rafmacalaba/datause-encoder-data huggingface dataset:
_Note:_ Profitability is measured by the banks’ loan loss reserves to total assets. The distribution of loan losses in the figures is based on the univariate kernel density estimation. DiD is calculated as the difference between the percentage change for the treated and control groups. DiD = difference-in-differences; p.p. = percentage points.

Against the general decline in profitability in EMDEs, the banks in the treated countries performed somewhat better, starting from higher profitability rates (table 11). The dispersion in bank profitability increased vastly in other EMDEs. Compared with that, the dispersion in the performance of banks in Basel III countries decreased mildly—albeit from higher values. The number of banks at the extremes (in the tails; kurtosis) and more banks with above-average performance (positive skewness) in Basel III countries did not change much. In contrast, many more banks in other EMDEs migrated to the extremes (tails) and more so to extremely worse performance.

Table 11: Higher moments of bank profitability: Treated versus control group

|**Variable**|**Treate**<br>|**d group**<br>|**Contr**<br>|**ol group**<br>|**Treated %**<br>|**Control %**<br>|**DiD in**<br>|
|---|---|---|---|---|---|---|---|
||**2012**|**2016**|**2012**|**2016**|**change **|**change **|**p.p. (%)**|
|Mean|4.60|4.01|0.33|-0.76|-6|-|-|
|Median|0.51|0.51|0.06|0.08|0|12|-12|
|Std. dev.|13.05|12.19|1.51|18.22|-3|108|-111|
|Skewness|4.76|4.73|2.02|-16.96|0|-|-|
|Kurtosis|27.77|27.87|39.52|293.21|0|87|-87|

_Note:_ Profitability is measured by the banks’ return on equity. The distribution of profitability in the figures is based on the univariate kernel density estimation. DiD is calculated as the difference between the percentage change for the treated and control groups. DiD = difference-in-differences; p.p. = percentage points.

# **6. Regression Framework**

We use the DiD approach to examine the consequences of Basel III implementation for SME financing. We opt for the DiD, given its simplicity, and utilize that some countries adopted the Basel III framework, while others did not. Therefore, our estimation framework shares common elements with Ongena et al. (2013) and Ferrando et al. (2017), who also apply the DiD method. Ongena, Popov, and Udell (2013) examine the consequences of domestic financial regulation on bank risk-taking abroad using data from Europe and the United States. Ferrando, Popov, and Udell (2017) study the effects of sovereign stress on SMEs’ access to finance in the euro area.

As in Ongena, Popov, and Udell (2013) and Ferrando, Popov, and Udell (2017), we also address the sample selection issues. In our case, this means an assessment of whether some countries are 23
D4 captures the fact that the changes in coefficients over time mattered less for the poor than the nonpoor since the poor had worse child survival detenninants to start with. These differences in determinants are weighted by the changes in the coefficients (assumed in this exercise to be the same for the poor and better-off). Given the joint insignificance of the interactions between the x's and the year dummy in Table 3, D4 should not be interpreted too literally. Differences in levels of household consumption stand out as the largest single contributory factor-the poor, by definition, are worse-off than the nonpoor, and the coefficient on consumption increased between 1993 and 1998, though not significantly so. A positive contribution is evident too from health services, reflecting the fact that the poor have lower levels of coverage, and the fact that, for the most part, the coefficient increased between 1993 and 1998. Again, however, the change was not significant.

**Table 6. Decompositions of changes in survival, based on eqn (4)**

|||_Poorestquartil_|_e_|_Ri_|_chestthree quar_|_tiles_|
|---|---|---|---|---|---|---|
||_Percent_<br>_changein_<br>_hazard_<br>A[-lnk]|_Changein_<br>_determinants_<br>AX198|_Changein_<br>_coefficients_<br>ADX93|_Percent_<br>_changein_<br>_hazard_<br>A[-InX]|_Changein_<br>_determinants_<br>AXP8|_Changein_<br> _coefficients_<br>A1PX93|
|Region|-0.768|0.417|-1.185|-1.124|0.069|-1.193|
|Childcharacteristics|-0.758|-0.026|-0.731|-0.846|-0.072|-0.774|
|Household<br>consumption|7.774|0.002|7.772|8.576|-0.018|8.594|
|Mother'seducation|-0.746|-0.065|-0.681|-0.704|0.080|-0.784|
|Water|-0.004|-0.024|0.020|0.335|0.284|0.051|
|Sanitation|-0.122|-0.030|-0.093|-0.451|-0.127|_-0.325_|
|Healthservices|-0.445|-0.771|0.325|0.945|0.513|0.432|
|Constant|-4.968|0.000|-4.968|-4.968|0.000|-4.968|
|_Total_|-0.037|-0.497|0.460|1.763|0.729|1.033|
|ImpliednewIMR|34.60|38.49|30.83|22.76|29.72|27.69|
|ImpliednewU5MR|50.19|55.78|44.76|33.10|43.16|40.23|

23
from reform may be offset by declines in factor accumulation. In that event trade reform may not be associated with higher output and GDP growth, though greater openness actually improved productivity.

Unfortunately, there is very little systematic cross-country evidence of openness-performance link in Sub-Saharan Africa that can question such skepticism. Those that exist either do not explicitly examine the openness-performance link or if they do, their focus is not Sub-Saharan Africa. For example cross-country analysis of the effect of recent reforms (including trade policy reforms) on performance in SSA (World Bank 1992, Elbadawi 1992) show that they have not significantly affected their GDP growth.<sup>2</sup> These studies use dummies in the regressions for countries receiving Worid Bank loans to proxy reforms carried out by those countries. Since loan dummies cannot indicate either the extent to which the reforms proposed in the loans were actually implemented or whether the reforms that were implemented actually increased openness they cannot support or negate the openness-performance relationship.<sup>3</sup> Most of the cross-country studies that explicitly examine the openness-performance link do not focus on SSA countries. There are only five studies that analyze a sub-set of countries that come close to the SSA sample. Four of them find a positive relationship between openness and performance: three find a positive relationship for low income countries (La[ and Rajapatirana 1987, Otani and Villanueva 1990, Moschos 1991) and one finds for African 2In one study (World Bank 1992) it is found that reforms under adjustment lending has restored growth in Sub-Saharan Africa to the moderate levels of the 1970s; the other study (Elbadawi 1992) using a different base period find that it has had no significant effect on growth. This is at variance with the strong positive result for all developing countries (Corbo and Rojas 1991). Another study (Faini et al 1990) also found weak effect of adjustment loans on growth **o0** iuw income countries.

3Another study (UNDP 1989) had tried to overcome this problem of using loans as proxy for reform by subjectively distinguishing adjustment loan countries that had implemented "strong' reforms from those that had implemented 'weak" or no reform. Again, the study did not distinguish between trade reforms and other reforms.

9n
**Table 4: Negative-Binomial Estimation Results:**

# **-** **<u>Determinants of Export Discovery Counts during 1994 2003 by Industry</u>**

||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|
|Ln(GDPpercapita)|8.436|8.245|8.310|6.657|13.927|2.382|
||(9.09)***|(8.83)***|(8.14)***|(3.76)***|(3.88)***|(0.55)|
|Ln(GDP per capita)^2|-0.507|-0.495|-0.498|-0.404|-0.812|-0.231|
||(9.24)***|(8.91)***|(8.32)***|(3.90)***|(4.04)***|(1.32)|
|Ln(historical<br>discoveries)|0.305|0.305|0.304|0.249|0.257|0.160|
||(3.39)***|(3.39)***|(3.37)***|(2.87)***|(2.88)***|(1.74)*|
|Factor Endowments|0.349|0.354|0.355|0.397|0.383|0.376|
||(0.98)|(0.99)|(1.00)|(1.08)|(0.98)|(0.97)|
|~~~~|0.368|0.501|0.493|0.434|0.523|-8.146|
||(1.71)*|(2.08)**|(2.04)**|(1.75)*|(2.06)***|(3.90)***|
|_barriers_<br><br>|0.674|0.748|0.736|0.652|0.774|2.711|
||(1.77)*|(2.13)**|(2.09)**|(1.80)*|(2.09)**|(3.88)***|
|_Barriers_|-0.003|0.026|0.237|0.583|4.733|-2.752|
||(0.04)|(0.25)|(0.28)|(0.56)|(1.82)*|(0.68)|
|)<br>ln(_GDPpc_<br><br>|||-0.022|-0.050|-0.476|0.437|
||||(0.24)|(0.45)|(1.72)*|(0.84)|
|Leamer-industry<br>dummies|Yes|Yes|Yes|Yes|Yes|Yes|
|Barriers index<br>excludes costs of<br>entry|No|Yes|Yes|Yes|Yes|Days to clear<br>customs for exports<br>only (log)|
|Regionaldummies|No|No|No|Yes|Yes|Yes|
|Regional dummies<br>times barriers||||No|Yes|Yes|
|Observations|510|510|510|510|510|510|

Notes: Absolute value of robust z-statistics reported within parentheses. Significant at 1%***, 5%**, 10%*. Note: See text and Appendix for definitions of variables. The excluded regional dummy variable in models 4-6 corresponds to South Asia, which in this sample is India. The other significant interactions between regional dummies and the barriers indexes are Africa (which is negative and significant at 1% level) and Eastern Europe and Central Asia (ECA, which is negative and significant at the 10% level). The excluded Leamer commodity group in all specifications is mining and petroleum.

27
Perhaps the most challenging question is what to do about the high out-of-pocket spending on medicines. In this regard, the paper raises more questions than it can answer using the available data. These include questions about the market structure, supply chain and quality of the regulatory environment (related to dispensing and pricing practices) for which additional analysis would be needed.

Achieving financial protection goals will require the combined effort of multiple entities because the responsibility for implementation of these policies, and therefore attainment of financial protection goals, lies not only with PhilHealth. The Department of Social Welfare and Development can improve the targeting of the PhilHealth indigent program through refining their NHTS-PR enumeration process. In order to improve physical access to care and improve quality, local governments (using their own resources) will need to invest in upgrading facilities for PhilHealth accreditation and the DOH will need to better target HFEP resources towards the most under-served areas; health care providers must strictly implement the no balance billing policy.

Finally, in order to ensure, and improve, measurement of progress toward the UHC financial protection goal, the Philippines Statistical Authority needs to help to ensure the continued collection of out-of-pocket payments data and work to progressively improve the contents of the relevant survey modules.

18
**Tahle** 8 PAKISTAN: PUBLIC EXPENDITlJRE **IN** AGRICULTURE (Current) Constant Rupees **-** millions

||19S2-.|319S3-S4|1984-S5|1985-86|19S6-S7|1987-8|19S2.89|1989-90|1990-91|1991-92|1992-93|1993-94|**Growih**Rate|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Puniab<br>||||||||||||||
|Crop Agriculture<br>|260.19|299.34|306.35|331.97|261.15|383.02|254.05|333.18|344.49|377.88|366.1S|37S.32|4.13|
|**Irri.**& Land Reclaim.<br>|876.10<br>|949.19<br>|1039.23<br>|1101.56<br>|150.54<br>|732.94<br>|973.76|90S.47|930.S6|1252.12|1070.57<br>|1302.07<br>|4.42<br>|
|TotalPunjab|**1340.2**|**6 1505.3**|**9****_1615hO_**|**_S_1756.00**<br>**15**|**1872.56**||**159S29**|**0 1579 52**|**1632i63**|1990.6|9<br>1gQ4L3|2 210471|2LB|
|**Slndh**||||||||||||||
|Crop Agriculture|88.21|122.89|109.14|127.14|134.31|144.52|140.20|157.79|166.83|185.25|156.02|153.63|6.74|
|Irrigation<br>|264.78|304.17|329.89|353.50|394.84|405.46|411.71|373.90|352.54|355.49|358.87|343.55|2.70|
|Total Sindh|43C076||22||639238|658.19|659.09|642L65|650.72|**67622**|**656.57**|a=|Lk|
|**NWFP**||||||||||||||
|CropAriculture|70.51|85.66|114.41|115.60|120.35|120.81|113.41|106.22|106.82|116.40|122.13|130.13|7.69|
|Irrigation|94.76<br>|108.55|129.00|147.65|222.00|216.0S|244.95|235.27|238.70|237.77|253.06|234.S7|13-34|
|Total NWFP|L42<br>16.49|2I|3A064||U24L1|426.21|450.86|a222|447.S8|||474.9S|D8|
|I'lnclllhtan||||||||||||||
|Crop**Argiculturc**|56.63|65.92|73.29|22.08|95.00|104.70|107.75|103.95|_100.09_|119.25|135.40|143.4|413.93|
|Irrigation<br>|71.21<br>|65.53<br>|66.41<br>|88.81<br>|84.72<br>|73.38<br>|76.97<br>|62.36<br>|60.01<br>|73.19<br>|73.52|76.13|0.63|
|Total Balochistan|167.66|t7.3|1=22|228.15|251.98|253s9|253|24236|244J1|ZI2A|2991s|3I4.75|Z21|
|Federal Govcmmient||||||||||||||
|Subsidies|na|na|2744.00|1318.00|112.00|236.00|4751.00|2501.00|2229.00|1914.00|978.00|492.00|na|
|Wheat & Sugar Suhsidy|na|na|1039.00|1318.00|112.00|236.00|3076.00|1852.00|1179.00|1830.00|978.00|492.00|-0.73|
|Edible Oil Subsidy<br>|na<br>|1179.00<br>|1704.55<br>|<br>0.00<br>|0.00<br>|122.00|1675.00|649.00|1050.00|<br>84.34|0.00|0.00|-10.00|
|TotalFederal<br>TotalNationalCurrent|14551|10946|2917.00|1497.60|3125|413 47|4919.00|2670.00|2393.0|2080|1196.00|66239|_so|
|Expenditurcs|UQ|2571.68|670s.83|5900.06|5337.21|5542.17|2Q22L|556347|634360|6320.94|5310.15|109947|<br>.L2|

Source: Economic Survey of Pak;istan 1993-94 and Statistical Supplement 1992-93. The considerable fluctuation in certain series from year to year indicates that the data may not always be reliable. The 1993-94 figures are Budget Projections and actual data could be considerably different. The growth rates for Federal items are calculated from 1984-85 to 1992-93. All grovth figures are calculated based on the first and last periods, except for National Expenditure growth, which is calculated using a regression trend line on a two-period moving average series. Other subsectors are not showAi **n** dividually, but are included in the total figures.

22
Figure 1: Estimates for Oil Production and Fiscal Revenues (2015-2070) Volumes of Production Oil Prices and Fiscal Revenues <!-- Start of picture text --> 180 10 120<br>160 9<br>8 100<br>140<br>7<br>120 80<br>6<br>100<br>5<br>60<br>80 4<br>60 3 40<br>2<br>40<br>1 20<br>20<br>0<br>02020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 -1 2020 2025 2030 2035 2040 2045 2050 2055 2060 2065 2070 2075 0<br>Oil, MBBL/year Gas, MBBoe/year Fiscal Revenue Oil Prices (RHS)<br>USD<br>Billion USD<br>MBBoe/year<br><!-- End of picture text --> and public investment are set at 16 and 8.6 percent of GDP, respectively, and private investment is set at 13.7 percent of GDP. We choose the shares of traded goods to be 60 percent in private consumption and 40 percent in government purchases, as government consumption typically have a larger component of nontraded goods than private consumption. The share of natural resources is 40 percent of GDP at the initial steady state.

- _Assets, debt and grants_ . Government savings are 0 percent of GDP ( _RFshare_ = 0) at the initial steady state. For government domestic debt, concessional debt, grants, as well as private foreign debt and government external commercial debt we rely on World Bank data. This implies _bshare_ = 0 _._ 268, _dshare_ = 0 _._ 114, _grshare_ = 0 _._ 01, _b_<sup>_∗_</sup> _share_<sup>= 0and</sup><sup>_dc,share_= 0</sup><sup>_._063.</sup>

- _Interest rates._ We set the subjective discount rate _ρ_ such that the real annual interest rate on domestic debt ( _R −_ 1) is 2 _._ 3 percent. Consistent with stylized facts, domestic debt is assumed to be more costly than external commercial debt. We fix the real annual risk-free interest rate ( _R_<sup>_f_</sup> _−_ 1) at 1 _._ 13 percent. The premium parameter _υdc_ is chosen such that the real interest rate on external commercial debt ( _Rdc −_ 1) is 7 percent, and the real interest rate paid on concessional loans ( _Rd −_ 1) is 1 percent. We assume no additional risk premium in the baseline calibration, implying _ηdc_ = 0. The parameter _u_ is chosen to have _R_ = _R_<sup>_∗_</sup> in the steady state. Based on the average

17
extremely accurate. For instance, the industrialized countries imported USD 310 billion worth of machinery and transport equipment in 1980, whereas their yearly payments for technology and technical and management services in the early 1980s only amounted to approximately USD 10 billion. Concurrently, the developing countries' imports of machinery and transport equipment from dleveloped countries reached USD 129 billion, and their payments of royalties, fees, and remuneration for technical services totaled about USD 2.5 billion. There are no data on the overall importance of joint ventures, but, for comparison, it can be noted that the industrialized countries' inward stock of foreign investment was estimated at approximately USD 401 billion in 1983, while the corresponding figure for developing countries was USD 138 billion (UNCTC, 1988, p. 25).' There are also some data available to describe the extent of MNC participation in licensing and gcoods trade. These data are interesting because they confirm that MINCs are the main sources of these types of technology, but also because they indirectly introduce foreign direct investment into the picture. MNCs control technology supplies by virtue of their ownership of proprietary technologies, but they also account for a significant share of the demand, via their foreign affiliates. This is most apparent for the transfers of "disembodied" technology that are captured by data on trade in royalties, licenses, and patent rights. Over 80 per cent of the registered payments to the United States for technology sales during the 1970-1985 period were made by foreigni affiliates of US firms (Grosse, 1989). More than 90 per cent of the technology payments from developing countries to Germany in the early 1980s, and over 60 percent of the payments to Japan, originated from their own foreign affiliates (UNCTC, 1988, p. 177).

The intra-firm character of the technology transfers that take place through trade in capital equipment and other products is less apparent, but still distinguishable. What we know about MNC involvement from statistics on goods trade is that between 70 and 80 per cent of the goods exports of both the US and the UK - the main suppliers of embodied technology together with Japan and Germany - are accounted for by MNCs (UNCTC, 1988, p. 90). Moreover, a significant share of the exports and imports of the major home countries (between one fifth and one third overall, and more for complex and technologically sophisticated goods that supposedly embody _5_
- Ghani, Ejaz, William Kerr, and Stephen O'Connell, “Input Usage and Productivity in Indian Manufacturing Plants,” World Bank Policy Research Working Paper (2013b).

- Ghani, Ejaz, Arti Goswami, and William Kerr, “Is India's Manufacturing Sector Moving Away From Cities?” National Bureau of Economic Research Working Paper No. 17992 (2012a).

- Ghani, Ejaz, Arti Goswami, and William Kerr, “Highway to Success: The Impact of the Golden Quadrilateral Project for the Location and Performance of Indian Manufacturing,” National Bureau of Economic Research Working Paper No. 18524 (2012b).

- Glaeser, Edward, Heidi Kallal, José Scheinkman, and Andrei Shleifer, “Growth in Cities,” _Journal of Political Economy_ 100:6 (1992), 1126–1152.

- Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova, “Imported Intermediate Inputs and Domestic Product Growth: Evidence from India”, _Quarterly Journal of Economics_ 125:4 (2010a), 1727-1767.

-

- Goldberg, Pinelopi, Amit Khandelwal, Nina Pavcnik, and Petia Topalova, “Multi product Firms and Product Turnover in the Developing World: Evidence from India,” _Review of Economics and Statistics_ 92:4 (2010b), 1042-1049.

- Henderson, J. Vernon, “Externalities and Industrial Development,” _Journal of Urban Economics_ 42:3 (1997a), 449–470.

- Henderson, J. Vernon, “Medium Size Cities,” _Regional Science and Urban Economics_ 27:6 (1997b), 583–612.

- Henderson, J. Vernon, Ari Kuncoro, and Matt Turner, “Industrial Development in Cities,” _Journal of Political Economy_ 103:5 (1995), 1067–1090.

- Hsieh, Chang-Tai, and Peter Klenow, “Misallocation and Manufacturing TFP in China and India,” _The Quarterly Journal of Economics_ 124:4 (2009), 1403–1448.

Jacobs, Jane, _The Economy of Cities_ (New York: Random House, 1969).

- Lall, Somik, Jun Koo, and Sanjoy Chakravorty, “Diversity Matters: The Economic Geography of Industry Location in India,” World Bank Policy Research Working Paper No. 3072 (2003).

- Lall, Somik, and Taye Mengistae, “The Impact of Business Environment and Economic Geography on Plant Level Productivity: An Analysis of Indian Industry,” World Bank Policy Research Working Paper No. 3664 (2005).

- Lall, Somik, Zmarak Shalizi, and Uwe Deichmann, “Agglomeration Economies and Productivity in Indian Industry,” _Journal of Development Economics_ 73:2 (2004), 643–673.

- McKinsey Global Institute, “India’s Urban Awakening: Building Inclusive Cities, Sustaining Economic Growth,” McKinsey & Company, April 2010.

- McKinsey Global Institute, “The Shifting Urban Economic Landscape: What Does it Mean for Cities?” McKinsey & Company, July 2012.

25
22 coordinating tax reforms: can lost tariffs be recouped?

tax rates are dropped. The cleaned data set that is ready for simulation consists of 265,194 import records spanning 4032 tariff lines from 133 economies.<sup>27</sup> The paper also incorporates domestic production data extracted from the latest quinquennial Census of Manufacturing Establishments that reports the domestic sale of manufactured goods (Government of Nepal 2008). For 3,079 of the 4032 import codes, there exists matching data for domestically sold products. This allows for substitution of imports by domestically produced goods when the price of imports rises, adding to the richness of simulation results. There are, however, two limitations. First, the latest production data are available only up to the fiscal year 2006-07, whereas the import data straddles the fiscal years of 2007-08 and 2008-09 (that is, calendar year 2008). Second, production data covers only manufacturing industries. For a little less than 25 percent of the tariff lines that belong to non-manufacturing industries, there are no data on domestic production. In the language of the model, for a subset of imports, the substitution between imports and domestically produced goods is _perfectly_ inelastic.

The 133 import trading partners of Nepal in 2008 are organized in eight groups: (1) India; (2) China, including the Tibet Autonomous Region; (3) Rest of South Asia (Afghanistan, Bangladesh, Bhutan, Maldives, Pakistan, Sri Lanka); (4) Northeast Asia (Japan, Republic of Korea, Hong Kong Special Administrative Region, and Taiwan); (5) Southeast Asia (Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam); (6) North America (Canada, Mexico, United States); (7) the European Union; and (8) the Rest of the World (ROW). The baseline scenarios assume an export substitution elasticity of 1.5, domestic substitution elasticity of 1, and import demand elasticity of 0.5.

> 27 The term “economies” is used in lieu of “countries” because Nepal’s customs data treat Tibet, Hong Kong, and Taiwan as sources of imports that are distinct from the People’s Republic of China even though the three economies are (politically) part of China.