rafmacalaba/datause-encoder-data huggingface dataset:
# IV. ECONOMETRIC ESTIMATION OF INVESTMENT MQDEL

We estimate the investment model in equation (7) by ordinary least squares (OLS) and by two stage least squares (TrLS). They yield similar coefficient estimates. Unlike earlier studies we use a stock variable for infrastructure, since it is more likely that private investment is affected by changes in the stock of capital rather than the figw of public investment. Similarly, instead of using current receipts of foreign exchange or current foreign exchange reserves we use lagged reserves in order to avoid any simultaneity bias.

The results suggest that lower availability of credit to the private sector and falling public infrastructure capital stock has reduced private investment over the 1980s. Similarly, lower foreign exchange reserves in the years following the collapse of the coffee boom has lowered capital imports. The negative supply-shock effect seems to dominate the impact of real depreciation. There is also econometric evidence to suggest that inability to control fiscal deficit and its money financing, as well as failure to restrain current expenditure crowded out private sector credit and lowered public investment in infrastructure.

# Estimation Results

Ordinary least square (OLS) and two stage least squares (TSLS) estimation used annual data on Kenya for the period 1968-1988.

To avoid spurious correlation and non-stationarity, the investment rate is used (i.e., share of investment in GDP) instead of the investment level. In 14
a country’s GDP, has a mean value of 111%. The countries with the largest banking share are Denmark in 2007 (178.5%), Hong Kong SAR, China, in 2003 (166%) and Switzerland in 2007 (158%), some of which also appear in Demirguc-Kunt and Maksimovic’s (1999) rankings. The size of the private bond market ( _bonds_ ) shows a large variation between the minimum value of 3.37% and a maximum value of 109.9%. The largest and the smallest values belong to Denmark in 2005 (152.9%) and Greece in 2003 (0.24%), respectively. The stock market size and trading volume variables also have quite dispersed distributions. The mean (standard deviation) values of the _stock_market_cap_ and _stock_market_turnover_ variables are 86.1% (47.2%) and 72.6% (39%), respectively.

17
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change to the classification methodology should be introduced gradually and carefully, perhaps with overlapping systems for the first few years following any adjustment.

# **3. A primer on the current system**

The Development Economics and Data Group (DECDG) of the World Bank is responsible for updating the Bank’s operational guidelines<sup>4</sup> each year, and the income classification is derived from the resulting dataset. The exercise involves gathering data from several sources to calculate preliminary GNI and population estimates for the previous calendar year. For most borrowing countries, the designated country economist provides national accounts aggregates from primary country sources around March; data for high income economies are obtained from the Organisation for Economic Co-operation and Development (OECD) and Eurostat. Population estimates are made by DECDG’s demographer using the UN Population Division’s biennial _World Population Prospects_ , with appropriate adjustments where necessary to estimate resident, rather than de facto, population numbers—so that they have a comparable basis to GNI. GDP deflators for the previous two years (for calculating the SDR deflator) are obtained from Eurostat and the IMF, and annual average exchange rates are obtained from the IMF.

The thresholds for both the operational guidelines and the income classification are maintained in current prices using a weighted average GDP deflator, based on the currencies in the IMF Special Drawing Rights: the SDR deflator. Countries are classified against these thresholds using the most recent GNI per capita estimates, valued in US dollars using currency conversion factors calculated by the Atlas method: this uses a three-year moving average, adjusted by national inflation relative to the SDR deflator, to reduce the effect of exchange rate instability.

The GNI per capita estimates for each country are circulated to the Bank’s regional chief economists for review, and updates to the thresholds are approved by DECDG management: this process provides separation between the data collection process and their end use for operational purposes.

> 4  World Bank Operational Policy 3.10.

7
Table 2: Descriptive statistics - Aggregate self-reported and behavioral measures

|||Men|W|omen|t-test|||
|---|---|---|---|---|---|---|---|
|||(1)||(2)|diference|||
|Variable|N|Mean/SE|N|Mean/SE|(1)-(2)|Min|Max|
|Self-reported: All|2231|-0.000<br>[0.021]|2228|-0.194<br>[0.020]|0.194***|-6.894|3.164|
|Self-reported: Intra|2231|-0.000<br>[0.021]|2228|-0.177<br>[0.021]|0.177***|-6.522|3.153|
|Self-reported: Inter|2231|0.000<br>[0.021]|2228|-0.186<br>[0.020]|0.186***|-7.887|2.979|
|Self-reported: Awareness|2231|-0.000<br>[0.021]|2228|-0.159<br>[0.021]|0.159***|-7.995|2.890|
|Self-reported: Management|2231|0.000<br>[0.021]|2228|-0.195<br>[0.020]|0.195***|-7.281|3.200|
|Self-reported: Self-awareness|2231|0.000<br>[0.021]|2228|-0.161<br>[0.021]|0.161***|-8.372|2.417|
|Self-reported: Social Awareness|2231|-0.000<br>[0.021]|2228|-0.110<br>[0.021]|0.110***|-5.327|2.599|
|Self-reported: Self Management|2231|-0.000<br>[0.021]|2228|-0.168<br>[0.021]|0.168***|-6.755|3.209|
|Self-reported: Rel Management|2231|0.000<br>[0.021]|2228|-0.188<br>[0.020]|0.188***|-8.524|2.715|
|Behavioral: All|2231|-0.000<br>[0.021]|2228|-0.030<br>[0.021]|0.030|-4.573|3.511|
|Behavioral: Intra|2231|0.000<br>[0.021]|2228|-0.029<br>[0.021]|0.029|-6.783|3.028|
|Behavioral: Inter|2231|0.000<br>[0.021]|2228|-0.021<br>[0.021]|0.021|-3.397|4.145|
|Behavioral: Awareness|2231|-0.000<br>[0.021]|2228|-0.002<br>[0.020]|0.002|-5.074|2.060|
|Behavioral: Management|2231|-0.000<br>[0.021]|2228|-0.038<br>[0.021]|0.038|-3.672|3.808|
|Behavioral: Self-awareness|2231|0.000<br>[0.021]|2228|0.019<br>[0.021]|-0.019|-6.765|1.215|
|Behavioral: Social Awareness|2231|0.000<br>[0.021]|2228|-0.025<br>[0.020]|0.025|-4.286|2.003|
|Behavioral: Self Management|2231|-0.000<br>[0.021]|2228|-0.057<br>[0.022]|0.057*|-5.895|3.784|
|Behavioral: Rel Management|2231|-0.000<br>[0.021]|2228|-0.012<br>[0.021]|0.012|-2.921|3.800|

_Notes:_ The value displayed for t-tests are the differences in the means across the groups. *** p _≤_ 0.01, ** p _≤_ 0.05, * p _≤_ 0.1.

29
13 of reforms. As a result, the investment rate is expected to increase. This increase may, however, come after a 'pause', according to the latest Bank-wide Review of Adjustment Lending (RAL IH), because adjustment programs in the short-run may have a negative effect on private investment because of reduction in aggregate demand and contractionary fiscal and monetary policies. However, as structural reforms are carried out, investment, particularly private investment, should go up in response to a new set of incentives. In the case of gross investmen **t** o GDP ratio, the projected values are generaUy close to the actual values. Both projections and actual outcomes have, on average, remained more or less at the same level indicating that not much immediat **e** ffect of adjustment on total investment was observed, ntor was it expected. The average projected ratio of the countries reviewed was up by one percentage point, from 17 in 1987 to 18 in 1991, the corresponding movement of actual outcome of investment ratio is that it remained more or less at the same level - 17 percent.

The closeness of the projected figures of investment ratios to actual figures is explained by the fact that in most SSA countries, a substantial part of public investment is donor financed. The projected figures reflect estimated future donor support to the public investment program, which generally is realized. Analysis done elsewhere shows that private investment in all of these countries continues to stagnate, indicating weak response to reforms during the period 1986-90.2/ The total investment figures for most SSA countries, therefore, reflect continued donor support in the short-run, which may not be sustainable in the medium- to long-run. The overall trend suggests that the E/ Faruqee, R. 'Private Investment in Sub-Saharan Africa' (World Bank, Africa Regional Office, 1992)
Table 12 Cotton Productivity Sensitivity to the Sample

|||Model 1|||Model 2||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_φ_|0_._009|0_._009|0_._011|0_._009|0_._009|0_._011|
||0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|0_._002_∗∗∗_|
|_F_ <sup>1</sup>|_−_0_._498|||_−_0_._499|||
||0_._058_∗∗∗_|||0_._058_∗∗∗_|||
|_F_ <sup>2</sup>|0_._199|||0_._197|||
||0_._039_∗∗∗_|||0_._039_∗∗∗_|||
|_R_<sup>1</sup>||_−_0_._421|||_−_0_._42||
|||0_._048_∗∗∗_|||0_._048_∗∗∗_||
|_R_<sup>2</sup>||0_._189|||0_._187||
|||0_._039_∗∗∗_|||0_._039_∗∗∗_||
|Dummy 1998|||0_._321|||0_._323|
||||0_._058_∗∗∗_|||0_._058_∗∗∗_|
|Dummy 1999|||_−_0_._157|||_−_0_._155|
||||0_._102|||0_._102|
|Dummy 2000|||_−_0_._366|||_−_0_._367|
||||0_._064_∗∗∗_|||0_._064_∗∗∗_|
|Dummy 2001|||0_._156|||0_._152|
||||0_._060_∗∗∗_|||0_._060_∗∗_|
|Dummy 2002|||0_._472<br>0_._056_∗∗∗_|||0_._473<br>0_._056_∗∗∗_|
|Constant|_−_0_._822|0_._816|0_._452|_−_0_._82|0_._818|0_._456|
||0_._310_∗∗∗_|0_._331_∗∗_|0_._524|0_._310_∗∗∗_|0_._331_∗∗_|0_._525|
|Observations|3462|3462|3462|3462|3462|3462|
|_R_<sup>2</sup>|0.15|0.15|0.17|0.15|0.15|0.17|

Robust standard errors in parentheses: * significant at 10%; ** significant at 5%; *** significant at 1%. Model 1: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in monetary units. Model 2: first stage includes total land tenure, family size, age, age squared, farm type, a dummy for male-headed farms, the proportion of males in the family, a dummy for livestock rasing households, and assets (harrows, ploughs) in physical units.

36
importance of health policy, and is still used to motivate discussions of health policy strategy (Jamison and Murray, 1997, World Bank, 1997a). There are two interesting points.

First, the ranking of countries depends heavily on factors besides income. Table 3 presents the "ten best" and "ten worst" countries from the "income alone" (I) and the "income plus other variables" (II) regressions in Table 1. Since the variables in addition to income add considerable explanatory power, it is not surprising the regressions identify different outliers. Among the "ten best" countries from (I), only five reappear in the "ten best" list for (II) (Jamaica, Sri Lanka, Costa Rica, Singapore, and Trinidad and Tobago). Similarly only five of the "ten worst" countries identified with income alone reappear in the "ten worst" list for the full regression (Indonesia, Mozambique, Peru, Brazil, and Bolivia). Income alone clearly does not allow the identification of countries that are doing well (or poorly) with respect to mortality in a way that can be used to identify good or bad health policies.

Second, the difference in public spending on health between the "ten best" and the "ten worst" is very small, while the differences in public spending among countries within the "ten best" or "ten worst" groups are very large. The average fraction of GDP devoted to public spending on health among the "ten best" is 2.04 percent, versus an only slightly lower average of 1.81 percent among the "ten worst". Jamaica, with mortality 80 percent better than predicted spends 2.89 percent of GDP while Brazil, with mortality almost 50 percent worse than predicted, spends 2.97 percent. Sri Lanka, the second best, spends 1.67 percent compared to South Korea, which spends 1.88 percent. These are stark examples of the similarities. Within the "ten best" and "ten worst" groups there are large variations in the public sector health spending as a share of 15
27 Table 5: Studies on the Effects of a Devaluation

|_AU7hOR_|_COVERAGE_|_ME77I0D_|_DEVALUA77ON_<br>_EFFECT_|
|---|---|---|---|
|Cooper (1971)|24 devaluations (1959-66) in LDCs<br>(Africa: Morocco, Tunisia)|Control group approach|Contraction: trade balance and<br>B&P improve|
|Shechey (1986)|16 Latin Americancountries|Economeiric approach|Highly contrctionary|
|Edwards<br>(1 986b p. 139)|12 devaluations in LDCs (1965-80)<br>Africa: South Africa<br>._____________|Economelric approach<br>(dependenteconomy model<br>with intermediate inputs.<br>wage indexation and foreign<br>debt)<br>-|Short-run contraction,long- run<br>ncutrl<br>.<br>____________________dcb|
|Ahluwalia and<br>Lysy<br>(1981)<br>-|Malaysia<br>.|Macromodel<br>.<br>.|Contraction when export demand<br>elasticityless than_0.5_<br>.<br>_ _|
|Gylfason and<br>Schmid (1983)|5DCs (Brazil, India, Pakistan,<br>Philippines, Turkey) and 5 DCs|Macro-simulation<br>model<br>(intemnediate goods and open<br>economy)|Devaluation iaexpansionary<br>in 8<br>of 10 countries.<br>Long-nm neutrality|
|Gylfason and<br>Risager (1984)|3LDCs, 7 DCs<br>(Africa:Kenya,Morocco)|Macro-simulation model|Contrction in LDCs; expansion<br>in DCs; imnproved<br>CA all|
|Hamilton<br>(1987)|Australia|Macromodel|Short-runexpansion:<br>long-run contraction|
|Diaz-Alejandro<br>(1965)|Argentina<br>_|lBcrorcand aftcrapproach<br>_|Short-run contraction<br>_<br>_<br>_|
|Kamin (1998)|107 devaluations in LDCs (SSA:<br>Botswana. Burmndi, Ghana, Rwanda,<br>Sicrra Leonc,Somalia. Sudan. Swaziland,<br>Tanzania, Zambia)|Control group approach|Expansion or no effect.<br>Contraction occurs before<br>devaluation and can last after<br>devaluation|
|Edwards<br>(1989a)(1939b<br>p.320.24)|18 devaluations in Latin America (1962-<br>1982)|Control group approach|Observed decline in growth<br>when observed not a<br>consequence of devaluation but<br>ratherdue toaccompanying<br>measures|
|Faini and de<br>Melo (1990)|Largc sample of LDCs|Econometrcapprtoacb|No effect on the trade balance<br>and output forexportersof<br>ptimary goods-In the long mru<br>expansionary<br>for cxporters of<br>rnanufactured goods|
|Gylfason and<br>Radetzki<br>(1985)|12 LDCs (SSA: Sudan, Botswana,<br>Burundi. Ethiopia, Malawi, Rwanda,<br>Somnalia, Sudan, Tanzania)|Macro-simulation model|CA improves, decline in output<br>if wages arm downwards<br>inflexible,specially if there is<br>wage indexation. Evidence of<br>contractionaryeffectsin LDCs|
|Donovan<br>(1981)|12 IMF-supported devaluations<br>(1970-76)|Control group approach|Export growth improves in the<br>longrun, thoughnoL initially(3<br>curve).Iportgrowth rise but<br>lessthan export growth|
<!-- Start of picture text --> Figure 4.3b. Urban employment declined in Textile and Chemical Fibers and other 8<br>manufacturing sectors<br>Textiles: # jobs lost: 507,140 Chemical fibers mfg:<br># job lost: 58, 335<br>2900<br>2800 2842 2849 270 263.37<br>8<br>2700 250<br>2600<br>230<br>2500<br>210<br>2400 204.73 205.04<br>2300 2,334 190 3 3<br>2200 170<br>2100<br>150<br>2000<br>20022003200420052006200720082009<br>Source: Banister and Cook, Monthly Labor Review March 2011, and NBS, China Labor Statistical Yearbook 2010<br>(for 2009 data). See Annex 1.<br><!-- End of picture text -->

- Evidently, rising wages in labor intensive industries have _already triggered relocation of low wage jobs overseas_ . Many lower wage countries in China‘s neighborhood such as Vietnam, Laos, Cambodia, and even Bangladesh are emerging as the new growth nodes of the garments, footwear and other labor intensive industries. How many jobs each country can attract depends on the incentives package it offers to investors.

- Perhaps the most critical channel presently is OFDI flows from China and other emerging market countries to the manufacturing sectors in lower wage countries. This is unsurprising. OFDI from South Korea and Hong Kong SAR, China fuelled much of the early investments in China‘s labor intensive industries when their wages increased and China followed them as flying geese. Now it is the turn for nearly all lower income labor abundant countries to fly after the leading dragon. Currently, the relocation of jobs from China‘s labor intensive sectors to Vietnam and other neighboring countries are financed by both transnational corporations including those from China, as well as local entrepreneurs given the high savings rates in East Asian countries. In low wage countries where local capital is scarce, the majority of the investors will invariably be foreign as apparent from Bangladesh‘s flourishing garments industry which is largely financed initially by FDI. It is notable that _the Leading Dragon Phenomenon is neutral with respect to the source of investment capital_ . What is of essence is  the number of labor intensive jobs that currently exist and will soon become redundant in China and can be relocated in some other country. The world demand for labor intensive products that China presently produces and exports can be satisfied by any country whose wage levels

46
5 principal (usually a private firm, an industry association, or a government agency), (iii) the purpose of the agency, including any US government entities contacted, and (iv) amount of money paid to the agencies for their services.<sup>4</sup> The results presented in this paper use data taken from the reports that covered calendar years 1990-1999.

Foreign lobbying encompasses a wide range of activities, including lobbying those connected with the US government, lobbying the media, and incurring expenditures on promoting trade through advertising (Husted, 1991). Our data do not include expenditures spent directly by the foreign principal on media or advertising but on their agents who, in turn, lobby policymakers. Many Caribbean principals are in fact government agencies, primarily tourism boards, entrusted with ensuring a steady flow of tourists every season. Jobst (2002, Table 7) indicates that 85 percent of all FARA expenditures by Latin American and Caribbean lobbies during the 1990s was spent on export promotion, which includes tourism promotion While FARA reports provide information about the money paid by foreign countries and the industry they represent, they do not provide information about how that money is used to achieve its objectives. In their studies, Krishna, Gawande and Robbins (2006) and Kee, Olarreaga and Silva (2007) assume that lobbying spending is a quid pro quo payment for changes in commercial policy either by lowering tariffs or resisting protectionist increases in tariffs. Since those studies are concerned with imports, and objective measures of policy (tariffs, nontariff barriers) may be targeted by lobbies, lobbying as payments-for-services is a reasonable assumption. Unlike trade in goods or services that are consumed in the home country (or “Mode 1” cross border trade) and may be regulated at the home country border, tourism involves consumption of foreign-produced goods and services within the foreign country’s borders (or “Mode 2” consumption abroad). Further, there are few, if any, domestic lobbies that demand regulation of tourism, unlike the ubiquitous import-competing lobbies that demand protection from imports and subsidies for exports. Thus, government’s motive behind regulating Mode 2 trade in services is distinct from government’s motive behind regulating Mode 1 trade in goods. The latter motive has its source in satisfying import-competing lobbies, and is best understood as a protection-for-sale interaction between lobbies and the domestic government. Foreign lobbies that play this game must also make similar quid-pro-quo payments. The former motive has its

> 4 Of the 1500 individual FARA entries in 1990, about 1000 reported a dollar amount of transactions.  We presume that the unreported amounts are either negligible or are randomly missing.