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15
<u>Table 12: Labor displacement, change in 2030 relative to baseline</u>

||||<br>BRIUB|<br>D||
|---|---|---|---|---|---|
||Agriculture||Total||Percent of labor<br>force|
||displ-|displ+|displ-|displ+||
|World Total|-821.8|6141.9|-14000.2|14000.2|0.37|
|BRI regions|-821.8|5074.8|-11966.2|11966.2|0.48|
|Cambodia|-22.7|0.0|-53.5|53.5|0.58|
|China|-438.6|0.0|-998.5|998.5|0.13|
|Indonesia|-27.4|0.0|-160.8|160.8|0.11|
|Lao PDR|-15.7|0.0|-22.6|22.6|0.54|
|Malaysia|-114.0|0.0|-382.6|382.6|2.36|
|Philippines|-19.7|0.0|-83.0|83.0|0.15|
|Thailand|-161.5|0.0|-516.9|516.9|1.32|
|Vietnam|-17.6|0.0|-180.4|180.4|0.39|
|Rest of East Asia|0.0|70.3|-190.0|190.0|0.32|
|Bangladesh|0.0|3090.8|-4775.3|4775.3|5.59|
|India|0.0|509.3|-972.0|972.0|0.15|
|Nepal|0.0|18.6|-31.3|31.3|0.15|
|Pakistan|0.0|761.8|-1885.0|1885.0|2.18|
|Sri Lanka|-0.6|0.4|-23.2|23.2|0.24|
|Rest of South Asia|0.0|27.5|-53.2|53.2|0.37|
|EU ECA|0.0|5.6|-21.9|21.9|0.08|
|Russian Federation|0.0|16.9|-118.3|118.3|0.17|
|Poland|0.0|0.1|-5.9|5.9|0.04|
|Rest of Eastern Europe|0.0|35.2|-67.6|67.6|0.26|
|Kazakhstan|0.0|1.4|-9.4|9.4|0.09|
|Kyrgyz Republic|-4.0|0.0|-48.9|48.9|1.70|
|Rest of Central Asia|0.0|2.3|-82.4|82.4|0.29|
|Turkey|0.0|27.5|-205.9|205.9|0.71|
|Egypt,Arab Rep.|0.0|18.6|-31.0|31.0|0.09|
|Iran,Islamic Rep.|0.0|68.8|-250.8|250.8|0.82|
|Rest of MENA|0.0|28.6|-380.5|380.5|0.34|
|Kenya|0.0|197.7|-201.2|201.2|0.78|
|Tanzania|0.0|193.3|-214.1|214.1|0.53|
|Non-BRI regions|0.0|1067.1|-2034.0|2034.0|0.15|
|Ethiopia|0.0|63.7|-186.2|186.2|0.27|
|Rest of Sub-Saharan Africa|0.0|542.4|-676.8|676.8|0.16|
|Latin America and<br>Caribbean|0.0|370.1|-588.0|588.0|0.18|
|United States|0.0|59.9|-194.6|194.6|0.11|
|Rest of High-income|0.0|19.1|-165.7|165.7|0.13|
|Rest of Western Europe|0.0|11.9|-222.8|222.8|0.11|

- 55 -
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# **Figure 3. Output since the COVID-19 pandemic in small states and other EMDEs**

_The pandemic disproportionately impacted small states, with an average economic contraction of about 11 percent in 2020, much greater than in other EMDEs. Tourism-dependent small states were hit particularly hard. Recoveries have also been slower in small states: their output in 2023 was only 5 percent higher than in 2019, far below the 15 percent increase in other EMDEs. Moreover, output in small states is set to continue on a lower path than expected before the pandemic._

## **A. GDP growth**

<!-- Start of picture text --> Percent<br>Small states EMDEs excl. small states<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>C. GDP change since 2019<br>Percent<br>35 Small states EMDEs excl. small states<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>2019 2020 2021 2022 2023 2024f 2025f 2026f<br>2019 2020 2021 2022 2023 2024f 2025f 2026f<br><!-- End of picture text -->

## **B. GDP growth in small states**

<!-- Start of picture text --> Percent<br>Small states: Tourism-reliant<br>Small states: Commodity exporters<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>D. GDP compared to pre-pandemic trends in<br>small states<br>Index, 100 = 2019<br>125<br>Output Pre-pandemic trend<br>115<br>105<br>95<br>85<br>2019 2020 2021 2022 2023 2024f 2025f 2026f<br>2018 2019 2020 2021 2022 2023 2024f 2025f 2026f<br><!-- End of picture text -->

**D. GDP compared to pre-pandemic trends in small states**

## _Source_ : World Bank.

_Note_ : f = forecast; EMDEs = emerging market and developing economies. Guyana is excluded from the small states sample.

A.-C. Country groups are GDP weighted at average 2010-19 prices and market exchange rates. Data for 2024-26 are World Bank forecasts. Based on a sample of up to 34 small states and 119 other EMDEs. B. Small states commodity exporters include 13 economies and tourism-reliant small states include 22 economies.

D. Blue line indicates GDP outcomes and current World Bank forecasts. Red line indicates the counterfactual had GDP grown in line with forecasts in the January 2020 _Global Economic Prospects_ , extended at constant growth rates beyond 2022. Based on a sample of up to 34 small states.

31
**Table 2: Regression Results for Step 1**

**Pass Through from International to Domestic Food Price Inflation**

||Full long-term pass-<br>through|R-squared<br>adjusted|
|---|---|---|
|_Using FAO food index_|0.16|0.25|
|_Using FAO cereals sub-index_|0.13|0.28|

These results are in line with the 0.16 weighted average pass-through of the IMF 2008a country-bycountry estimate, and the 0.19% estimate of El-Sakka and Ghali (2005). Yet, these results may seem much lower than the 0.34 median long-term pass-through of international food price shock in EMEs, (IMF, 2011). This may be due to expressing international prices in local currency in IMF, 2011, blending thus together changes in the exchange rate and changes in international food prices. Yet, our results remain unexpected given the large size of the food basket in Egypt‟s CPI.

The estimation of the second-step equation showed that the long-term the spill-over from domestic food prices to other domestic prices is sensitive to the variables used both in step 1, the international price index being (either the FAO food index or the FAO cereals sub-index), and step 2, the domestic price index (core versus non-food inflation) and the economic activity proxy variable (output gap versus output growth gap). In fact, the eight equations that were estimated showed that the long-term spill-over effects ranged between 0.15 (from domestic food prices impacted by FAO cereals price changes to core inflation) and 1 (complete spill-over of the shock to domestic food price inflation to non-food price inflation when the FAO food index is used) (table 3).

To solve the inconclusiveness of the results of the spill-over estimates, the authors resorted to an out-ofsample forecasting method to compare the forecasting power of various models. As shown in table 3, ceteris paribus, having a lower RMSE, the growth gap variable produces more accurate forecasts than the output gap; and the FAO Cereals sub-index produces more accurate forecasts than FAO food index. Consequently, based on the RMSE criterion, the most accurate relation would be that of the non-food price inflation regressed, among other things, on the predicted values of domestic food prices estimated from the bivariate regression of domestic food and FAO cereals sub index, and the growth gap variable. This relation indicates that a 1% increase in the domestic food price inflation is estimated to increase nonfood inflation by almost 0.6%, and that changes in domestic food price inflation explain almost 75% of changes in non-food inflation. Moreover, for the same specification, the spill-over to core inflation is also close to 0.6%.

14
negative cash flow do on average show a lower growth of sales, are more indebted than firms with a positive cash flow, which is consistent with the information in Section 3.1. Another important observation is that firms with positive cash flow had on average much smaller long-term debt than firms with negative cash flow.

**Table 3 Summary Statistics for 1003 Bulgarian Firms**

||_TOTAL SAMPL_|_E_<br>_CA_|_SHFLOW>_|_O_<br>|_CASHFLOW<O_||
|---|---|---|---|---|---|---|
||MEAN<br>M|EDIAN M|EAN<br>ME|DIAN|MEAN<br>M|EDIW4|
|NEMPL|709|286|644|283|<br>844|301|
|K|14.51|2.41|15.51|2.09|<br>12.42|3.31|
|IB/K|0.25|0.19|0.26|0.20|<br>0.22|0.17|
|LTDEBT/K|0.10|0.00|0.06|0.00|<br>0.19|0.02|
|LOAN/K|0.58|0.30|0.46|0.25|<br>0.82|0.46|
|CREDIT/K|0.16|0.07|0.17|0.06|<br>0.16|0.08|
|A(SAL)/K|1.18|0.69|1.47|0.89|<br>0.58|0.38|
|PAY/K|0.11|0.04|0.06|0.02|<br>0.20|0.09|
|CF/K|0.06|0.04|0.19|0.08|<br>-0.21|-0.09|

**Variables description:** NEMPL - number of employees, K - tangible assets, I=d(K) is the net investment, IB - gross fixed investment including depreciation, LTDEBT is the long term debt, NCLIAB - non-current liabilities, CLIAB - current liabilities, LOAN - short term loans, CREDIT -trade credit, d(SAL) proxies future profits, IPAY - interest paid, CF - cash flow. All variables, except NEMPL (number of employees) and K (millions $), are scaled by the capital stock K.

# **4 Estimation Results for the Full Sample**

We estimate two versions of equation (1') for the full sample. The first version is simply the accelerator model without the liquidity variable, whereas in the second version the cash-flow variable is added. Note that the variables used in all the regressions are scaled by the capital stock K in order to control for the size effect. The full sample estimation results are shown in Table 4. The first column in table 4 represents the estimation of a neoclassical investment equation for Bulgarian companies, where investment only depends on the first difference of sales and capital market imperfections are thought to be absent. Although relatively small, the coefficient on first difference of sales is positive and significant. The second column estimates our liquidity constrained model of investment. The cash flow variable has a significant and positive impact on investment.'<sup>2</sup>

> 12 The size of the cash flow coefficient isrelatively small but not extremely so. See for instance Hoshi et aL. (1991), who using exactly the same methodology, find a cash flow coefficient of 0.1 for Japan.

13
UNIDO. 2009. _Scaling up Renewable Energy in Africa_ .12th Ordinary Session of Heads of State and Governments of the African Union, Addis Ababa, Ethiopia. Vienna: United Nations Industrial Development Organisation (UNIDO).

van der Werf, G. R.; D. C. Morton; R. S. DeFries; J. G. J. Olivier; P. S. Kasibhatla; R. B. Jackson; G. J. Collatz; and J. T. Randerson. 2009. “CO2 emissions from Forest Loss,” _Nature Geoscience_ 2: 737 – 738.

WHO. 2006. _Fuel for Life: Household Energy and Health_ . Geneva: WHO.

Zhang, J., K. R. Smith, Y. Ma, S. Ye, F. Jiang, W. Qi, P. Liu, M. A. K. Khalil, R. A. Rasmussen, and S. A. Thorneloe. 2000. "Greenhouse Gases and other Airborne Pollutants from Household Stoves in China: a Database for Emission Factors." _Atmospheric Environment_ 34 (26):4537‐4549.

19
# **1 Introduction**

If trade matters, what can we then say about the countries that do not trade? Many countries in Africa, for example, are consistent underperformers. While ”globalizers,” as defined by Dollar and Kraay (2004), appear to be catching up with the OECD, the countries that are not are falling further behind, in both trade and growth terms. This begs the obvious question ”why?” Why do they not trade, or why do they trade less relative to the recent set of globalizers? The issues involved are important ones. To address them, negotiators within the World Trade Organization have been given a ”leave no country behind” mandate focused on integration of developing countries into the trading system. Emphasis has been placed on North-South tariffs, and there has been a massive mobilization of institutional resources (political, fiscal, legal and research-based) focused on trade promotion through liberalization of tariff and nontariff barriers. The underlying magnitudes are highly relevant, as the mobilization of resources focused on trade policy, in a world of limited institutional resources, implies shifting away resources from other possible priority issues, like institutional development and improvements to basic infrastructure.

The renewed emphasis on growth through trade follows a tumultuous period of shifting perceptions on the role of trade in the empirical literature. Outward oriented policies emerged as a consensus growth prescription in the 1980s. This consensus was backed by cross-country studies of openness and growth. A pioneering attempt to classify trade regimes was conducted in an NBER study directed by Bhagwati (1978) and Krueger (1978). The common message carried from this work was that the degree of openness of the trade regime was positively correlated with export growth, which was in turn positively correlated with real GDP growth. A second
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between the per capita incomes of the rich and poor countries.<sup>7</sup> Underlying this general pattern is a large degree of variation in individual country performance, with growth peaks and valleys across various regional groupings and time periods. However, the overall trend is of an increasing distance between countries in different income brackets, although Pritchett (1997) also shows evidence of convergence at the top of the distribution (that is, among the group of today's high-income countries).

Once different weights are assigned to countries based on their population (using the international inequality approach), the global income distribution appears to have improved. For example, Bourguignon, Levin, and Rosenblatt (2004) demonstrate a decrease in world income inequality between 1980 and 2002, as long as the relevant inequality measures are not too sensitive to the distance of mean income from the bottom.<sup>8</sup> Using the Gini, the Theil index, and mean logarithmic deviation, Atkinson and Brandolini (2004) observed a similar decrease in global income inequality between 1970 and 2000.

The extent of changes in these conclusions once a within-country inequality dimension is added has been an intensely debated subject. Milanovic (2002) suggests that ignoring intra-country income inequality may lead to a completely different understanding about the levels and trends of global inequality. On the other hand, Bourguignon and Morrison (2002) argue that inequality between countries has been responsible for most of the time-series variation in global inequality. Thus, despite the finding of the World Bank (2005) that within-country inequality has been steadily increasing since the late 1980s, the overall direction of change in global inequality since the 1980s is not clear.<sup>9</sup>

> 7   The ratio of per capita incomes of the richest and poorest country in Pritchett’s (1997) sample has grown by a factor of more than five between 1870 and 1990.

> 8   Bourguignon, Levin, and Rosenblatt (2004) show that it is possible to produce rising inequality statistics if, for example, the sensitivity of the Atkinson inequality index to deviations from mean income at the bottom of the distribution is set sufficiently high (over five).

> 9   Some of the studies examining global inequality have relied on parameterized Lorenz curves to add the within-country dimension to the analysis: see for example, Sala-i-Martin (2002a), Sala-i-Martin (2002b), and Bhalla (2002). Others, such as Milanovic (2002) and World Bank (2005), have built up the global distribution from household surveys.

6
is significant in all OLS specifications; in the IV specification the point estimate is smaller, and the standard errors are larger.

How large are these effects of the BDH on the food share? As is discussed above, there was unfortunately substantial contamination of the control group in the BDH experiment. This will tend to dampen any effect the program may have had on food shares estimated in (1) and (2). The coefficient δ can therefore best be understood as a lower bound of the underlying program effect. As an alternative, it is possible to use the randomized assignment into study groups as an instrument for actual receipt of BDH transfers (Imbens and Angrist 1994). For this purpose, we obtained data from _Banred_ , the consortium of banks that pay BDH benefits, on total transfers collected by every household in the sample between January 2004 and July 2005. (Results are very similar when, instead of the banking data, we use the responses provided in the follow-up survey about receipt of BDH transfers.) As is well known, these regressions estimate Local Average Treatment Effects (LATE). These are estimates of the effect of BDH transfers on the food share for households whose probability of receiving transfers was affected by the lottery—“compliers”, in the language of Angrist, Imbens, and Rubin (1996). In these regressions, the coefficient on instrumented receipt of BDH transfers implies an effect on the food share of between 6.4 and 7.4 percentage points in the single-difference estimates, and of between 10.5 and 12.6 percentage points in the double-difference regressions.<sup>5</sup> We present two additional pieces of evidence that are consistent with an increase in expenditures on food by BDH lottery winners. First, we reproduce a result from the paper by Paxson and Schady (2007) on the impact of the BDH on child health and development. The follow-up survey used by Paxson and Schady asked BDH beneficiaries, all of whom were women, what they used BDH transfers for. Table 4, reproduced from their paper, shows that nearly half (49.2 percent) reported that they spent all or most of the transfer on food, with much smaller fractions reporting that they spent all or most of the transfer on clothing (11.4 percent), education (10.7 percent), and health care (7.9 percent). The survey also asked mothers who in the household (the mother, her partner or husband, or both) decided whether the transfers should be spent on food, clothing, etc. For each type of expenditure, fewer than 2 percent of women reported that her husband or partner alone made decisions on how to spend the BDH transfer, and the majority indicated that they made spending decisions alone. Second, we note that Paxson and Schady find that the BDH program resulted in

> 5 The first stage is highly significant: In a regression of the dummy for households that received transfers on a dummy for lottery winners and the full set of controls, the coefficient on lottery winners is 0.347, with a standard error of 0.048.

12
**Table** _5.4:_ **Marginal Effects for Correspondence of School Age and Grade -- NE**

|Probitestimat|es|||||Numberof <br>LRchi2(9)<br>Prob>chi2|obs=25423<br>=14444.79<br>=0.0000|
|---|---|---|---|---|---|---|---|
|Log likelihood|=-10026.|014||||Pseudo R2|=0.4187|
||dF/dx|Std.Error|z|P>Iz||x-bar|[95%|C.I.]|
|female*|0.073|0.007|10.170|0.000|0.499|0.059|0.087|
|Age|-0.489|0.013|-32.880|0.000|12.430|-0.515|-0.463|
|age<sup>2</sup>|0.013|0.001|23.960|0.000|164.834|0.012|0.014|
|P0*|-0.143|0.007|-18.880|0.000|0.565|-0.157|-0.128|
|primlH*|-0.020|0.009|-2.220|0.026|0.289|-0.038|-0.002|
|prim2H*|0.261|0.007|31.300|0.000|0.152|0.248|0.274|
|secH*|0.389|0.005|52.020|0.000|0.076|0.379|0.399|
|femHH*|0.009|0.008|1.090|0.274|0.236|-0.007|0.026|
|m5yNESE*|0.039|0.029|1.280|0.202|0.015|-0.019|0.096|
|obs.P:0.5854|94|||pr|ed.P:0.68|7906(atxb|ar)|

(*) dF/dx is for discrete change of dummy variable from 0 to I z and P>|z| are the test of the underlying coefficient being 0

_Note:_ Variable uniH was dropped during probit estimation.

_Source:_ Author's own calculations based on PNAD 1999.

The negative correlation between NE-SE migration and school attendance as well as school performance, and evidence from descriptive statistics in Table 5.1 indicate that children of NE-SE migrants have more difficulties in catching up in school than children of SE-NE migrants. This could be due to lower quality of education in the NE. Children of NE-SE migrants therefore have more difficulty adapting to new school curricula in the SE. Therefore it might be useful to provide additional instruction to children from NE-SE migrants. Alternatively, efforts to improve the educational quality in the NE might be warranted.

**Table** 5.5: **Marginal Effects for Correspondence of School Age and Grade --** SE

|Probitestimat|es||||<br>|Numberof <br>LRchi2(9)<br>Prob >chi2|obs=22929<br>=8922.30<br>=0.0000|
|---|---|---|---|---|---|---|---|
|Loglikelihood|=-7966.8|103||||PseudoR2|=0.3590|
||dF/dx|Std.Error|Z|P>Iz||x-bar|[95%|C.I.]|
|female,*|0.036|0.005|7.490|0.000|0.492|0.027|0.046|
|age|-0.127|0.009|-11.970|0.000|12.371|-0.146|-0.109|
|age2|0.001|0.000|3.000|0.003|163.765|0.000|0.002|
|P0*|-0.077|0.007|-11.650|0.000|0.209|-0.092|-0.063|
|primlH*|-0.120|0.008|-14.810|0.000|0.356|-0.136|-0.103|
|prim2H*|0.090|0.005|14.660|0.000|0.241|0.079|0.100|
|secH*|0.238|0.005|51.610|0.000|0.154|0.227|0.248|
|femHH*|-0.014|0.006|-2.380|0.017|0.210|-0.026|-0.002|
|m5ySENE*|-0.093|0.027|-3.940|0.000|0.014|-0.146|-0.039|
|obs.P:0.767|718|||p|red.P:0.86|7629(atxb|ar)|

(*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>Izl are the test of the underlying coefficient being 0 _Note:_ Variable uniH was dropped during probit estimation.

34