rafmacalaba/datause-encoder-data huggingface dataset:
the regressions, it is obtained as the first difference of tangible fixed assets.<sup>8</sup> Investment opportunities, X in equation (1), are an important explanatory variable. Theoretically marginal Q could be used for the approximation of present and expected future investment opportunities. Since marginal Q is unobservable, many investment/liquidity studies for industrialized countries use average Q as a proxy. However, in order to be able to calculate average Q, the country concerned should have a well-developed stock market. In Bulgaria this is still not the case, and only a limited number of companies are listed. As a consequence for most Bulgarian firms average Q simply cannot be calculated.<sup>9</sup> We therefore use the first differences of sales as a proxy for the investment opportunities of the firm. This proxy is also used in other studies on transition economies (see e.g. Lensink and Sterken, 1998) and does more often than not outperform Tobin's Q (Fazzari, Hubbard and Petersen (1988), pp. 165-175). Cash flow is used as a proxy for the liquidity variable in equation (1).1° So in our empirical study, equation (1) becomes

# **(IIK)j** = co + **c1** A(SAL)/K **i + c2** CF/K **i + -i (1')**

where i denotes the ith firm, Ii is gross investment, ASAL denotes the change in sales and proxies for investment opportunities, and CF represents the cash-flow variable., es is the error term and K, the scalar is the capital stock (at the beginning of each year).

The mean and median of most relevant variables" for the full sample are presented in columns 1 and 2 of Table 3. The considerable differences between the mean and the median of many variables indicate a skewed distribution. It turns out that there is a minority of very large firms (mainly (former) state owned enterprises) with many employees and a large capital stock. Columns 3-6 in Table 3 illustrate that firms with a

> 8 Ideally, one would like to measure investment directly. However, as in all related studies the micro-data are based on the balance sheet of the firms, which leaves no other option then to proxy for investment by taking the first differences of the capital stock (see Fazari et al., 1998 and Hubbard, 1998).

> 9 The use of an accelerator model of investment is open to the criticism that, as opposed to Tobin's Q, it does not deal sufficiently with expected profitability with the result that the liquidity variables in equation (1) mnight pick up this effect (see Hoshi et al, 1991).

IO We experimented with other proxies for the liquidity variable, notably we also looked at the _stock_ of liquidity, this proved immaterial to our results so in the remainder of the paper we stick to the use of cash flow as our proxy for the measurement of the impact of liquidity constraints on investment. l All the variables in this data set are averages for the period 1993 - 1995.

12
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46

# **Table 1a**

## **Dependent Variable: Growth Rate of Real GDP Per Worker Educational Pillar Indicators**

|**Years : 1960 - 2000**|**Reg 1a.1**|**Reg 1a.2**|**Reg 1a.3**|**Reg 1a.4**|**Reg 1a.5**|**Reg 1a.6**|
|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**per worker**|**0.2119*****<br>**(0.0424)**|**0.2116*****<br>**(0.0451)**|**0.2464*****<br>**(0.0375)**|**0.2095*****<br>**(0.0436)**|**0.2128*****<br>**(0.0464)**|**0.2455*****<br>**(0.0398)**|
|**Initial GDP (1960)**|**-3.7103*****<br>**(0.8717)**|**-3.9131*****<br>**(0.7780)**|**-3.5760*****<br>**(0.9164)**|**-3.7197*****<br>**(0.9234)**|**-3.9317*****<br>**(0.8032)**|**-3.6712*****<br>**(0.9496)**|
|**HC15**|**4.4661****<br>**(1.9854)**||**4.5116*****<br>**(1.5779)**||||
|**Sq of HC15**<br>**HC15 with extended**<br>**educational returns**|**-1.0965****<br>**(0.4726)**|**1.5718****<br>**(0.7644)**|**-1.0374*****<br>**(0.3752)**||||
|**Sq (HC15 with**<br>**Extended**<br>**Educational Returns)**||**-0.2617****<br>**(0.1139)**|||||
|**HC25**||||**3.8946***<br>**(2.0153)**||**4.3664*****<br>**(1.5611)**|
|**Sq of HC25**||||**-0.9925****<br>**(0.4738)**||**-0.9995*****<br>**(0.3638)**|
|**HC25 with extended**<br>**educational returns**|||||**1.5373***<br>**(0.8991)**||
|**Sq (HC25 with**<br>**Extended**<br>**Educational Returns)**|||||**-0.2845***<br>**(0.1446)**||
|**Log (Educational**<br>**Quality)**|**0.3719**<br>**(0.5412)**|||**0.4324**<br>**(0.5411)**|||
|**Log (Educational**<br>**Quality Extended)**||**0.3877**<br>**(0.5730)**|||**0.4288**<br>**(0.5654)**||
|**Log (Educational**|||**0.1743**|||**0.1913**|
|**Quality) -**<br>**Bosworth & Collins**|||**(0.1110)**|||**(0.1148)**|
|**Log (Patents)**|**0.1956*****<br>**(0.0701)**|**0.1847****<br>**(0.0788)**|**0.1373****<br>**(0.0534)**|**0.1984*****<br>**(0.0714)**|**0.1873****<br>**(0.0774)**|**0.1331****<br>**(0.0543)**|
|**Phones**|**0.0040*****|**0.0044*****|**0.0039*****|**0.0040*****|**0.0043*****|**0.0039*****|
||**(0.0011)**|**(0.0012)**|**(0.0018)**|**(0.0011)**|**(0.0012)**|**(0.0012)**|
|**Constant**|**-4.5304****|**-2.3031**|**-3.9558****|**-4.066***|**-2.2720**|**-3.7559****|
||**(2.2424)**|**(1.8845)**|**(1.7167)**|**(2.2225)**|**(1.8833)**|**(1.6724)**|
|**R squared**|**0.5912**|**0.5607**|**0.7240**|**0.5842**|**0.5578**|**0.7213**|
|**Number of Countries**<br>*, **, *** denotes significanc<br>Robust standard errors obta|**85**<br>e at the 90, 95 a<br>ined usingWhite|**92**<br>nd 99 percent c<br>'s correction fo|**73**<br>onfidence level r<br>r heteroscedasti|**84**<br>espectively<br>city.|**90**|**72**|
implemented in 2006 included both groundnut and maize, we present a hypothetical maize-only package to simplify interpretation of results. Maize is highly sensitive to water stress, represents varieties that have been relatively well characterized for agronomic modeling, requires a substantial investment in inputs, and is supported by relatively good historical data.

# **4.1 Insurance pricing**

We calculate what insurance payouts would have been if the 2006 maize contract for Kasungu had been applied to rainfall observed from 1962 to 2006. The 2006 insurance contract calculated the premium as: _Premium_ = _Average Payout_ + _Loading_ ( ∗ _Value at Risk_ - _Average Payout_ ) (1) with _Loading_ set to 6.5% and _Value at Risk_ based on the 99th percentile. Because distributional assumptions are required for an estimation of the 99th percentile when there are approximately 50 years of data, for the sake of transparency, the 2006 insurance was officially priced using the maximum payout as an approximation of the 98th percentile, with a loading factor of 6.5%, which was increased to adequately load the lower 98th percentile size _Value at Risk_ . We do not use that pricing in our analysis because it is based entirely on the largest payout, which could lead to idiosyncratic results. The plans for future pricing of the 2007 implementation of the Malawi insurance are not based on largest historical payout. Historical burn pricing, used here, relies on payouts determined from historical data, without attempting to characterize the underlying distributions. Although this technique may be overly simplistic, we utilize it because it is highly transparent, and because was the pricing method used for determining the official price of the Malawi insurance.

12
groups need well over 2 years to achieve the same outcome (50 percent success rate). Unfortunately, these relatively successful worker groups are a minority. For a majority of the jobless chances of finding a job quickly are very slim. This is yet more evidence of a stagnant unemployment pool.

# **Table 16 Duration of Uncompleted Unemployment Spells (2001)**

**A. Distribution of Unemployment by Duration**

||Shareinunempl|oyment|
|---|---|---|
|Lessthan1 month|10.1||
|1-6months|25.4||
|7-12months<br>13-24months<br>25monthsormore|16.0<br>16.3<br>32.3||
|B.AverageDuration|||
||Meanduration|Medianduration|
||_months_||
|All unemployed|21.1|12|
|**_Gender_**|||
|Men|22.5|14|
|Women|19.1|12|
|**_Age_**|||
|15-24|14.0|9|
|25-54|22.3|14|
|55-64|26.2|23|
|**_Education_**|||
|University|14.6|8|
|College|20.7|12|
|Uppersecondary|20.1|12|
|Lowersecondary|25.3|14|
|Primary|25.3|19|

> _Source Lithuanian Labor Force_ **_Survey,_** _May 2001, Bank staff calculations._

47
13 difference is more than 50 percent for 45<sup>countries.'6 Under the GATT(2) definition of manufactures 52</sup> countries have a difference of 50 percent or<sup>more from the UNCTAD/GATfI1BRD totals.</sup> Given the magnitude of the differences<sup>between results using the IBRD/UNCTAD/GAWT</sup> definition as opposed to that employed by<sup>UNIDO or GATT(2), a question of importance concerns the impact</sup> of individual products on the discrepancies.<sup>Specifically,</sup> if the differences are due mainly to a relatively<sup>small</sup> number of products it might be easier to<sup>reconcile the definitions than it would be if all the products UNIDO</sup> or GATT(2) added to the core group (see Appendix<sup>Table 1) had a more or less equal impact.</sup> Table 3 provides information for assessing<sup>the contribution of individual products to the</sup> overall discrepancies between the core and other<sup>definitions of manufactures. The table shows the total value</sup> of the 72 developing countries' exports of each<sup>product that was added by UNDO to the core group as well</sup> as the items' share in this trade.'<sup>7</sup> In addition, information is also provided on the<sup>importance of each</sup> product in the exports of individual countries.<sup>Specifically, these tabulations show the percentage of the 72</sup> developing countries for which the added product<sup>accounted for 25 to 49, 50 to 74, or 75 percent or more of</sup> the total exports of non-core manufactured products.<sup>Finally, drawing on statistics from the .S. Genwus of</sup> Manufacture, the table shows indices of the<sup>capital intensity of each product relative to that for all United</sup> 16 It should be noted that the UNIDO and GATT(2)<sup>definition of manufactures closely corresponds to</sup> that employed by the United States Department<sup>of Commerce for its national Census of Mfares.</sup> In terms of U.S. SIC codes, the latter includes meat<sup>products (201), dairy products (202), preserved fiti-s and</sup> vegetables (203), grain mill products (204), bakery<sup>products (205), sugar and confection products (206), fats</sup> and oils (207), beverages (208) and miscellaneous food<sup>(209). The U.S. Census also classifies tobacco products</sup> (21) and petroleum refining (29) as manufactures.<sup>See U.S. Department of Commerce, Bureau of he Census,</sup> 1982Census of Manufacture.YVolumes l.Summary and Subject Statistics, (Washington: Government<sup>Printing</sup> Office, 1985).

t7 Similar tabulations were made for the products<sup>added by the GATT(2) definitions of manufactures.</sup> Since the results were so similar to those shown<sup>in Table 3 they have not been reproduced.</sup>
estimate of future conditions. The project’s economic analysis concludes that “[...] the rehabilitated plant will be the least cost option for Turkey under any scenario [...]” (The World Bank, 2006:58).

**Table 1. Uncertainties evaluated in the original analysis and in this study’s RDM analysis.**

||**Original**|**Analysis**|**RDM**|**Analysis**|
|---|---|---|---|---|
|**A. Deep Uncertainties**|**B. Projected**<br>**Value**|**C. Sensitivity**<br>**Test Values**<sup>**b**</sup>|**D. LH**<br>min|**S Range**<br>max|
|Wholesaleprice of electricity (US$/kWh)<sup>a</sup>|0.05|0.04,0.045|0.04|0.10|
|Discount Rate<sup>a</sup>|0.1|—|0.01|0.20|
|Estimated Life of the Plant(years)|||||
|Rehabilitation; New local lignite plant;<br>New gas-fired plant;|20|(10, 16)|5|25|
|New imported coal plant; New lignite<br>fluidized bed plant|40|—|5|45|
|Capacity Utilization|||||
|Rehabilitation; New gas-fired plant; New<br>lignite fluidized bed plants|0.76|(0.6, 0.68)|0.5|0.9|
|New local lignite plant; New imported<br>coalplant<br>|0.90|—|0.6|0.95|
|Capital Costs(US$,in millions) <sup>7</sup>|||||
|Rehabilitation|683|(819,1024.5)|600|1030|
|New local ligniteplant|1998|—|1800|3000|
|New imported coalplant|586.7|—|500|880|
|Newgas-firedplant|420|—|350|630|
|New lignite fluidized bedplant|451.84|—|400|670|
|Length of Construction Time (years)|||||
|Rehabilitation|4|—|2|6|
|New local lignite plant|6|—|3|9|
|New imported coal plant; New gas-fired<br>plant;New lignite fluidized bedplant|3|—|1.5|4.5|
|Cost of energy inputs|||||
|Local lignite (US$/ton, for the<br>rehabilitated and the new plant|6.05|(7.06, 9.08)|3|12|
|Imported coal (US$/ton)|60|—|30|120|
|Gas (US$/tcm, for the new gas-fired<br>plant)<br>|220|—|110|440|
|Improved coal<sup>8</sup>for the new lignite<br>fluidized bed plant (US$/ton)|21|—|10|42|

> _~~a~~ Only the wholesale price of electricity and the discount rate have the same ranges across all options’ cost effectiveness models, both in the original and in the RDM analyses. b The original analysis conducted sensitivity tests only for the IRR of rehabilitating the existing plant._

> 7 Project capital costs included two key technological improvements. Given the poor quality of local lignite, both the rehabilitated and the new local lignite-fired power plants would have flue-gas desulfurization (FGD) to remove sulfur dioxide from exhaust flue gases. During project preparation, dust reduction technology was also made compulsory in all options with low quality coal.

8 The three types of coal – local lignite, coal for the fluidized bed plant, and the imported coal -- have a different energy values. Local lignite has the least and imported coal has the most potential energy.

9
_Note:_ This figure shows the mine locations and the district in which the mines are located.

The baseline specification is shown in the following equation:

# Y��� �  β�active_district�� � ∝�� g� �λX�� �ε���            �3�

The outcome for individual _i_ in district _d_ in time period _t_ is regressed on district and year fixed effects, an indicator for whether the individual lived in an active mine district at the time of the interview, and time varying individual-level factors. Even though the treatment is defined at the district level, we use individual-level data to be able to control for individual- level factors and explore heterogeneity at the individual level. The standard errors are, however, clustered at the district level to take into account the interdependence induced by the higher-level treatment. Since the treatment variable is at the same level as our district fixed effects, the _β1_ coefficients are directly interpretable as difference-in-differences estimates. That is, they capture the difference between mining districts and nonmining districts before and after mining starts.

In estimating the district-level effects of mine openings on birth outcomes, we control for birthyear fixed effects instead of survey-year fixed effects, as we are interested in the effect of mining at birth. In investigating the effects on birth outcomes and infant mortality, we further classify a child as treated if he or she is born in a district with active mining during the birth year (in contrast to whether the mine is active when the mother was interviewed). We also include controls for the age of the child in the survey year in the child and birth outcome regressions (but, naturally, not in the infant mortality regressions).

# **5. Results using individual-level difference-in-differences strategy**

In this section, we present results using the two difference-in-differences strategies. Since the individual analysis contains district fixed effects, the two strategies are complementary. While the district-level analysis informs us about differences across and within districts over time, the local-level analysis gives us the additional impact at the very local level. This means that any differences in effects across district and local analysis should not be interpreted as inconsistencies, but rather as differential and additional impacts.

In a difference-in-differences setting, it is important that the sample is balanced, assuming that the treatment and control groups are on similar trajectories. Table 2 shows the summary statistics for the women’s surveys across four different groups, close and far away, and before and during the mine’s production phase. Columns 1 and 3 show mean values of the population that live far away from mines, before and during mining respectively. Columns 2 and 4, in 14
characteristics are entered simultaneously. SSP had broad appeal, with few baseline characteristics predicting take-up and dropout. Strikingly, while heavy overdrafters were just as likely as light overdrafters to sign up for SSP, they were 12 percentage points more likely to drop out (column 2). This is consistent with the hypothesis that some heavy overdrafters are partially-naïve and underestimate their future desire to undo commitments.

# **3.2 Direct Results from Administrative Data**

We now analyze how SSP impacted study participants’ use of NVRB accounts. Here, we focus on use of overdrafts, overdraft fees paid, net deposits (total deposits minus total debits, a measure of savings flow), and the average daily account balance (a measure of savings stock).<sup>11</sup> Our administrative data span the commitment period, the post-commitment period covered by follow-up surveys, and a post-commitment, post-survey period. We therefore estimate separate treatment effects for these three periods to understand how effects evolve over time.<sup>12</sup> We use the following regression specification: Where _yit_ is the outcome of interest for account holder _i_ in month _t_ , _treati_ is a dummy variable equal to one if account holder _i_ is in the treatment group, _duringt_ identifies the commitment period, _after_ 1 _t_ identifies the post-commitment, follow-up survey period, _after_ 2 _t_ identifies the post-commitment, post-survey period, _γs_ are strata fixed effects, _δt_ are month fixed effects, and _yi_ 0 is the average outcome of interest, measured in the year before the survey.

The first two columns of Table 2 show that during the commitment period SSP had no impact on overdrafts. After SSP savings were released, the share of accounts in overdraft declined by 6 percentage points (18 percent), while overdraft charges declined by GHS 3.77 (21 percent) per month – both significant at the 10 percent level. In the longer run, however, participants slid back into debt cycles: we find no treatment effect on overdrafts 7-12 months after the commitment amount was released.

During the commitment period, SSP increased net deposits by GHS 19.5 (column 3 of Table 2). The 72 percent takeup rate implies a treatment-on-the-treated effect of GHS 27, – roughly two-thirds of the average SSP contribution of GHS 42 thus, there is evidence that participants were able to partially undo their commitments by drawing down other

> 11When constructing variables we sum across all accounts each respondent holds with NVRB. Note that average daily balances can be negative, e.g. when an account holder takes an overdraft.

> 12The commitment period lasted from December 2013 to May 2015, the post-commitment, follow-up survey period covered June to November 2015, and the post-commitment, post-survey period covers six months from December 2015 to May 2016.

8
**<u>Table 9: Marketing Regulation</u>**

||Types andTerms|
|---|---|
|Australia|Occupational plans are not active providers of annuities.<br>Marketing ofannuitiesis subject to ordinary conductrules.|
|Chile|.<br>Licensing requirements for pension advisors.<br>Caps on broker commissions.<br>Electronic quotationsystem.|
|Denmark:ATP|Nomarketingisinvolved.|
|Denmark: Other|Annuities are provided through occupational plans.<br>Marketing focuses on enhancing brand names, competition<br>for mandates, declarationofbonuses.|
|Sweden: PPM|No marketing is involved other than creation of brand<br>names by decentralized assetmanagers.|
|Sweden: Other|Annuities are provided through occupational plans.<br>Marketing focuses on enhancing brand names, competition<br>for mandates, declarationofbonuses.|
|Switzerland|Annuities are provided through occupational plans.<br>Little marketing is involved in view of federal regulation of<br>products and prices.|

Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

However, the marketing of the two main payout options, phased withdrawals and life annuities, is highly asymmetrical and one-sided. The pension companies (AFPs) focus on the very profitable accumulation phase of the pension business and adopt a passive marketing stance on phased withdrawals. The commission income that pension companies can raise from offering phased withdrawals is a modest fee of around 1 percent of benefit payments, whereas the fees during the accumulation phase, excluding premiums for term life and disability insurance, still amount to close to 15 percent of contribution amounts.

In contrast, life insurance companies engage in very active marketing of annuities, using employees and company agents as well as independent brokers. They have strong incentives to market annuities, which represent the core of their business. Pension advisors play an important part not only in the choice of the annuity option but also in the decision to retire early.<sup>12</sup> During the 1990s when commissions paid to brokers reached very high levels of 5 to 6 percent of the value of the annuity contract, brokers reportedly offered kickbacks to their clients, effectively increasing the amount of funds that early retirees could withdraw as lump sums.

Chile applies an extensive regulation of marketing activity in the annuity market. Pension advisors have to pass a certification test administered by the supervisory agency as well as a basic 'fit and proper' test. Most applicants take a course on annuities that comprises a total of 120 hours. Licensed pension advisors are legally obligated to represent their clients and generate their income from commissions on the sale of annuities. They are not

> 12 In 2008, brokers were replaced by pension advisors. Stricter certification requirements have been imposed.

22
<!-- Start of picture text --> Figure 7<br>Contract Duration: 1990- 1995 and 1996 - 200<br>50_0%<br>45.0% 44%<br>40.0%<br>35%<br>35.0%<br>30.0%<br>25.0%<br>22%<br>20.0%<br>1N _~~~~~~~~~~~~~~7<br>15.0%<br>2%<br>10.0% tso%~~~~~~~~~~~~~~~~~~~~3 2i -,2<br>5.0% 4% 5% 4%5% 4% 3%<br>00% ,/ 1iU 2% 0%0% 0%0%<br>0-2 2-4 4-6 6-8 8-10 10-12 12-14 14-16 16-18 18-20 20-22 morethan<br>22<br>Years<br>*0 1990-1995 01996-2000<br><!-- End of picture text --> Source: Resolution 25/2000, Secretariat of Energy, Argentina Recognizing that the increment in the duration of contracts between oil companies and gasoline stations in Argentina could be following a worldwide policy adopted by the multinational oil companies operating in this country (RepsolYPF, Exxon, Shell), the SDCyC studied the recent international experience in this area.

The SDCyC correctly focused the study of the international experience on Europe. Many markets in this continent share similar characteristics with the Argentine gasoline market, especially Spain, where the gasoline market was fully deregulated in 1998 and RepsolYPF has a significant market share (Contin, 1999).

In December of 1999, the European Commission issued a Resolution (2790/99/CE) that imposed new conditions for exclusive contracts. The direct implication of this resolution in the gasoline market is that contracts between oil companies and gasoline stations can not exceed five years. It is implicit in this Resolution the idea that 22