rafmacalaba/datause-encoder-data huggingface dataset:
**TABLE 18:  STATISTICALLY SIGNIFICANT CORRELATES OF TRUCK UTILIZATION** **<u>(1000 KILOMETERS PER YEAR)</u>**

|Significant variables for Utilization|Coefficient|
|---|---|
|Hours of wait time per ton-kilometer|-1,256*|
|Speed (km/hour)|1.95***|
|Fleet age|1.13**|
|Percentage of trips to national capital city|-29.30**|
|Panama=1|-26.35***|
|Significant Variables for Average Speeds (kil|ometers per hour)|
|Percentage of tires used tires|-0.0464*|
|Road type/quality (0 worst 11 best)|1.09***|
|Competitors=2-5|-11.59***|
|Competitors > 5|-9.56***|

***=significant at 1 percent level; **=5 per cent level; *=10 percent level, ~=almost significant

**FIGURE 13:  AVERAGE INFORMAL PAYMENTS PAID ON INTERNATIONAL ROUTES BY TYPE OF REQUESTER (US CENTS PER TON-KM)**

<!-- Start of picture text -->
1.4<br>1.2<br>Agricultural authorities at border<br>1<br>Customs at border<br>0.8 Inmigration at border<br>0.6 Other local authorities (eg municipal)<br>Other individuals<br>0.4<br>Police while in route<br>0.2<br>Authorities at weight stations<br>0<br>CR SV GUA HON NIC PAN<br>FIGURE 14:  AVERAGE INFORMAL PAYMENTS ON NATIONAL ROUTES BY TYPE OF<br>REQUESTER IF REQUESTED (US CENTS PER TON-KM)<br>4<br>3.5<br>Agricultural authorities at border<br>3<br>Customs at border<br>2.5<br>Inmigration at border<br>2<br>Other local authorities (eg municipal)<br>1.5<br>Other individuals<br>1 Police while in route<br>0.5 Authorities at weight stations<br>0<br>CR SV GUA HON NIC PAN<br>US cents per ton‐km<br>US cents per ton‐km<br><!-- End of picture text -->

Note: The value of payments to police in Panama this was truncated, and equals 11 US cents per ton-kilometer.

26
||Rev. 3.1 level. There are 7 industries in our<br>sample.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|---|---|
|Financially Constrained (CI avg.)|The ES asked firms if they applied for a loan<br>during the last fiscal year. If they did not, they<br>were asked to choose the main reason for not<br>doing so from the following list: No need for a<br>loan as the firm has enough internal funds;<br>application procedures were too complex;<br>interest rates were not favorable; collateral<br>requirements were too high; loan size and<br>maturity was insufficient; or did not think it<br>would be approved. For firms that did apply<br>for a loan, the ES asks if the loan application<br>was rejected, still pending, approved partially,<br>or approved in full. Using this information, we<br>first define a firm as financially unconstrained<br>if it either did not apply for a loan because<br>there was no need to do so (has enough internal<br>funds) or if it applied for a loan and the loan<br>amount was approved in full. The remaining<br>firms are classified as financially constrained.<br>Next, we compute the proportion of all firms<br>in the same country-industry cell as the firm in<br>question that are financially constrained. This<br>proportion is computed overall all firms other<br>than the firm in question. Industry grouping is<br>defined at the 2-digit ISIC Rev. 3.1 level.<br>There are 7 industries in our sample.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|Finance: A Major Obstacle (CI avg.)|The ES asked firms if (lack of) access to<br>finance is no obstacle (0), minor obstacle (1),<br>moderate obstacle (2), major obstacle (3), and<br>a very severe obstacle (4) for the firms’ day to<br>day operations. We first define a dummy<br>variable equal to 1 if the firm reported major or<br>very severe obstacle and 0 otherwise. Next, we<br>take the average of the dummy variable where<br>the average if taken over all other firms (that<br>is, excluding the firm in question) in the same<br>country-industry cell. Industry grouping is<br>defined at the 2-digit ISIC Rev. 3.1 level.<br>There are 7 industries in our sample.|

48
Figure 6. Policy measures during the pandemic <!-- Start of picture text --> A. Policy rates B. Asset purchases in EMDEs<br>Percent World Percent of GDP<br>8 Advanced economies 7<br>EMDEs<br>6<br>6<br>5<br>4<br>4<br>3<br>2<br>2<br>1<br>0 0<br>2010 2012 2015 2018 2021 EAP ECA LAC SAR SSA<br>C. Fiscal measures in response to the D. Fiscal measures in response to the<br>COVID-19 pandemic COVID-19 pandemic, by region<br>Percent of GDP Percent of GDP<br>20 8<br>Additional spending and forgone revenue<br>15 Additional spending and 6 Equity, loans, and guarantees<br>forgone revenue<br>10 Equity, loans, and 4<br>guarantees<br>5<br>2<br>0<br>LICs EMDEs Advanced 0<br>economies EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> Sources: Bank for International Settlements; Haver Analytics; International Monetary Fund; Kose et al. (2021); Kose, Sugawara, and Terrones (2020); Organisation for Economic Co-operation and Development; World Bank. A. Quarterly nominal policy rates. Aggregates are calculated using real GDP in U.S. dollars as a weight. Sample includes 153 countries, consisting of 36 advanced economies and 117 EMDEs. Last observation is 2021Q2. B. Announced or completed purchases (where no announcement exists) relative to 2019 nominal GDP as of November 2020. Bar shows average in each region. Orange whiskers show regional range. Red line shows average of advanced economy programs launched in 2020.

C.D. Country-level data are as reported in October 2021 Fiscal Monitor by the International Monetary Fund. Country groups are weighted by GDP in U.S. dollars for 2019. Revenue and spending measures exclude deferred taxes and advance payments.

26
was progressively reduced to partly dampen the impact of higher world oil prices on the retail fuel price; only three years earlier the tax had been 87 cents per gallon.

**Table 2: Selected Parameter Assumptions for the Baseline Simulations**

|||Mil|eageparame|ters|Total or|
|---|---|---|---|---|---|
||Auto|Minibus|Bus|Rail|average|
|Annual passenger miles per capita|3,723|1,067|371|427|5,588|
|Annual vehicle miles per capita|2,296|139|12|2|2,449|
|Average vehicle speed, mph|14.3|10.5|11.4|20.5|14.1|
|Average vehicle occupany|1.6|8|31|174|2.3|
|Current fares or tolls, cents per passenger mile|na|6.0|6.0|4.0||
|Marginal operating cost, cents per passenger mile|na|6.0|10.5|6.8||
|Vehicle miles per gallon|17.4|7.8|4.3||16.8|
|Passenger miles per gallon|28.3|60.1|131.6||39.1|
|_Marginal external costs_||||||
|Congestion, cents per vehicle mile|27|81|136|0|30.7|
|Congestion, cents per passenger mile|17|11|4|0|13.4|
|Accidents, cents per vehicle mile|1.9|1.9|1.9|0|1.9|
|Accidents, cents per passenger mile|1.2|0.2|0.1|0|0.8|
|total, cents per passenger mile|17.9|10.8|4.5|0|14.3|
|local and global pollution, cents per passenger mile|3.5|1.5|0.8|0|2.7|
|total external cost, cents per passenger mile|21.4|12.3|5.2|0|17.0|
|_Mileage elastcities_||||||
|own mileage elasticity wrt own fuel price or fare|-0.30|-0.30|-0.5|-0.3||
|_Relative price coefficients for mileage_||||||
|Fraction of reduced auto travel going to mode|na|0.46|0.16|0.18||
|Fraction of reduced microbus travel going to mode|0.35|na|0.16|0.19||
|Fraction of reduced bus travel going to mode|0.35|0.25|na|0.10||
|<br>Fraction of reduced rail travel going to mode|0.35|0.26|0.09|na||
|Fraction of reduced auto/microbus travel going to other mode|na|na|0.28|0.32||
||Gasolineparam|eters||||
|Fuel use, gallons per capita|149.4|||||
|Current (effective) fuel tax, c/gal.|17|||||
|Retail price of fuel, c/gal.|221|||||
|_External cost per gallon_||||||
|Local pollution, cents per gallon|90|||||
|Global pollution, cents per gallon|9|||||
|total|99|||||
|_Own-price fuel elasticity_|-0.60|||||

Notes. Figures are for year 2005 or thereabouts. All monetary data (prices, taxes, externalities, operating costs, etc.) are expressed in US dollars. See text and Appendix for details. Since emissions resulted from electricity generation are accounted for in the electricity sector and electricity sector specific policy instruments are normally implemented to reduce the emissions, we have not accounted for those emissions in the transport sector to avoid double counting.

21
Our survey data provide additional information which can help us interpret the results in Tables 5 through 8. The fact that subsidies have a temporary effect and that there is little evidence that the increase in profits is greater than the subsidy amount suggests that labor markets work without major frictions for the micro enterprises in our sample. This picture is consistent with the view that urban informal labor markets working reasonably well. One of the advantages of experiments is that they create shocks to opportunities that allow researchers to observe how actors respond. The survey data provide details that help us understand the aggregate responses we observe in Tables 5-8.

We begin by looking at responses as they relate to key aspects of the heterogeneity examined in Table 8.

- _Learning about type and managerial ability_ : Did owners learn about their types, for example, did they learn about their ability to manage workers? Among those with a worker hired under the program who left, we asked which among several reasons the worker left. One option was that the owner had come to realize he was not able to properly manage the worker. Across several rounds and more than 30 such cases, this reason was only ever given once. There is a somewhat larger sample of owners who were eligible for the subsidy but never hired a worker. In October 2009, we asked these owners whether any of 12 reasons for not hiring were important in their case. Only 13.3 percent of the owners said concern about their ability to manage an employee was a reason for not hiring. The far more common responses for not hiring related to a lack of demand for labor, including that the additional employee would not be profitable (43.4 percent), that the enterprise does not require an additional employee (26.5 percent, and that the subsidy is not large enough (13.3 percent). Together, these data suggest that learning about managerial ability is not a central issue among these firms; the lack of hiring appears mainly to reflect a lack of demand for labor. However, we note that a sizeable number of respondents not hiring workers stated that they had not yet found the right employee (22.3 percent) or that they lacked the capital required to make the employee productive (27.1 percent). Hence, these data do indicate some potential role for search frictions and capital constraints.

- _Importance of access to capital_ : Among those not hiring workers, responses from the October 2009 survey indicate that only 40 percent would need to make a capital investment

24
# **Tables**

Table 1: Summary Statistics for Sub-Saharan Africa

|Variable|Irrigated<br>Cropland|Rain-fed<br>Cropland|Difference|p-value|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Harvested Area (1000 Ha)|0.00|0.09|-0.08|0.00***|
|Production (1000 T)|0.57|0.23|0.33|0.00***|
|Yield (T/Ha)|14.70|5.00|9.70|0.00***|
|Production Gap(1000 T)|0.57|0.63|-0.06|0.00***|

Notes. In this table we compare agricultural outcomes across rain-fed croplands and irrigated croplands. Columns (1) and (2) compute the averages of the outcomes for rain-fed- and irrigated- croplands, respectively. Column (3) indicates the difference in their means, whereas column (4) denotes whether these mean differences are statistically significant. *p _<_ 0.1 **p _<_ 0.05 ***p _<_ 0.01.

Table 2: Aquifer Types Across SSA

|Aquifer<br>Type|Share of Area<br>(%)|Volume<br>(Billion Liters)<br>V|olume Per Capita<br>Per Day<br>(Liters)|
|---|---|---|---|
|(1)|(2)|(3)|(4)|
|Local Shallow|60.77|1698302|4530|
|Karstic|4.97|138873|370|
|Major Alluvial|8.16|228088|608|
|Complex|26.10|729357|1945|

Notes. This table presents descriptive statistics on the four aquifer types in Sub-Saharan Africa. Column (1) specifies the aquifer type. Column (2) is the mean share of the aquifer grid area that is covered by the aquifer type. Column (3) presents the corresponding total groundwater volume. Column (4) presents the total groundwater volume available per person per day. To compute the volume per capita per day, we divided the volume by the total population of the country that each groundwater aquifer grid lies in and then summed up the volume per-capita measure. Subsequently, we divided the measure by 365 days to obtain an estimate of groundwater availability per person per day. We use the World Bank’s curated groundwater aquifer dataset to construct this table.

26
<!-- Start of picture text --> a POA Test of krawn beta eq.ta betaft) [RE]<br>fa See!<br>te tee a for Sa ah a ‘<br>an See<br>138: 1380 1s 138 13 1sa7 124 1<br>1 inthe 4 signee bevel<br><!-- End of picture text --> <!-- Start of picture text --> ne5 Tout of= inewn bese eq.te baat)<br>oe eee acey nese PN Fal ce ave ncnne ney masse [—ee ees ||<br>1 a<br>We,<br>a ett meats I : gael etcetera en F<br>an cao . iF : bee Say Pk :<br>on NY Bs<br>ast T<br>195 128 aT 15 1<br>Tisthe 5 Sigraiicences bevel<br><!-- End of picture text -->

<!-- Start of picture text --> ‘s Test ofknows Beta eg, bo beta{e)<br>AA =e<br>Vp: “|A i fy f \<br>i ' \ pe | Ce a Hate<br>| a<br>gl woe | spepesaaca ee ee eee ae eee<br>ce] \ T _— mi,<br>bbe 1583 aH 155 ee} Tar 1a<br>‘Tis the 7 sigeticance evel<br><!-- End of picture text -->

<!-- Start of picture text --> . Tet of knows Beta eq. to betaiel<br>: ;<br>i E — |<br>oe error aE |: at i a re : |<br>; L.<br>1960 19) 154 Tas 158 107 ras Ter)<br>eeed |<br><!-- End of picture text -->

<!-- Start of picture text --> om Teatol knight beta ea te betalt)<br>[—_irez<br>:- : er‘i ae Heaueots oe vee se<br>f 4 ak,<br>0 : 2 ays \. ar : a a .<br>i<br>ow | - - : nie , : iplaratty Tati ies tec<br>aa<br>oat - ‘<br>1a Tats eat 13 1a<br>ee |<br><!-- End of picture text -->

<!-- Start of picture text --> 7 Teat ofKina beta ej.te etait)<br>- [=<br>5 sy \\ Ay\ a<br>re | Hate ae I i \ \ ‘A oe) '<br>a \] \<br>ie) Se eee ‘ at Soha :<br>ol T T ———"<br>ime TH beet 1866 14 is 71<br>Tisthe fh cogretcemicsbowel<br><!-- End of picture text -->
- 18 -

**<u>Table 6. Summary statistics</u>**

|Variable|Abb.|Obs.|Mean|Std.Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Dummy variable for rail freight users|_RAIL_|900|0.03|0.17|0.00|1.00|
|Price paid per value-km (TSh)<sup>1</sup>|_PRICE_|900|43.36|526.28|0.00|13231.41|
|Distance (km)|_DIST_|900|425.70|491.87|0.00|2346.48|
|Average speed (km/hour)|_SPEED_|900|2.57|5.70|0.00|49.65|
|Value of shipment (TSh billion)|_VALU_|900|5.42|54.71|0.00|900.00|
|Age of firm|_AGE_|900|14.68|12.88|1.00|82.00|
|Education level attained by firm manager|_EDU_|900|3.48|1.33|0.00|5.00|
|interviewed (0: No education; 1: Primary; 2:|||||||
|Secondary; 3: Vocational; 4: Bachelor degrees; 5:<br>Above)|||||||
|Foreign ownership share (%)|_FORN_|900|12.00|31.42|0.00|100.00|
|Dummy for local market oriented firms|_LOCL_|900|0.80|0.40|0.00|1.00|
|Dummy for national market oriented firms|_NATN_|900|0.64|0.48|0.00|1.00|
|Dummy for import transactions|_IMPT_|900|0.07|0.25|0.00|1.00|
|Dummy for export transactions|_EXPT_|900|0.11|0.32|0.00|1.00|
|Memorandum items:|||||||
|Transport spending (TSh billion)||900|0.17|2.40|0.00|70.00|
|Transport cost estimate (US$ per ton)|_TC_||||||
|Alternative = Road||911|29.78|34.84|0.00|181.74|
|Alternative = Rail (and Road)||911|27.66|31.48|0.00|143.78|
|Transport time estimate (hours)|HR||||||
|Alternative = Road||911|6.53|7.77|0.00|43.86|
|Alternative = Rail(and Road)||911|7.40|8.85|0.00|44.93|

1 Multiplied by 100 for presentation purposes.

**<u>Table 7. Two-sample t test statistic tests between road and rail users</u>**

||Road users<br>Mean<br>|<br>Std. Err.|Rail benefi<br>Mean|ciaries<br>Std. Err.|Difference<br>Coef.|<br>Std. Err.|
|---|---|---|---|---|---|---|
|Foreign share (%)|6.152|(1.288)|<br>20.294|<br>(9.220)|<br>-14.142|(6.023) **|
|Market orientation dummy:|||||||
|Global market|0.131|(0.019)|<br>0.294|<br>(0.114)|<br>-0.163|(0.086) *|
|National market|0.482|(0.028)|<br>0.882|<br>(0.081)|<br>-0.401|(0.123) ***|
|Local market|0.857|(0.019)|0.529|<br>(0.125)|0.327|(0.090)***|

# **IV. ESTIMATION RESULTS**

Multinomial logit regression is performed: The results are shown in **Table 8** . Road transportation is used as a baseline. It is found that prices have a negative impact on rail use. The impact is statistically significant: When rail prices are higher, firms are less likely to
39

# **Table 6**

_<u>Content—Diagnostic table</u>_

|ContentVariables|Top<br>Performers|Obs|At-scale programs|Obs|
|---|---|---|---|---|
|_Robust characteristics_|||||
|Focus is counseling|0%|16|3.60%|139|
|Focus is a specific tool|0%|16|6.47%|139|
|No subject focus|0%|16|8.33%|48|
|Training involves lesson enactment|62.50%|8|72.66%|139|
|_Characteristics with large coefficients_|||||
|Focus is subject content|81.25%|16|27.34%|139|
|Subject focus is math|12.50%|16|54.17%|48|
|Subject focus is information technology|6.25%|16|22.92%|48|

Obs refers to the number of PD programs in each sample (top performing, evaluated programs, and at-scale programs) that report whether or not they have a given characteristic.
|P157416|2017|2017|3/26/2017|1 Active|Europe and Central As|
|---|---|---|---|---|---|
|P157416|2017|2018|11/6/2017|2 Active|Europe and Central As|
|P157416|2017|2018|5/22/2018|3 Active|Europe and Central As|
|P157416|2017|2019|12/3/2018|4 Active|Europe and Central As|
|P157416|2017|2019|6/4/2019|5 Active|Europe and Central As|
|P157416|2017|2020|12/19/2019|6 Active|Europe and Central As|
|P157416|2017|2020|6/16/2020|7 Active|Europe and Central As|
|P157425|2017|2017|3/13/2017|1 Active|Ecuador|
|P157425|2017|2018|10/19/2017|2 Active|Ecuador|
|P157425|2017|2018|5/3/2018|3 Active|Ecuador|
|P157425|2017|2019|11/29/2018|4 Active|Ecuador|
|P157425|2017|2019|6/10/2019|5 Active|Ecuador|
|P157425|2017|2020|12/31/2019|6 Active|Ecuador|
|P157425|2017|2020|6/26/2020|7 Active|Ecuador|
|P157473|2017|2017|6/30/2017|1 Active|Gabon|
|P157473|2017|2018|12/31/2017|2 Active|Gabon|
|P157473|2017|2019|7/6/2018|3 Active|Gabon|
|P157473|2017|2019|1/25/2019|4 Active|Gabon|
|P157473|2017|2020|9/12/2019|5 Active|Gabon|
|P157473|2017|2020|4/8/2020|6 Active|Gabon|
|P157484|2016|2018|7/10/2017|1 Closed|Caribbean|
|P157484|2016|2018|1/29/2018|2 Closed|Caribbean|
|P157489|2017|2018|2/15/2018|1 Active|Serbia|
|P157489|2017|2019|8/11/2018|2 Active|Serbia|
|P157489|2017|2019|10/19/2018|3 Active|Serbia|
|P157489|2017|2020|9/26/2019|4 Active|Serbia|
|P157490|2016|2017|11/10/2016|1 Active|Indonesia|
|P157490|2016|2017|6/29/2017|2 Active|Indonesia|
|P157490|2016|2018|2/28/2018|3 Active|Indonesia|
|P157490|2016|2019|12/20/2018|4 Active|Indonesia|
|P157490|2016|2019|6/26/2019|5 Active|Indonesia|
|P157490|2016|2020|12/26/2019|6 Active|Indonesia|
|P157490|2016|2020|6/29/2020|7 Active|Indonesia|
|P157507|2018|2018|6/29/2018|1 Active|Pakistan|
|P157507|2018|2019|2/5/2019|2 Active|Pakistan|
|P157507|2018|2020|9/13/2019|3 Active|Pakistan|
|P157507|2018|2020|3/24/2020|4 Active|Pakistan|
|P157507|2018|2021|7/19/2020|5 Active|Pakistan|
|P157521|2017|2018|10/23/2017|1 Active|Zambia|
|P157521|2017|2018|5/9/2018|2 Active|Zambia|
|P157521|2017|2019|11/8/2018|3 Active|Zambia|
|P157521|2017|2019|5/20/2019|4 Active|Zambia|
|P157521|2017|2020|11/7/2019|5 Active|Zambia|
|P157521|2017|2020|5/18/2020|6 Active|Zambia|
|P157531|2017|2017|6/26/2017|1 Active|Haiti|
|P157531|2017|2018|12/31/2017|2 Active|Haiti|
|P157531|2017|2018|6/28/2018|3 Active|Haiti|
farm employment, especially through rural-to-urban labor migration, has played an important role in the nation’s structural transformation and has been a source of economic growth (Zhang and Song 2003; Cai and Wang, 2010). The rise of off-farm employment for the farm population was one of the most salient features of China’s development during the 1980s and 1990s. According to the 2000 China National Rural Survey, only 15 percent of the rural labor force had a job off-farm in the early 1980s. By 2000 the share of the rural labor force that worked off the farm reached 45.3 percent (Wang et al., 2011). With a rural labor force exceeding 500 million, this means that in 2000 more than 218 million individuals were working fully or part time off the farm (Gile, 2006; NSBC, 2001). The upward trend in the share of the rural labor force with off-farm employment continues to rise. From 45.3 percent in 2000, more than 60 percent of the rural labor force is working off the farm in 2011.

When reviewing the process of adopting labor-saving technology in agriculture, it is generally assumed that this can be achieved through substituting machine-based engineering technology for labor (Hayami and Ruttan, 1985). This, in turn, helps to save more labor time for nonfarm activities, potentially increasing income from other sources.

Initially, the effort to promote appropriate mechanization dates back to the stage of the collective system before 1978. Even though large inefficiency is attributed to this collective institution, causing less motivated production and other adverse social effects, certain remarkable achievements have been acknowledged (Lin, 1991; 1992). Specifically, agricultural machinery stations at different administrative levels were established to provide machine operation services at the fixed price. Projects were designed to provide machine operations including plowing, sowing and reaping within villages, or production teams who were equipped with large or medium-sized machines, especially tractors. This institutional mechanism also facilitated mutual aid among neighboring farmers to operate small motorized farming machines in peak seasons. As a result, mechanical farm operations increased gradually, e.g., about 45
16 a "Solow residual" defined as real per capita GDP growth minus (0.3) times the growth rate of the capital stock per person. The analyses include a matrix of conditioning information, X, that controls for other factors associated with economic growth (e.g., income per capita, education, political stability, indicators of exchange rate, trade, fiscal, and monetary policy). KL estimated the following regressions: G(j) = _a_ + _b_ DEPTH + _cX_ + u.

Adapted from KL, Table 1 indicates that there is a statistically significant and economically large relationship between DEPTH and (a) long-run real per capita growth, (b) capital accumulation, and (c) productivity growth. The coefficient on DEPTH implies that a country that increased DEPTH from the mean of the slowest growing quartile of countries (0.2) to the mean of the fastest growing quartile of countries (0.6) would have increased its per capita growth rate by almost 1 percent per year. This is large. The difference between the slowest growing 25 percent of countries and the fastest growing quartile of countries is about five percent per annum over this 30-year period. Thus, the rise in DEPTH alone eliminates 20 percent of this growth difference. The illustrative example, however, ignores causality and the issue of how to increase DEPTH.

KL also examine whether the value of financial depth in 1960 predicts the rate of economic growth, capital accumulation, and productivity growth over the next 30 years. As shown in Table 2, the regressions indicate that financial depth in 1960 is a good predictor of subsequent rates of economic growth, physical capital accumulation, and economic efficiency improvements over the next 30 years even after controlling for income, education, and measures