rafmacalaba/datause-encoder-data huggingface dataset:
It is interesting to compare African countries’ performance in exporting apparel under AGOA with changes in their exports to the EU. Access to the EU is duty and quota free under either the Cotonou Agreement or Everything but Arms. Figure 3 shows that exports of apparel from African least developed countries (LDCs) to the EU have stagnated despite preferences, while exports to the US under AGOA have grown very strongly. Exports of apparel from African LDCs to the EU and US were almost equal in 2000, but the value of exports to the US in 2004 was almost four times greater than the value of exports to the EU.

<!-- Start of picture text --> Figure 3: Non-restrictive rules of origin have strongly stimulated US imports of<br>apparel from African LDCs; but EU imports have stagnated in the face of strict<br>rules<br>900<br>US Imports of apparel from African<br>800 LDCs<br>700<br>600<br>500 EU Imports of apparel from<br>African LDCs<br>400<br>300<br>200<br>100<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> The key factor explaining why exports to the US have grown much faster than to the EU is the rules of origin. EU rules stipulate production from yarn. This entails that a double transformation process must take place in the beneficiary with the yarn being woven into fabric and then the fabric cut and made-up into apparel. These rules prohibit the use of imported fabric, although cumulation provisions allow for the use of inputs produced in other ACP countries. To obtain preferences, apparel producers must use local, EU or ACP fabrics. They may not use fabrics from the main fabric-producing countries in Asia and still qualify for EU preferences—a binding restriction, since few countries in Africa have competitive fabric industries. The EU rules do not allow producers in African LDCs the flexibility they currently have under AGOA to source fabrics globally. It is worth remembering that the EU has granted preferences to African countries for apparel subject to these strict rules of origin for more than 20 years under the Lome and then Cotonou agreements. However, these strict rules have done little to encourage the development of an efficient fabric industry in Africa and are likely to have severely constrained the impact of preferences in stimulating the apparel industry.<sup>12</sup> 12 See Brenton and Ozden (2005) for a more detailed analysis of the impact of the EBA and AGOA on apparel exports from African LDCs and the role of the rules of origin.

9
Table 5: The choice of how much to export: before and after the crisis

||OLS|OLS|OLS|OLS|OLS|IV|IV|
|---|---|---|---|---|---|---|---|
|Crisis period?|15.817***|-16.874|18.451***|-16.260|16.571***|47.058|28.722|
|(1=Yes)|(4.190)|(19.947)|(5.974)|(12.695)|(4.081)|(31.907)|(21.548)|
|Subj. fnancial||-4.595|-1.056|||||
|constraint||(4.979)|(1.140)|||||
|Subj. fnancial||1.990|-0.840|||||
|constraint _×_ Crisis?||(5.127)|(1.569)|||||
|Unable to obtain a||||-11.479|-2.017|8.960|7.650|
|loan? (Yes=1)||||(7.634)|(1.542)|(28.602)|(11.337)|
|Unable to obtain a||||7.880|-1.514|-86.938*|-19.250|
|loan? (Yes=1) _×_ Crisis?||||(7.956)|(2.498)|(50.341)|(33.585)|
|Foreign shock|0.199***||0.198***||0.197***||0.202***|
|(%∆GDP _×_ %Export)|(0.010)||(0.010)||(0.010)||(0.014)|
|Foreign shock|-0.074***||-0.074***||-0.071***||-0.073**|
|(%∆GDP _×_ %Export) _×_|(0.028)||(0.027)||(0.027)||(0.029)|
|Crisis?||||||||
|Domestic shock|-0.037***||-0.037***||-0.037***||-0.038***|
|(%∆GDP _×_ (100-%Export))|(0.002)||(0.002)||(0.002)||(0.002)|
|Domestic shock|-0.007||-0.007||-0.007||-0.005|
|%∆GDP) _×_ Crisis?|(0.005)||(0.005)||(0.005)||(0.008)|
|Log(Labor|-2.364***|-2.864***|-2.428***|-2.881***|-2.449***|-5.280***|-2.643**|
|Productivity)|(0.635)|(0.677)|(0.640)|(0.686)|(0.645)|(1.604)|(1.058)|
|Firm Age|-0.126**|-0.242***|-0.123**|-0.241***|-0.121**|-0.140|-0.111|
||(0.055)|(0.056)|(0.055)|(0.056)|(0.055)|(0.100)|(0.074)|
|Small Firm? (Yes=1)|-3.175*|-4.331**|-3.180*|-4.527**|-3.353*|-7.822**|-3.724|
||(1.842)|(2.001)|(1.836)|(2.022)|(1.842)|(3.469)|(2.269)|
|Foreign Ownership?|10.122***|10.050***|9.972***|10.092***|9.958***|6.592*|9.591***|
|(1=Yes)|(2.361)|(2.485)|(2.364)|(2.471)|(2.334)|(3.623)|(2.641)|
|Industry F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year F.E.|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|41.796|41.796|41.796|41.796|41.796|41.796|41.796|
|F stat||||||5|76|
|Kleibergen-Paap LM test||||||0.050|0.113|
|Hansen J stat||||||0.036|0.000|
|Endog. test||||||0.430|0.000|
|N|6275|6275|6275|6275|6275|6275|6275|

The dependent variable is the _Share_ ( _ExportedSalesit_ ). Robust standard errors are reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level. Under the null hypothesis of the Kleibergen-Paap LM test, the structural equation is underidentified, that is instruments are not relevant. Under the null hypothesis of the Hansen J test, the instruments are valid. The null of the endogeneity test is that the endogenous regressor(s) can be treated as exogenous.

19
# **7. References**

Banerjee, A., Duflo, E., Glennerster, R. and Kinnan, C. (2009), The miracle of microfinance? Evidence from a randomized evaluation, Mimeo, May Barth, J., Caprio, G., Levine, R., (2004), Bank regulation and supervision: What works best? _Journal of Financial Intermediation_ Vol. 13, pp. 205-248.

Beck, T. and de la Torre, A. (2006), The basic analytics of access to financial services, _World Bank Policy Research Working Paper No. 4026._ Beck, T., Demirgüç-Kunt, A. and Martinez-Peria, S, (2007), Reaching out: Access to and use of banking services across countries, _Journal of Financial Economics_ , Vol. 85 (1), pp. 234-266, July.

Beck, T., Demirgüç-Kunt, A. and Martinez-Peria, S. (2008), Banking services for everyone? Barriers to bank access and use around the World, _The World Bank Economic Review_ , Vol. 22(3) pp. 397-430 Bruhn, M. and Love, I. (2009), The Economic Impact of Banking the Unbanked: Evidence from Mexico, _World Bank Policy Research Working Paper No. 4981._ Burgess, R. and Pande, R. (2005), Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment, _American Economic Review_ , vol. 95(3), pp. 780-795, June.

Calem, P. and Nakamura, L., (1998) Branch banking and the geography of bank pricing, _The Review of Economics and Statistics_ , MIT Press, vol. 80(4), pp. 600-610, November.

Christen, R. Jayadeva, V. and Rosenberg, R. (2004) Financial Institutions with a Double Bottom Line: Implications for the future of microfinance, _CGAP Occasional Paper No.8_ .

Collins, D., Morduch, J., Rutherford, S., and Ruthven, O. (2009), _Portfolios of the Poor: How the World's Poor Live on $2 a Day_ , Princeton University Press.

37
# **IV. Empirical strategy**

We estimate the effect of robotics on export quality using a difference-in-difference estimation, complemented with additional fixed effects, in the next section we discuss our instrumental variable: Where ln quality p p refers to the log quality for a product , from an exporter , in a sector , at time (defined by equation 3 in the previous section). r p represents is the log robot stock pp qq ss in exporter , for sector at time t. Note we add one to the robot stock before taking logs, to avoid pp qq qqll r qq dropping some zero values. p represents a broad range of fixed effects and control variables. qq ss We include exporter*HS 10-digit product and exporter*year fixed effects, in order to control for pp χχ potential trends across countries overtime, as well as potential time-varying multilateral resistance terms highlighted by the trade literature (Anderson and van Wincoop, 2003).<sup>17</sup> Note the seminal paper in the literature, Graetz and Michaels (2018) estimate a similar specification for European country-industries, also allowing for country-trends, but measuring long-differences in productivity rather than differences-in-differences as we do here. We also add control variables for (log) industry employment, which is similar to estimating log robots per worker, but allows for differential coefficients on log robots and log employment. We include real value added per worker to control for potential scale effects and the effects of differential productivity growth across sectors. p represents the error term. All standard errors are clustered at exporter-industry level. pp εε First, we examine heterogeneous effects by exporter country income – developed vs developing economies. Exporter income status is defined in 2000, the first year of our data, using the World Bank classification. We interact our main robot measure with a developed and developing country dummy, in order to report the estimated effect for each country group against zero. We do this in preference to sample splitting in order to maximize the power in our IV estimations.

> 17 We also examine including product-year fixed effects, thereby fully saturating the model with dummies.  This clearly works the data extremely hard and first-stage F statistics fall to around 5.  We find broadly similar results, with similar signs and size of coefficients, but these are identified more noisily, with some evidence of statistical significance.  Results are available upon request.

18
that yearly turnover might exceed the exemption threshold, even if ex-post it does not in fact exceed it.<sup>14</sup> Our results suggest that this is unlikely. In Figure 6 we show that even when conditioning on taxpayers filing 12 months (balanced sample), 25%-30% of taxes due are from firms that fall under the exemption threshold. Alternatively, if we only consider partial filers and annualize their revenues, we still observe that 81% would have been under the threshold. Both of these facts are also consistent with what we observe in the full distribution of turnover (Figure 7). Most taxpayers that are under the exemption threshold are not simply slightly under that level – in 2022, for example, 48% had annual turnover below Ksh. 500,000, less than half the exemption threshold. It is unlikely that partial filing or uncertainty over yearly turnover would encourage these taxpayers to file and pay.

An alternative explanation is lack of knowledge: taxpayers might be unaware of the exemption threshold, for example, believing that businesses with any amount of income are liable for the TOT. This explanation is supported by the results of both surveys. The in-person survey showed that only 4.1 percent of registered businesses could correctly identify the minimum threshold for TOT. The SMS survey showed that only 13.7 percent of TOT payers correctly identified the minimum threshold, and almost half stated there was no minimum threshold.

Another possible mechanism to explain the fact that so many taxpayers are paying TOT despite being below the exemption threshold relates to the costs of proving their exemption status. Taxpayers might be aware of the exemption rules and know (or strongly believe) their revenue is low enough to be exempt, but since they often do not have bookkeeping, they may perceive that it will be hard to convince the KRA of this. If not filing/paying TOT led to hassle costs, such as the need to justify their behavior to tax officers or appeal fines, taxpayers might be better off filing TOT and avoiding those costs. Note that a complementary explanation is that many of the taxpayers

> 14The Kenyan Revenue Authority has no automatic check on the total amount of turnover declared to exempt businesses with little revenue. This is partially driven by the fact that declarations are due monthly while the relevant exemption threshold refers to annual revenues, so even very little turnover in a single month might end up summing to a yearly turnover above the threshold if revenues are high enough in other periods.

20
COST RECOVERY, EQUITY, AND EFFICIENCY IN WATER TARIFFS IN AFRICA developing country tariff is less than half the average tariff of countries in the Organisation for Economic Co-operation and Development (OECD).

**Table 7. Comparison of water tariffs in Sub-Saharan Africa and other regions ($/m**<sup>**3**</sup> **)**

|Sub-Saharan Africa|4 m<br>3|10 m<br>3|15 m<br>3|40 m<br>3|
|---|---|---|---|---|
|Average|0.55|0.49|0.52|0.65|
|Median|0.41|0.38|0.40|0.51|
|Comparable tariffs (average consumption = 15 m<br>3)|||||
|OECD|||1.04||
|Latin America and the Caribbean|||0.41||
|Middle East and North Africa|||0.37||
|East Asia and Pacific|||0.25||
|Europe and Central Asia|||0.13||
|South Asia|||0.09||

_Source:_ AICD WSS Survey Database, 2007; Foster and Yepes (2006).

The average price at the minimum consumption of 4m<sup>3</sup> is higher than at 10 or 15m<sup>3</sup> , and lower than the price at the high-end consumption level of 40 m<sup>3</sup> . In fact, excluding ELECTRA, the average prices at 4 m<sup>3</sup> and 40 m<sup>3</sup> is almost the same (figure 7). At the minimum consumption of 4 m<sup>3</sup> , the average tariff across the 45 utilities is $0.55/m<sup>3</sup> , and $0.50/m<sup>3</sup> excluding ELECTRA. At 4 m<sup>3</sup> , two utilities in Namibia—Windhoek and Oshakati—have effective prices of more than $1, besides ELECTRA, and six utilities have tariffs very close to $1. The number of utilities that enforce an effective tariff of more than $0.8/m<sup>3</sup> is highest at the consumption level of 4 m<sup>3</sup> . At the average consumption level of 10 m<sup>3</sup> , the majority of utilities impose a tariff between $0.4 and $0.8/m<sup>3</sup> . At the high-end consumption level of 40 m<sup>3</sup> , very few utilities—REGIDESO in DRC, Kaduna WB, Katsina WB, and JIRAMA in Madagascar— have an effective tariff of less than $0.2/m<sup>3</sup> . The majority of utilities charge at least $0.4/m<sup>3</sup> at this level of consumption (annex F).

**Figure 7. Effective prices at different levels of household consumption ($)**

<!-- Start of picture text --> 100%<br>9%<br>27% 20%<br>80% 27%<br>60% 24% 45%<br>40% 20% 49%<br>20% 25%<br>29%<br>16%<br>9%<br>0%<br>4m3 10m3 40m3<br><0.2 >0.2 & <0.4 >0.4 & <0.8 >0.8<br>% of Utilities<br><!-- End of picture text --> _Source:_ AICD WSS Survey Database, 2007.

15
**Table 1:** Impact of no-till management of fields on PM2 _._ 5

||(1))|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|1[Tillage time] _×_ Avg. no-till pct|-0.013<sup>_∗∗∗_</sup>|-0.014<sup>_∗∗∗_</sup>|-0.013<sup>_∗∗∗_</sup>|-0.013<sup>_∗∗∗_</sup>|-0.014<sup>_∗∗∗_</sup>|-0.015<sup>_∗∗∗_</sup>|
||(0.003)|(0.003)|(0.001)|(0.003)|(0.003)|(0.003)|
|N|3,206,977|3,206,977|3,206,977|2,899,677|3,152,195|3,206,927|
|**Outcome:**|||||||
|Mean|8.87|8.87|8.87|8.97|8.87|8.87|
|SD|5.46|5.46|5.46|5.53|5.47|5.46|
|**Controls:**|||||||
|Temp & Precip||Yes|Yes|Yes|Yes|Yes|
|Upwind No-till|||||Yes||
|Additional controls||||||Yes|
|**Fixed Efects:**|||||||
|County|Yes|Yes|Yes|Yes|Yes|Yes|
|State_×_Year|Yes|Yes|Yes|Yes|Yes|Yes|
|DOY|Yes|Yes|Yes|Yes|Yes|Yes|
|DOW|Yes|Yes|Yes|Yes|Yes|Yes|
|Day of sample|||Yes||||
|Omit spring tillage||||Yes|||

Notes: Robust standard errors are clustered at the state level. Tillage time is calculated as the period in a four week window following the most active harvest week in each state according to the USDA. “Avg. no-till pct“ is the percent (0-100) of pixels in a county that are assessed as employing no-till during a given year. PM is measured using data from.<sup>22</sup> All regressions include quadratic controls for daily precipitation and daily maximum temperature. Coefficients on the individual indicator for tillage time and the continuous measure of no-till are suppressed in the table. Additional controls are measures of the height of the boundary layer, a quadratic in wind speed and a set of indicators for which direction the prevailing wind each day. Coefficients indicate the change in PM ( _μg/m_<sup>3</sup> ) during the period of tillage compared to the non-tillage period in a given county for a percentage point increase in the amount of land in no-till in that county. The average level of PM and the standard deviation for the counties included in each sample is reported in the rows labelled mean and SD. Compiled 18 Apr 2022.

6
- 10 -

tariff for general goods is TSh1.5 million per large wagon for the first 100 km, which translates into about 61 U.S. cents per ton-km ( **Figure 9** ).<sup>3</sup> But when the shipping distance is greater than 500 km, the tariff is TSh2.6 million per wagon, which is equivalent to about 11 U.S. cents per ton-km. The unit rate is even lower when the distance exceeds 1,000 km, i.e., TSh3.9 million per wagon or 8 U.S. cents per ton-km.

Such a pricing strategy is rational because loading and unloading goods from rail wagons incur significant fixed costs, especially when transported goods are uncontainerized bulk cargo. Waiting times that are required for each train to have sufficient wagons to carry may also add additional economic costs to shippers. Because of these fixed costs, short-haul rail shipping tends to be relatively expensive, and long-haul bulk shippers are more induced to use rail transportation. Although no detailed data are available, most rail cargo are large volumes, such as minerals, cement, agricultural products and fertilizer.<sup>4</sup> According to the port statistics, at the Port of Dar es Salaam, wheat accounted for about 40 percent of total imports, followed by fertilizer, cement and rice ( **Figure 10** ). Similarly, wheat and fertilizer are among major commodities exported through the Port of Dar es Salaam.

> 3 Average loaded weight per wagon is assumed to be 36.5 tons.

> 4 This is consistent with our survey data. See the following sections.
Second, we create **Small Firm Industry** which is a dummy variable that takes the value 1 if the industry’s share of employment in firms with less than 20 employees is ≥ the median value of the index of small firm domination from Beck, Demirguc-Kunt, Laeven, and Levine (2008) and takes the value 0 if it is < the median index value. Beck et al. (2008) use the methodology in Rajan and Zingales (1998) described above to argue that each industry has a technological firm size that depends on that industry’s particular production process and that these technological differences should persist across countries. They construct each industry’s share of employment in firms with less than 20 employees in the US using data from the 1997 US Census.<sup>15</sup> Finally, we follow Hasan and Jandoc (2012) in constructing **Labor Intensive Industries** which is a dummy variable that takes the value 1 for labor intensive industries and 0 for capital intensive industries.<sup>16</sup> We use data on informal firms from the Unorganized Manufacturing sector for 1994/95 that is collected by the National Sample Survey (NSS) Office of the Ministry of Statistics. The survey uses a stratified two-stage sampling design where the first-stage units are the villages in the rural areas and urban blocks in the urban areas and the second-stage units are the enterprises. The allocation of sample first-stage units between rural and urban areas is in proportion to population as per the 1991 Census with double weightage to the urban sector. As seen in Appendix B, the informal sector in India is very large and dwarfs the formal sector in terms of

> 15 Beck et. al. present alternate indices including using 100 employees as the cut-off and using 1992 census to construct the index. Our results are robust to these alternate classifications.

> 16 Hasan and Jandoc (2012) classify the following industries in India to be capital intensive industries: Machinery, Electrical Machinery, Transport, Metals and Alloys, Rubber/Plastic/Petroleum/Coal and Paper/Paper Products. The labor-intensive industries are: Beverages and Tobacco, Textile Products, Wood/Wood Products, Leather/Leather Products and Non-Metallic Products. The remaining industries are not as clearly distinguishable and include: Food Products, Textiles, Basic Chemicals, Metal Products and Other Manufacturing

15
7 We will focus on the most common poverty measure, namely the headcount index ( _H_ ), which gives the proportion of the country’s population that live in households with per capita incomes below the poverty line. Other measures are the poverty gap index ( _PG_ ), which gives the average shortfall of income from that line, where the average is taken over the entire population (with the gap set to zero for incomes higher than the poverty line); the squared poverty gap index (Foster et al., 1984); and the Watts (1968) index. The latter two measures penalize inequality amongst the poor, and so are better at picking up differences in the severity of poverty. _PovcalNet_ provides all these measures. In some of the discussion, we also multiply _H_ by the country’s population, to yield the absolute number of poor people.

Table 1 presents the two inequality measures (Gini and MLD) and _H_ for the two poverty lines for every country for which we have household-survey data.<sup>13</sup> Wherever possible, we present results for two periods: (i) the 1990s (centered on 1994), and (ii) the 2000s (centered on 2004). Since most surveys have less-than-annual frequency and since countries field their surveys on different schedules, for each country we use the survey nearest to the two period centers, and indicate the year in the table.

The range of inequality measures across the 130 countries in Table 1 is very large indeed. The Gini index ranges from 0.20 in the Slovak Republic, to 0.74 in Namibia. The MLD ranges from 0.12 in Hungary to 0.71 in Bolivia using data for the 2000s; using data for the 1990s, the range is from 0.07 in Slovak Republic to 1.13 in Namibia. In terms of country groupings, the high-income economies (including the OECD) and Eastern Europe and Central Asia (ECA) record the lowest inequality measures, and Sub-Saharan Africa (SSA) and Latin America and the Caribbean (LAC) have the highest. The predominance of measures using income, rather than consumption, in LAC is a contributing factor to the high inequality measures for that region. The high-level of inequality in SSA thus deserves special mention, as many of the indices refer to distributions of consumption expenditures. The commonly-held view that LAC is unambiguously the most unequal region in the world needs to be qualified accordingly.

Figure 1 plots inequality (measured by the latest available Gini coefficient) against GDP per capita for each country listed in Table 1. The figure reveals a negative correlation between inequality and mean incomes (measured by GDP per capita). The correlation coefficient is –0.44 (statistically significant at the 1% level). In addition, the variance of inequality is higher among

> 13 An extended version of Table 1 is available from the authors giving _PG_ for both poverty lines.