rafmacalaba/datause-encoder-data huggingface dataset:
- 22 -

# The Comprehensiveness of the Budget

48. Few developing countries currently prepare a consolidated public sector budget. This failure not only places severe limitations on investment programing and its full integration with tta budget, it also reduces the possibility of using fiscal policies for stabilization purposes. A 1980 study of Costa Rica's economic administration noted that some 550 autonomous institutions enjoyed legal financial independence and operated outside the control of the central authorities. Moreover, even within the Central Government, about 10 percent of expenditures took place outside the budget. Some specialized ministries had their own independent budgets while about half of total tax collections were earmarked for specific purposes. In 1979, a major step forward was taken with the setting up of a National Budgetary Authority; prior to that the responsibility for what little central budget preparation and control existed was split between the Ministries of Finance and Planning. Drastic reduction of earmarking, and progress in centralizing and consolidating the budgetary process were major components in Costa Rica's structural adjustment program supported by a policy-based lending operation in 1984.

49. Other examples abound. In Ecuador, the national budget was found to cover only 62 percent of public revenues and 43 percent of expenditures, with the rest accounted for by special funds and autonomous agencies and public enterprises. In Brazil, the growth of autonomous federal agencies and powerful public enterprises progressively weakened the once strong hold which the Federal Treasury had on central government receipts and expenditures. In the Ivory Coast in the late 1970's, the budget was split between the Treasury and two other agencies, while a multitude of extrabudgetary accounts were maintained. In Egypt,<sup>14</sup> a 1980 budgetary reform brought a number of special funds and separate accounts into the central budget, and the current budget was thus unified.<sup>15</sup> However, responsibility for financing public investments was transferred to a newly established National Investment Bank so that there were in effect two central budgeting agencies.

50. Frequently, the budget coverage reflects definitional problems resolved more by custom than by economic considerations. Defining the concept of "public investment" is not, for example, without its difficulties. Should it cover only investment by central government agencies? Should it include only those projects financed wholly or partially through the budget? What about investment by public enterprises?

- 14/ "Public Finance in Egypt" by S. Ahmed, Staff Working Paper, 1984.

- 15/ This should not necessarily be regarded as salutory since it may limit the autonomy and accountability of enterprise management.
<u>Table A.11: Robustness to Annual Data (DOLS and FMOLS)</u>

||VEC|OLS|DOLS-1|DOLS-2|FMOLS|
|---|---|---|---|---|---|
|Cointegration relation|||(HAC)|(HAC)||
|Terms of trade (tot)|1.681***|1.001**|1.174***|1.360**|0.996***|
|Productivity diff (tnt_ag_xchn)|0.642**|0.729**|0.607**|0.513*|0.721*|
|Reserves (resY)|0.644***|0.246**|0.457***|0.591***|0.335**|
|Trade openness (tradeY)|-2.025***|-1.158***|-1.531***|-1.766***|-1.213***|
|Constant|-13.661***|-4.736**|-8.537***|-11.239***|-6.474**|
|Lag Specification||||||
|Lags|1|0|-|-|-|
|Leads and lags|-|-|0|1|-|
|Cointegration Test||||||
|Johansen's cointegration rank|1|-|-|-|-|
|Hansen Parameter Instability 1/|-|-|0.131|0.145|0.512|
|(Probability)|-|-|(> 0.2)|(> 0.2)|(> 0.2)|
|Single cointegration relation?|Yes|-|Yes|Yes|Yes|
|Residual Tests||||||
|Jarque-Bera test for normality 2/|74.955***|2.244|1.396|0.596|0.988|
|_Probability_|_0.000_|_0.326_|_0.498_|_0.742_|_0.610_|
|LM test for autocorrelation||||||
|_Reject null for lags 1-4?_<br>1/ Fails to reject the null hypothesis<br>2/ Rejects the null hypothesis of res|_No_<br>that series ar<br>idual normalit|_Yes_<br>e cointegrat<br>y only for th|_-_<br>ed.<br>e VEC spec|_-_<br>ification.|_-_|

<u>Table A.12: Robustness to Annual Data (Alternative Indicators)</u>

||Baseline||Alternative T|OT and Prod|uctivity diff.||TNT r|obust.|
|---|---|---|---|---|---|---|---|---|
|Adj. REER|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Reserves (resY)|0.644***|1.299***|1.428***|0.707***|1.586***|0.952***|0.631***|0.666***|
|Trade openness (tradeY)|-2.025***|-2.390***|-3.649***|-2.105***|-3.286***|-2.231***|-2.010***|-2.182***|
|Constant|-13.661***|-19.620***|-10.419***|-13.616***|-8.593***|-15.612***|-13.465***|-13.915***|
|Terms of trade<br>tot|1.681***||0.934|1.624***|-0.607|0.899|1.699***|1.734***|
|psoy_cpiae||0.552*|||||||
|Productivity differential|||||||||
|tnt_ag_xchn|0.642**|0.074|||||||
|tw_gdppc|||-2.022*||||||
|tnt_ag||||0.424|||||
|tnt_ag_ind|||||-1.944*||||
|tnt_ag_ind_xchn||||||0.464|||
|tnt_ag_xchn (2)|||||||0.636**||
|tnt_ag_xchn (3)||||||||0.649*|
|Error correction coeff.|-0.121|-0.145|-0.081|-0.122|-0.097|-0.150|-0.121|-0.106|
|Half-life in years|5.4|4.4|8.2|5.3|6.8|4.3|5.4|6.2|
|Johansen cointegration rank|1|0|0|1|1|1|1|1|

40
the regions of the world. 30.4 percent of our formal sample is in Africa, 33.5 percent in Eastern and Central Europe, 32.4 percent in Latin America, and 3.7 percent in Asia. Thirtysix percent of sample firms have female ownership participation; 28 percent in Africa and 39 percent in the rest of the world.

Table 1 Panel A provides descriptive statistics of the variables used in the analysis. Thirty-six percent of firms in our sample have at least one female owner. Thirteen percent of formal firms across the sample have 5 or fewer employees, while in Africa, these represent 21 percent of the firms. In the overall sample, 30 percent of enterprises are sole proprietorships, 13 percent of firms export at least 10 percent of their sales and 10 percent have foreign owners holding at least 10 percent of capital. In terms of location, 40 percent are located in the capital or cities with populations of 1 million or more. The average age of firms is 16 years. We also find that enterprises finance, on average, 18 percent of their fixed asset investment with bank finance and 9 percent of their working capital needs with bank finance, while, on average, 55 percent of firms have a bank loan or overdraft facility.

Table 1 Panel B shows the sample distribution by gender and type of company. If we focus on formal companies we confirm that in Africa only 28 percent of companies have female ownership participation, while the percentage in the entire sample (including Africa) is 36 percent. The percentage of informal companies with female ownership participation in Africa is 33 percent.<sup>5</sup> Finally, when focusing on formal sole proprietorships (often micro and small ones) the difference in prevalence of female ownership participation between Africa and the entire sample is only three percentage points (24 percent in Africa versus 27.1 percent in the entire sample). In our multivariate analysis, we will present results for the whole sample as well as for a subsample of sole proprietorships, as this allows us to isolate enterprises that are completely run by a female owner, as opposed to other firms where some

> 5 We cannot compare this to the rest of the world because informal sector surveys where not performed in other regions outside Africa.

7
# **V. Concluding Remarks**

This paper provides new evidence on the unique role of trade credit as a competitive gesture and risk management tool used by suppliers and as a potentially inexpensive source of working capital financing for large buyers.

We use a novel dataset on almost 30,000 supplier contracts for 56 large buyers in Europe and North America to document contract terms across buyers and suppliers of varying size and investment quality.

The bilateral, multi-contract nature of our dataset is truly unique and is a major improvement on (generally survey based) datasets that have previously been used to study the determinants of credit terms used in trade credit. This multi-contract structure of our dataset allows us to abstract from unobserved buyer and supplier firm characteristics, something previous empirical work has not been able to do.

We find that the largest and most creditworthy buyers receive contracts with the longest maturities, as measured by net days, from smaller, investment grade suppliers, consistent with existing trade credit theories. In particular, these results are consistent with a market power explanation (smaller suppliers are squeezed more by large buyers), a screening explanation (large and investment grade buyers have lower default risk and need not be screened as much), a bonding explanation (small suppliers have to offer better bonds), and a financing explanation (investment grade suppliers have lower financing costs, allowing them to offer longer terms).

In comparison, early payment discounts seem to be used as a risk management tool to limit the potential nonpayment risk of trade credit. In particular, early payment discounts are generally offered by smaller, non-investment grade suppliers to smaller and non-investment grade buyers. This is consistent with the screening explanation (early payment discounts offer suppliers a screening mechanism to gauge buyer default risk). Our result that discounts are more common from small suppliers is also consistent with a market power explanation, whereby large buyers demand discounts from small suppliers.

Our results suggest that contract terms are jointly determined by supplier and buyer characteristics, based on market power, information asymmetries, and alternative financing costs.

17
important in terms of getting at the true co-movement between inflows and outflows and we see that there is a high degree of correlation between bank inflows and bank outflows. This is clearly the case in AEs; furthermore, banks still have the strongest positive inflow-outflow correlation in EMs, though with lower magnitude. As a result, the key to understanding the inflow-outflow comovement is the banking sector. All of the negative correlations in this table involve the public sector, reinforcing the point that the public sector often behaves differently than the private sector.

Table 1:

||All<br>|I|nfows||O|utfow|s|
|---|---|---|---|---|---|---|---|
|Co|untries|Public|Bank|Corp|Public|Bank|Corp|
|ws|Public|1.00||||||
|fo|Bank|-0.10|1.00|||||
|In|Corp|0.04|0.10|1.00||||
|ws|Public|0.24|0.17|0.03|1.00|||
|tfo|Bank|0.15|0.77|0.19|-0.08|1.00||
|Ou|Corp|0.14|0.33|0.50|-0.03|0.32|1.00|
|Ad<br>|vanced<br>|I|nfows||O|utfow|s|
|Ec|onomies|Public|Bank|Corp|Public|Bank|Corp|
|ws|Public|1.00||||||
|fo|Bank|-0.14|1.00|||||
|In|Corp|0.04|0.10|1.00||||
|ws|Public|0.27|0.20|0.01|1.00|||
|tfo|Bank|0.14|0.81|0.21|-0.03|1.00||
|Ou|Corp|0.14|0.35|0.50|-0.02|0.34|1.00|
|Em|erging|I|nfows||O|utfow|s|
|M|arkets|Public|Bank|Corp|Public|Bank|Corp|
|ws|Public|1.00||||||
|fo|Bank|-0.08|1.00|||||
|In|Corp|-0.04|0.04|1.00||||
|ws|Public|0.23|0.18|0.11|1.00|||
|tfo|Bank|0.08|0.27|0.02|-0.33|1.00||
|Ou|Corp|0.03|0.07|0.21|-0.04|0.01|1.00|

Sample consists of 31 countries (15 advanced, 16 emerging) over 2004q1-2014q4, and is described in Appendix A.4. N=1408, 660, and 704 respectively for each panel. Correlations are conditional on country fixed effects, lagged log VIX, and lagged GDP growth.

25
29 security PAYGO system. The Chilean plan Is best viewed as an intermediate system since, like a defined contribution plan, it required that worker contributions be invested in a pension fund. However, the govemment initially restricted fund Investmen **t** o Chilean assets, and mainly to govemment bonds, rathers than requiring it to have an intemationally diversified portfollo.20 In addition, the Chilean govemment continues to serve as guarantor of the system in many ways, including the promise of a minimum benefit payable irrespective of actual fund performance. In the mid-19809 the govemment also bailed out the country's financial institutions, including the national pension fund.

Irrespective of whether a retirement plan is PAYGO, fullyfunded, or in between, a well-run system should be administratively efficient. Many nations have apparently suffered problems in this regard, with negative rates of return characterizing many Latin American countries. Well performing systems have been identfied in which administrative costs total only one or two percent of total collections, whereas other national systems are reported to spend up to 20 percent on administrative costs.21 Data problems also plague many countries making the transition away from a socialist system; for ,nstance, eamings records are apparently unavailable in Hungary and Poland despite the retirement system's need to link benefits with wage histories. In other countries no data were kept on employees' years of coverage under the retirement system, making it almost impossible to determine benefits. This clearly opens the possibility for fraudulent claims and massive administrative inefficiencies.

# Strength* and Weaknesses of Public and Private Retirement Systems

Having reviewed some of the key institutional features of retirement systems, it remains to address the overall rationales for public versus private plans more generally. This section reviews several important motivations for the different pillars of a multiple-pillarsystem. Depending on a country's needs and abilities, one or another mix may be more appropriate. Since retirement systems offer retirement insurance, it is importan **t** o ask how the insurance varies with characteristics of the group being covered, the nature of the benefits being provided, and the funding mechanisms used to pay for the coverage.
# Figure 9.B. Financial markets during global recoveries

## A. Capital flows

## B. Credit

<!-- Start of picture text --> Percent of GDP Index, t = 100<br>50 130<br>1975 1982 1975 1982<br>40<br>1991 2009 120 1991 2009<br>30 Average Average<br>110<br>20<br>100<br>10<br>0 90<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br>C. Equity prices D. House prices<br>Index, t = 100 Index, t = 100<br>260 110 1975 1982<br>1975 1982 1991 2009<br>220<br>1991 2009 Average<br>Average 105<br>180<br>140<br>100<br>100<br>60 95<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br>E. Financial conditions F. Inflation<br>Percent Percent<br>1975 1982<br>2 16<br>1991 2009 Average 1991 2009<br>Average<br>1 12<br>0 8<br>-1 4<br>-2 0<br>t-2 t-1 t t+1 t+2 t+3 t+4 t-2 t-1 t t+1 t+2 t+3 t+4<br><!-- End of picture text --> Sources: Bank for International Settlements; Bloomberg; Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank. Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to an average of four global recessions with available data. Equity prices, house prices, financial conditions, and inflation are weighted by GDP in U.S. dollars. Credit, equity prices, and house prices are index numbers equal to 100 in recession years. For details, see Appendix 2.

42
labor-relevant domains to be completely closed and by accounting also for the remaining domains modeled.

We continue by parametrizing the additive sub-module for which convergence is not necessarily relevant or accounted for, namely the non-tradable production. We parametrize the former by drawing statistical measures from the HIES 2014 and the UN Women (2019). According to these sources, we calibrate the time spent on non-tradable production activities to be different between men and women and therefore modeling the higher allocation of female time to unremunerated activities. The female population aged 15+ is estimated to allocate 8.1 percent of their time to non-tradable activities (or 1.95 hours a day) compared to 3.9 percent (or 0.93 hours a day) allocated by men. Note that under a more equitable redistribution between men and women, the total value of non-tradable production would still be higher for women than men since the female population aged 15+ is larger than the male population aged 15+.

Our approach to modeling closures in the gender gap for a given factor assumes that convergence occurs instantaneously in the starting period, and the effects of this convergence are therefore immediately reflected in subsequent periods. To some extent, this modeling feature reflects the upper bound of the potential impact of closing gender gaps since the effects of an instantaneous convergence are able to accrue over the longest time horizon.

# **5 Results**

We present several of estimates, including macroeconomic measures as well as estimated labor-market and earning-relevant statistics. We begin by depicting the current economic contribution of Liberian women, which we refer to as the “status quo.” This is followed by the estimates of the gender dividend for each sub-module or characteristic studied. For each, we describe the initial condition of the sub-module and provide the macro-economic estimate, depicted as a share of GDP, GDP per capita, GDP per worker and GDP per sector-gender output. In relevant cases, estimated differences in the labor-market composition or wages are given. Finally, we present the estimates given by the simultaneous convergence of all the individually studied inputs to output. Appendix 4 summarizes the convergence experiments for each domain while Appendix 5 presents the results of each convergence experiment. Table 1 summarizes the experiments and estimates presented throughout this section.

**Table 1: Convergence Estimated by Domain, by the Calibration Exercise**

|**Category**|**Estimated increase to GDP, %**|**Calibration**|
|---|---|---|
||8.4|Closure|
|Education|4.9|SSA|
||8.65|OECD|
||3.1|net-reproduction rate|
|Fertility|0.57|SSA|
||3.7|OECD|
|LFPR|2.3|Closure|
|Pa-Ga|4.8|Closure|
|yp|2.0|OECD|
|Sectoral|5.3|Closure|

13
<!-- Start of picture text -->
-25a-<br><!-- End of picture text -->

FIGURE I 0 <!-- Start of picture text --> Setoral Proes/GDP Deflato<br>130<br>120<br>110<br>100<br>80<br>70<br>60<br>71 72 73 74 75 76 77 70 79 60 St 62 63 64<br><!-- End of picture text -->
_ut_ is the error term.<sup>13</sup> The inclusion of the post-reform indicator and its interaction with the time trend allows both the trend and the level of the outcome to change with the reform. In our preferred specification, we set _p_ = 1, fitting a linear trend. Figure 1, Panel B, plots the de-seasonalized outcomes _log_ ( _Z_<sup> ̃</sup> _t_ ) = _log_ ( _Zt,m_ ) _− g_ ˆ _m_ .

Our coefficient of interest is _γ_ 0, which measures the VAT-rebate-driven jump in the outcome in August 2014, under the assumption that no other policy or economic change coincides with the reform to provoke a change in the outcome. Put differently, the outcomes are assumed to evolve smoothly around the reform time in the absence of the reform. Our preferred specification uses weekly outcome data and weeks as running variable. Weeks are defined such that the first day of a week coincides with the first post-reform day. In auxiliary analyses, we also estimate a firm-level version of Equation 1 in which we include firm fixed effects, hence estimating the average effect of the reform across firms while weighting all firms equally.

Ideally, we would also like to examine the estimate for _γ_ 1, capturing whether the reform was associated with a change in the growth rate of the outcome. However, a causal identification of _γ_ 1 would require us to make the very strong assumption that the outcome would have evolved according to the same growth trajectory before and after the reform, in the absence of the reform. This is unlikely to be true. Instead, we conduct a non-parametric comparison of the month-on-month growth-rate distributions before and after the reform, to evaluate the presence of suggestive evidence for a trend acceleration.

# **4.2 Results**

Considering first the raw and de-seasonalized data (Figure 1), it is clear that the number of card transactions jumps sharply in August 2014, precisely when the VAT rebates first become available. This immediate and large response is not surprising, as the VAT rebates were large in size, were introduced with great media fanfare, and were very salient to consumers (Figures A.2-A.5).<sup>14</sup> The second outcome of interest, the volume of card transactions, also increases with the reform, but the increase here is less pronounced. The increase in the number of transactions is hence driven by smaller transactions. This is consistent with the fact that the VAT rebates were proportionally smaller for larger transaction amounts, and that a larger share of large transactions was likely already carried out through electronic payment methods before the

> 13Here, _t_ can be a week or a month. For weeks that stretch across two months, we consider that each week falls into the month in which it has more days.

> 14We do not observe prices or the incidence of the VAT rebate, but the strong consumer response suggests that a substantial share of the rebate was passed through to consumers.

12
# **Table lOf. Transparency and** FOIA

# _DATA SET.ICRG, Averaged over the period 1984-1997_

||ICRG|Corruption|RuleofLaw|Bureaucracy<br>Quality|Contract<br>Repudiation|Expropriation<br>Risk|
|---|---|---|---|---|---|---|
|SocialistLegalCode|.39|.67|1.12***|-.06|.27|.65**|
||(1.01)|(1.63)|(3.87)|(-.14)|(.62)|(2.00)|
|FrenchLegalCode|-.30*|-.09|-.24|-.60***|-.20|-.33|
||(-1.92)|(-.41)|(-1.06)|(-2.68)|(-1.27)|(-2.35)|
|GerTnanLegal Code|.75**|.67|.83*|.92**|.92***|.53**|
||(2.62)|(1.23)|(1.70)|(2.42)|(5.48)|(3.36)|
|ScandinavianLegalCode|.77***|1.40***|1.12***|*79***|.53***|35**|
||(4.41)|(5.65)|(4.41)|(3.54)|(2.76)|(2.37)|
|Income|.47***|.43**'|.73***|.57***|.46**|.41***|
||(3.92)|(2.74)|(4.96)|(3.73)|(3.86)|(4.30)|
|Transparency|-.35***|-.39***|-.34**|-.36***|-.33***||
||(-5.80)|(4.17)|(-3.7)|(-3.99)|(4.87)|(-5.50)|
|FOIA(reverse)|.52***|.64**|.40|.72***|.45***|.30*|
||(2.74)|(2.57)|(1.48)|(2.81)|(2.47)|(1.95)|
|Constant|.92|.68|-1.47|-.07|1.10|1.95**|
||(1.04)|(.56)|(-1.29)|(-.07)|(1.23)|(2.53)|
|#obs|93|93|93|93|93|93|
||.71|.58|.63|.64|.67|.68|

_*indicates significance at.1 level,_ ** _at .05 level and_ *** _at .01 level._ Under the assumption that more information has greater value in democratic rather than autocratic governance regimes, an indicator for autocracy was added to the right hand side of the regressions. The inclusion of the indicators affects some of the variables; more information in more autocratic environments is less useful than in more democratic environments. However, the effect still significant at the 5 and 10 percent levels for several of the governance indicators.

These relationships do not prove one way causality since it may also be that governments that govern well have over time also been more likely to publish data. However, combined with the observation that information gives power to monitor and make good choices a significant and positive correlation between transparency and improved governance gives us pause to think: just giving better data to people can help countries do better.

29
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