rafmacalaba/datause-encoder-data huggingface dataset:
25

# **Table 1.  Summary Data on Non-Remittance and Remittance-Receiving Households, Ghana, 2005/06 (sub-sample)**

|Variable|Receive no<br>remittances<br>(1)|Receive internal<br>remittances (from<br>Ghana)<br>(2)|Receive international<br>remittances (from<br>African or other<br>countries)<br>(3)|t-test (Internal<br>remittances vs. no<br>remittances)<br>(4)|t-test (International<br>remittances vs. no<br>remittances)<br>(5)|
|---|---|---|---|---|---|
|**Human Capital**||||||
|Number of members over<br>age 15 with primary school|0.35<br>(0.62)|0.32<br>(0.57)|0.21<br>(0.45)|-0.027<br>(-1.27)|-0.139***<br>(-3.26)|
|Number of members over<br>age 15 with junior secondary|0.72|0.55|0.89|-0.166***|0.172***<br>(2.78)|
|school|(0.90)|(0.78)|(0.94)|(-5.36)||
|Number of members over|||||0.121***|
|age 15 with senior secondary|0.11|0.07|0.23|-0.030**|(4.60)|
|school|(0.38)|(0.30)|(0.50)|(-2.30)||
|Number of members over<br>|0.03|0.01|0.07|-0.027***|0.040***<br>|
|age 15 with university|(0.22)|(0.10)|(0.27)|(-3.90)|(2.89)|
|**Household Characteristics**||||||
|Age of household head|43.38|48.42|45.34|5.037***|1.954*|
|(years)|(14.13)|(18.40)|(15.46)|(9.10)|(1.77)|
||4.19|3.68|3.28|-0.516***|-0.907***|
|Household size|(2.78)|(2.58)|(2.20)|(-5.39)|(-4.75)|
||1.18|0.91|0.97|-0.273***|-0.212***|
|Number of males over age 15|(0.90)|(0.90)|(0.85)|(-8.50)|(-3.31)|
|Number of children under|.56|0.49|0.24|-0.074***|-0.319***|
|age 5|(0.81)|(0.73)|(0.52)|(-2.69)|(-5.81)|
|Mean per capita expenditure|6,404|5,545|12,600|-859***|6,196***|
|(thousand cedis)|(7,615)|(4,699)|(11,500)|(-3.38)|(12.24)|
|N|2515|1159|210|||
(2005) and Li and Cheng (2007). We combine the established methodology with the latest and most comprehensive information on applied service trade policies in the maritime shipping sector.

The construction of a liner shipping STRI proceeds in two principal steps: first individual policy measures relevant to the maritime shipping sector are selected, scored and aggregated for each country. Further details are provided in Annex 5. Figure 1 displays the liner shipping STRI by country and income group. As suggested in the literature, the liner shipping sector is relatively open to foreign trade in comparison to other services sectors (Borchert _et al._ , 2011; Kumar and Hoffmann, 2002), yet there is considerable variation across countries, which bodes well for identifying the impact of regulatory regimes. At the same time the index exhibits plausible correlations with some geographical characteristics; for instance, island countries for which international shipping is crucial tend to exhibit a low STRI (Australia, New Zealand, Mauritius and Trinidad and Tobago, one of the Caribbean’s main maritime transport hubs).

**Figure 1: Liner Shipping STRI,  Mode 3, across per-capita income group**

<!-- Start of picture text --> High income Upper middle income Lower middle income Low income<br>4<br>3<br>2<br>Liner Shipping STRI<br>1<br>0<br>AUS KOR NZL CAN TTO USA JPN BHR QAT KWT OMN SAU BGR MUS LTU TUR ROM ARG NAM RUS ZAF MEX COL DOM PER BRA VEN URY CRI CHL PAN POL LBN MYS GEO NGA MAR PAK UKR CIV ECU GTM HND CMR ALB JOR NIC CHN IDN THA PHL LKA TUN EGY IRN KEN GHA MOZ MDG SEN IND KHM TZA VNM BGD<br><!-- End of picture text --> Sources: Author’s calculation. Note: Level of development according to World Bank classification.

Second, country scores are combined—or ‘bilateralized’—so as to obtain an indicator of restrictiveness that varies at the route level. Recall that activities specifically tied to having a commercial presence are administration and organization of vessels’ calls, management of 8
approved credit card exposures and revolving credits. We use the classification scheme of Basel II and split the credit portfolio into seven asset classes including: ( _i_ ) Corporates

- ( _ii_ ) SMEs (retail)

- ( _iii_ ) Consumer Mortgage Loans

- ( _iv_ ) Consumer Loans (QRE retail)

- ( _v_ ) Other Consumer Loans

- ( _vi_ ) Sovereigns (loans to public institutions and state-owned enterprises)

- ( _vii_ ) Banks (loans to credit institutions).

For the sake of simplicity and tractability, we abstract from all other types of credit risk exposures.<sup>29</sup> The exposures at default for bank _j_ in asset class _i_ ( _EADi_ , _j_ ) are constructed as the following weighted sum: where _EADi_<sup>_ON_</sup> , _j_ and _EADi_<sup>_OFF_</sup> , _j_ are, respectively, the on-balance sheet and off-balance sheet EADs for bank _j_ and asset class _i_ . Notice that the on-balance sheet EADs receive a weight of unity in the sum in (11), while the weight on the off-balance sheet EADs is determined by the _δi_ , _j_ parameter.<sup>30</sup> For example, for loan commitments, _δi_ , _j × EADi_<sup>_OFF_</sup> , _j_ estimates the

> 29A detailed overview of other types of credit risk exposures can be found in BIS (2010).

> 30Off-balance sheet exposures include commitments (including liquidity facilities), unconditionally cancellable commitments, direct credit substitutes, acceptances, standby letters of credit, trade letters of credit, failed transactions and unsettled securities (Basel II, 2006).

37
|Dependent. Variable: ln<br>labor productivity (**_ln Y_**)|Low forward<br>and low<br>backward<br>GVC part.|High forward<br>and high<br>backward<br>GVC<br>particip..|Low forward<br>GVC<br>particip.|High forward<br>GVC<br>particip.|Low<br>backward<br>GVC<br>particip.|High<br>backward<br>GVC<br>particip.|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_ln FDI_|0.005*|-0.007*|0.006***|-0.005*|0.005**|-0.004|
||(1.96)|(-1.884)|(2.72)|(-1.765)|(2.39)|(-1.343)|
|_Constant_|3.531***|4.021***|3.397***|3.998***|3.627***|3.765***|
||(275.66)|(308.60)|(264.01)|(340.77)|(309.95)|(346.82)|
|Country-sector FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of country-|692|669|933|884|922|942|
|Observations|2,878|2,850|3,947|3,901|3,929|3,919|
|R-squared|0.651|0.562|0.581|0.535|0.639|0.549|

**Note:** The dependent variable is the log of labor productivity. The standard errors are clustered at the country-sector level. Country-sector, country-year and sector-year fixed effects are included. Column (1) uses the subsample of sectors with low backward and low forward GVC participation, column (2) sectors with high backward and forward GVC participation, column (3) sectors with low forward GVC participation, column (4) sectors with high forward GVC participation, column (5) sectors with low backward GVC participation, column (6) sectors with high backward GVC participation. *** p<0.01, ** p<0.05, * p<0.1.

# **Robustness check**

We are aware that it could take more than a year to complete the foreign investment, and the investment might have lagged spillover effects in future years. Therefore, the preliminary results might only demonstrate the partial effect of FDI. To address this issue, we use the two-year moving average of the FDI instead of the contemporaneous one as our explanatory variable in columns (1) and (2) of Table 5. Column (1) shows that the positive correlation between labor productivity and the FDI still exists with the coefficient of similar magnitude as in our main results on Table 3. The correlation becomes negative _and significant_ for the high GVC participation subsample (Table 5, column (2)). Therefore, the main conclusions in the previous analysis still hold even after considering lagged effects. For columns (3) and (4), we exclude the oil extraction and mining sector from the sample because it may be difficult to compare measures of labor productivity in sectors that use or produce raw and refined commodities with manufacturing: the pricing is highly volatile so that it is difficult to disentangle whether the coefficient is capturing the real effect or the
Subsequent graphs follow a similar sequence. In Figure 10 one can observe that the real export value of high productivity goods grows steadily over the period with few exceptions. Also, looking at Figure 11, one observes that the shares of the products have tended to converge in each graph except for the top 25 products. That indicates that, none of the most high-productivity products, are increasing as a share of total exports. In terms of product income, depicted in Figure 12, there has been an increasing trend with very few exceptions.

# **Figure 10 Top Argentine Exports: High Productivity Products, Export Value 2000 US$**

<!-- Start of picture text --> Top 25 Next in the Top 50<br>1,800,000 Wheat and meslinParts and accessories of the motorTubes,pipes and hollow profiles,seaFish fillets and other fish meat, f Motor cars and other motor vehiclesMotor vehicles for the transport ofMedicaments of mixed or unmixed proUnwrought aluminium 200,000180,000 Chemical wood pulp, soda or sulphatFish, frozen, (excl. those of 03.04Cyclic hydrocarbonsPolyethers and epoxide resins; polyMalt extract; food preparations of<br>1,600,000 Accessory parts suitable for engine<br>160,000 Shaving preparations, personal deod<br>1,400,000<br>140,000<br>1,200,000 120,000<br>1,000,000 100,000<br>800,000 80,000<br>600,000 60,000<br>400,000 40,000<br>200,000 20,000<br>0 0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 75 Next in the Top 100<br>Cheese and curd Other plates..., of plastics, not r 140,000<br>140,000 Fibreboard of wood or other ligneou Chocolate and other food preparatio Wool, not carded or combed<br>120,000 Compression-ignition,combustion pisUncoated paper..., for writing... iTapes,valves,for pipes pressure red Polymers of propylene or of other oUncoated paper..., for writing... i 120,000100,000 Other vegetables preserved other thPumps for liquids,with or without mAutomatic regulating,controlling inTractors(other than tractors of heaFood preparations not elsewhere spe<br>100,000 Edible offal of bovine animals...,<br>Hormones; derivatives thereof used<br>80,000 Paper, paperboard, etc, coated...,<br>80,000 Halogenated derivatives of hydrocar<br>60,000<br>60,000<br>40,000<br>40,000<br>20,000 20,000<br>0 0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Next in the Top 125<br>Nonwovens Photographic film in rolls, instant<br>Prepared glues and adhesives, nes; Animal or vegetable fats and oils..<br>100,000 Synthetic rubber derived from oils, Polyamides in primary forms<br>All types of electrical ingnition,g Barley<br>Air or vacuum pumps,exhausting and Printed books, brochures, leaflets<br>Machines,mechanical appliances havi Carboxylic acids with oxygen functi<br>80,000 Refrigerators,freezers electric or Mixtures of odoriferous substances<br>60,000<br>40,000<br>20,000<br>0<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br><!-- End of picture text --> 15
While the discussed impacts are small in general, this assessment illustrates the broader point that the EPA should not be taken in isolation and that changes in the Nigerian trade regime can be made to offset the potential negative effects of the discriminatory nature of tariff removal under an EPA. One such change in Nigeria’s trade policy is the introduction of the ECOWAS CET. To the extent that the adopted CET makes the Nigerian import regime more restrictive (e.g., a high fifth band), the EPA is likely to yield more severe revenue and welfare losses. On the other hand, taking the CET process as an opportunity to adopt a more open regime would help minimize the potentially damaging impact of the discrimination introduced by an EPA.

**Table 8. Welfare comparison: EPA and MFN reform**

|Scenario<br>EPA with no<br>sensitive<br>productlist<br>MFN cap<br>at20%|EPA and<br>MFNcap|
|---|---|
|**Tariff and VAT revenue [mln. US$]**<br>_Change_<br>-675<br>-231|-828|
|**Consumer Surplus [mln. US$]**<br>_Change_<br>638<br>270|835|
|**Welfare (Revenue + Consumer Surplus) [mln. US$]**<br>_Change_<br>-37<br>39|8|

Source: Computed by authors based on TRIST model and database What will happen to the import bans discussed earlier in the context of the EPA? It is unlikely that the bans could be lifted only on imports from the EU but maintained on imports from other sources. One possibility is that they are converted into a relatively high tariff (say 35 percent), but kept in the 20 percent exclusion list of the EPA tariff phase-out schedule. We now discuss a very simple counterfactual exercise that provides a rough indication of the likely magnitude of the trade response to such a switch.

In the absence of observations prior to the imposition of the ban it is not possible to estimate well defined import demand equations for each of the products for Nigeria. We adopt a simple gravity type econometric approach using data for a range of countries that import the products concerned. In this model, the share of any given product in a countries import bundle is determined by its economic size (GDP), wealth (per capita income), geographic location, the 20
Policy Research Working Paper 5046

# **Abstract**

Development aid and policy discussions often assume that poorer countries have less internal capacity for redistribution in favor of their poorest citizens. The assumption is tested using data for 90 developing countries. The capacity for redistribution is measured by the marginal tax rate on those who are not poor by rich-country standards that is needed to cover the poverty gap or to provide a poverty-level of basic income, judged by developing-country standards. For most (but not all) countries with annual consumption per capita under $2,000 (at 2005 purchasing power parity) the required tax burdens are found to be prohibitive—often calling for marginal tax rates of 100 percent or more. By contrast, the required tax rates are quite low (1 percent on average) among all countries with consumption per capita over $4,000, as well as some poorer countries. Most countries fall into one of two groups: those with little or no realistic prospect of addressing extreme poverty through redistribution from the “rich” and those that would appear to have ample scope for such redistribution. Economic growth tends to move countries from the first group to the second. Thus the appropriate balance between growth and redistribution strategies can be seen to depend on the level economic development.

This paper—a product of the Director’s Office, Development Research Group—is part of a larger effort in the department to help inform development aid and policy choices aiming to reduce poverty. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mravallion@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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and the after-tax profits of public commercial and industrial enterprises, the latter being included as earmarking to the extent that profits ere government purposes. Ac the opposite end of the spectrum would be earmarking in the broadest sense--the unconditional sharing of revenues between levels of government. Between these two ends of the spectrum lie a variety of earmarking varying in the specifieity of revenue sources, the specificity of government expenditures, the degree to which the benefit principle applies, and the mix of financing between earmarked and nonearmarked sources.

B. **TY1es** of Earmarking Found in Colombia 3. Among free-market LDCs, Colombia--together with some other South American countries--have long been known for extensive earmarking. Table 1--which cites the major instances of earmarking in existence today with the agencies involved, the tax bases and rates, and the purposes to which revenues are put--gives some notion of the breadth of earmarking as currently practiced. It should be clear from the outset that earmarking in Colombia runs the full spectrum of experience. It has earmarking which follows the benefit principle (e.g., the highway funds, the airport funds, the valorization tax). It also has taxes with no clear benefit rationale (e.g., taxes on alcohol, gambling and tobacco used for health, sport6, and neighborhood roads). It has payroll taxes used for traditiona'. social security purposes (pensions and medical payments) and also for other purposes-vocational training, family welfare and nutrition. It also has revenue sharing between levels of government and an extensive set of decentralized agencies and enterprises at all the levels of government. 4. This paper has two broad objectives. The first is the examination of the trends in the size and structure of earmarking sincr 1970,
India’s Net Domestic Product (NDP) per capita in the period 1958-1991 was under 2% per annum, but it was more than double this rate in the period since 1992.<sup>13</sup> The picture that emerges from the accumulating evidence from India’s National Sample Surveys (that started in the 1950s) indicates that economic growth in India had in fact been poverty reducing. Ravallion and Datt (1996) showed that the elasticity of the incidence of poverty with respect to mean household consumption was -1.3 over 1958-1991.<sup>14</sup> Given the modest rate of growth over this period, success at avoiding rising inequality prior to the 1990s was key to this finding.<sup>15</sup> Many observers came to the view that too little growth was the reason for India’s slow pace of poverty reduction. However, a deeper exploration of the data suggests that the sectoral pattern of growth also played a role. Using data up to the early 1990s, Ravallion and Datt (1996) found that rural economic growth was more poverty reducing, as was growth in the tertiary (mainly services) and primary (mainly agriculture) sectors relative to the secondary (mainly manufacturing and construction) sector. They also found that spillover effects across sectors reinforced the importance of rural economic growth to national poverty reduction. Urban growth and secondary sector growth had adverse distributional effects that mitigated the gains to the urban poor, while urban growth brought little or no benefit to the rural poor. The slow progress against poverty reflected both a lack of overall growth and a sectoral pattern of growth that did not favor poor people.

There was much hope in India that the higher growth rates attained in the wake of the economic reforms that started in earnest in the early 1990s would bring a faster pace of poverty reduction.<sup>16</sup> However, there have also been signs of rising inequality in the post-reform period, raising doubts about how much the poor have shared in the gains from higher growth rates.<sup>17</sup> <mark>The changes in India’s labor markets since around 2000 have potentially important implications. Th</mark> ere has been a tightening of rural casual labor markets, with rising real wage

> 13 NDP is gross domestic product less depreciation of capital. In order to better approximate incomes at the household level, we use net rather than gross product series in our analysis, though the two are highly correlated. Note that per capita incomes in the national accounts are also based on net product series.

> 14  They also found higher absolute elasticities for measures of the depth and severity of poverty, indicating that those well below the poverty line have benefited from economic growth, as well as those near the poverty line.

> 15  For evidence on this point for developing countries more generally see Bruno et al. (1998).

> 16  For an overview of India’s reform agenda since the early 1990s see Ahluwalia (2002) and Kotwal, Ramaswami and Wadhwa (2011).

> 17  Evidence of rising inequality in India since 1991 is reported in Ravallion (2000), Deaton and Drèze (2002), Sen and Himanshu (2004a,b) and Datt and Ravallion (2011).

4
**Table 5: Regression Results with BEEPs Variables (years 2004 and 2008 only)**

|**_Variables_**|**_(1) Beeps_**<br>**_Only_**|**_Variable_**|<br>**_(2) Including_**<br>**_Characteristics_**|**_Firm_**<br>|
|---|---|---|---|---|
|_∆teleobs_|_-0.08_<br>_(0.035)_|_**_|<br>_-0.09_<br>_(0.037)_|_**_|
|_∆ transobs_|_-0.06_<br>_(0.037)_|_*_|<br>_-0.07_<br>_(0.039)_|_*_|
|_∆broadband_|_0.06_<br>_(0.032)_|_*_|<br>_-0.06*_<br>_(0.032)_||
|_∆waterfreq_|_-0.104_<br>_(0.058)_|_*_|<br>_-0.12_<br>_(0.059)_|_**_|
|_Constant_|_9.17_<br>_(3.287)_|_***_|<br>_10.2_<br>_(3.563)_|_***_|
|_Observations_|_19,385_||_19,385_||
|_R-squared_|_0.03_||_0.04_||

*** significant at the 1 percent level, **   significant at the 5 percent level, *     significant at the 10 percent level. ∆teleobs = How much of an obstacle is telecommunications?, ∆ transobs= How much of an obstacle is transportation? ∆broadband= do you have high-speed broadband internet? ∆waterfreq= frequency of insufficient water supply?

# **5. Conclusion**

**This note presents the results of an empirical analysis of firm-level productivity growth in Russia’s manufacturing sector during the period 2003-08 using a rich Amadeus database as well as the recent BEEPs surveys.** The principal conclusions are as follows:

First, productivity grew steadily between 2003 and 2008, with an annual growth rate averaging **_4 percent_** over the period, showing **_no signs of a slowdown_** from the previous period after the 1998 crisis.

Second, **_firm characteristics_** like size, location, age and the structure of firm ownership are important determinants of productivity. Specifically

- **Firm size matters** : large firms tend to be most productive, followed by mediumsize firms, and small firms, reflecting the economies of scale and scope.

- **Location is important** : the “primary city effect” of Moscow––capturing the Moscow city agglomeration effects on productivity––is statistically and quantitatively significant although it is weaker for medium and large firms. In other words, while location matters, size matters even more.

20
# Table 2. Exogenous U.S. Agricultural Policy Variable Definitions for the USAGMKTS Model.

|Variable|Definition|
|---|---|
|ICCCA-<br>BAC-<br>BAGS-<br>YLDFGP-|Interest charged on CCC non-recourse loans in percent<br>Base acreage of corn in million acres<br>Base acreage of grain sorghum in million acres<br>Program yield of feed grains (corn & grain sorghum) in bushels per<br>acre|
|TPC-<br>SPRC-<br>DRFG-<br>DPC - <br>VDFG-|Target price of corn in dollars per bushel (support price and<br>additional support payment prior to 1973)<br>Support price of corn in dollars per bushel<br>Diversion requirement of feed grains in percent of base acreage<br> Diversion payment for corn (paid diversion) in dollars per acre<br>Additional voluntary paid diversion for feed grains in percent of<br>base acreage|
|VDPC-<br>NOPROG-<br>SPRC-<br>SPFORC-<br>RELFORC-|Additional voluntary diversion payment for corn in dollars per acre<br>Dummy variable, 1 if a feed grain program is in effect, 0 if not<br>Regular CCC support price of corn in dollars per bushel<br>Support price for farmer owned reserve corn in dollars per bushel<br>Release price for the farmer owned reserve corn in dollars per<br>bushel|
|DMYPIK-|Dummy variable for PIK Program, 1 if third or fourth quarter of<br>1983, 0 if not|
|ICCC-|Interest rate charged for CCC non-recourse loans in percent|

25