rafmacalaba/datause-encoder-data huggingface dataset:
ownership just under and just above this one half acre threshold to finance. Pitt and Khandker found small but significant effects of the use of credit on household expenditures, household assets, labor supply, and the likelihood that children attend schools.

Others, however, challenged the finding that the Grameen Bank’s micro-credit programs boosted economic outcomes. Many doubted that the half-acre rule was systematically and rigorously applied, leading to concerns that selection bias still plagued the analysis. Using the same data, but a difference-in-differences approach and comparing microfinance recipients with a control group of households in areas not served by any microfinance program, Morduch (1998) failed to find any significant impact of microcredit on borrowers’ income, consumption or likelihood of sending children to school, although he did find an effect on consumption smoothing, i.e. a substantially lower variation in consumption and labor supply across seasons. Furthermore, Pitt and Khandker (1998) could only include fixed-effects at the village level, and could not deal with the endogeneity of household participation because they did not have a household panel. In an extension, Khandker (2003) used a panel of household surveys to control for unobserved, time-invariant borrower characteristics. The resulting estimates suggest a much smaller impact of credit on household welfare than the original findings with Mark Pitt.

In another influential study, Coleman (1999) finds that microcredit did not materially help borrowers in northeast Thailand. To deal with selection issues, he exploited the fact that six communities had been identified as future locations for village banks, and that there was a list of self-selected villagers who wanted to apply for loans once the banks were established. Instead of simply surveying a random group in the not-yet-served villages, Coleman (1999) compares actual borrowers with borrowers-in-waiting, in a technique known as “pipe matching.” He found no significant impact of microcredit on physical assets, savings, production sales, productive expenses, labor, or expenditures on health care or education.

A cleaner way of dealing with selection bias is to construct a genuine experiment in which the subjects and the control group are chosen randomly. One such experiment was conducted by Karlan and Zinman (2006) who convinced a South African consumer lender to choose a subset of marginally-rejected applicants at random and reverse the loan decision. Two years later, those granted access were in better shape than a control group that remained without 27
# **1 Introduction**

The latest push for industrialisation in Ethiopia has attracted much academic and public interest. While the domestic manufacturing sector is still relatively small, Ethiopia has made large investments to promote industrial development and has witnessed some early success (Oqubay, 2015). The flagship project is the creation of several industrial parks across the country. The most famous of these parks, in the southern city of Hawassa, has attracted significant foreign direct investment from international garment manufacturers. This park alone currently employs more than 15,000 workers and exports 100 million USD worth of goods, doubling the country’s foreign exchange earnings from garment manufacturing (Butler, 2018).

These early results are surprising in the context of the recent literature on ‘premature deindustrialization’ (Rodrik, 2015). This literature argues that in the last decades developing countries have moved out of manufacturing much faster than today’s rich countries did in the past, when they had comparable levels of income. This early exit from a labour-intensive sector is feared to be an obstacle to generating employment and broad-based gains from growth. Also, the international experience with special economic zones and industrial parks has been mixed (Khandelwal and Teachout, 2016).

In this paper, I assess Ethiopia’s competitiveness and attractiveness as an investment destination by comparing the productivity and input costs faced by firms based in Ethiopia to a sample of manufacturing exporting countries. Given the current strategic importance of the garment sector, I select comparison countries that are garment exporters in Africa (Kenya), and Asia (Bangladesh, India, Vietnam). I use harmonized data from the World Bank Enterprise Survey, which enables me to construct aggregates figures for sales for worker (a measure of productivity), total labor costs, capital stock, firm size, and a somewhat noisy measure of value added. I compute these figures for the whole manufacturing sector and for the garment sector. Further, I use data from the World Management Survey to study the quality of management.

My main finding is that Ethiopia’s labor cost advantage is more than offset by low productivity with respect to all but one of the comparison countries (Bangladesh). Ethiopia’s industrialization is thus on a knife’s edge. Its fundamentals make it competitive with respect to the countries at the bottom of the productivity distribution. In recent years, the increase in labour costs has also been modest compared to these countries. However, given the likely pressure on wages that will result from further expansion of the manufacturing sector, additional gains in productivity will be crucial in order to secure Ethiopia’s position as an attractive investment destination.

The low productivity of Ethiopian firms is not explained by differences in size, cap2
Table 6: Neighborhood effects: Geographic vs. political neighbors

||Citi<br>|esinthe sam<br>|e province<br>||Citiesin<br>|otherprovi<br>|nces<br>|
|---|---|---|---|---|---|---|---|
||(1)<br>All|(2)<br>350km|(3)<br>400km|(4)<br>450km|(5)<br>350km|(6)<br>400km|(7)<br>450km|
|Ave. neighbors’ over-reporting|0.635<sup>∗∗∗</sup>|0.255<sup>∗∗∗</sup>|0.400<sup>∗∗∗</sup>|0.521<sup>∗∗∗</sup>|0.061<sup>∗∗</sup>|0.042|0.011|
||(0.016)|(0.072)|(0.091)|(0.105)|(0.030)|(0.042)|(0.040)|
|PS YearsInOffice|0.108<sup>∗∗∗</sup>|0.101<sup>∗∗∗</sup>|0.099<sup>∗∗∗</sup>|0.098<sup>∗∗∗</sup>|0.096<sup>∗∗∗</sup>|0.098<sup>∗∗∗</sup>|0.098<sup>∗∗∗</sup>|
||(0.030)|(0.028)|(0.027)|(0.027)|(0.028)|(0.028)|(0.028)|
|PS YearsInOffice<sup>2</sup>|−0.011<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|−0.009<sup>∗∗</sup>|
||(0.005)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Lag own over-reporting|0.138<sup>∗∗∗</sup>|0.219<sup>∗∗∗</sup>|0.215<sup>∗∗∗</sup>|0.212<sup>∗∗∗</sup>|0.225<sup>∗∗∗</sup>|0.225<sup>∗∗∗</sup>|0.226<sup>∗∗∗</sup>|
||(0.028)|(0.033)|(0.032)|(0.032)|(0.033)|(0.033)|(0.033)|
|City Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Leader Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Provincial Controls|Yes|||||||
|Provincial Trend|Yes|||||||
|City FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Province-Year FE||Yes|Yes|Yes|Yes|Yes|Yes|
|# of Observations|3264|3264|3264|3264|3264|3264|3264|
|# of Cities|272|272|272|272|272|272|272|

_Notes_ : This table presents the results of a dynamic, spatially autoregressive Durbin model estimated by MLE following Yu, De Jong, and Lee (2008). Each column corresponds to a different spatial weighting matrix in terms of geographic distance defined in the header of each column. City controls include city-level time-varying socioeconomic controls (the share of industrial and tertiary value added in GDP, log population, and growth of nighttime lights) of their own and their neighbors, and leader controls are the same as the ones displayed in the table of baseline results. Provincial controls include _YearsInOffice_ , _YearsInOffice_<sup>2</sup> and _Native_ for the provincial party secretaries, provincial growth target, and three dummies indicating the timing of the provincial People’s Congress as well as one year before and after the Congress. Year fixed effects are also controlled in column (1). Standard errors clustered at the city level are reported in parentheses.<sup>***</sup> ,<sup>**</sup> and<sup>*</sup> denote significance at 1%, 5% and 10%, respectively.

44
TABLE 5: regressions on binary ratings of outcome and borrower

||OLS (1)|FE (1)|FE (2)|
|---|---|---|---|
||Binary Outcome|Binary Outcome|Binary Outcome|
|Borrower<br>Size<br>GDP p.c.|0.688***<br>(0.016)<br>-0.008<br>(0.006)<br>0.000005<br>(0.00004)|0.687***<br>(0.016)<br>-0.010<br>(0.007)<br>-0.000006<br>(0.00004)|0.683***<br>(0.017)<br>-0.009<br>(0.007)<br>-0.000001<br>(0.00002)|
|Volatility<br>Growth|-0.00002<br>(0.00002)<br>0.104***<br>(0.040)|-0.00001<br>(0.00002)<br>0.075<br>(0.062)|-0.00001<br>(0.00002)<br>0.057<br>(0.067)|
|Infrastructure<br>Bureaucracy|0.139<br>(0.212)<br>-0.006<br>(0.011)|-0.014<br>(0.046)<br>-0.045**<br>(0.023)|-0.088<br>(0.483)<br>-0.050**<br>(0.023)|
|Corruption<br>Accountability<br>Stability<br>Ethnic<br>Ext. conflict|-0.002<br>(0.010)<br>-0.010*<br>(0.005)<br>0.001<br>(0.005)<br>0.005<br>(0.006)<br>0.003<br>(0.005)|-0.017<br>(0.017)<br>0.032**<br>(0.012)<br>-0.003<br>(0.006)<br>0.021<br>(0.016)<br>-0.005<br>(0.008)|-0.011<br>(0.018)<br>0.027**<br>(0.012)<br>-0.002<br>(0.012)<br>0.017<br>(0.017)<br>-0.007<br>(0.009)|
|Country FE||Yes|Yes|
|Year FE|||Yes|
|<br>|Other<br>|Statistics<br>|<br>|
|Observ.<br>|1710|1710|1710|
|Adj. R<sup>2</sup>|0.545|0.557|0.556|
|Prob(F)|0.00|0.00|0.00|

Note: both the project outcome variable and the borrower performance variable are reported on a 0 (unsuccessful) and 1 (successful) scale. For further references, see the text.

22
The architects of the RFR reform had not anticipated that the expanded agricultural tax, within the context of a substantial overall reduction in “burdens” on farmers, would become a lightening rod for criticism. The reality, though, is that while urban citizens are exempted from personal income taxes (PIT) on the first RMB 9,600 of annual income –an amount that was doubled in 2005, no such exemption existed for the much poorer rural citizens since agricultural taxes were unrelated to incomes. In this light, the argument that fair treatment of all citizens under the tax law required the abolition of the agricultural tax found resonance among policymakers.<sup>54</sup> From the perspective of revenue considerations, this was also easily affordable since even the expanded agricultural tax, together with the animal husbandry tax had produced only RMB 33.4 billion in 2003, equal to 1.7 percent of current revenues (Finance Yearbook 2004). In net revenues terms, the “yield” was even lower for these two taxes since they were notoriously costly to collect from the two hundred million rural households.

While the ATA was welcomed for bringing further reductions in the farmers’ burdens, it injected some confusion into the ongoing reforms and up-ended all the careful plans for revenue-replacement under the RFR. Moreover, following so quickly on the RFR, the ATA directly undermined two principal objectives of the RFR – that of imposing a more structured framework on rural public finance and hardening the budget constraints on grassroots governments. Abolishing the agricultural taxes also further diminished the already slim local tax base, especially in inland, rural localities where, aside from agriculture, there is little economic activity to tax.

The immediate effect of the ATA was twofold. First, it imposed a much greater burden on central transfers, both in quantity and duration. While transfers were always required under the RFR to fill the revenue gap, they were meant to be transitional, and would be reduced or streamlined as cost savings are realized from improved efficiencies and associated reforms. With the elimination of the agricultural taxes, however, the gap is now larger, and transfers will be needed for a long period until new taxes can be found to rebuild the revenue base in rural communities.

Second, it has created new urgency for implementing the associated reforms to generate

> 54 See, for example, Yang et al. (2003).

50
||Employed|Durables|In<br>Tuition|put:<br>Livelihood|Politics|Bank|Mobility:<br>Taluk Office|Police Station|
|---|---|---|---|---|---|---|---|---|
||(-1.46)|(-0.04)|(-0.65)|(-0.35)|(-0.61)|(3.38)|(0.72)|(-1.18)|
|BC: Others|-0.0806***<br>(-3.70)|0.00856<br>(0.41)|-0.0187<br>(-0.82)|-0.00469<br>(-0.18)|-0.0139<br>(-0.64)|0.0506<br>(1.89)|-0.0572*<br>(-2.09)|-0.0328*<br>(-2.39)|
|Observations|5190|3284|3280|3283|3279|3299|3299|3299|
|Adjusted R-squared|0.279|0.117|0.117|0.149|0.077|0.136|0.149|0.050|

Notes: (1) Source: Authors' calculations based on data collected by Social Observatory, World Bank and Government of Tamil Nadu. . Notes (2) - (5) of Table 5 apply..

34
1

# **Appendix Table 1. Nominal Rates of Assistance of Policies Assisting Producers of 28 Covered Farm Products, All 75 Focus Countries, 1960 to 2004 (percent)**

||**1960-64 **|**1965-69**|**1970-74**|**1975-79**|**1980-84 **|**1985-89**|**1990-94 **|**1995-99**|**2000-04 **|
|---|---|---|---|---|---|---|---|---|---|
|**Grains and tubers**|**20**|**15**|**9**|**9**|**-1**|**25**|**20**|**14**|**17**|
|Rice|39|6|11|12|-10|26|25|23|39|
|Wheat|15|22|7|2|9|30|23|12|6|
|Maize|4|8|5|2|-3|11|3|6|7|
|Cassava|0|0|-3|1|1|-1|-2|-4|-3|
|Barley|40|38|23|33|10|85|73|20|2|
|Sorghum|61|56|47|17|14|24|11|12|9|
|Millet|-19|-6|-4|-1|1|0|1|-3|-2|
|Oat|38|52|33|69|12|54|45|28|0|
|**Oilseeds**|**-3**|**2**|**-3**|**-7**|**-2**|**10**|**8**|**2**|**1**|
|Soybean|0|1|0|-2|-1|-2|1|7|4|
|Groundnut|-21|2|-14|-27|-1|34|3|-10|-14|
|Palmoil|-20|-24|-23|-15|-4|-5|8|-5|-3|
|Rapeseed|12|29|14|5|12|72|47|7|13|
|Sunflower|13|1|-9|-14|-23|46|19|-10|-12|
|Sesame|-53|-64|-65|-68|-60|-48|-46|-49|-39|
|**Tropical crops**|**1**|**22**|**-8**|**-13**|**-10**|**0**|**3**|**9**|**21**|
|Sugar|78|157|-4|9|15|38|28|39|60|
|Cotton|-10|0|9|-9|-12|-8|-10|-6|3|
|Coconut|-29|-24|-8|-3|-11|-19|-34|-22|-8|
|Coffee|-20|-31|-33|-43|-43|-31|-8|-10|0|
|Rubber|-16|-14|-8|-19|-19|-14|-16|5|4|
|Tea|-32|-31|-26|-26|-25|-24|-27|-19|-12|
|Cocoa|-27|-50|-45|-56|-47|-32|-32|-31|-35|
|**Livestock products**|**38**|**41**|**36**|**48**|**29**|**39**|**33**|**28**|**25**|
|Pigmeat|33|47|36|31|-16|-12|4|10|10|
|Milk|96|97|91|140|138|152|85|62|53|
|Beef|15|14|12|13|25|42|29|31|23|
|Poultry|21|20|26|26|29|20|26|20|19|
|Egg|-8|-3|-6|12|11|17|15|19|6|
|Sheepmeat|41|48|61|99|64|51|30|13|11|
|Wool|0|0|6|4|7|4|5|1|1|
|**All of the above**<br>**28 commodities**|**26**|**27**|**17**|**19**|**9**|**27**|**23**|**19**|**20**|

Source: Anderson and Valenzuela (2008), based on NRA estimates reported in national studies covering 75 focus countries.
per person while the number of lost-years corresponds to 0.0044 years per person.

Table 1: Estimation of the pandemic's welfare costs in six countries as of early June 2020 (baseline, distribution-neutral contraction)

||(1)<br>Belgium|(2)<br>Sweden|(3)<br>UK|(4)<br>Pakistan|(5)<br>Peru|(6)<br>Philippines|
|---|---|---|---|---|---|---|
|Economic and demographic cha|racteristi|cs|||||
|GDP p.c. in 2017 (2011 PPP$)|43,133|47,261|40,229|4,764|12,517|7,581|
|National poverty line (2011 PPP$)|27|28.9|25.8|2.8|5.3|2.6|
|Population (in millions)|11.59|10.10|67.88|221.0|32.98|109.5|
|Life expectancy at birth|81.18|82.31|80.78|65.98|80.24|69.51|
|Age (mean)|41.42|41.14|40.62|25.86|32.53|28.53|
|Residual life expectancy (mean)|42.01|43.06|42.40|46.25|50.55|44.92|
|Covid-19 mortality, current sce|nario||||||
|Number of deaths|9,605|4,639|48,848|2,056|5,465|1,002|
|LYs per death|9.467|9.479|10.14|18.46|21.97|16.90|
|LYs per person|0.00785|0.00435|0.00730|0.000172|0.00364|0.000155|
|Covid-19 economic shock|||||||
|On GDP per capita (in %)|-8.5|-11.5|-14.5|-6.7|-13.1|-8.4|
|On poverty HC (in million)|0.32|0.41|4.37|7.39|1.58|2.96|
|On poverty HCR|0.0279|0.0409|0.0644|0.0335|0.0480|0.0270|
|Break-even ˆ_α_|3.553|9.383|8.816|194.8|13.20|174.8|

The main nding of this section, from an inspection of Table 1, is that the poverty costs are very substantial relative to the mortality costs. This is obvious in the case of ˆ Pakistan and the Philippines, for which the break-even _α_ are 195 and 175, respectively. It is relatively clear as well in the case of Peru, for which break-even _α_ ˆ is 13. If one were to take the view that _α_ = 10 , then Sweden and the United Kingdom would be very near the break-even point at which the welfare costs of the pandemic arise in equal parts from greater poverty and mortality. Under that assumption ( _α_ = 10 ), Belgium (with ˆ _α_ = 3 _._ 6 ) would be the only country in our sample for which Covid-induced mortality is the dominant source of welfare losses from the current crisis. All things considered, it seems safe to say that, given individual and public policy responses, the poverty costs of the pandemic in these six countries are not dwarfed by its mortality costs. Indeed, for most plausible parameter estimates, the reverse is true (at least) in Pakistan and the Philippines.

# 3.2 The whole world

The ndings above suggest that the poverty eects of the pandemic are substantial, even in relation to its mortality eects. They also hint at a possible pattern where that cost ratio is much larger for developing countries relative to developed countries. To investigate those conjectures further, we go beyond our six-country sample, and extend the analysis to as many countries as possible.

11
# **Figure C5.3** _:_ **Evolution of the premium on industry-level Suharto connectedness –– broad connections**

<!-- Start of picture text --> Entry Exit<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>PCM PE<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>Herfindahl MS4<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>lnN lnP<br>'93'94'95'96 '00'01'02'03'04'05'06'07'08'09 '93'94'95'96 '00'01'02'03'04'05'06'07'08'09<br>Suharto era Post Suharto era Suharto era Post Suharto era<br>.2 .15<br>.1<br>.1<br>.05<br>0<br>PC broad PC broad 0<br>β β<br>-.1 -.05<br>-.2 -.1<br>.4<br>2<br>.2 0<br>PC broad 0 PC broad -2<br>β β<br>-.2 -4<br>-.4 -6<br>.2 .1<br>.1<br>0<br>PC broad 0 PC broad<br>β -.1 β<br>-.1<br>-.2<br>-.3 -.2<br>.4 .4<br>.2 .2<br>0<br>0<br>PC broad PC broad<br>β -.2 β<br>-.2<br>-.4<br>-.6 -.4<br><!-- End of picture text --> _Note:_ The figure depicts annual variation in the estimated impact of the market share of broadly defined politically connected firms on the outcome of interest. The dots depict the coefficient estimates ′93 ′94 ′09, estimated using the regression: = ∑ Y Y which are αα , αα , ... ... . αα presented in Table C5.3, with 1996 as the omitted year. jjtt ii=1993,..,2009 ααiiPC (MMSS)jj ∗DD(Y = tt) + ∑ represent industry characteristics. ii=1993,..,2009 γγiiSSjjii ∗DD(Y = tt) + ππjjmeasures the aggregate + ττii + εεjjii YY Suharto-era market share of firms with broad connection to Suharto (the average of their aggregate market share in 1996 and jjii jj SS PC (MMSS) 1997). represent Suharto-era industry characteristics. is a vector of industry fixed effects, and is a vector of year fixed effects. The vertical bars indicate the 95% confidence interval associated with the estimates. jjii jj ii SS ππ ττ 69
**Guyana:** Figure differs from the one reported originally due to an error in the earlier calculation.

**Iceland:** Includes "basic", "supplementary" and "additional" pensions of the old age, anticipatory, special anticipatory and survivors' branches.

**Ireland:** Includes non-contributory old age and widows' pensions, invalidity pensions and retirement pensions as well as estimated public employees' pensions assuming that real spending on PE schemes did not change between 1986 and 1988. Invalidity spending was erroneously omitted from original table.

Israel: Figure reflects addition of disability pensions which are estimated assuming constant ratio of old age to disability pensions between 1987 and 1989.

**Italy:** OECD figure is increased by .3 percent of GDP to reflect early retirement spending.

**Jamaica:** Figure reflects revisions to GDP.

Kenya: GDP is weighted average of 1988 and 1989 GDP to correspond to fiscal year reporting.

**Luxembourg:** Figure reflects addition of invalidity/early retirement/redundancy spending based on Eurostat 1992a,b which is added to the figures reported in OECD 1993.

Malaysia: Includes public employees' scheme and Employees' Provident Fund withdrawals. Assumes same proportion of housing withdrawals as are made in Singapore.

**Mauritius:** Includes universal flat scheme, main and public employees' contributory scheme. GDP is a weighted average of 1990 and 1991 to reflect fiscal year reporting.

Mexico: Data does not include the pensions of PEMEX, the national petroleum company.

Morocco: Reflects revisions made to GDP since publication.

**Netherlands:** Figure which appears in the book and figure reported by OECD 1993 do not include invalidity payments under the General Disablements Act (AWW) or redundancy payments (Wgf) which raises the total significantly.

**Nicaragua:** Reflects revisions made to GDP since publication. Includes all "pensiones ordinarias" but excludes

"pensiones especiales" which appear to be political pensions or pensions related to war injuries. Including these pensions increases the ratio to 1.4%.

Norway: Includes "basic", "supplementary" and "additional" pensions of the old age, anticipatory, special anticipatory and survivors' branches.

Pakistan: GDP is weighted average of 1988 and 1989 to correspond to fiscal year reporting.

**Panama:** Note that real GDP fell dramatically after 1986. The more typical pension spending ratio which prevailed during 1983-1986 was 3.4%.

Poland: Includes KRUS, FUS and other pensions.

**Romania:** Figure replaces the one reported in the original table and is considered more reliable. **Rwanda:** Reflects revisions to GDP since publication.

11