rafmacalaba/datause-encoder-data huggingface dataset:
FIGURE 6: SHARE OF WHOLESAL AND RETAIL TRADE IN VALUE ADDED' <!-- Start of picture text --> FIGItRE<br>USuS<br>oSCD<br>20% ~ ~ ~ ~ ~~~~~~~~0<br>| VR<br>GRC<br>; ~~~GRC 4RcIDt<br>D<br>INL INL<br>is,FN f3<br>D0% IUU DEU<br>I-L<br>% Lo(;OFPERC<br>LO OF PIt C IAP INCOME<br><!-- End of picture text -->
<!-- Start of picture text --> $7.0 39.6%<br>$6,000<br>49.5%<br>Suu<br>$4000 30.2%<br>Sse<br>$7,000 ATS 1A%“ 40%©<br>147%<br>Stoo .<br>sn<br>CCA New CU Turkey Western Dslkans Russia, South Caucasus Centre! Asis<br>Mamber States. Moldewa,<br>Ukraine and<br>Belarus<br>=GDP percapits 1990 m GDP per capita2006<br><!-- End of picture text --> <!-- Start of picture text --> 500 16.2<br>45.0 -<br>aun srw<br>BO 35.6<br>ann - ne<br>7a -<br>200<br>mo - yaa 146 14 id<br>: 10.2<br>band<br>una - 83 6s<br>vo — =<br>Ecar New EU Turkey Western Balkans Russia, South Ceuceus Central Asie<br>Member States Moldova,<br>Ukraine and<br>Belerus<br>= Poverty Hesdevunt 1990 Puverly Headevunt 2005<br><!-- End of picture text -->
41 rather than to actual inflation. Thus, according to the theory of optimal taxation, tax rates and expected inflation should move in the same direction. Moreover, any innovation in government spending or the tax bases should result in unexpected changes in actual inflation. While Judd's distinction between expected and uwsexpected inflation is important, his argument that only expected inflation has welfare costs is not generally valid (for instance, it relies on the government neglecting the redistributions within the private sector that are brought about by unexpected inflation or deflation). We return to this point below.

As Grilli (1989) has pointed out, a necessary (but not sufficient) empirical implication of the optimal taxation theory is that both the inflation rate and the tax rate should have a unit root.28 In Tables A.1 and A.2 we use the augmented Dickey-Fuller (ADF) test for unit roots on these two variables for a large number of developing countries. While Table A.1 presents results obtained using annual data for kQgh inflation and tax rates, Table 5 presents ADF tests on quarterly data for the inflation tax rate only. (There are no quarterly data on tax rates.)29 As can be seen from Table A.1, in all cases, except inflation in India, the unit root hypothesis cannot be rejected. The results in Table A.3 show that, as in the case of Table A.2, for the vast majority of the countries the hypothesis that inflation follows a unit root cannot be rejected.

28That is, their time series should have the following form: xt - xt1 + ut, where ut is an error term.

29The taxation rate is computed, as In Mankiw, as the ratio of goverrnent tax revenues to GDP. The raw data were obtained from the IMF Government Financial Statistics. While Table 2 contains data for those 21 countrles with data on both f and *, Table 3 presents data on w only for a larger group of 44 nations.
yarn stock, introducing restocking norms for future purchases, and monitoring inventory on a daily basis, the firms reduced their inventories. But this took time as the reduction in inventories primarily arose from lowering stocking norms and using old yarn for new products.

Table 2 columns (5) to (7) shows the regression results for log of raw material (yarn) inventory. The results are presented for the 18 plants for which we have yarn inventory data (two plants do not maintain yarn stocks on site). In column (5) we present the fixed-effects result which regresses the weekly yarn on the plant level management practices, plant fixed-effects, and a set of weekly time dummies. The coefficient of -0.639 says that increasing management practices adoption rates by 1 percentage point would be associated with a yarn inventory reduction of about 0.6%. In Table 2, column (6), we see the impact of management instrumented with the intervention displays a point estimate of -0.929, somewhat higher than the FE estimates in column (1).<sup>20</sup> Again, the IV estimator is higher than the OLS estimator, suggesting that the adoption of better management practices may be endogenous (or at least downward biased by measurement error). In column (7) we see the intervention causes an average reduction in yarn inventory of (exp(-.179)-1=) 16.4%.

These numbers are substantial but not unprecedented. Japanese automotive firms achieved much greater reductions in inventory levels (as well as quality improvements) from the adoption of lean manufacturing technology. Many firms reduced inventory levels from several months to a few hours by moving to just-in-time production (Womack, Jones and Roos, 1991).

Finally, as with the quality defects estimates, the IM confidence interval for the IV estimator finds the coefficient significant at the 5% level. However, the IV permutation tests cannot exclude zero. Looking at the ITT coefficient, we see that under IM the results are significant at the 10% level, although again not significant using the standard permutation tests.

# **_V.C Output_**

In Figure 5 we plot output over time for the treatment and control plants. Output is measured in physical terms, as production picks<sup>21</sup> . The results here are less striking, although output of the treatment plants has clearly risen on average relative to the control firms, and this difference is point-wise statistically significant in some weeks towards the end of the period.

> 20 We do not report the IV first-stage as this is very similar to the first stage for quality shown in column (2).

> 21 A production pick is a single crossing of the shuttle, representing the weaving of one thread of weft yarn.

18
reduction. In particular, if the UNHS data are correct, very little has changed in the last decade.<sup>9</sup>

## **Table 1:  Sectoral composition of primary employment, 1992/93 - 2009/10 show sluggish movement out of agriculture**

|||**Ye**|**ar**||
|---|---|---|---|---|
||**1992/93**|**2002/03**|**2005/06**|**2009/10**|
|Labor force|100.0|100.0|100.0|100.0|
|Agriculture|82.6|70.2|74.9|70.0|
|Industry|4.5|7.2|5.4|8.0|
|<br>Services|12.9|22.6|19.7|22.0|

**Source:** Authors calculations based on the IHS 1992/93, UNHS 2002/03, UNHS 2005/06 and UNHS 2009/10

# **5. Uganda’s structural transformation and its impact on poverty**

# **5.1 Nature of the transformation**

If the movement of labor from the low productivity sector (agriculture) to the higher productivity sectors was limited, then what were the drivers of the extensive poverty reduction? Our analysis suggests three channels:

- (i) transformation in the type of employment in each sector;

- (ii) transformation in household livelihood portfolios; and

(iii) transformation within the agricultural sector, from ―traditional subsistence‖ to new, higher value export crops, the use of livestock, and modern marketing channels.

To see the first transformation, the change in the structure of primary <u>employment by sector</u> is broken down by type of employment within the sector (see Figure 2). While the reported primary sector of employment of those in the labor force

> 9 We found significant inconsistencies in the 2009/10 employment data, and suspect that the 2009/10 questionnaire undercounted non-wage employment, especially secondary employment. We made some adjustments to correct for this, ad are reasonably confident about the data on primary employment by sector shown here.

15
countries (1.1 percentage points per year) while the smallest growth benefits are attained by oil rich countries in the region (0.6 percentage points per year).

**Table 6. Comparative Statistics, Trade Openness** _2010-2014_

||||SSA<br>Leader|World Excl.<br>SSA(75th<br>percentile)|MIC<br>Leader|LIC<br>Leader|
|---|---|---|---|---|---|---|
||7|SSA Average|0.86%|0.58%|1.02%|0.66%|
|Growth|13|SSA oil RR|0.59%|0.31%|0.75%|0.38%|
||0.0|SSA non-oil RR|0.76%|0.48%|0.93%|0.56%|
|||SSA non-RR|1.10%|0.82%|1.26%|0.90%|
||4|SSA Average|0.34%|0.23%|0.41%|0.26%|
|Caital|54|SSA oil RR|0.23%|0.12%|0.30%|0.15%|
|p|.00|SSA non-oil RR|0.30%|0.19%|0.37%|0.22%|
||0|SSA non-RR|0.44%|0.32%|0.50%|0.36%|
||2|SSA Average|0.64%|0.43%|0.76%|0.49%|
|TFP|10|SSA oil RR|0.44%|0.23%|0.56%|0.29%|
||0.0|SSA non-oil RR|0.57%|0.36%|0.69%|0.42%|
|||SSA non-RR|0.82%|0.61%|0.94%|0.67%|

_NOTE: The estimations are calculated based on the results from regressions in Table 5 Columns [4] [5] and [6]._ Similarly, we look at the channels of transmission of closing the gap in trade volumes by assessing their benefit in terms of (potential) growth of physical capital per worker and total factor productivity (TFP) growth. The second panel in Table 6 shows the sensitivity of physical capital accumulation to trade openness (0.00544 as reported in column [5] of Table 5) and the third panel reports the sensitivity of TFP (0.0102 as in column [6] of Table 5). In all cases, the largest gains in physical capital accumulation and TFP growth from closing the trade openness gap vis-à-vis the SSA leader would be attained by the non-resource rich countries (0.44 and 0.82 percentage points per year). Again, oil abundant countries in the region register the smallest benefits in terms of capital accumulation and TFP growth. Finally, the potential gains in capital accumulation and TFP growth for the region as a whole vis-à-vis the leader in Sub-Saharan Africa amount to 0.34 and 0.64 percentage point per year.<sup>12</sup> Next, we analyze the likely benefits of SSA and other country groups within the region in narrowing the gaps in international trade integration relative to the MIC leader. For instance, narrowing the trade openness gap of SSA relative to the MIC leader would improve economic growth by 1.02 percentage points, capital accumulation by 0.41 percentage points, and TFP by 0.76 percentage points. Oil rich countries in the region reap the smallest growth benefits in all three variables: growth, capital and TFP. The non-resource rich countries would have greater than average

> 12Note that closing the gap in international trade integration relative to the SSA leader would render greater potential benefits in terms of growth, capital accumulation and TFP growth than by closing the gap with respect to the top quartile of the rest of the world excluding SSA (75th percentile) and the LIC leader. For the sake of space, the results are reported in Table 6, but they are not discussed in the document.

_page 26_
increase in interest rate in response to the increase in money demand. Although evidence for developed countries suggests that about two third. of real balances are held by households, the extrapolation of this conclusion to developing countries could be controversial as firms may have much less sophisticated cash management procedures. The resolution of this question, however, should be empirical. We address this issue by using both income and consumption as the scale variable.

Next we describe the data and investigate the time series properties of the variables. The section is closed with an assessment of the fixed-intercept money demand equation in the light of recent cointegration literature. 3.1 Data We estimate the model with quarterly dat.a from Chile during the period 1975-1989 and Mexico 1980-1989<sup>11</sup> • The data are described in Appendix B. For each country we used data on Ml, short-term interest rate, consumption and GDP. The series of money, consumption and GDP are normalized by total population, as suggested by the theory.

Using standard Dickey-Fuller and Augmented-Dickey-Fuller tests it can be seen that there is strong evidence that most of the series are well characterized by processes with unit roots. The results are presented in Table Bl, Appendix B. All the series are the log of the respective variable. The tests in levels do not reject the null hypothesis that the series have a unit root. The tests in first differences reject the null hypothesis of unit root. Therefore the evidence supports the hypothesis that the variables are difference stationary or I(l). The only exception is GDP in Mexico where stationarity around a deterministic trend

> 11 In Chile market interest rates start in 1975, and in Mexico quarterly consumption starts in 1980.

11
than the control group two years after training. These results highlight the challenge in getting subsistence-level female-owned microenterprises to grow, and suggest that the binding constraints on their growth may lie outside the realm of capital and skills. One option is more intensive (and expensive) one-on-one personalized mentoring and consulting, which Valdivia (2011) finds to increase sales by 18 percent. Another is addressing constraints to female participation in wage work, as these labor market failures are potentially the reason that many women are operating businesses in the first place (Emran et al, 2007).

In contrast, our results are more encouraging for using business training to help women who are out of the labor force start enterprises more quickly, and for improving the management and profitability of these businesses. The impact on business start-ups is consistent with results from recent randomized experiments with microfinance (Banerjee et al, 2011; Attanasio et al, 2011) which find that giving microfinance to poor women with a high propensity for business ownership leads to some new business start-ups. Taken together, these results suggest getting women to start a subsistence business is easier than getting these businesses to grow.

A final point is that our study highlights the importance of tracing out the trajectory of impacts and not just relying on a single follow-up survey. The impacts of business training vary over the time frame in which they are measured, particularly for potential enterprise owners.

# **References**

Attanasio, Orazio, Britta Augsburg, Ralph de Haas, Emla Fitzsimons, and Heike Harmgart (2011) “Group lending or individual lending? Evidence from a randomized field experiment in Mongolia”, BREAD policy paper no. 27. Banerjee, Abhijit and Esther Duflo (2008) “What is middle class about the middle classes around the world?”, _Journal of Economic Perspectives_ 22(2): 3-28.

Banerjee, Abhijit, Esther Duflo, Rachel Glennerster and Cynthia Kinnan (2011) “The miracle of microfinance? Evidence from a randomized evaluation”, Mimeo, MIT.

Berge, Lars Ivar Oppedal Kjetil Bjorvatn and Bertil Tungodden (2011) “Human and financial capital for microenterprise development: Evidence from a field experiment in Tanzania”, Mimeo.

Bruhn, Miriam and David McKenzie, (2009), “In Pursuit of balance: Randomization in Practice in Development Field Experiments,” _American Economic Journal: Applied Economics_ 1(4): 200-232.

Bruhn, Miriam and Bilal Zia (2011) “Business and Financial Literacy for Young Entrepreneurs: Evidence from Bosnia-Herzegovina”, Mimeo. World Bank.

De Mel, Suresh, David McKenzie, and Christopher Woodruff (2008). “Returns to Capital: Results from a Randomized Experiment.” _Quarterly Journal of Economics_ 123(4): 1329–72.

25
Overall, we observe a decrease in applied tariffs over the study period across all countries (Figure 4a). We observe a similar pattern across sectors with a little more reduction in the manufacturing sector compared to the agriculture sector where protection remains high globally (Figure 4b). The trends we see for tariffs and NTMs are quite different: tariffs have been steadily decreasing over time, while NTMs are becoming more widespread. Whereas these patterns reflect substitution between NTMs and tariffs as market access tools, empirical evidence confirms the existence of both complementarity and substitution effects (Niu et al., 2020).

Figure 4: Trend in applied tariffs across countries and sectors over time <!-- Start of picture text --> Malawi Agriculture<br>Burkina Faso Manufacturing<br>Senegal<br>2000 2003 2006 2009 2012 2015 2018 2000 2003 2006 2009 2012 2015 2018<br>Years Years<br>(a) Across origin countries (b) Across sectors<br>15 20<br>15<br>10<br>10<br>Applied tariffs (%) 5 Applied tariffs (%)<br>5<br>0 0<br><!-- End of picture text -->

# **4 Empirical Strategy**

## **4.1** **_Model specification_**

To examine the effects of NTMs imposed by destination countries on firm-level exports, we estimate the following model: where _yicpdt_ is the dependent variable for firm _i_ , in origin country _c_ , exporting HS6-digit product _p_ , to destination _d_ , in year _t_ . _yicpdt_ is defined to include firm exit dummy, firm entry dummy and log of firm export value, as defined in subsection 3.2. The variable _NTMpdt_ is the count of the number NTM imposed by the importing country _d_ , on product _p_ , in year _t_ (as discussed in subsection 3.1). The variable _Tari f fcphs_ 4 _dt_ is the specific bilateral tariff imposed by the destination country _d_ at the HS4-digit product level, on imports from the exporting country _c_ . To reduce the number of missing values, we access the tariff data at the HS4 digit level.

Equation 4.1 also includes a host of fixed effects. _ωipd_ is the firm-origin-HS6 product fixed effect, which accounts for all time-invariant firm unobservable characteristics. _λipt_ is the firmHS6 product-year fixed effect, which controls for all firm-level time-varying characteristics, 11
whether this effects the relation between trade orientation of firms and their evolution, I include a dummy variable for multi-plant firms. Since this information is only available for 2006 and 2008 surveys, with its inclusion, number of observations decreases significantly. Being a part of a multiplant firm does not contribute to growth or innovation significantly. Two-way traders perform significantly better than non-traders in both growth rates and innovation. For technological innovation variable only importers innovate more than only exporters.

Next, I add total number of hours worked per week to control for capacity utilization. Becheikh et al. (2006) present evidence that firms that use their resources more efficiently are more likely to innovate and grow. This is especially valid for process innovation. If firms are producing at a capacity close to their limits, they might be inclined to improve their processes that will lead to more access capacity. Estimation results show that firms with higher capacity utilization grow faster<sup>16</sup> . However the magnitude is small, a 10 hour increase in total hours worked per week leads to 0.3% increase in employment growth. Although not presented, I use an alternative measure of capacity utilization which is measured as the firm’s actual output relative to its maximum possible output. Using this measure also gives similar results and doesn’t affect the relation between trade and growth.

Finally I analyze whether the link between trade and firm evolution is affected by the degree of market competition that the firm faces. The literature on relation between market competition and innovation gives mixed results. On one hand, it is predicted that innovation should decline with competition, because competition reduces monopoly rents that the innovations yield. A classical example is Aghion and Howitt (1992). On the other hand, Shaked and Sutton (1987) argue that innovation increases product differentiation and this should cause it to increase with competition. A more recent study by Aghion et al. (2005) introduces a model that combines these two relations and gets a negative-U shaped relation between competition and innovation. To measure competition, I look at the markup that the firm charges. For this variable, data is only available for ECA 2002 and 2005 surveys. For both growth rate and product innovation, two-way traders perform better than only exporters who perform better than only importers. As the amount of markup increases (implying less competition in the market), probability of innovating new products increases.

> 16 I only included firms with total hours/week ≥ 25 .

26
To set a context for the results under full regional trading, Tables 3a through 3d present key findings from the baseline analysis.<sup>14</sup> Tables 3a and 3b show how generation capacity evolves out to 2040 without increased cross-border cooperation and trade. Total regional capacity would increase by 3.8 fold, from 276 GW in 2013 to 1,067 GW in 2040. More than 70% of the total regional capacity would be installed in India. Bangladesh, India, Pakistan and Sri Lanka would have large shares of coal in total electricity capacity. As of 2020, the share of coal-fired generation capacity in the total installed capacity would be 19%, 60%, 34% and 16%, respectively in those four countries. The corresponding shares in 2040 would be 67%, 58%, 45% and 37%. The systems in Afghanistan, Bhutan, and Nepal are dominated by expansion in hydro generation capacity.

**Table 3a: Baseline Scenario Installed Capacity (GW)**

||**2020**|**2030**|**2040**|
|---|---|---|---|
|**Afghanistan**|1.1|6.0|7.0|
|**Bangladesh**|27.2|48.7|67.8|
|**Bhutan**|6.9|14.7|14.8|
|**India**|387.2|586.5|783.9|
|**Nepal**|3.4|7.9|9.3|
|**Pakistan**|57.5|108.7|173.0|
|**Sri Lanka**|5.8|9.1|11.7|
|**Total**|489|782|1067|

**Table 3b: Baseline Installed Capacity Mix in 2020 and 2040 (%)**

||Year|Diesel|Gas<br>Turbine|Hydro|Solar|Coal|Wind|Combin<br>e Cycle|Biomass|Nuclear|
|---|---|---|---|---|---|---|---|---|---|---|
||**2020**|19.2%|54.1%|24.6%|0.0%|0.0%|2.2%|0.0%|0.0%|0.0%|
|**Afghanistan**|**2040**|3.0%|8.5%|88.1%|0.0%|0.0%|0.3%|0.0%|0.0%|0.0%|
||**2020**|14.8%|9.2%|1.5%|17.3%|19.4%|0.4%|37.5%|0.0%|0.0%|
|**Bangladesh**|**2040**|5.9%|3.7%|0.6%|6.9%|66.8%|0.1%|15.0%|0.0%|0.9%|
|**h**|**2020**|0.1%|0.0%|99.9%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**Butan**|**2040**|0.1%|0.0%|99.9%|0.0%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**Idi**|**2020**|0.3%|3.1%|15.5%|3.2%|59.9%|9.7%|3.8%|1.4%|3.0%|
|**na**|**2040**|0.1%|6.9%|12.7%|2.6%|57.7%|9.3%|4.2%|2.9%|3.7%|
|**l**|**2020**|1.6%|0.0%|97.8%|0.6%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**Nepa**|**2040**|0.6%|0.0%|99.2%|0.2%|0.0%|0.0%|0.0%|0.0%|0.0%|
|**ki**|**2020**|0.8%|4.3%|29.8%|0.0%|34.3%|16.7%|10.7%|0.8%|2.6%|
|**Pastan**|**2040**|0.3%|1.6%|22.1%|0.0%|45.4%|20.0%|6.0%|0.3%|4.3%|
||**2020**|3.1%|10.7%|32.7%|1.5%|16.3%|17.8%|17.1%|0.8%|0.0%|
|**Sri Lanka**|**2040**|0.8%|5.7%|17.6%|1.1%|37.1%|24.5%|12.3%|0.8%|0.0%|
|**Tl**|**2020**|1.2%|3.7%|18.4%|3.6%|52.7%|9.9%|6.6%|1.2%|2.7%|
|**ota**|**2040**|0.6%|5.7%|15.9%|2.3%|54.3%|10.3%|5.1%|2.2%|3.4%|

> 14 All the baseline results are presented in Appendix A.

12