rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A19: Coping strategies for not having been able to access the desired amount of fertilizer.**

||On<br>pa<br>are|ly fertilized<br>rt of cultivated<br>a|Used lower rate of<br>fertilizer|Cultivated a<br>smaller area|Supplemented<br>with organic<br>fertilizer|Practiced<br>legume<br>intercropping|
|---|---|---|---|---|---|---|
|**Burkina F**|**aso**||||||
|2024/01|Mean|27.8|50.1|10.3|47.0|10.3|
||Std. Err.|3.3|3.6|2.2|3.6|2.2|
||_N_|189|189|189|189|189|
|**Ethiopia**|||||||
|2024/02|Mean|23.3|78.3|5.6|40.9|4.6|
||Std. Err.|2.8|2.7|1.5|3.2|1.4|
||_N_|234|234|234|234|234|
|**Malawi **|||||||
|2023/06|Mean|25.4|61.8|3.9|8.9||
||Std. Err.|1.9|2.2|0.9|1.3||
||_N_|510|510|510|510||
|**Nigeria**|||||||
|2023/06|Mean|60.6|62.3|40.9|64.7||
||Std. Err.|2.2|2.1|2.2|2.1||
||_N_|516|516|516|515||
|**Tanzania**|||||||
|2023/11|Mean|37.4|49.0|13.6|8.2|2.9|
||Std. Err.|3.8|3.9|2.7|2.2|1.3|
||_N_|162|162|162|162|162|
|**Uganda**|||||||
|2023/10|Mean|10.4|43.1|0.0|23.5|23.9|
||Std. Err.|5.4|8.8|0.0|7.5|7.5|
|Note: Cop|_N_<br>ing strategies fo|33<br>r not having bee|33<br>n able to access the des|33<br>ired amount of fer|33<br>tilizer. All means in p|33<br>ercent.|

40
# **References**

Andersson, Martin. 2007. “Entry Costs and Adjustments on the Extensive Margin – an analysis of how familiarity breeds exports”, CESIS Working Paper, Royal Institute of Technology, Stockholm Andersson, Martin, Sara Johansson and Hans Lööf. 2007. ”Firm Performance and International Trade – evidence from a small open economy”, Royal Institute of Technology, Stockholm Bartelsman, Eric J. and Mark Doms. 2000. “Understanding Productivity: Lessons from Longitudinal Data.” _Journal of Economic Literature_ XXXVIII (3), 569-594.

Bernard, Andrew B. and J. Bradford Jensen. 1995. “Exporters, Jobs, and Wages in U.S. Manufacturing: 1976-1987.” _Brookings Papers on Economic Activity: Microeconomics_ . 67-119.

Bernard, Andrew B. and J. Bradford Jensen. 1999. “Exceptional exporter performance: cause, effect, or both?” _Journal of International Economics_ 47, 1-25.

Bernard, Andrew B. and J. Bradford Jensen. 2004. “Exporting and Productivity in the USA.” _Oxford Review of Economic Policy_ 20, 343-357.

Bernard, Andrew B. and Joachim Wagner. 1997. “Exports and Success in German Manufacturing.” _Weltwirtschaftliches Archiv / Review of World Economics_ 133, 134157.

Damijan, Joze P., Saso Polanec and Janez Prasnikar. 2004. “Self-selection, Export Market Heterogeneity and Productivity Improvements: Firm Level Evidence from Slovenia.” Katholieke Universiteit Leuven, LICOS Discussion Paper 148.

Damijan, Joze P. and Crt Kostevc. 2006. “ Learning-by-Exporting: Continuous Productivity Improvements or Capacity Utilization Effects? Evidence from Slovenian Firms.” _Review of World Economics_ 142, 599-614.

24
Figure C.4: Life Cycle of Firms during Acceleration Episodes: Chile 1980-2011 <!-- Start of picture text --> 2<br>ss 1980<br>ss Chile post−reform<br>t=1995<br>1.8 t=2011<br>t=1980 (per 1 of reform)<br>Cohort from 1980<br>1.6<br>1.4<br>1.2<br>1<br>0 10 20 30 40 50 60<br>Age<br>Employment relative to Age 1<br><!-- End of picture text --> In terms of understanding the source of this sluggishness, recall that the shape of the cross-sectional life-cycle is determined by a combination of age and cohort eects. On the one hand, newly created rms are innovating at a pace consistent with the more friendly economic environment and are, therefore, making the life-cycle look steeper. On the other hand, older cohorts comprise low productivity, formerly subsidized entrepreneurs whose protection is being withdrawn by the reform and are consequently cutting down on innovation and headed towards exit. Since these low productivity rms have accumulated investments in productivity, the productivity process implies that it takes time for these rms to drift down towards the exit threshold. Hence, they contribute to making the life-cycle look atter.

The sluggishness in the convergence of the cross-sectional distribution of employment across age raise a word of caution to using it as an input to back out the underlying idiosyncratic distortions in the economy. Suppose a researcher were to observe the cross-sectional distribution of employment over age for Chile in 2011, and one were to use a stationary model of rm dynamics to infer the degree of allocative distortions that are necessary to replicate the cross-sectional life cycle in the data.<sup>26</sup> Since the life cycle of rms in the cross section of the model for 2011 is well below

> 26This is the kind of counterfactual constructed in Hsieh and Klenow (2014) to quantify the aggregate implications of the dierences in the life-cycle of rms between the U.S., India, and Mexico

51
# **Reducing Trade Costs in East Africa: Deep Regional Integration or Multilateral Action**

# **by**

# **Edward J. Balistreri David G. Tarr and Hidemichi Yonezawa**<sup>**1**</sup>

# **1. Introduction**

Evidence is now substantial that with the progressive global decline in tariffs over several decades, trade costs are often a much more substantial barrier to trade than tariffs.<sup>2</sup> Moreover, trade costs are especially high in Sub-Saharan Africa compared to other regions in the world. For example, the World Economic Forum (2012) found that it is still considerably more expensive to trade with Africa than with other regions, and, in many cases, the cost of trading is a more important obstacle to trade development than trade policies.<sup>3</sup> Some Sub-Saharan countries, notably the members of the East African Customs Union (EACU, also known as the East African Community)<sup>4</sup> are addressing the high trade costs through regional initiatives, which may or may not become multilateral. In this paper, we assess the impacts of reducing trade barriers among the EACU members and also assess how much more there is to gain if the trade cost reduction

> 1 Corresponding author: David G. Tarr, is Consultant and Former Lead Economist, the World Bank. His email is: dgtarr@gmail.com. Edward Balistreri is Associate Professor at the Colorado School of Mines and Hidemichi Yonezawa is with the University of Ottawa. The authors gratefully acknowledge the financial support of the Government of the Netherlands under the Bank-Netherlands Partnership Program, project TF012466, entitled “Reducing Trade Costs in East Africa: Analytical Development and Capacity Building." The authors thank: Ana Margarida Fernandes Yaghoob Jafari, Zoryana Olekseyuk, Josaphat Kweka, John Randa, George Gandye, Paul Brenton, Maryla Maliszewska, Yutaka Yoshino, Jacques Morisset and Victoria Cunningham and an anonymous referee for their contributions. The views expressed are those of the authors and do not necessarily represent those of the World Bank or its Executive Directors or those acknowledged.

> 2 See, for example, Hummels (2007) or Hummels _et al_ ., (2007).

> 3 Brenton and Isik (2012) have also documented the high costs of trading in Sub-Saharan Africa. See also, the estimates of Hummels _et al_ ., (2007) and Minor (2013).

4 The Treaty on the Establishment of the East African Community among Kenya, Tanzania and Uganda came into force on July 7, 2000. It stipulated that the three countries would continue to trade preferentially. On March 2, 2004, however, the Protocol for the Establishment of the East African Customs Union was signed by the Heads of State of Kenya, Tanzania and Uganda. Rwanda and Burundi joined the Customs Union in 2008. See <u>http://www.customs.eac.int/index.php?option=com_content&view=article&id=123&Itemid=78. Although the five</u> country grouping of Kenya, Tanzania, Uganda, Rwanda and Burundi is sometimes referred to as the East African Community (EAC), given the later developments of 2004 and 2008, we typically refer to the five country grouping as the East African Customs Union (EACU).

1
Banerjee et al. (2012), the four nodal cities of the GQ network were established hundreds or thousands of years ago, making this concern less worrisome in our context.<sup>11</sup> The exclusion restriction embedded in the straight-line IV is that proximity to the minimum-distance line only affects districts in the 1999-2009 period due to the likelihood of the district being on the GQ network and experiencing the highway upgrade. This restriction could be violated if the regions along these straight lines possessed characteristics or policies that are otherwise connected to financial growth during this period. To guard against these concerns, we focus on IV specifications with state fixed effects. We will thus only exploit variation within states in the likelihood that a district would have been on the GQ network. We also continue to control for initial financial development in the district.

IV Route 1 is the simplest approach, connecting the four nodal districts outlined in the original Datta (2011) study. We allow one kink in the segment between Chennai and Kolkata to keep the straight line on dry land. IV Route 1 overlaps with the GQ layout and is distinct in places. We earlier mentioned the question of Bangalore's treatment, which is not listed as a nodal city in the Datta (2011) work. Yet, thinking of Bangalore as a nodal city is visually compelling on a map. We thus test two versions of the IV specification, with and without the second kink for Bangalore.

Panel A of Table 4 provides a baseline OLS estimation. For these IV estimations, we exclude nodal districts (sample now contains 302 districts) and measure all effects relative to districts more than 10 km from the GQ network. This approach only requires us to instrument for a single variable-being within 10 km of the GQ network.

The first-stage relationships are quite strong. IV Route 1, which does not connect Bangalore directly, has a first-stage elasticity of 0.43 (0.05) and an associated F-statistic of 74.5. IV Route 2, which treats Bangalore as a connection point, has a first-stage elasticity of 0.54 (0.05) and an associated F-statistic of 138.1.

Panel B presents the second-stage results. The IV specifications generally confirm the OLS findings. Column 1 shows a modest growth in the estimated impact of proximity to 11 Banerjee et al. (2012) provide an early application and discussion of the straight-line IV approach, and Khanna (2014) offers a recent application to India. Faber (2014) provides an important extension to this methodology. Faber (2014) uses data on local land characteristics and their impact on construction costs to define a minimum-cost way of connecting 54 key cities that were to be linked by the development of China's highway network.

14
**Table 6: Determinants of education completion of generation II**

||**Prim**<br>**Female**|**ary**<br>**Male**|**Secon**<br>**Female**|**dary**<br>**Male**|
|---|---|---|---|---|
|Young|0.077**|-0.042|0.090|0.106|
||(0.016)|(0.227)|(0.053)|(0.055)|
|Reform*Young|0.151**|0.138|0.061|-0.033|
||(0.016)|(0.227)|(0.053)|(0.055)|
|Observations|524|388|517|384|
|R-squared|0.218|0.270|0.166|0.245|

_Note:_ Definitions are equivalent to those used in table 4 and 5, respectively. All regressions include village fixed effects. Figures in brackets are robust standard errors clustered by state. * significant at 10%; ** significant at 5%; *** significant at 1%.

21
-18-

# IV. DATA AND RESULTS

The data source for all our variables, but the proxy for the level of education, is The Penn World Table (Mark 5), constructed by R. Summers and A. Heston (1991). This table provides annual information for a number of national accounts variables from around 1950 to 1988. However, data for most countries is available only for a shorter period of time, namely, 1960 to 1985. We work with regular non-overlapping intervals of five years each. Thus, our five cross sections correspond to the years 1965, 1970, 1975, 1980, and 1985. Let us explain each of the variables of the model in tum. The dependent variable is the natural logarithm of real GDP per worker, that is, log(y;65), .-. , log(y.5)When no information as to the country-specific factors is available, the regression equation has three explanatory varables (equation (8)). The first one is the natural logarithm of the working-age population average growth rate plus (g+c); we follow Mankiw, et. al. (1992) in assuming that (g+6)=0.05. The avenage of the working-age population growth rate is taken over the previous five-year interval; then we also have five observations of this variable _far_ each country, that is, log(n.65+0.05), ... , log(ni,85+0.05).

The second explanatory variable is the natural logarithm of the average ratio of real investment (including government investment) to real GDP. These averages are also taken over the previous five-year interval, so that we have five observations for each country, that is, l0g(s;65), ..- , 10g(Si.s)The last explanatory variable is the natural logarithm of real GDP per worker lagged one period, that is, five years back; therefore, the observations for each country are,
approach yields systematically lower (overall) opportunity shares of inequality, ranging from 17% in Colombia to 34% in Brazil. This is true in all countries, although the difference between θ _rN_ and θ _rP_ is only approximately 3% (and statistically insignificant) in the case of Brazil. The differences are larger, but either borderline significant or insignificant at the 5% level, in Colombia, Ecuador, Guatemala, and Peru. The country ranking is identical for the two estimation procedures.

These systematic differences are consistent with the expectation (discussed in Section 3) that the large sampling variance within cells with very few observations may cause an upward bias in the non-parametric estimates. Although it can not be ruled out that the (linear) functional form assumption implicit in the parametric estimate might lead to underestimates, the fact that in the only country for which we have a substantially larger sample size (Brazil) the difference almost vanishes provides some support for the suspicion that the bias might come from the sampling variance in small cells in the nonparametric estimates. Nevertheless, given the remaining uncertainty, we make two recommendations: (i) wherever possible, surveys that may be used for measuring inequality of opportunity should collect larger sample sizes; and (ii) where that is not possible, both parametric and non-parametric estimates should be reported to provide a plausible range for the true lower-bound value of inequality of opportunity.

Regarding the effect of each individual circumstance, Θ _rJ_ is highest for family background variables in all countries. This is particularly true for mother’s education which is associated with between 9% and 12% of total inequality. The relative shares of inequalities associated with ethnicity and place of birth vary across countries, with ethnicity being more important in Brazil, Guatemala, and Panama – where it accounts for between 3% and 7% of inequalities – and the geography of birth having more effect in Peru, Brazil and Panama, where it accounts for 4-6% of overall inequality. Finally, inequality of opportunity related to gender ranges from a low of 0-1% in Panama and Colombia, to a high of 5% in Guatemala. In Brazil and Ecuador, gender accounts for 3- 4% of overall inequality.

> earlier, labor market participation is almost 100% for men in this age group, but much lower for women. As implied by the reduced-form specification, we are estimating inequality of earnings opportunities _conditional_ on being active in the labor market, so it would be inappropriate to correct for selection.

19
crop cycles, after debt relief, these effects represent a persistent medium-term response to debt relief.<sup>19</sup> They also provide evidence that the program failed to achieve its aim of reintegrating beneficiaries into the formal credit market and providing liquidity through new bank loans.

# **6.2 The Composition of Household Debt**

Table 6 turns to the effect of the bailout on the composition of household borrowing, distinguishing between the share of financing obtained from banks and informal sector lenders.<sup>20</sup> The coefficient estimates reported in Columns (1)–(8) of Panel A, reveal that in addition to reducing the overall debt burden of households, the bailout also led to a persistent shift in the _composition_ of household borrowing. One and a-half years after debt relief, formal sector debt among households that benefited from unconditional debt relief declined by 8-10% compared to households in the control group. Over the same period, their relative reliance on informal credit increased by 5-6%. Columns (3)–(8) show that the magnitude of these estimates is unaffected by the inclusion of additional controls and alternative specifications of the parametric control function. Taken together, these results indicate that, while debt relief led to a persistent reduction in the overall level of household debt, it did not enable recipients to substitute high-interest debt from the informal sector with cheaper bank financing. Instead, households became relatively more reliant on informal credit.

Exploring this shift towards informal credit in greater detail, Table D.7 in the Supplemental Appendix disaggregates the estimates further and considers the relative share of post-program credit obtained from commercial banks, cooperative banks, moneylenders and traders, and friends and relatives, respectively. The results show that the reduction in formal sector borrowing is primarily driven by a decline in borrowing from cooperative banks, while the greater share of informal sector financing is primarily due to a greater percentage of loans obtained from friends and family, rather than high-interest loans from moneylenders or credit from shopkeepers and traders. These results indicate that, in the absence of bank credit, most households are in fact able to increase their relative reliance on internal financing through family networks, rather than having to resort to loans from moneylenders –the most

> 19These estimates are based on self-reported data, in which borrowers may tend to underreport high-interest rate informal sector loans (Karlan and Zinman, 2008). To mitigate this concern, the next section reports evidence based on questions asking about relative shares of financing obtained from different lenders rather than loan amounts. 20Results distinguising between different informal sector lenders are reported in the Supplemental Appendix.

19
# <u>Figure 6: Progressivity over Development</u>

<!-- Start of picture text --> (a) Formal Taxed (b) Non-Food Taxed (c) Formal Non-Food Taxed<br>SN<br>MZ<br>PG<br>SN<br>CD BF RW BF RW<br>MZ<br>KM PG CD NE KM RS<br>TZ SN TD TZ MA<br>BI TD TZ MA BI ST<br>NE BJ ST CMBO CG TNSZ DOECCORSPEMEZA CRUYMXBR BI CDNE MZ BFTD RWBJ ST KMCMBO PG MA CG TNSZ DOECCORSPEMEZA CRUYMXBR CL BJ CMBO CG TNSZ DOEC CO PEMEZA CRUYMXBR CL<br>CL<br>5 6 7 8 9 10 5 6 7 8 9 10 5 6 7 8 9 10<br>Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD Log GDP per capita, Constant 2010 USD<br>5 5 5<br>4 4 4<br>3 3 3<br>2 2 2<br>1 1 1<br>0 0 0<br>Budget Share Ratio Top 20-Bottom 20<br><!-- End of picture text --> These panels plot the ratio of effective tax rate paid by the richest quintile relative to the poorest quintile in each country as a function of the country’s level of economic development. The left panel corresponds to a scenario where a uniform tax is levied on all goods, but purchases in informal stores are de facto not taxed (scenario #1 in Section 4). The middle panel corresponds to a scenario where food goods are de jure exempt, but purchases in both formal and informal stores are taxed (scenario #2 in Section 4). The right panel combines de facto informality exemption and de jure food exemption (scenario #3 in Section 4). In each panel, the ratio is plotted for all 31 countries of our sample against log GDP per capita.

43
Figure 2. Global demand, supply, oil price, and interest rate shocks A. Global oil price shocks

- B. Global supply shocks

<!-- Start of picture text --> Percent Percent<br>4 4<br>2 2<br>0 0<br>-2 -2<br>-4 -4<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->

# C. Global demand shocks

D. Global interest rate shocks <!-- Start of picture text --> Percent Percent<br>4 4<br>2 2<br>0 0<br>-2 -2<br>-4 -4<br>1970 1980 1990 2000 2010 2020 1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Note: The structural shocks are estimated with the global FAVAR model. Grey-shaded areas indicate global recessions (1975, 1982, 1991, 2009, and 2020) and red-shaded areas indicate periods of oil price spikes (1974, 1978-80, 1990, 2008). Orange-shaded areas indicate periods of oil price plunges (1985-86, 1990-91, 1997-98, 2001, 2007-09, 2014-16, and 2020).

25
17 provides information on cost per death averted and per DHLY gained for a wide range of nutrition interventions.

The cost per death averted figures in table 9 are $1482 for (hig>'ly targeted) supplementary feeding in Tamil Nadu, and $1522 for a vitamin A capsule distribution scheme in Bangladesh. These figures (as discussed in section I) are comparable to some other health interventions, but definitely more costly than EPI and ORT interventions. Tamil Nadu is evidently at the low end of cost per death averted as compared to food subsidies and face-to-face nutrition education: the cost per child removed from moderate or severe malnutrition in Tamil Nadu is $33 compared to $331 for a food subsidy in the Philippines, and $493 for a nutrition education/growth monitoring project in the Dominican Republic. Comparable costs for mass media nutrition education efforts are lower ($12 for Indonesia NIPP and $5 for Morocco), although the outcome data for these projects are not as well controlled as some of the other studies.

Table 10 presents estimates of costs per death averted from the Narangwal project. Data are available for 3 different types of intervention (nuttition, medical care, and combined nutrition and medical care), for three different age groups (perinatal, infants, and children 1-3). The study was unusual in that it included a control group. It was also primarily a research study and not a largescale service-delivery project. There are some interesting findings. Firstly, cost per death averted increases with age: perinatal deaths are the least expensive to avert, followed by infant deaths, with child deaths being the most costly to avert. The type of intervention which is the least costly varies with age. For perinatal deaths, nutrition interventions (pre-natal maternal supplementation) alone are the cheapest, whereas for infant and child deaths, medical interventions alone are most cost-effective. The high figures for cost per death averted for children 1-3 ($3053 for nutrition interventions alone, $1617 if combined with medical interventions) underscore the discussion in section I earlier. Another useful source of information to supplement cost-effectiveness calculations, are