rafmacalaba/datause-encoder-data huggingface dataset:
Efficiency improvements in China’s and India’s high-tech and heavy industries have much stronger trade effects than a uniform efficiency improvement of the same magnitude. This scenario will lead to severe competition in the high-tech sectors and entail substantial structural change with China and India displacing other countries in markets for high-tech products, but leaving space for other countries to increase production of light manufactures.

Some caveats are important. First, these are thought experiments and not precise predictions. While they show that China’s and India’s growth could be beneficial to nearly all other countries, and that the impact on particular countries will depend on those countries’ own trade, production and consumption profiles and on the patterns of growth in China and India, they offer only the broadest indications of likely effects. Likewise, our results strongly suggest that benefiting will depend on adapting to the new opportunities and challenges. But by themselves these results cannot dictate the necessary adjustment. They must be supplemented with sector-specific case studies both to identify the emerging patterns in general and to consider particular products, as well as services which we did not discuss in the analysis of growth and trade effects. Our aggregation hides important information on intra-industry trade in components as part of the global production sharing arrangements.

Note that the adjustment costs of this economic transformation could be substantial, but are not factored into the analysis. Finally, recall also that the paper focuses on the static trade aspects of growth in China and India; it ignores important investment-growth linkages that may amplify the effects discussed here and affect the welfare results.

# **References**

- Acemoglu, D. and Ventura, J. (2002), “The World Income Distribution”, _Quarterly Journal of Economics_ 117(2), pp. 659-94.

- Amiti, M. and Freund, C. (2007), “An Anatomy of China’s Export Growth”, paper prepared for Global Implications of China’s Trade, Investment and Growth Conference, IMF Research Department, April 6 2007.

- Ando, M. and Kimura, F. (2003), ‘The formation of international production and distribution networks in East Asia’ NBER Working Paper 10167, National Bureau of Economic Research, Cambridge, MA.

- Dimaranan, B. V., ed. (2006), _Global Trade, Assistance, and Production: The GTAP 6 Data Base_ , Center for Global Trade Analysis, Purdue University.

27
3. The share of households using traditional stoves as a whole dropped from 92% during the baseline to 50.7% by round 3, but the percentage of households using “other” traditional stoves recorded a steady increase from 4.1% to 14.8% and 17.5% over time.

**<u>Table 1: Summary statistics of household fuel consumption variables</u>**

|**Variables**|**Baseline**|**Round 2**|**Round 3**|
|---|---|---|---|
|_Fuel use_||||
|%Households using wood|37.6|31.9|32.3|
||(48.5)|(46.7)|(46.8)|
|%Households using charcoal|75.8|64.8|63.0|
||(43.9)|(47.8)|(48.3)|
|%Households using “other” biomass fuels|6.5|14.8|17.5|
||(24.7)|(35.6)|(38.0)|
|%Households using ethanol|0|18.9|18.3|
||(-)|(39.2)|(38.8)|
|_Stove use_||||
|%Households using traditional three-stone fire stoves|29.0|10.1|10.7|
||(45.4)|(30.1)|(30.9)|
|% Households using traditional metal charcoal stove|59.1|26.0|22.5|
||(49.2)|(43.9)|(41.8)|
|%Households using other traditional stoves|3.9|14.8|17.5|
||(19.3)|(35.5)|(38.0)|
|%Households using improved biomass stove|0|9.2|8.6|
||(-)|(28.9)|(28.0)|
|% Households using improved charcoal stove|8.0|21.0|22.5|
||(27.2)|(40.8)|(41.8)|
|%Households using ethanol stoves|0|18.9|18.3|
||(-)|(39.2)|(38.8)|
|_Fuel consumption_||||
|Average wood consumption among users (kg/week)|75.8|57.5|58.8|
||(144.9)|(54.5)|(49.6)|
|Average charcoal consumption among users (kg/week)|21.7|20.2|21.8|
||(14.7)|(13.5)|(16.1)|
|Average ethanol consumption among users (liters/week)|0|4.0|4.7|
||(-)|(2.0)|(2.0)|

* Note: Figures in parentheses are standard deviations. Information on the consumption of other inferior fuels was not collected. The sum of the percentage figures within the sample can exceed 100 because households used multiple fuels and multiple stove types. The averages of the quantity of fuel consumption were calculated for users only, that is, users of zero-consumption were excluded. Source: World Bank household survey (2009-2010)

_<u>Fuel Consumption:</u>_ As far as the quantity of fuel consumption is concerned, information was self-reported by respondents. There was virtually no change in the average quantity of charcoal consumption from the baseline (21.7 kg/week) to round 3 (21.8 kg/week) despite the distribution of improved charcoal stoves to a subset of households, which is counter-intuitive. On 8
countries in the region in June and July 2009 and the Business Environment and Enterprise Performance Survey (BEEPS), which was conducted in 28 countries in the region in 2002, 2005, and late 2008 and early 2009.<sup>7</sup> The fourth section discusses the estimation methodology used to test the various hypotheses; the fifth discusses the empirical results and presents robustness checks. The final section concludes.

# **II. Related Literature and Hypotheses**

In this section, we develop hypotheses about how the economic crisis affected firms in the ECA region. Although our regressions will control for a number of factors that could affect firms‘ financial constraints and survival, the hypotheses in this section focus on two main themes: the pecking order theory of external finance and foreign bank participation.

# **A. Pecking Order Theory of Financial Services**

The first set of hypotheses derives from the pecking-order theory of financial services. This theory holds that internal funds are less costly than external funds (Myers and Majluf, 1984), because borrowing firms have better information about the quality of their investment opportunities than lenders do. The lender, therefore, requires compensation in the form of higher interest rates. Because external funds are more costly, firm owners first exhaust internal funds before turning to credit markets, leading to a pecking order with respect to the two sources of finance.

Informational asymmetries between borrower and lender tend to be more severe for small, young firms, and lenders, therefore, charge them higher interest rates or do not extend credit to them at all. As a result, small, young firms use less external credit than others. This is the case in Eastern Europe and Central Asia. Whereas about 64 percent of large firms had loans or lines of credit in 2008-2009, only about 38 percent of small firms did.<sup>8</sup>

> 7 Though BEEPS was conducted in 28 countries, our sample is comprised only of firms that were interviewed in all three BEEPS rounds. That sample spans 23 countries.

> 8 Data from www.enterprisesurveys.org based upon BEEPS data.  The regional averages are unweighted averages of the weighted country averages (i.e., all countries weighted equally but weighted averages are used for each country).

6
**TABLE O COF I**

||pan|
|---|---|
|II_._|THE STRUCTURAL ADJUSTMENT: BACKGROUND<sup>AIiDPOLICYRESPONSES .....</sup><br>3|
||A.<br>BACKGROUND ...............................................<br>3|
||B.<br>POLICY RESPONSES ......................|
|IIl.|THRFINANCIAL<br>SECTOR IN EGYPT**...................................**<br>**.**5|
|IV.|THE ROLE OF INTEREST RATES IN STAB:LIZATION<sup>AND ADJUSTMENT .....</sup><br>10|
||A.<br>INTEREST RATES AND CURRENCY SUBSTITUTION<sup>.................</sup><br>11|
||DECOMPOSITION OF FINANCIAL ASSETS ...........|
||REALINTEREST RATE DT.YFERENTIALS<br>**...............**<br>**...**16|
||B.<br>INTEREST RATES ANC WORKERS' REMITTANCES<br>1.................19|
||C.<br>INTEREST RATE AND INVESTMENT EFFICIENCY<sup>.............</sup><br>20...20|
|V.|IMPLICATIONS<br>OF HIGH INTEREST RATES**.............**<br>**2**<br>**.3........23**|
||A.<br>INTEREST RATE AND BUSINESS INVESTMENT<sup>....................</sup><br>24|
||B.<br>IMPACT ON THEBANKING<br>SECTOR.......................<br>......26|
|VI.|POLICY IMPLICATIONS<br>..........................<sup>**.**</sup><br>**......**<br>.<br>29|
|REFER|ENCES .*...... ..*...... ...<sup>#**..</sup><br>o..... 35o......<br>**LIST**OF FIGURES**AND**TABLES|
|FIGUR<br>FIGUR<br>FIGUR<br>FIGUR<br>FIGUR|E1, DEPOSIT BANKING INSTITUTIONS<sup>.........................</sup><br>..<br>8<br>E 2, (FOREIGN CURRENCY DEPOSITS)/(M2)<sup>...........................</sup><br>12<br>E 3, EVOLUTION OF LOCAL REAL MONEY<sup>-</sup><br>BALANCE .....................<br>18<br>E 4, EVOLUTION OF REAL BANK TIME<sup>DEPOSITS ........................</sup><br>18<br>E S, EVOLUTION OF BANK DEPOSITS<sup>IN FOREIGN CURRENCY ..............</sup><br>18|
|TABLE<br>TABLE|1, CURRENT STRUCTURE OF INTEREST<sup>RATES ...........................</sup><br>9<br>2, COMPOSITION OF HOUSEHOLD<sup>FINANCIAL ASSET HOLDING AND MEAN</sup><br> <br>****<br>14|
|TABLE<br>|NOMINAL RETURN<br>......<br>....#so..............................<br><br>3, GROWTH OF MONETARY AGGREGATES<sup>ANDINFLATION</sup><br>TAX ..............<br>19<br> <br>|
|TABLE<br>TABLE<br>TABLE|4, REAL RETURN ON A STANDARD<sup>PROJECT ...........................</sup><br>21<br> 5,MAXIMUM PAYABLE INTEREST<sup>RATE AND REAL RETURN ON INVESTMENTS 22</sup><br>6, SIMULATINGTHEIMPACT<sup>OF HIGHER INTEREST RATES ONlTolREAL</sup><br> <br>26|
|TABLE|COST OF CAPITAL ......................**.** <sup>.....</sup><br>.......<br><br>7, NET LIABILITY OF MAJOR BORROWERS<sup>TO THE BANKING SECTOR ... ...</sup><br>27|

We would like to thank Millard Long,<sup>Spiroo</sup> Voyadzi., Marcelo Giugale. M. G. Maid, Emile Sawaya, David Scott, and Dimitri<sup>Vittas for thfair</sup> helpful comments _sne_ suggestions.
17 2 R -0.83 DW-2.08 Sample Period:1977.I-1986.IV

> **A** th Pt, the proxy for expected inflation, is approximated by the forecasts of an univariate VAR of inflation on its own past values. The results are reasonable, with all the coefficients of the right sign and almost all highly significantly different from zero. Seasonal dummies were also included but their coefficients are not reported here. Adjustment is quick, with a mean lag of less than 5 months (1/(1-0.28) quarter).

A similar equation for demand deposits yields the following results: (9) R2-0.98 DW-1.85 Sample Period:1977.I-1986.IV Again a reasonable equation, with a relatively high adjustment speed and strong sensitivity to interest rates and expected inflation.

Applied to time deposits, the portfolio approach yields:
# **Impact on health care use**

Table 6 reports both the average treatment effect on the treated (ATT) estimated by comparing healthcard owners and their matched controls, and the unmatched differencein-difference effect obtained by comparing healthcard owners and all potential controls.

# _Use of public sector services_

Household survey data indicate that public sector utilization rates declined substantially for children during 1997-2000 [11, 49]. The healthcard, which was introduced during this period, entitled household members to free outpatient and inpatient care services at public facilities. I tested the hypothesis that the net impact of the healthcard on the change in public sector use rates was positive from 1997 to 2000. I find that the healthcard resulted in a net increase of 3.6% in public sector utilization for healthcard children relative to others. The difference-in-difference estimates for the unmatched groups are not statistically significant and smaller in magnitude than estimates of ATT.

Results presented earlier in Table 3 point to a significant decline in public sector use rates for children between 1997 and 2000, a finding consistent with earlier work. The unmatched difference-in-difference results presented in Table 6 suggest that the decline in public sector use rates was less dramatic for children in healthcard owning households compared to others, although the estimates are not significant. ATT estimates obtained by comparing healthcard children with similar children without healthcards confirm that use of government health facilities fell significantly less for the former. In the aftermath of 38
this threshold. This phenomenon, known as ’bunching’ in the literature, has been a subject of investigation by Saez (2010). However, recent research by Mascagni and Molla (2018) reveals that there is no noticeable bunching effect below the VAT registration threshold.

Subsequently, we examine the gendered distribution of taxpayers within the categories of ’A’ (potential VAT registrants) and ’B.’ The actual disparity in registering for Category A is relatively minor, with 81% of businesses owned by men registering for category A and 79% representing women. Notably, the difference is not statistically significant, as indicated in Table 8.

Table 8: Share of Individuals by Gender and Tax Category

||Men|Women|Diferences|
|---|---|---|---|
|Share of Category A TP|0.81|0.79|0.016|
||0.01|0.02|0.881|
|Share of Category B TP|0.19|0.21|-0.016|
||0.01|0.02|-0.881|
|Number of Observations|1,654|666|.|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 We report standard errors below the proportion estimate and z-statistics below the difference.

# _4.1.4. Industrial Classification_

Examining industrial classification is vital, as it may have a close connection with gender. Our administrative data provides the industrial classification of businesses, based on the Ethiopian Standard Industrial Classification Rev. 1.<sup>8</sup> The summary statistics reveal that women-owned businesses represent a significant share of the taxpayers in specific sectors, including hotels and

> 8It is important to note that, within our dataset, approximately 30% of the taxpayers did not disclose their industrial classification in their tax registry.

18
have to come from increased use of inputs, intensification will need to be sustainable over time both economically and ecologically. African farmers have used low levels of fertilizers compared with farmers in other developing countries. Although some authors (e.g., Marenya and Barrett 2007) argue that once land is degraded, fertilizer will be ineffective, Matsumoto and Yamano (2009) do not find a diminishing yield effect of fertilizers as soil fertility decreases.

Reardon et al. (1999) suggest that intensification will have to be capital-led instead of being accomplished by employing more labor, and that abstaining from using fertilizers and other non-labor inputs can lead to soil mining and is hardly profitable. But capital-led intensification of agricultural production has its own negative environmental effects and has to be properly managed to be sustainable. Inappropriate use of fertilizers can, for example, lead to poisoning of humans and animals and water pollution, which in turn can have indirect consequences for larger ecosystems. Such negative effects have been observed in rice-wheat systems in South Asia (World Bank 2007). Hence, what is called for is well managed, capital-led intensification of agriculture, taking into account agro-ecological factors and avoiding environmental damage—what Conway and Toenniessen (2003) call the ―Doubly Green Revolution.‖ Global climate change will also have to be taken into account. Increasing global temperatures could lead to a drop in agricultural output mostly in tropical countries; and less rainfall could damage rain-fed agriculture and cause more frequent droughts. This is a special concern for Sub-Saharan Africa where most agriculture is rain-fed, so that climate change implies an increased risk for farmers. Compared with a no-climate-change scenario, agricultural GDP in Sub-Saharan Africa could decline by 2 to 9 percent. Adapting to the changing climate is, hence, a necessity for African farmers. New crop varieties that are more drought resistant and greater investment in irrigation systems are needed (World Bank 2007). Such adaption efforts should be part of the overall agricultural development strategies. Thanks to agricultural R&D, new technologies to cope with climate change already exist; but many households in Africa do not adjust their planting techniques (World Bank 2007). This is partly due to various barriers to adoption, an issue we now address.

20
Given the low entry rate from the initial sample of likely participants we will also estimate the mean BWR for sub-samples of participants only, defined as those who were found to have participated in at least one survey round.

# **4. Estimation results**

The simple OLS coefficient of DB receipts on income net of DB implies a BWR of 6.5% (t=5.4) (Table 3). Adding time effects this rises slightly, to 7.2% (t=5.1). We also give results for subsamples of DB participants only. The sub-sample gives similar results to the full sample. So instead of a 100% marginal tax rate these calculations suggest a rate of only around 7%. However, this may well be a large under-estimate, as discussed in Section 2.

Table 3 also gives results for household fixed-effects regressions of DB receipts on income net of DB using our three-year panel. This gives an even lower BWR of 3.1% (t=-3.9) using all households in sample and 4.9% (t=-2.3) using only those households who receiving DB income at least once (Table 3). However, as discussed in Section 2, we expect that greater noise in the changes over time may well be imparting a downward bias in the fixed effects estimator.

The IV results are in Table 4. Recall that the IVs are lagged regular salaried income and lagged property income. These were very significant predictors for net income in the first stage regressions.<sup>24</sup> The IV estimator gives an appreciable higher BWR of 12-14%, roughly double the OLS estimate. As noted in Section 2, we cannot rule out remaining bias due to correlated incidence heterogeneity, stemming from a positive serial correlation in incomes and latent heterogeneity in the BWRs. Under our assumption of a latent pro-poor incidence (consistent with its objective of reducing poverty), the true value of the mean BWR will be no greater than our IV estimate. Although the BWR implied by our IV estimator is about twice the OLS estimate, it is still appreciably lower than the value of 100% implied by the scheme’s design.

Table 5 gives both estimates by city. (We only give results for the whole sample given the sample sizes.) The IV estimate of the BWR exceeds OLS for all except Pingliang. We see a marked variation across cities with the IV estimates ranging from 6% to 27%. For the IV estimates (but not OLS) there is a positive correlation between the BWR and mean income (from Table 1); the correlation coefficient is 0.67 which is significant at the 10% level. Of course, with

> 24 On the full sample, the F-statistic for the regression of income net of DB on the two IVs was 80.61, with prob.<0.00005; for the participant sub-sample it was 38.03 with prob.<0.00005.

14
quite high, in both cases above 25%<sup>14</sup> . Both projects finance investment in maintenance of roads and aim at improving the quality of transport infrastructure.

The economic analysis of both cases was carried on using the Highway Design and Maintenance Standard Model (HDM-III), which simulates the deterioration of the road on the basis of existing conditions, the traffic on the road, and measures the incremental benefits to the road users from a base “without project” alternative. A sensitivity analysis<sup>15</sup> was also carried out in both cases.

**Cost-Benefit Analysis Case 1: Bolivia’s Road Rehabilitation and Maintenance Project** An economic analysis was undertaken as part of project preparation. The investment component includes the rehabilitation of the Calamarca-San Pedro and the Boyuibe-Yacuiba road segments. Table 10 below summarizes the results of the analysis of the rehabilitation component. The present value of the economic and financial benefits has been calculated using a 12 percent discount rate.

# **TABLE 10: BOLIVIA CASE: SUMMARY OF ECONOMIC ANALYSIS**

||PresentValue of Flo<br>|ws<br>|Fiscal Impact<br>||
|---|---|---|---|---|
||EconomicAnalysis|Financial Analysis|Taxes|Subsidies|
|Benefits:US$million|45.16|.99<br>60|15.83|-|
|Costs:US$million|23.40|27.51|4.11|-|
|Net benefit (NPV):<br>US $million|21.76|33.48|11.72|-|
|IRR(%)|27.0 %|31.0 %|||
|_Source: World Bank, 2001:_|_Bo_<br>_d Rehabilitation_<br>_livia, Roa_|_&_<br>_ance Project_<br>_Mainten_|||

The summary of benefits and costs suggests that the proposed project would reduce road-user transport costs by (a) lowering vehicle operation, accident, and travel time costs, (b) removing physical constraints to road transport of goods and people within Bolivia and from Bolivia to the neighboring countries of Argentina and Chile, and (c) allowing the provision of more reliable and safer transport services.

The sensitivity analysis show that both subprojects will continue to show rates of return above a cutoff of 12 percent<sup>16</sup> (rate most commonly used in World Bank projects) in the event of a 20 percent increase in the investment cost, a 20 percent decrease in the benefits or a combination of the two. In these latter events, the rate of return results to be 16.9 percent.

> 14 It should be highlighted that the cost-benefit analysis presented are ex-ante, no ex-post information is available to test whether the estimated benefits differ from actual benefits.

> 15 Analysis of how sensitive outcomes are to changes in the assumptions. The assumptions that deserve the most attention should depend largely on the dominant benefit and cost elements and the areas of greatest uncertainty of the program or process being analyzed.

> 16 The World Bank adopted a 12 percent discount rate for all its investment projects. This discount rate has been applied to all Cost -Benefit analysis in investment projects since 2000.

36