: Robustness of Results—Alternative Data Sources**

|||Depe<br>|ndent variable: Per<br>|cent change of GDP/<br> <br>|worker<br>||
|---|---|---|---|---|---|---|
||Includes time<br>trends starting in<br>1985 and 1999|4 percent<br>depreciation of<br>capital stock|<br>Disaggregated<br>capital stock<br>estimate<sup>b/</sup>|<br> <br>Barro and Lee<br>(2001) education<br>data<sup>c/</sup>|<br>Cohen and Soto<br>(2001) education<br>data<sup>c/</sup>|Population 15-64<br>instead of labor<br>force data|
||IV<br>1951-02|IV<br>1951-02|OLS<br>1971-02|IV<br>1951-00|IV<br>1961-02|OLS<br>1951-02|
|Explanatory variables|(1)|(2)|(3)|(4)|(5)|(6)|
|Constant|-0.071**|-0.075**|-0.042*|-0.073**|-0.061**|<br>-0.013|
||(-4.05)|(-3.46)|(-2.57)|(-4.31)|(-4.43)|(0.66)|
|Percent change of capital/worker|0.865**|0.780**|0.827**|0.730**|0.847**|0.507*|
||(8.44)|(5.59)|(33.0)|(10.3)|(9.18)|(2.44)|
|Percent change of capital/worker [-1]|0.119*|0.168*|0.167**|0.160**|0.138**|...|
||(2.41)|(2.54)|(4.37)|(3.28)|(3.16)||
|log GDP/worker [-1]<sup>a/</sup>|-0.259**|-0.243**|-0.333**|-0.279**|-0.272**|<br>0.040|
||(-6.14)|(-4.88)|(-7.06)|(-5.45)|(-5.40)|(0.72)|
|log capital/worker [-1]|0.113**|0.078|0.180**|0.080*|0.108*|-0.127*|
||(3.19)|(1.63)|(5.47)|(2.40)|(2.10)|(-2.02)|
|log average years of schooling [-1]|0.074**|0.083**|0.130**|0.133**|0.072**|0.026|
||(4.54)|(4.58)|(6.65)|(4.86)|(5.53)|(1.53)|
|Trend 1985|0.002**<br>(2.83)|...|...|...|...|...|
|Trend 1999|-0.008*|...|-0.008**|...|...|...|
||(-2.50)||(-3.63)||||
|Long-run elasticity of capital|0.436|0.322|0.541|0.288|0.399|N.A.|
|Long-run elasticity of schooling|0.287|0.344|0.392|0.476|0.266|N.A.|
|Adjusted R<sup>2</sup>|0.972|0.958|0.982|0.955|0.975|0.648|
|F-statistic|57.55|43.28|180.4|59.61|67.88|14.92|
|Durbin Watson|2.440|2.162|2.174|2.441|2.151|1.879|
|S.E. of regression|0.011|0.013|0.009|0.013|0.010|0.014|
|N|50|50|31|49|40|51|

Note: The regressions include a step dummy for 1977 and impulse dummies for 1963 and 1982 significant at 1%. a/ Asymptotic critical values of the t-ratio are from Banerjee et al. (1998). b/ Includes 1976 and 1982 impulse dummies significant at 1%. c/ Data is interpolated.

t-statistics in parenthesis. ** Significant at 1%, * significant at 5%.

_Source:_ Author’s calculations.

-45-
- 6 -

In the 3G mobile telecommunications experiences in European countries, for instance, the number of bidding firms per license ranges from 0.75 to 2.6. In the United Kingdom, five licenses were auctioned among 13 bidders. On the other hand, there was no competition in the Austrian and Swiss cases, meaning that the number of bidders was eventually the same as the number of licenses to be issued (Klemperer, 2002; van Damme, 2002). NOA (2007) indicates that half of the past U.K. PFI projects attracted at most two or three bids. Similarly, in the railway concession cases in Argentina and Brazil during the 1990s, two to four firms participated in the bidding process. Particularly, the freight train contracts, which may be less profitable from the private sector point of view, attracted only one or two bidders.

How many bidders are required for sufficient competition? Gupta (2002) shows that the highway construction market (in Florida) becomes competitive with about six to eight bidders.<sup>3</sup> As per Iimi (2006), large-scale official development projects—mostly in the infrastructure sector—require eight firms to minimize the contract prices. In the U.S. offshore oil lease market, a similar level of bidder participation seems necessary for competition purposes (Brannman _et al_ ., 1987).

Developing countries may have an even more limited competition in PPP infrastructure auctions. The PPI Database shows that about 60 percent of PPP infrastructure transactions in developing countries involved one to three bidders (Figure 1).<sup>4</sup> About 15 percent of contracts attracted only one bidder even under the competitive bidding circumstances. The degree of competition varies depending on sector. It is the lowest at 3.6 in the water sector. By contrast, the telecommunications and road sectors could attract more participants on average, as expected.

> 3 It means that the marginal impact of one more bidder on the equilibrium bid is not statistically significant when the number of participants exceeds eight.

> 4 The database covers only transactions of which total investment commitments exceed US$ 1 million.
JAPAN

## **-** 2. Projections NRR-1 by 2030

|**Tes**|**Pepd atin**|**Se.**<br>**ratlo**|**Period**|**GSrot**<br>rate|Ca|c|**TFR**|Holm|**sO**<br>b<br>**Fmtes**|**IF**|xlO00|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|123504|43.5|1990-94|0.42|11.4|7.2|1.68|76.4|82.2|4|6|
|1995|126112|45.0|1995-99|0.41|11.7|7.6|1.68|77.4|83.2|4|6|
|2000<br>2005|128723<br>130776|49.4<br>54.7|2000-04<br>**2005-09**|0.32<br>0.17|11.3<br>10.8|8.1<br>9.1|1.75<br>1.81|78.2<br>78.7|84.1<br>84.6|4<br>4|5<br>5|
|2010|131888|60.0|2010-14|0.02|10.4|10.2|7.87|79.1|85.1|3|5|
|2015|132024|66.5|2014-19|-0.07|10.4|11.1|1.93|79.6|85.7|3|4|
|2020|131543|69.5|2020-24|-0.11|10.6|11.8|1.98|80.1|86.2|3|4|
|2025|130795|70.1|2025-49|-0.19|11.0|12.9|2.06|81.0|87.3|3|4|
|2050|124749|80.2|2050-74|-0.15|11.5|13.0|2.06|81.8|88.5|2|3|
|2075|120197|75.0|2075-99|0.00|11.7|11.7|2.06|82.3|89.1|2|3|
|2100|120073|74.4|2100-24|0.02|11.6|11.5|2.06|82.7|89.5|2|3|
|2125<br>**2150**|120579<br>**120868**|74.5<br>74.9|2125-49|0.01|11.6|11.5|2.06|82.8|89.7|2|3|

# 3. Age structure (percent): 1990 and 2025

<!-- Start of picture text --> 75 +<br>70-74<br>65-89<br>60-84<br>55-59<br>50.54<br>45-49<br>40-44<br>35-39<br>30-34<br>32g %m 25-29<br>20-24<br>15-19<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10 10 8 6 4 2 0 2 4 6 81l<br><!-- End of picture text -->

# 4. Comparison of Country with Regi.on and Income Group, 1985-90

<!-- Start of picture text --> TPR e0<br>. *.a<br>Coun~try Region Income group Country Region Income group<br><!-- End of picture text --> <!-- Start of picture text --> Growth rate<br>Coantry Region Income group<br><!-- End of picture text --> 61
level of cooperation with their spouse affects their business investment behavior. A study in Madagascar found that marriage is associated with benefits for men’s businesses but not for women’s, again suggesting that intrahousehold dynamics affect women’s businesses (Nordman and Vaillant, 2014). Together, these studies provide compelling evidence that intrahousehold dynamics regarding resource generation and management play a role in women’s businesses. This study of microentrepreneurs in Accra, Ghana builds on this evidence by examining _how_ intrahousehold dynamics influence women’s decision-making for business investment.

# **Methods**

This study took place in Accra, Ghana between January 2016 and June 2017. While findings from our research have relevance to female entrepreneurs across Ghana and other locales, this research is specific to urban women microentrepreneurs in Accra. The Metropolis area has a population of about 1.6 million. Most women, at some point in their lives, are either married or living in an informal consensual union with a partner. At the same time, divorce and separation are relatively common, and polygyny is practiced across many parts of Ghana. Some of the women we spoke with expressed concern over their husband or partner taking a second wife or a girlfriend. According to Demographic and Health Survey data from 2014, 37 percent of urban household are female-headed and the average household size in urban areas is 3.1. In the greater Accra area, average fertility for women is 2.8 (Ghana Statistical Service et al., 2015). These demographic characteristics are important because our findings indicate that many women, for a myriad of reasons, treat marriage as a somewhat insecure institution, which impacts their longterm planning strategies, and hence their business investment behaviors.

8
# **_2.2 General equilibrium biofuel analyses_**

General equilibrium analyses provide a more encompassing assessment of the biofuel emergence on the whole economy and food prices because these multiple linkages are explicit in the model. The disadvantages of many of these analyses are the aggregation of crops in a few sectors, the lack of realistic biofuel policy parameterization, and trade elasticities, which tend to dampen trade responses to changing relative agricultural prices. Taheripour et al. (2007) describe extensions of the GTAP 6 database to account for biofuel production and their linkages, upstream and downstream, calibrated to 2001 data. The impact from biofuel expansion on land is through reallocation of land into corn, sugar cane, and oilseeds sectors. Biodiesel is treated as crushing oilseeds and producing meal as a byproduct (as DDGS are in the case of ethanol), <!-- Start of picture text --> Li Lj Ln<br>σ1<br>L _<br><!-- End of picture text --> Figure 1. Land allocation in the standard GTAP model a drastic assumption. Land supply is not explicitly described but presumably it follows the standard one level CET assumption incorporated in the GTAP model structure as shown in Figure 1. Land adjusts sluggishly according to relative rents obtained in respective activities. All countries have a common cost structure. The trade matrix is rather limited. It analyzes several policy shocks such as the increase in crude oil prices, the phasing out of MTBE, and biofuel subsidies. Keeney and Hertel (2009) propose a model incorporating land, yield, and trade responses to the biofuel expansion. Land demand is derived from a constant-return-to-scale <!-- Start of picture text --> L1 Lj Lk Ln<br>σ2 = -0.5<br>L L L<br>forest & other pasture crops<br>σ1 = -0.11<br>L _<br><!-- End of picture text --> Figure 2. Land allocation in Keeney and Hertel (2009).

7
In the case of Toronto, cost savings were likely to be fairly small since the largest expenditures (welfare assistance, transit, and policing) were Metro responsibilities prior to the amalgamation in 1998.<sup>17</sup> These three services accounted for 70 percent of the total upper-tier and lower-tier expenditures combined. This means that any cost savings that could be achieved only applied to 30 percent of the total budget of the new city.

Most of the savings that did occur resulted from staff reductions. Reports following the amalgamation suggest that between 1998 and 2002, 2,700 positions were eliminated through amalgamation. Over the same time period, however, an additional 3,600 positions were added to improve service levels, thus resulting in a net increase in employment over the period (Schwartz 2004, 101-102). Unfortunately, it is not possible to determine if these new positions were created in response to the amalgamation or to other provincial initiatives that were introduced in the same year.<sup>18</sup> When municipalities are amalgamated, some duplication is obviously eliminated. In particular, the number of politicians and bureaucrats is reduced. On the other hand, when municipalities with different service levels and different wage scales merge, expenditures may increase. For example, when the fire departments of several municipalities are amalgamated, it is possible to reduce costs by eliminating a number of fire chiefs. However, the newly amalgamated city faces thousands of fire fighters who are doing the same job and working for the same employer – the new city. Understandably, they will want to be paid comparable salaries and benefits, and none will be willing to take a pay cut. Salaries and benefits thus tend to equalize up to the highest expenditure municipality. This upward harmonization of wages and salaries generally outweighs

> 17 Prior to the amalgamation, the provincial government commissioned a study on the potential savings from the Toronto amalgamation – KPMG Consulting (1996). This study, however, estimated cost savings in the area of policing, a service that had already been amalgamated in 1967. The study also did not estimate the potential impact of the harmonization of wages and salaries and services that would occur following the amalgamation.

> 18 Local services realignment across the province, for example, resulted in the transfer of responsibility for many services such as water, sewers, roads, transit, social housing, public health, ambulances, and increased responsibility for social services to municipalities. In return, the provincial government uploaded primary and secondary education and a few other services to the provincial level.

40
interest payments), expenditures on personnel, other current expenditures, and interest payments—in order to assess potentially different degrees of cyclicality for revenue and expenditure categories.

Individual regressions are conducted for each fiscal item of gross state product using OLS, fixed effects (FE), and feasible generalized least squares (FGLS).<sup>5</sup> Given that in almost all states expenditure and revenue components and the gross state product data demonstrate pronounced upward trends and non-stationary behavior, all variables are expressed in growth rates. The specifications are as follows: (1) ΔRevenuesit = αi + β (ΔGDPST)it + Σγt (Yeardum)t + εit, (2) ΔExpendituresit = αi + β (ΔGDPST)it + Σ γt (Yeardum)t εit ; i=1,...,27 ; t=1991,...,2006 where Δ refers to the rate of change. Revenues (all in logs) are: Total Revenues, Tax Revenues, Sales Tax (ICMS), and Intergovernmental Transfers. Expenditures (again all in logs) are: Total Expenditures, Primary Expenditures, Personnel Expenditures, Maintenance and Capital Expenditures, and Interest Payments. ΔGDPST is the rate of change in the gross domestic state product.

The coefficients should be interpreted as how much fiscal revenues or expenditures change in response to a 1 percent change in gross state product, or the changes in fiscal revenues or expenditures due to by a 1 percent increase in gross state product. In the case of the estimation con FE and FGLS the coefficients should be interpreted as “within” each state or jurisdiction. All regressions include time (year) dummies to control for common effects experienced by all states in a particular year (i.e. national economic downturns or changes in the federal macroeconomic policy with symmetric effects on all states) so the coefficient on the gross state product hone in on the effects of asymmetric provincial income shocks. The products of this methodology include the correlation (degree of association) between output and fiscal variables but not causality effects.

Table 3 reports the results using the three different estimation methods. As expected, revenue variables (total revenues, tax revenues and VAT) show a pro-cyclical relationship to the gross state product The main source of pro-cyclicality is related to the main tax revenue directly collected by the states (the sales tax or ICMS). Regarding expenditures, total and primary expenditures show a pro-cyclical behavior with respect to gross state product. Among expenditure categories expenditures on personnel have a higher degree of pro-cyclicality than maintenance and capital expenditures.

Changes in intergovernmental transfers are not associated with changes in gross state product, which would suggest the potentially a-cyclical behavior of federal government transfers with respect to the states. However, intergovernmental transfers are pro-cyclical

> 5 This estimation method allow us to present fixed effects regressions with panel-corrected standard errors, correcting for first order autocorrelation of residuals, and assuming the disturbance terms to be heteroskedastic and contemporaneously correlated across panels.

9
<!-- Start of picture text --> Micro (0-4) Small (5-19)<br>0 0<br>-20 -20<br>-40 -40<br>-60 -60<br>-80 -80<br>Production shock Demand shock Finance Shock Production shock Demand shock Finance Shock<br>No shock suffered Shock suffered No shock suffered Shock suffered<br>Medium (20-99) Large (100+)<br>0 0<br>-20 -20<br>-40 -40<br>-60 -60<br>-80 -80<br>Production shock Demand shock Finance Shock Production shock Demand shock Finance Shock<br>No shock suffered Shock suffered No shock suffered Shock suffered<br>Change in sales (%) Change in sales (%)<br>Change in sales (%) Change in sales (%)<br><!-- End of picture text --> Figure 9: Change in sales by types of shock

# **4.4 Impact of access to programs**

Support programs matter for a firm’s outlook towards the future, investment decisions and financial fragility.<sup>11</sup> Our analysis of the BPS data suggests that increased access to public support programs is weakly positively associated with firm’s expectations of future sales. This highlights the considerably low access to firm support among South Asian firms and the potential for such intervention to influence their behavior (Figure 10, left panel).

Access to public support is also associated with a decline in the probability of falling into arrears for firms that had initially low access. By contrast, financial fragility cannot be addressed at higher levels of policy access (Figure 10, right panel).<sup>12</sup> This finding can be an indication of difficulties in targeting firms that need the most help and are otherwise viable. Some firms that had high access to public support by virtue of their size or belonging to the hardest-hit sector (e.g., large firms in accommodation and food services) may also have _a priori_ high financial fragility that cannot be addressed by support through instruments relating to access to finance when the underlying root of their distress is depressed demand. In fact, prior evidence suggests that as additional liquidity

> 11It is probably too early to detect the effect of public support on liquidity, costs, and survival. Notwithstanding, Bennedsen et al. (2020) find a strong relationship between support on labor aid and reporting lower layoffs and more furlough among Danish firms. They do not find similar results for fiscal aid.

> 12Although the chart shows a quadratic relationship, the estimates could be biased at the upper end due to the low number of higher support access bins.

17
**Table 6: Sectoral Export Shares and Female Attainment Measures**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Panel A|: OLS||||
|Dep. Var.: Sectoral Share of|Exports||||||
|Female Labor Intensity *|0.08|-0.39|||||
|Fertility|(0.259)|(0.322)|||||
|Female Labor Intensity *|||0.09**|0.08*|||
|Female Labor Force|||(0.036)|(0.043)|||
|Participation|||||||
|Female Labor Intensity *|||||0.29|3.97|
|Ratio of Female to Total|||||(4.122)|(5.487)|
|Education<br>|||||||
|Capital Intensity * Capital||1.72**||1.67**||1.53**|
|Abundance||(0.791)||(0.784)||(0.751)|
|Skill Intensity * Skill||-1.53||-1.42||-0.44|
|Abundance||(1.583)||(1.546)||(1.532)|
|Country and Sector|Yes|Yes|Yes|Yes|Yes|Yes|
|Dummies<br>|||||||
|_R_<sup>2</sup>|0.245|0.253|0.246|0.254|0.227|0.239|
|Observations|8,533|6,190|8,533|6,190|7,361|5,732|
|||Panel B|: 2SLS||||
|Dep. Var.: Sectoral Share of|Exports||||||
|Female Labor Intensity *|-2.27***|-1.59*|||||
|Fertility|(0.761)|(0.941)|||||
|Female Labor Intensity *|||0.23***|0.19**|||
|Female Labor Force|||(0.072)|(0.096)|||
|Participation|||||||
|Female Labor Intensity *|||||11.07*|4.51|
|Ratio of Female to Total|||||(6.450)|(7.125)|
|Education|||||||
|Capital Intensity * Capital||1.96***||1.80**||1.90**|
|Abundance<br>||(0.815)||(0.784)||(0.789)|
|Skill Intensity * Skill||-1.69||-1.283||-0.57|
|Abundance<br>||(1.619)||(1.536)||(1.588)|
|Country and Sector<br>|Yes|Yes|Yes|Yes|Yes|Yes|
|Dummies<br>|||||||
|Observations|8,416|6,075|8,416|6,075|7,244|5,501|
||||First|Stage|||
|Dep. Var.:|_FLi_*|_FLi_*|_FLi_*FLFP|_FLi_*FLFP|_FLi_*Ratio|_FLi_*Ratio|
||Fertility|Fertility|||of Female<br>to Total|of Female<br>to Total|
||||||Education|Education|
|Female Labor Intensity *|2.71***|2.54***|-30.53***|-29.67***|-0.23***|-0.20***|
|Muslim Share of|(0.111)|(0.156)|(1.151)|(1.775)|(0.012)|(0.014)|
|Population|||||||
|Female Labor Intensity *|0.68***|0.37***|-12.61***|-11.30***|0.08***|0.12***|
|Christian Share of|(0.098)|(0.138)|(0.883)|(1.499)|(0.010)|(0.013)|
|Population|||||||
|F-test|367.44|252.18|359.32|180.25|604.16|740.47|
|Partial _R_<sup>2</sup>|0.911|0.920|0.947|0.942|0.977|0.981|

Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. _Female Labor Intensity_ ( _FLi_ ) is the average fraction of female employees in a sector at the ISIC Rev. 3 three-digit level; _Fertility Rate_ is total births per woman; _Female Labor Force Participation (FLFP)_ is female labor participation rate (% of female population ages 15+); _Ratio of Female to Total Education_ is the ratio of the Barro-Lee measures of female to total educational attainment. All of the variables are averages 33 over the period 1962-2007, except for _Female Labor Force Participation,_ which is averaged over the period 1980-2007. Variable definitions and sources are described in detail in the text.