rafmacalaba/datause-encoder-data huggingface dataset:
Table 5. GDP, GNDI and external accounts in Lebanon (in percent of GDP)

||**Share to GDP (%)**|**1972**|**1994**|**2004**|
|---|---|---|---|---|
||Agriculture|9.9|12.1|5.2|
||Transport and communication|7.5|2.8|7.4|
|**ces**|Industry|15.9|17.7|11.9|
|**our**|Trade|31.5|28.8|24.0|
|**Res**|Construction|4.6|9.5|7.4|
||Public sector|7.5|8.3|11.2|
||Services and housing|23.1|20.9|32.9|
||**GDP (uses = resources)**|**100.0**|**100.0**|**100.0**|
||Consumption-public|9.0|10.6|15.0|
||Consumption- private|78.7|110.9|85.5|
|**s**|Investment-public|3.1|4.1|3.1|
|**Use**|Investment-private|17.3|32.3|18.8|
||Exports of goods and services|38.0|8.4|19.9|
||Imports of goods and services|46.0|66.3|42.1|
||**Net exports (trade balance)**|**-8.0**|**-57.8**|**-22.3**|
||Net transfers and factor income|6.3|32.6|11.9|
||Current account deficit|-1.7|-25.2|-10.4|
||Net foreign savings|13.8|37.7|11.2|
||Net<br>change<br>in<br>foreign<br>assets<br>(negative is an increase)|-12.0|-12.4|-0.7|
||**GNDI = GDP + net transfers and**<br>**factor income**|**106.3**|**132.6**|**111.9**|

Sources: National Accounts for 1973, 1994 and 2004. Economic dislocation and recovery in Lebanon - IMF, 1995. Back to the future, postwar reconstruction and stabilization in Lebanon – IMF, 1999.

In 1972, Lebanon remained open to international flows of goods, services, and capital. The foreign inflows of income, transfers, and capital amounted altogether to 20 percent of GDP, which is high but much lower than the 54 percent observed 60 years earlier. Interestingly, only part of these inflows poured into the local economy to finance imports for local consumption and investment. The largest part of inflows helped to build foreign currency reserves and purchase gold. The biggest source of foreign currencies was a dynamic exporting sector with 38 percent of GDP that helped to cover the largest part of imports. Consumption remained at 79 percent of GDP.

After the destruction of the war and the large resource movements in the form of strong outmigration between 1975 and 1990, a radically different picture emerged in 1994 compared to 1972. The second globalization was in full motion and, in 1994, the economy was receiving up to 70 percent of GDP in income, transfers, and capital flows. These inflows financed imports reaching 66 percent of GDP and boosted private consumption up to 111 percent of GDP. Exports shrunk to less than 9 percent of GDP. Investments reached a high of 32 percent of GDP, but large parts of these investments were in housing and real estate, with little impact on the productive capacity. If we look at the distribution of GDP by sector, we notice an increase in the shares of industry and agriculture, two sectors that benefited from the devaluations of 1985-88. Transport and communication declined due to severe damages inflicted to infrastructures and to the redirection of regional transport away from Lebanon during the war. Trade and services also suffered, especially with the decline in tourism.

9
and typically consists of a few rooms around a common courtyard with shared latrines and other facilities. After stratifying compounds by average household assets, number of eligible children, and location, each was assigned to the subsidy-only group, the CHW-only group, the full treatment group, or the control. The healthworkers in the two CHW treatment groups were trained and managed separately, to avoid spillovers.<sup>9</sup> The focus of this study is to understand the demand for health care of the typical quality a local medical professional (trained and certified in-country) can provide, holding the supply side constant. The reader may have two concerns in this regard. First, quality of care in public health care systems in developing countries is often low, which may mean in practice that patients receive care from an untrained individual or not at all (e.g. if the doctor is actually absent). Such quality problems affect how desirable the use of the health care system is, both from the parents’ and the policymaker’s point of view (see e.g. Das (2011); Das and Hammer (2005, 2007, 2014); Das et al. (2016); Das and Sohnesen (2006); Leonard and Masatu (2010); Leonard et al. (2002)). The clinics in this study were financially supported by Mali Health during the study and subject to regular quality control. We assume that the standard of care provided reflects the level of care that the WHO expects when they formulate treatment recommendations for low-income countries. The second concern is that subsidizing care might create supply side responses to our interventions, such as demand inducement and moral hazard by doctors. While we cannot rule out that such effects occurred, Mali Health controlled costs in the subsidy group using treatment guidelines for common diagnoses, spot checks on diagnosis and prescriptions using bills submitted to Mali Health, and the clinic’s treatment records and accounting. We also found that the subsidy did not change the average value of services received per visit (see section 2.3). Lastly, the study sample is small relative

> 9It was not possible to assign healthworkers across the entire intervention area while keeping their travel distances manageable. Mali Health therefore paired healthworkers of similar experience and quality, and one of each pair was assigned to the healthworker only or the healthworker and subsidy group. The pairs were then assigned in an overlapping pattern by stratum. For example, pair 1 and pair 2 were each randomly assigned half of the families in the same stratum, and half of the families in a different stratum, each shared with another pair of healthworkers. In this manner, each stratum was assigned four healthworkers (two in each treatment group), the quality of the healthworkers in the treatment groups was matched, and no two strata had identical sets of healthworkers.

11
eliminate, price arbitrage between countries. Taken together, the results suggest that after three months, 52 percent of a given shock is dissipated when there is no ban – compared to 42 percent under a ban (Figure 11).

As expected, the influence of weather anomalies on local price changes is exacerbated during an export ban.<sup>25</sup> A 10 percent decline in the NDVI is associated with a 3.8 percent increase in prices during a ban and only a 2.7 percent increase when there is no ban. These estimates are significantly different from zero and also from each other at the 1 percent level. The Southern and Northern zones, the main maize export areas, experience the largest differential impacts of weather disturbances under the two trade regimes. It is worth noting that the mechanisms through which export bans influence market prices involve changes in the impacts of both external and domestic shocks and not merely lower price levels (as shown in table 5).

In the case of the Northern and Southern zones, which are the main export areas, bans exacerbate weather shocks and consequently engender greater price uncertainty during the harvests. Consequently, maize producers are adversely affected by both lower prices at harvest as well as greater uncertainty due to the larger impacts of domestic shocks.

# **7 The Impact of the 2011 Export Ban**

What path would maize prices have taken in the absence of export bans? This question is important because it constitutes an essential first step to understand the welfare impacts of trade this is because of the en- policies. Answering question precisely difficult likely dogeneity of export bans. First, ad-hoc agricultural trade policies may exert an adverse long-run influence on economies through mechanisms that disrupt market institutions and engender sub-optimal investments along the supply chain.<sup>26</sup> Indeed, the majority of Tanzanian maize markets analyzed in this study are characterized by price volatility that is significantly higher than external markets (Table 3), suggesting that these mechanisms indeed be at work. This of is and has been a feature may type endogeneity systemic, of Tanzania’s maize sector all along, so it is not expected to bias our results. It would, however, be a more relevant concern in the context of cross-country comparisons.

Second, the timing of the imposition of a particular ban may be endogenous as well. As noted earlier, bans are more likely to be imposed when prices are elevated. Though our econometric analysis accounts for harvest shocks and consequently removes one (po-

> 25These results are similar to those of Burgess and Donaldson (2010), who found (based on 1875-1915 data for India) that the tendency of rainfall shortages to cause famines diminished as trade increased (as measured by railroad expansion).

> 26Tschirley and Jayne (2010) discuss the adverse long-term consequences of discretionary (and unpredictable) trade policy regimes.

16
- 6 -

## Table 1

# The Relationship Between Excises as Percentage ol Total Tax Revenue and GNP per Capita, For All Countries and by Region

|Estimated Per||Excises as Percentage|
|---|---|---|
|Capita GNP<br>(US dollara)|Number of<br>Countries|of Total Tax<br>Revenve|
|Below 500|||
|All countries|30|14.7%|
|Africa|20|11.3%|
|Asia|8|22.3%|
|Europe|0|--|
|North and Centra! America|1|29.9%|
|South America|1|7.4%|
|501-2000|||
|All countries|37|13.4%|
|frica|I1|9.9%|
|Asia|8|15.8%|
|Europe|1|7.7%|
|North and Central America|11|14.9%|
|South America|6|14.6%|
|2001-6000|||
|All countries|16|10.5%|
|Africa|2|0.2%|
|Asia|2|6.6%|
|Europe|4|14.1%|
|North and Central America|3|8.3%|
|South America|5|14.4%|
|Above 6000|||
|All countries|25|9.4%|
|Africa|0|--|
|Asia|6|8.8%|
|Europe|16|10.3%|
|North and Central America|3|5.8%|
|South America|0|--|

Source of Data: International Monetary Fund. Goverunment Finance Statistics Yearbook, 1990 (Washington: 1991). More detailed analysis is presented in the article by John F. Due and Carrie Meyer, "Major Determinants of the Tax Structures of Market Economy Countries, Tanzania Journal of Economics, Vol. I (July 1989), pp. 47-63.
graphs the change in poverty on the horizontal axis, and the sum of the growth and distribution components on the vertical axis, using the dataset of long spells. The slope of the OLS regression line is the share of the variance in poverty changes due to the growth and distribution components, and one minus the slope is the share due to the residual term. It is clear from this graph that changes in poverty are largely accounted for by the sum of the growth and distribution components, with very little of the variation due to the residual.

The middle panel of Tables 2 and 3 does the same variance decomposition, but now to assess the importance of the growth component relative to the distribution component of changes in poverty. For the sample of all spells, between one-third and one-half of the variation in changes in poverty is due to the growth component, with the remainder due to changes in distribution. The story is quite different for the long spells, where the growth component of changes in poverty dominates, accounting for between 65 and 90 percent of changes in poverty. In both tables, the growth component is relatively less important for bottom-sensitive poverty measures such as the poverty gap and the squared poverty gap. The middle panel in Figure 4 graphically summarizes this second decomposition for the long spells sample, plotting the growth component of changes in poverty on the vertical axis, and the sum of the growth and distribution components on the horizontal axis. Again, the slope of the OLS regression line can be interpreted as the share of the variation on the horizontal axis due to the growth component. Visually inspecting this graph, it is clear that if poverty reduction is large, it is mostly because the growth component of poverty reduction is large.

The bottom panel of Tables 2 and 3 further disentangles the growth component into growth in average incomes, and the sensitivity of poverty to growth in average incomes, i.e. it separates the first term in Equation (3) into its two components. Since the decomposition we have been using applies to sums of random variables, I take the logarithm of the absolute value of the growth component, which then becomes the sum of the logarithm of the absolute value of growth, and the logarithm of the absolute value of the average sensitivity of poverty to growth, and apply the decomposition to this sum. Tables 2 and 3 show that over 80 percent of the cross-country variation in the growth component of changes in poverty is due to cross-country differences in average income growth, and very little is due to cross-country differences in the sensitivity of poverty to 16
|||log(trade)||
|---|---|---|---|
|Time|RTAm|RTAin|RTAout|
|-5|-0.0056|-0.1614***|0.0601***|
||(-0.18)|(-14.39)|(5.42)|
|-4|0.0014|-0.1615***|0.0308**|
||(0.05)|(-14.08)|(2.72)|
|-3|-0.0080|-0.1376***|0.0291*|
||(-0.26)|(-11.62)|(2.48)|
|-2|0.0429|-0.1073***|0.0618***|
||(1.37)|(6.91)|(10.67)|
|-1|0.0737*|-0.0735**|0.0747***|
||(2.31)|(-5.22)|(5.35)|
|0|0.1040**|-0.0615**|0.0938***|
||(2.79)|(-3.03)|(4.62)|
|1|0.2019***|-0.0042|0.1385***|
||(5.85)|(-0.29)|(9.54)|
|2|0.2405***|-0.0150|0.1423***|
||(6.91)|(-1.13)|(10.67)|
|3|0.2429***|0.0023|0.1329***|
||(6.88)|(0.19)|(10.46)|
|4|0.3067***|-0.0093|0.1213***|
||(8.67)|(-0.75)|(9.75)|
|5|0.3287***|0.0287*|0.1063***|
||(9.19)|(2.32)|(8.63)|
|6 and more|0.5777***|0.0962***|0.2773***|
||(0.2990)|(10.02)|(28.97)|
|Country pair FE|Yes|||
|Country_×_time FE|Yes|||
|Observations|796,107|||
|_R_<sup>2</sup>|0.7990|||

t statistics in parentheses. * p<0.05, ** p<0.01, *** p<0.001 Table 5: Timing of the effects of RTA on total trade flows 17
<!-- Start of picture text --> 2,500<br>2,000<br>1,500<br>1,000<br>500<br>0<br>ETHIOPIA_2011-12 TANZANIA_2010-11<br>MALAWI_2010-11 UGANDA_2010-11<br>Agriculture Industry Services Unknown<br>ag ind serunkn ag ind serunkn ag ind serunkn ag ind serunkn<br>Mean hours per person per year<br><!-- End of picture text --> Figure 3: Average hours supplied by individuals to all sectors, categorized by each individual’s primary sector of participation.

23
8 the high rate of private consumption (especially compared to low rate of savings) in three key bubble economies, Spain, the UK and the US. Concomitant with these imbalances has been the growth of foreign exchange reserves in the two leading surplus economies (China and Japan), which together accounted for nearly half of total global foreign exchange reserves (Table 3).

**Table** **<u>3: Foreign Exchange Reserves (US$ Millions) 2009</u>**

||**Country**|**($ Millions)**|**% of World**<br>**Total**|
|---|---|---|---|
||**World (sum of all countries)**|**7,520,566**||
|2009|China(includingHongKong)|2,292,300|30%|
|2009|Japan|1,044,327|14%|
|2008|Eurozone (EU member states which have<br>adopted the EURO,incl.ECB)|569,213|8%|
|2008|India|313,354|4%|
|2009|Brazil|223,713|3%|
|2008|Germany|150,377|2%|
|2008|United Kingdom|99,956|1%|
|2008|UnitedStates|67,000|1%|

Source: The SWF Institute, accessed November 2009 (www.swfinstitute.org).

The imbalances in trade – feeding off the financial bubble – represents a core structural feature which is unsustainable in the medium and long term, particularly for very large global economies such as the US and China. To be resolved they require either (or a combination of) a reduction in consumption in the surplus economies, or a rise in consumption in the deficit economies, resulting in a fall in net exports in surplus economies and a rise in net exports in the deficit countries. These changes may work their way through the system through changes in exchange rates, personal consumption expenditure and government expenditure, and may or may not involve price deflation or inflation. The precise mechanisms involved in the resolution of the imbalances are less important for our discussion than the level of output and output growth in which the structural rebalancing will be achieved.

Some changes are already occurring. Thus household savings rates are beginning to rise, with consumption falling and trade deficits narrowing in key deficit economies. At the same time, payments surpluses have been falling in some economies, including China (Table 4).

**Table 4:** **<u>Changes in Trade and Savings for Major Economies (2008-2009)</u>**

||**Current**<br>**Bala**<br>**(Percent **|**Account**<br>**nce**<br>**of GDP)**<br>|**Gross N**<br>**Savi**<br>**(Percent **|**ational**<br>**ngs**<br>**of GDP)**<br>|**Tr**<br>**(% Change i**<br>**2008/0**|**ade**<br>**n $ value June**<br>**9 YOY)**|
|---|---|---|---|---|---|---|
||**2008**|**2009**<sup>**1**</sup>|**2008**|**2009**<sup>**1**</sup>|**Imports**|**Exports**|
|Germany|6.4|2.9|26|20|33|34|
|Japan|3.2|1.9|27|23|26|24|
|United|||||||
|Kingdom|-1.7|-2.0|15|12|31|31|
|United States|-4.9|-2.6|13|11|24|-0.29|

Source: Calculated from IMF World Economic Outlook and DOTS Database, accessed November 2009.

1 Estimated by the IMF WEO.
the fact that determinants of earnings gaps between men and women vary across the earnings distribution. Accordingly, policymakers who seek to close the gap between high earning businesses should target different variables than if they are trying to help low-earning businesses become more successful. Secondly, while our results are descriptive, we hope they will motivate future research on the causal determinants of earnings gaps between men and women. For instance, we argue that the increasing role of structural factors across the earnings distribution in the Republic of Congo, Rwanda, and Tanzania suggests that a glass ceiling is holding back female entrepreneurs in these countries. Future research could test whether this glass ceiling is indeed in place by examining differences in deteriminants of business success such as access to inputs, business networks, and consumers. Additionally, future data collection efforts could collect information on determinants of profits such as risk aversion, ability (both cognitive and non-cognitive), and discount rates, allowing researchers to confirm that the structural differences hold up to controls for a wider range of observable differences in profits. Overall, we hope that our results contribute to both academics and policymakers interested in understanding and closing earnings gaps between male and female entrepreneurs.

# **References**

(2005-2013): “World Development Indicators,” .

- Bates, T. (1999): “Existing self-employment: an analysis of Asian immigrant-owned small businesses,” _Small business economics_ , 13(3), 171–183.

- Chi, W., and B. Li (2008): “Glass ceiling or sticky floor? Examining the gender earnings differential across the earnings distribution in urban China, 1987–2004,” _Journal of Comparative Economics_ , 36(2), 243–263.

- Chuhan-Pole, P., M. Angwafo, A. D. Mapi Buitano, V. Korman, and M. L. Fox (2011): “Africa Pulse: An analysis of issues shaping Africa’s economic future, vol. 4,” _Washington, DC: The World Bank_ .

- Croson, R., and U. Gneezy (2009): “Gender differences in preferences,” _Journal of Economic Literature_ , pp. 448–474.

- De Mel, S., D. J. McKenzie, and C. Woodruff (2009): “Measuring microenterprise profits: Must we ask how the sausage is made?,” _Journal of Development Economics_ , 88(1), 19–31.

- DiNardo, J., N. M. Fortin, and T. Lemieux (1995): “Labor market institutions and the distribution of wages, 1973-1992: A semiparametric approach,” Discussion paper, National Bureau of Economic Research.

22
# Tables 3 & 4 [about here]

Our baseline regression model is based on Hassan, Sanchez and Yu (2011), but focusing on sectoral rather than aggregate growth rates. Let denote the real value added growth rate of sector _i_ in country _k_ in year _t_ . Diminishing returns in the neoclassical growth model imply a positive convergence parameter λ such that: where denotes the long-run real value added per worker implicitly determined by structural parameters in the economy. According to equation (2), the growth rate of real sectors is expected to diminish as their value added per worker converges to the latent potential level of output per worker, . Since the technological frontier in each sector in each country is unobservable, following Hassan, Sanchez and Yu (2011) we assume that it is a function of economic fundamentals in each country. Therefore, for each sector _i_ we estimate the following model: The first term on the RHS captures any persistence in the annual value added growth rate of sector _i_ in country _k_ . The second term on the RHS captures perturbations due to changes in the growth rate of the financial sector and third term (the vector of controls, ) represent determinants of long-run growth. The vector of country-level controls includes: 8
- 24 -

Recall that ';eightcd Government Growth measures (G/Y'lG. Assuming an average 5.7 percentage point growth rate G, the regression in column (2) predicts that a per.-,anent three percent increase in G/Y financed by i..¢reasing the tax effort will reduce output growth rates by 0.3 percentage points, although this prediction is not significant at conventional levels.

The third column once again uses Tax Zffort to proxy for the overall degree of tax distortions, although in this case it is entered linearly; as in (9), rather than interactively. The coefficient is negative and significant.

Column (4) expands the regression to include different measures of tax distortion which are entered linearly. One notable aspect of these equations is the significant and negative impact of direct taxation (corporate and personal) on output growth rates. A one percentage point increase in the personal tax (equivalent to a 17 percent increase in personal tax rates) is predicted to reduce output growth rates by 0.36 percentage points. The coefficients for the import, export, and sales taxes, however, are insignificant, with coefficients near zero.

The possibility that mineral exporting countries (Liberia, Sierra Leone, Zaire, Guinea, and Zambia; see Wheeler, 1984) subject to high corporate tax rates suffered output downturns because of trade-related problems rather than high corporate tax rates was tested by including a mineral exporting dummy variable. The regression (not reported) showed only minor differences in the taxation coefficients.

Landau (1983; 1986) has suggested that the ratio of government consumption to GDP, G/Y, be entered as a component in GDP growth rates. While it is difficult to justify its inclusion on a theoretical basis (as discussed in the previous section), it is included nonetheless in the final