rafmacalaba/datause-encoder-data huggingface dataset:
We use indicators of growth in aggregate output, income per capita, inflation, and population to control for the economic growth during the global crisis period, for the difference in economic development, for the change in price level, and for the difference in the country size. The nominal GDP (henceforth GDP in short), GNI per capita, inflation and total population data were obtained from World Bank’s WDI database and all three variables other than inflation were log transformed. To control for the impact of GDP growth on deposit growth, we calculate the GDP growth over the period during which the deposit growth declined most in each country.

We included indicators for deposit insurance, bank stability, bank liquidity, banking concentration, deposits to GDP ratio, loan to deposit ratio, and capital account openness to capture the cross sectional variation in financial sector structure, development, and openness. Deposit insurance is a dummy variable indicating existence of an explicit deposit insurance scheme as documented in Demirguc-Kunt, Karacaovali, and Laeven (2005). We use banking system z-score as the indicator for financial stability and the ratio of liquid assets to deposits and short-term funding as the indicator for bank liquidity. The data on both indicators were obtained from World Bank’s Global Financial Development Database. Capital account openness is measured by Chinn-Ito financial openness index (Chinn and Ito, 2006). Banking concentration is approximated by total assets of a country’s three largest banks as a share of assets of all commercial banks. The data for bank concentration, loan to deposit ratio and deposits to GDP ratio were obtained from World Bank’s FinStats database. Summary statistics of all variables are provided in Table A3 and pairwise correlations of variables are shown in Table A2.

Given concerns that small island countries could have very different macroeconomic and institutional characteristics compared to other countries of the same income-group, we removed such countries from our sample.<sup>3</sup> Our sample is further reduced to 95 countries for regression estimation due to data availability on banking sector indicators.

# **5. Baseline Estimation Results**

This section discusses the baseline estimation results presented in Table 1. Namely, Table 1 shows both the full model estimation results and the parsimonious estimation results based on adjusted R-squared maximization. Further, the columns labeled OLS show the OLS estimation results, the columns labeled Robust show the robust regression estimates, and the GMM(1) column shows the GMM estimation of the model where gdpgr is treated as possibly endogenous variable and instrumented by the log of GDP at the peak of the deposit growth cycle prior around 2008. In addition to GMM(1), GMM(2) uses D-K&K(2012) measure of access to deposits from 2011, which we instrument by Honohan(2008)—the pre-crisis measure of access to deposits.

> 3 The actual impact of excluding small island countries from the sample on the sample size used in the regression analysis is negligible since many of such countries do not have banking sector data available.

11
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.5** . **Port performance compared across African regions**

||East Africa|Southern Africa|**Central/ West**<br>**Africa**|Global best<br>practices|
|---|---|---|---|---|
|**Performance**|||||
|Container dwell time (days)|5–28|4–8|**11–30**|<7|
|Truck-processing time (hours)|4–24|2–12|**6–24**|1|
|Crane productivity (containers per hour)|8–20|8–22|**7–20**|20–30|
|Crane productivity (tonnes per hour)|8–25|10–25|**7–15**|>30|
|**Charges**|||||
|Container handling (US$ per TEU)|135–275|110–243|**100–320**|80–150|
|General cargo handling (US$ per tonne)|6–15|11–15|**8–15**|7–9|

_Source:_ AICD Ports database. _Note:_ TEU = twenty-foot equivalent unit.

Individual port performance varies significantly across ECCAS countries (table 2.6). But even the best performers do not measure up to good global performance standards. Container dwell times are particularly high, around 25 days in Matadi in the Democratic Republic of Congo. Container dwell time is between 10 and 25 days, significantly worse than the global benchmark of 4 to 8 days. Truck-processing times are also relatively high in ECCAS’s ports. In Matadi it takes as many as 18 hours for truck processing. Douala, which is considered one of the better performers in ECCAS, records a high crane productivity of 18.5 containers an hour—but this is only around 60 percent of global productivity standards. Prices are also exorbitant in most ports in ECCAS. The ports of Luanda and Owendo, for example, are among the most expensive in Africa, charging $320 per twenty-foot equivalent unit (TEU). Douala, which is one of the most used ports in Central Africa, charges $220 per TEU. General cargo handling is around $16 per tonne in Owendo, about twice the global benchmark. Douala and Pointe-Noire charge $5.50 to $6.50 per tonne, which is consistent with global good standards.

**Table 2.6.** P **erformance compared across West African ports**

||Luanda<br>(Angola)|Douala<br>(Cameroon)|Matadi<br>(Congo,<br>Dem. Rep.<br>of)|Owendo<br>(Gabon)|Pointe-Noire<br>(Congo, Rep.<br>of)|
|---|---|---|---|---|---|
|Container dwell time(days)|12|12|25|10|18|
|Truck-processing time (hours)|14|12|18||12|
|Container crane productivity (containers per hour)|6.5|18.5|6.5||6.5|
|Container-cargo-handlingcharge(US$ per TEU)|320|220|120|340|140|
|General-cargo-handling charge (US$ per tonne)|8.5|6.5|10|16|5.5|

_Source:_ AICD ports database. _Note:_ TEU = twenty-foot equivalent unit.

23
-26-

- What type of organization structure should we have to achieve the goals set by management?

- What type of staff structure is most appropriate? What are the needs in terms of staff rationalization, retraining, recycling? How can we entice staff to quit and yet provide adequate compensation?

- What are the impediments to abandon branch lines where the railway has no comparative advantage?

- Is our costing system I/ providing disaggregate information which allows us to assess adequately the profitability of each commodity ? What can be done to improve costing?

- Is our accounting system providing accurate and on time information for financial management purposes?

- Is our planning and statistics department providing the accurate information required for day to day management?

- = Should we commission a study to investigate where management information systems J/ can help in improving productivity and propose a M.I.S. plan?

- z Are there any other ways of tapping revenue such as selling or leasing railway land, sell idle workshop time, etc.?

- e Can any of the present activities of the railway be subcontracted to the private sector? Should joint ventures be undertaken and why ?

/ For a good discussion of railway costLng see 'Railway Costing: A Review' Canadian Transport Commission, Rep. 3 1986/023. **The Bank has financed the preparation** of a Lotus based spreadsheet to **compute long run marginal costs in developing railways using default values whore the basic information in not used. The name of this program is** 'OCAR' and **is prosently belng tested in African Railways**

**2/** For a good **discussion** of Management Information Systems, see D. Porter NModular Management Information Systems for Railways*, IDRD, 1988.
<!-- Start of picture text --> 16,000<br>Figure 1: U.S. Actual Income and Model Simulation, 1990-2005<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>y1990 y1991 y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009<br>Source: Authors Simulations Equilibrium_Income Actual_Income<br><!-- End of picture text --> <!-- Start of picture text --> Table 1: Simulated Multipliers Government Expenditure ������ � 1 0.39<br>�� |�|<br>Tax ������ � ��� -0.27<br>��� |�|<br>Trade ������ � ���� 0.19<br>�� � |�|<br>Source: Authors Simulations<br><!-- End of picture text -->

# **7. Conclusions**

We extended an IS-MP model to incorporate a few realistic features and derived and discussed fiscal and trade multipliers. The main finding is that government spending, tax cut, and trade multipliers are relatively smaller in magnitudes when more realistic features are incorporated in a model. The model simulation shows that the model can track the actual US nominal GDP reasonably well. However, further work is needed to improve the model 19
competition appears positively correlated with both sales and profits. One possible reason is that large firms simply have more competitors for rather obvious reasons: they need to market their products more widely. Arguably, documenting the effect of competition on productivity is more interesting, and we return to this issue in Section 3.3.3. In addition, we find evidence that enterprise profits are higher in areas where wages are higher, though there is no significant association between wages and enterprise sales.

Regarding the role of shocks, there is strong evidence that droughts reduce sales and profits in the nonfarm enterprise sector. In addition, households which experienced a death of a household member have significantly higher levels of sales, but not profits. Interestingly, no other shock variable has a significant coefficient in these regressions. The drought result is economically rather substantial, indicating that one additional drought-year reduces sales by about 24%, and profits by 30%. Taken together with the positive effect of agricultural performance alluded to above, this finding really reinforces the notion that the performance of the agricultural sector impacts strongly on the performance of the nonfarm enterprise sector.

Finally, we consider the variables meant to proxy for credit availability and the household’s financial capacity to deal with shocks. Column 2 in Table 16 shows that the level of sales in enterprises linked to households that are able to raise 100 Birr in one week for emergency needs is about 35% higher than sales of other enterprises. The coefficient is significant at the 5% level. This result could suggest enterprises linked to households in a relatively strong financial position have a lower cost of capital (i.e. less likely to be unable to finance investment), though it is also possible the result is driven by reverse causation (i.e. high sales strengthens the financial position). Indeed, the coefficient on the financial capacity dummy is totally insignificant in the profit regression, and so overall, at this point, its impact will have to be considered quite weak. As we shall see below, further probing of the effects of this variable in the context of modeling investment will prove quite informative. The variables proxying for the availability of credit are all insignificant at the conventional 5% significant level, both individually and jointly, though most of them have the expected positive signs.

# _Investment and labor growth_

We now turn to investment and labor growth. Table 17 shows results from probit regressions modeling the likelihood of buying equipment (investment), and growth regressions where the dependent variable is the change in labor days worked per year since startup. As regards the model specifications, we adopt the same strategy as for sales and profits, i.e. we consider both a parsimonious and an extended specification. The explanatory variables are the same as before, except we have added controls for initial conditions with respect to capital and labor. Adding these latter variables is natural, given that we are now modeling aspects of firm growth.

-16-
13 Table 1: DESCRIPTIVE STATISTICS OF THE DISTRIBUTIONS OF INDIVIDUAL INFLATION RATES

|_quarter_|_inflation_|_var_|_sk_|_wvar_|_wsk_|_Tvar_|_Tsk_|
|---|---|---|---|---|---|---|---|
|89Q1|0.295|0.275|6.523|0.342|1.345|9.481|0.721|
|89Q2|0.249|0.020|_0.655_|0.219|2.554|1.946|0.030|
|89Q3|1.053|1.615|2.510|17.433|3.943|243.370|0.277|
|89Q4|1.230|1.015|4.542|5.658|2.588|43.326|0.384|
|90Q1|1.319|0.980|0.889|4.740|1.955|55.905|0.125|
|90Q2|0.163|0.032|0.861|0.247|2.752|2.778|0.115|
|90Q3|0.103|0.035|2.127|0.064|0.151|1.527|0.050|
|90Q4|0.174|0.041|2.083|0.302,|3.298|4.161|0.251|
|91Ql|0.257|0.039|0.991|0.216|1.459|7.068|0.367|
|91Q2|0.106|0.081|3.523|0.191|2.762|6.105|0.322|
|91Q3|0.050|0.029|0.679|0.119|-2.203|2.550|-0.107|
|91Q4|0.098|0.011|2.328|0.079|2.216|1.198|0.225|
|92Ql|0.116|0.005|0.632|0.084|3.273|1.241|0.652|
|92Q2|0.096|0.005|0.645|0.044|2.156|0.450|0.124|
|92Q3|0.097|0.011|1.481|0.091|1.799|1.195|0.027|
|92Q4|0.077|0.004|2.388|0.039|2.846|0.594|0.208|
|93Q1|0.099|0.003|2.250|0.033|2.031|0.431|0.184|
|93Q2|0.056|0.003|-0.909|0.025|1.623|0.465|-0.061|
|93Q3|0.060|0.012|-1.497|0.122|-1.790|1.968|-0.227|
|93Q4|0.119|0.015|2.930|0.112|3.056|1.632|0.248|
|94Q1|0.050|0.019|-5.071|0.036|4.340|1.638|-0.399|
|94Q2|0.071|0.006|2.819|0.038|4.490|0.729|0.326|
|94Q3|0.079|0.012|3.616|0.019|0.971|0.726|0.334|
|94Q4|0.067|0.013|5.183|0.023|1.993|0.696|0.447|
|95Q1|0.081|0.005|-0.598|0.030|2.490|0.541|0.040|
|95Q2|0.052|0.002|-0.248|0.010|1.524|0.169|0.018|
|95Q3|0.025|0.006|-4.429|0.043|-3.863|0.988|-0.349|
|95Q4|0.047|0.003|3.936|0.021|4.308|0.459|0.333|
|96Ql|0.065|0.002|1.257|0.020|2.117|0.265|0.104|
|96Q2|0.047|0.002|-0.997|0.013|2.293|0.238|0.005|
|96Q3|0.023|0.007|-4.009|0.073|-2.641|1.259|-0.297|
|96Q4|0.041|0.003|5.195|0.034|6.192|0.585|0.424|
|97Q1|0.049|0.001|0.696|0.013|2.215|0.198|0.072|
|mean<br>st. dev.|0.197<br>0.330|0.131<br>0.359|1.302<br>2.647|0.925<br>3.214|1.947<br>2.086|11.996<br>43.199|0.151<br>0.257|

_source: author's calculations using GUS data_
Our estimates do not account for general equilibrium effects on the returns to schooling caused by the Cultural Revolution’s impact on the aggregate relative supply of workers with different levels of education. Accounting for such effects would be important if our goal were to evaluate the welfare effects of the Cultural Revolution which is beyond the scope of this paper.<sup>16</sup> But such concerns do not undermine our use of the Cultural Revolution as an exogenous source of variation in educational attainment to identify the returns to schooling in China’s urban labor market in 2001.

We find that IV estimates of the returns to college are notably lower than OLS estimates, while the opposite is true for the returns to high school. We interpret these results to be evidence of selectivity bias associated with higher ability students entering college. This is an important finding because it suggests that simple estimates of the returns to schooling in countries with highly selective entrance exams that affect school progression may overestimate the private returns to higher education. If policymakers view the high return to college education as an indication of the scarcity of university graduates, rather than evidence of selection effects associated with entrance examinations, this may lead to support for more rapid expansion in the capacity of higher education institutions than would be otherwise warranted.

# **References**

Almond, Douglas (2006). “Is the 1918 Influenza Pandemic Over? Long-term Effects of In Utero Influenza Exposure in the Post-1940 US Population,” _Journal of Political Economy_ v114, n4 (August 2006): 672-712.

Angrist, Joshua D., and Alan B. Krueger (1991). “Does Compulsory School Attendance Affect Schooling and Earnings.” _Quarterly Journal of Economics_ 106: 979-1014.

> 16 For example, if estimated returns to high school and college are taken to be exogenous, we estimate that wages of the the Cultural Revolution cohort would have been 9 percent higher in the absence of shocks to high school and college attainment.

24
Administration of Taxation (SAT).<sup>56</sup> These estimates vary significantly, especially in terms of the size of illegal fees and levies and the valuation of compulsory labor – two components of the farmers’ burdens for which aggregate statistics do not exist. Under the higher estimate by Yang Yuanwei and his colleagues (Yang et al. 2003), prior to the RFR total resources available to township governments may have been as high as RMB 216 billion. With the elimination of fees and the abolition of agricultural taxes, township governments are almost entirely dependent on transfers from higher levels. Despite a rapid escalation in recent years, central subsidies for the RFR and agricultural tax replacement totaled only RMB 98.6 billion in 2006, leaving a shortfall of as much as RMB 120 billion for township governments in the aggregate. Put another way, according to the estimate of Yang and his colleagues, post-RFR grassroots governments have at their disposal less than half as much as before the RTFR. Even under the more benign scenario described by (then-Vice) Premier Wen, they have 14-19 percent less.

**Table 24.  The Effect of the RTFR on Rural Revenues**

|**(billion RMB)**|**WenJiabao**|**Yang et el.**|
|---|---|---|
|Figures from:|1997|circa 2000|
|Total collected revenues:|114.3|160|
|of which: agricultural taxes||34|
|townshipand village unified levies||59|
|other fees and levies||27|
|irregular fees and levies|20|40|
|plus: compulsorylabor contribution|8.1|59|
|Total mobilized resources|122.4|219|
|Replacement revenues|||
|Central transfers(2006)*|98.6|98.6|
|Revenue loss under the RTFR(1)|-17.5(-14%)|-61.4(-28%)|
|Revenue loss under the RTFR(2)|-23.6(-19%)|-120.4(-55%)|

Note: * includes rural fee reform transfer 75.1 billion and transfer to counties and townships in difficulties (three rewards and one subsidy) 23.5 billion. (1) Includes only legally permitted fees and levies, and excludes compulsory labor valuation. (2) Includes total mobilized resources.

Source: Wen Jiabao speech at National Conference on Anhui’s RFR Experience, April 13, 2000 (reprinted in State Council 2001); Yang et al. (2003); Xu (2006) and Jin (2006, 2008).

Of course, the revenue impact of the RTFR varies by region and by administrative level, in accordance with the respective weight of the agricultural taxes in their total revenue – hitting

> 56 This group is led by Yang Yuanwei, a leading figure in the Tax Policy Department at the SAT.

52
firm level analysis,<sup>51</sup> _UnreliableSupplyit_ , is a dummy variable equal to 1 if a firm reports electricity outages as a major constraint to its operation and 0 if otherwise. Arguably, exposure to outages is non-random across space and time, hence estimates from equation (9) cannot be ascribed causal interpretations. In other words, _φ_<sup>ˆ</sup> shows the association between exposure to outages and net entry of firms. Finally, I also explore how the association between outages and (net) firm entry varies between ”high energy-intensive” and ”low energy-intensive” industries. Standard errors are clustered at the location level.

# **Results**

Table 7 presents the results on the association between exposure to outages and firm density, and extent of operation. Starting with the district-level analysis, column (1-2) shows the relationship between outage intensity and the number of firms operating in a district. According to the results, a 1 pp increase in the share of firms experiencing outages is associated with a 1.7% reduction in the number of firms operating in the district.<sup>52</sup> In other words, moving from a district with (fully) reliable supply of electricity to one with an unreliable supply reduces the number of firms operating in the district by almost one-fifth. Obviously, proximity to markets is a key determinant of firm location (concentration). As a result, more (manufacturing) firms are likely to be located in densely populated areas like large cities relative to small towns.<sup>53</sup> To account for this, in column 3-4, I use firm density (i.e., number of firms per 1000 people) as the outcome and explore the association with intensity of outages experienced by firms. Again the results show a negative association. These results suggest that unreliable electricity provision has negative impacts on the entry (and exit) of firms across locations. To further understand the effects of

> 51Despite being an annual survey, information on unique firm identifiers in the LMMIS are scant (Hjort and Poulsen, 2019; Abebe et al., 2018). This limits the ability to exploit the panel structure of firms. Thus, in this analysis, I treat the data as a repeated cross-section rather than estimating within-firm variations.

> 52 _expβ_<sup>ˆ</sup> _−_ 1 = _exp−._ 0191 _−_ 1 _≈_ 0 _._ 17

> 53Access to infrastructure like roads, electricity, water, and internet are also more readily available in densely populated areas cities relative to small towns

38
# **Figure 4 OECD index of regulatory restrictions in accounting services**

<!-- Start of picture text --> Turkey<br>Italy<br>Tunisia<br>Czech republic<br>Canada<br>France<br>Netherlands<br>Luxembourg<br>Germany<br>Belgium<br>Sweden<br>Japan<br>Finland<br>Slovak Republic<br>Iceland<br>Korea<br>0 1 2 3 4 5<br>Restrictiveness index<br><!-- End of picture text --> Data source: Based on OECD (2005a).

26
# _Distributional Impact of Tariff Increases_

In this section, I examine the welfare impact of electricity price changes on consumers in different income quintiles based on the estimated demand function. The welfare loss from price increases can be approximated by the consumer surplus change. For a log linear demand function, , the consumer surplus change of households in income quintile _i_ is given by: (3) where is the price elasticity; _Q1i_ and _P1_ are the initial consumption and price, _Q2i_ and _P2_ are the new consumption and price. _Ai_ is a constant and is calculated at the means of the exogenous variables for households in income quintile _i_ . Details on the calculation of consumer surplus change of a log linear demand function are provided in the Appendix.

Table 7 summarizes the estimated consumer surplus change by income quintile. The annual welfare loss from 2008 price increases is estimated to be 164 TL for an average bottom income quintile household, or 2.16 percent of household disposable income, compared with 330 TL, or 0.75 percent of disposable income for the top quintile. Notably, the consumer surplus change is greater than the financial loss, because it also captures the welfare loss from the reduced consumption.

The results demonstrate that how price elasticities vary with household income has important welfare implications. The burden on the poorest quintile, measured by the consumer surplus change as a percentage of income, is 2.9 times of that of the wealthiest quintile. The welfare effects of electricity price changes would evidently be disproportionally borne by the lowincome segment of the population distribution.

Nonetheless, the overall welfare impact from the price spikes seems to be limited. This is because Turkish households spent a relatively small amount of their budget on electricity. Indeed, according to the World Bank, the share of electricity expenditures in household total expenditures (excluding expenditures on health, durables and rents) in Turkey was still the eighth lowest among the European and Central Asian countries after price hikes in 2008.

# _Model validation – out-of-sample test_

As discussed earlier, the empirical model does not capture the impact of regional variation in weather conditions on electricity demand. To examine the model’s validity, I predict household demand in 2009 using Equation (1) estimated with 2008 HBS data. Figure 6 and 7 compare the out-of-sample predictions to actual consumption levels in 2009. In Figure 6, the vertical-axis is the sum of the log of the monthly consumption; while Figure 7 plots the sum of monthly electricity consumption. In general, the actual and predicted series are close. Although, as more clearly shown in Figure 7, the model under-predicts overall consumption but it picks up the trend 14