rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 2. Graphical Representation of Progressivity and Regressivity of Taxes and Transfers**

# a. Government Transfers

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Cumulative proportion of population (ordered by pre-transfer income)<br>Regressive<br>Pre-transfer Income Lorenz curve. Neutral in relative terms if on this line<br>Progressive in relative terms<br>45 degree line; population share. Neutral in absolute terms<br>Progressive in absolute terms<br>Cumulative proportion of benefits or income<br><!-- End of picture text -->

- b. Government Taxes

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Cumulative proportion of population (ordered by pre-tax income)<br>Relatively progressive<br>Pre-tax Income Lorenz curve. Neutral in relative terms if on this line<br>Regressive<br>45 degree line; population share. Upper bound of regressive<br>Cumulative proportion of taxes or income<br><!-- End of picture text --> Source: adaptation from Lustig (2018).

**_Poverty reduction effect:_** This captures the marginal contribution of the fiscal system element(s) to poverty defined at a particular poverty line. Again, the marginal contribution is understood as the difference

9
negative, global trade volume declined and remained suppressed for years thereafter, and global liquidity froze temporarily. Indian growth adjusted to a lower level. After the global financial crisis, India’s growth drifted down to about 7 percent, and some of the same drivers of growth that had played a prominent role during the pre-crisis boom were the ones that accounted for the slowdown. Slowdown has been most pronounced in investments and exports, both of which more than halved their contribution to growth.

**Figure 8: Three Phases of Growth**

8 A: Growth accelerated to an average rate of 8 B: Real per capita GDP growth followed the 5.4 percent during 1991–2003, followed by 8.8 GDP growth trajectory percent during 2004–08, and a slowdown thereafter <!-- Start of picture text --> 13 10<br>7.2<br>8.8 5.5<br>5.4 7.1 3.4<br>8 5 2.0<br>4.3<br>3 0<br>-2 -5<br>-7 -10<br>Real GDP Growth Average 1971-1990 Real per capita GDP Growth Average 1971-1990<br>Average 1991-2003 Average 2004-2008 Average 1991-2003 Average 2004-2008<br>Average 2009-2017 Average 2009-2017<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Note: Central Statistics Office data. Years refer to respective fiscal years.

Below we analyze the period of economic boom and the succeeding period of deceleration, situating it in a global context. We compare the Indian experience with that of the other large emerging markets, and track its constituents. Comparing growth acceleration in 2004-2008 with that of other emerging countries, we note that the spurt in growth rate that India experienced during this period was larger than in many other emerging countries (Figure 9). Starting from a modest level, its creditto-GDP ratio increased rapidly, surpassing the levels in EM7 countries. The rate of investment in India also outpaced the rate in EM7 countries, and India’s share in world export markets increased at a pace faster than in other emerging markets. The growth exuberance and the “credit bubble” were partly financed by large capital inflows during this period.<sup>24</sup>

> 24 Gupta (2016) notes a rapid pickup in capital inflows to India starting in the early 2000s. The surge in capital inflows during 2003–04 to 2007–08, was prominently evident in all forms of capital inflows—portfolio flows, FDI flows and other flows. Capital inflows accelerated to an average $44 billion a year during 2004 and 2008, compared to $10 billion a year in the three prior years. At their peak in 2007–08, capital flows exceeded $100 billion in one year. The pace of capital inflows mirrored global trends and was thus vulnerable to reversal. There was a sudden stop of capital flows in 2008–2009, when capital flows declined precipitously to $7 billion.

18
**Figure 2: Cumulative Number of FDI projects from HICs to LMICs, by sector, 2004-2015 (natural logarithm scale)**

<!-- Start of picture text --> 12.00<br>Electrical/electronics<br>11.00<br>Motor vehicles and other transport<br>equipment<br>pharmaceutical and other chemical<br>10.00 products n.e.c.<br>Industrial machinery<br>Textiles, apparel, and leather products<br>9.00<br>Food and beverages<br>8.00 All other manufacturing branches<br>Rubber and plastics products, and other<br>non‐metallic mineral products<br>7.00 Metals and metal products<br>Wood and furniture<br>6.00<br>coke and refined petroleum products<br>5.00<br>4.00<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Source: Calculations based on fDi Markets Database

# b) <u>Use of robots</u>

The data on industrial robots are taken from the International Federation of Robotics (IFR) database. It is the single source of data on the use of robots, which is comparable across countries and industries, and has been used by several studies in the literature that estimates the effect of automation on labor markets and other economic outcomes. High-income countries were the largest users of industrial robots in the manufacturing sector between 1995 and 2015. However, China is projected to have more than 400,000 industrial robots in operational stock in the manufacturing sector by 2018, more than doubling the number in 2015 (Hallward-Driemeier and Nayyar 2017). This would give China the distinction of having the highest number of installed industrial robots in the world, accounting for about one-fourth of total industrial robots projected to be installed globally. While other large emerging markets (including Brazil, India, Indonesia, Malaysia, Mexico, Thailand, and Turkey) also had nontrivial stocks of industrial robots in 2015 (appendix figure 2), Thailand leading the pack at approximately 14,000 robots was a fraction of China’s at more than 200,000.

What matters more than the absolute stock of industrial robots is how the stock has changed, relative to labor input. These data on robots are combined with data on labor input taken from the World Input-Output Database Socio-Economic Accounts to compute the intensity of robot use, as measured by the stock of industrial robots per 1,000 employees. The intensity of robot use varies widely not only across sectors, but also across countries. Figure 3 plots the evolution of robot per 7
**<u>Table 2</u>** <u>: Descriptive statistics</u>

|<br>Variable|<br>All<br>(N=618)|DS Beneficiary<br>(N=108)|<br>PW Beneficiary<br>(N=186)|<br>Non-Beneficiary<br>(N=344)|
|---|---|---|---|---|
|**Panel A: 2009**|||||
|Head is male (=1)|0.58|0.37|0.60|0.63|
|Age of head (yrs.)|48.32|58.47|43.57|48.16|
|No schooling (=1)|0.52|0.64|0.48|0.50|
|Primary started (=1)|0.05|0.06|0.06|0.05|
|Primary completed (=1)|0.37|0.24|0.40|0.40|
|Secondary or more (=1)|0.05|0.06|0.05|0.05|
|Disabled (=1)|0.48|0.70|0.38|0.48|
|# of HH members|4.71|3.43|4.89|4.95|
|Share of adults (18-65 yrs.)|0.47|0.37|0.47|0.49|
|Share of children (0-5 yrs.)|0.16|0.11|0.19|0.16|
|Share of children (6-18 yrs.)|0.30|0.27|0.31|0.30|
|Share of elders (65+ yrs.)|0.09|0.26|0.03|0.07|
|Landholding 0-0.25 ha (=1)|0.50|0.69|0.58|0.43|
|Landholding 0.25-1 ha (=1)|0.31|0.21|0.28|0.35|
|Landholding >1 ha (=1)|0.18|0.10|0.15|0.22|
|Index of housing structure|-0.16|0.04|-0.08|-0.27|
|Ubudehe 1 (=1)|0.13|0.29|0.17|0.08|
|Ubudehe 2 (=1)|0.36|0.51|0.42|0.29|
|Ubudehe 3 (=1)|0.35|0.16|0.30|0.42|
|Ubudehe 4 or higher (=1)|0.16|0.05|0.10|0.22|
|Owns goat (=1)|0.35|0.30|0.30|0.38|
|Owns rabbit (=1)|0.17|0.09|0.18|0.19|
|Owns pig (=1)|0.12|0.03|0.10|0.15|
|Owns chicken (=1)|0.20|0.07|0.18|0.24|
|Owns sheep (=1)|0.09|0.03|0.09|0.10|
|Owns cow (=1)|0.28|0.09|0.19|0.38|
|Livestock (TLU)|0.77|0.26|0.53|1.03|
|**Panel B: 2011**|||||
|Head is male (=1)|0.57|0.33|0.60|0.63|
|Age of head (yrs.)|49.93|61.53|45.55|49.23|
|No schooling (=1)|0.49|0.70|0.48|0.44|
|Primary started (=1)|0.07|0.07|0.08|0.06|
|Primary completed (=1)|0.36|0.19|0.39|0.39|
|Secondary or more (=1)|0.08|0.04|0.05|0.10|
|Disabled (=1)|0.41|0.62|0.38|0.37|
|# of HH members|4.64|3.35|4.89|4.85|
|Share of adults (18-65 yrs.)|0.48|0.37|0.49|0.50|
|Share of children (0-5 yrs.)|0.13|0.10|0.16|0.13|
|Share of children (6-18 yrs.)|0.32|0.30|0.34|0.31|
|Share of elders (65+ yrs.)|0.09|0.25|0.03|0.07|
|Table continues next page.|||||

16
|migration),|**BF10**<br>0.640<br>(0.22)_∗∗∗_<br>-0.179<br>(0.11)_∗_<br>0.011<br>(0.03)<br>-0.086<br>(0.08)<br>0.125<br>(0.15)<br>0.024<br>(0.07)<br>1.603<br>58.775<br>-1.653_∗_<br>S-GMM<br>124<br>341<br> and is hence<br>e in log form.<br> (GMM only)<br> percent level,|
|---|---|
|(with|**BF9**<br>1.993<br>(2.87)<br>-0.268<br>(0.16)<br>-0.498<br>(0.58)<br>1.721<br>(1.65)<br>0.506<br>(0.50)<br>0.211<br>(0.77)<br>1.281<br>0.146<br><br>IV-2<br>5<br>33<br>mple size,<br>riables ar<br>t variable<br>nce at 10|
|polarity|**BF8**<br>0.724<br>(0.33)_∗∗_<br>-0.221<br>(0.12)_∗_<br>-0.013<br>(0.05)<br>-0.075<br>(0.14)<br>0.179<br>(0.22)<br>0.253<br>(0.32)<br>0.994<br>36.516<br>-1.572<br>S-GMM<br>97<br>202<br>aximize sa<br>nel). All va<br>d dependen<br>tes signifca|
|growth|**BF7**<br>3.581<br>(1.64)_∗∗_<br>-0.442<br>(0.19)_∗∗_<br>0.073<br>(0.16)<br>-0.863<br>(0.59)<br>1.104<br>(0.62)_∗_<br>-1.802<br>(1.22)<br>3.245<br>0.058<br>IV-2<br>4<br>40<br>lected to m<br>8–2008 (pa<br>es. A lagge<br>d. _∗_indica<br>evel.|
|nants of|**BF6**<br>0.832<br>(0.32)_∗∗_<br>-0.224*<br>(0.11)_∗_<br>0.001<br>(0.06)<br>-0.093<br>(0.16)<br>0.219<br>(0.20)<br>1.085<br>36.068<br>-1.650_∗_<br>S-GMM<br>97<br>202<br>g frame se<br>n) and 196<br>n parenthes<br> not reporte<br> 1 percent l|
|determi<br>es*|**BF5**<br>3.335<br>(1.71)_∗_<br>-0.543<br>(0.24)_∗∗_<br>-0.181<br>(0.14)<br>-0.365<br>(0.27)<br>0.722<br>(0.43)_∗_<br>3.446<br>0.058<br>IV-2<br>4<br>69<br>s. Samplin<br>ross-sectio<br> reported i<br>ssions, but <br>ifcance at|
|damental <br>rowth rat|**BF4**<br>1.383<br>(0.48)_∗∗∗_<br>-0.303<br>(0.15)_∗∗_<br>-0.062<br>(0.07)<br>-0.010<br>(0.19)<br>0.446<br>(0.23)_∗_<br>9.388_∗∗∗_<br>IV-1<br>3<br>37<br>0 U.S. dollar<br>1999–2003 (c<br>rd errors are<br> in the regre<br>ndicates sign|
|regressions for fun<br>8, with real GDP g|**BF2**<br>**BF3**<br>2.037<br>0.659<br>(0.94)_∗∗_<br>(0.23)_∗∗∗_<br>-0.396<br>-0.198<br>(0.15)_∗∗∗_<br>(0.13)<br>-0.135<br>-0.015<br>(0.09)<br>(0.03)<br>2.289_∗∗_<br>5.750_∗_<br>2.478<br>75.080<br>-1.636<br>IV-2<br>S-GMM<br>3<br>82.000<br>75<br>349<br>m GDP data in real 200<br>y fve-year averages for <br>correlation-robust standa<br>cifcations) were included<br> percent level, and _∗∗∗_i|
|nchmark <br> 1968–200|**BF1**<br>1.364<br>(0.51)_∗∗∗_<br>-0.344<br>(0.14)_∗∗_<br>-0.071<br>(0.06)<br>9.826_∗∗∗_<br>IV-1<br>2<br>42<br>lculated fro<br>ta periodicit<br>ty and auto<br>erm (all spe<br>ifcance at 5|
|Table A.5: Be<br>1999–2003 and|Institutions<br>Integration<br>Geography<br>Democracy<br>Fractionalization<br>Social capital<br>Human capital<br>_F_<br>Anderson LR<br>Hansen _J_<br>AR(2)<br>Estimator<br>Instruments<br>N<br>*Growth rates ca<br>unbalanced. Da<br>Heteroskedastici<br>and a constant t<br>_∗∗_indicates sign|

37
Table 5: Self-reported allocation of the grant, by expenditure category

||Treatment|arm|Diferen|ce|(N=475)|
|---|---|---|---|---|---|
|Expenditure category|Cash & therapy|Cash only|Coef.||_p_ value|
||(1)|(2)|(3)||(4)|
|Living expenses (such as food, clothing, rent)|28%|26%|0.04||0.13|
|Durable assets|7%|6%|-0.01||0.53|
|Drugs, alcohol, gambling & sex|4%|4%|0.00||0.95|
|Gifts and transfers to others|11%|11%|0.01||0.65|
|Business investments and expenses|23%|24%|-0.04||0.20|
|Savings and debt payments|20%|20%|0.02||0.57|
|Own health and education|8%|8%|0.00||0.89|

_Notes:_ Using pictures of different types of spending and plastic chips, grant recipients indicated how they used the grant. Columns (1)–(2) report the means for each treatment arm. Columns (3)-(4) report the coefficients and _p_ values from an OLS regression of the proportion spent on an indicator for assignment to therapy then cash controlling for block fixed effects and baseline covariates.

We assessed grant spending in two ways. Using pictures of different types of spending and plastic chips, we first asked grant recipients to indicate how they used the grant. Table 5 reports self-reported allocations of the grant by treatment arm. We see little effect of the recent therapy on allocations. Little of the grant seems to have spent on drugs, alcohol, gambling and prostitution. Even if men underreport these expenses, we see no difference between cash recipients who did and did not receive therapy.

We can also look at expenditure data, which included a range of business investments in the two weeks prior to the 2- and 5-week surveys. As reported in Table 4, those who received only cash reported $56 more investment in each 2-week period. Thus the total 5-week investment treatment effect is at least $112—around 60% of the grant. Meanwhile, the therapy only group resembled the control group in terms of investment. Labor inputs also rise, as total hours of work per week rise by 20% in the short run.

These short run investments did not last. In the cash only group, the stock of business assets after a year is only $19 greater than in the control group, not statistically significant. We also see no long run difference in total work hours.<sup>38</sup> What happened? From qualitative interviews, insecure property rights were a major barrier to capital accumulation. A large number of men reported the theft of all their assets, or all their wares, on a regular basis, by criminals or (for market wares) the police.<sup>39</sup> At each

> rising about 36% relative to the control mean.

> 38All three treatments caused individuals to substitute from illicit work to non-agricultural low-skill business in the short term, but the effects were most pronounced and longer lasting for the group that received both cash and therapy (see Appendix E.7).

> 39In some cases this was theft by a friend, family member, or stranger. Also common was confiscation of

23
locations as Ugandan policy makers consider policies for accelerating growth. Public infrastructure investments in other locations are likely to attract few private investors, and will pose a spatial efficiency – equity tradeoff.

Following this introduction, section 2 describes the data and clustering of manufacturing. Section 3 specifies the estimation model and variables, and discusses the findings from the empirical analysis. Section 4 provides alternate scenarios for transport improvements. Section 5 concludes.

# **2. Clustering of industrial activity**

_Is industrial activity in Uganda located throughout the country_ ? No. Much of industrial activity is clustered around large cities and along transport corridors. Consider the map of industrial location in Uganda (figure 1). Mapping the location of industrial firms onto the country’s geographic profile makes it clear that industrial activity in Uganda is concentrated (see the map on the left in figure 1). Most of the country’s 12,000 manufacturing firms with 5 or more employees are clustered along the industrial corridor stretching between the country’s major urban agglomerations—Masaka, Kampala, Jinja, and Mbale. Also clear is that the location of industrial activity closely follows the distribution of infrastructure networks. Seventy percent of the manufacturing firms with 5 or more employees are located within 10 kilometers of a major road. And, most of these firms are located in regions that are close to national markets, measured using travel times to cities of 100,000 or more (see the map below).

These manufacturing data are drawn from the Uganda Business Registry of 2001 which provides a comprehensive listing of all establishments in the country. A total of 165,000 establishments are included in this database. For each establishment, we know its physical location (measured by a GPS system), four-digit industrial classification, year that it started operations, and number of employees. The GPS coordinate provide each establishment with a unique location identifier (latitude and longitude) at accuracies of ten to fifteen meters. To our knowledge, the Uganda Business Registry is the only

-  5
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

**Figure 27: Pure DC reforms make Government expenditure for teachers much lower than in the baseline scenario***

<!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>ref I.1A ref I.2 base<br>2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072<br><!-- End of picture text -->

**Notes** : * Comparison of Government expenditure for teachers – Reform I.1A, I.2 with baseline (% of GDP). **Source** : WB calculations based on PROST.

From the expected benefit point of view, Reform I.2 may sound more appealing to current system members, but only assuming the government manages to cope with the fiscal pressure which may exceed even the baseline projections during some periods in the medium term.

Replacement rates for those staying in the old scheme are compared with replacement rates projected for funded scheme members in Figure 28 and Figure 29. Here, replacement rates are calculated as a percentage of the group average wage for the total covered population in both the old and the new scheme. One may notice that members of the defined benefit scheme obtain somewhat higher replacement rates compared to the baseline scenario. This can be explained by the assumed changes in behavior resulting in changes in the age distribution of new retirees: once the system is closed to new entrants, the average retirement age and hence the average wage and length of service of new retirees increase producing higher replacement rates in terms of the general group average wage.

- 60 -
inputs and because they are typically applied roughly in proportion to the amount of land cultivated. Farmers therefore have strong motives to know and use the true plot size in deciding how much of those inputs to apply. We define log-transformed input use ( and objectively measured plot size ( ) and estimate the following input demand function for each input: YY) XX<sup>∗</sup> 0 1 2 3 p 4 5 56 (2) pp where the dependent variable is the input intensity (i.e., input use per unit of land), with YY−XX<sup>∗</sup> = ββ + ββ TT+ ββ XX<sup>∗</sup> + ββ RR + ββ + ββ + ββ θθ + εε standing for log-transformed input use in the post-planting round. All other terms are as defined in equation YY (1). The most important parameters in equation (2) are 1, 4, and 5. The parameter 1 estimates the impact of the information treatment on farmers' input self-reporting at the average plot size and ββ ββ ββ ββ measurement error in our sample. Rejecting the null hypothesis that 1 = 0 indicates information spillovers across self-reported variables. 4 ( 5) captures differential spillover effects associated ββ with plot size (pre-treatment measurement error in plot size). To allow for asymmetric and ββ ββ heterogenous responses, we employ continuous over/underestimation rates in the pre-treatment survey.

One important assumption in the estimation of equation (2) is that recall length does not affect reporting differently for treatment and control groups. As discussed previously and shown in Appendix Table A1, the control group households were subject to a single post-harvest visit, while the treatment group households were visited once during the post-planting period (at the time of the information treatment) and once during the post-harvest period (together with the control group households in the same EAs). This assumption seems reasonable, for at least two reasons. First, Wollburg et al. (2021), studying earlier rounds of the same Malawi household survey, show that self-reported unconditional fertilizer quantity per plot is insignificantly associated with recall length and that self-reported farm labor activity (e.g., land preparation and planting, weeding and fertilizing) per plot is inconsistently and only modestly associated with recall length. Second, when we regress post-harvest self-reported fertilizer inputs against postplanting self-reports of the same variable among the treated households, the resulting coefficient estimate (0.955) is not statistically significantly different from one and the simple bivariate relationships explains almost half of variation, indicating a close correspondence between reports of the same early season input use regardless of whether collected post-planting or post-harvest.

25
countries; hence tomorrow’s global middle class will be formed, primarily, by today’s citizens from poor countries. The total increase in the global middle class is explained by (1) population growth rates of cohorts within this class that are above the world average, and (2) by higher economic growth rates in developing countries which pull their citizens out of _poverty_ and into the global middle class. The population growth rates of households within the global middle class (as classified in 2000) was relatively low with an average rate of 18 percent over the entire period, as opposed to the world average of 32 percent. Therefore, the great majority of the increase in the global middle class is explained by high economic growth rates taking place in developing countries.

**Table 3 The global middle class is growing, and its composition is changing**

||20<br>Pop.|Shar<br>00<br>Income|es<br>2<br>Pop.|030<br>Income|Growt<br>(% 200<br>Pop.|h Rates<br>0-2030)<br>Income|
|---|---|---|---|---|---|---|
|Poor|82.0|28.7|63.0|17.0|2|29|
|Middle class, of which:|7.6|13.8|16.1|14.0|178|0|
|_Developed country nationals_|_3.5_|_6.8_|_1.2_|_1.0_|_-52_|_-2_|
|_Developing country nationals_|_4.1_|_7.0_|_14.6_|_12.9_|_363_|_3_|
|Rich|10.5|57.5|20.9|69.0|163|28|
|Total|100|100|100|100|32|109|

_Notes:_ (1) totals may not sum to 100 because of rounding.

(2) Poor are defined as individuals with an income below the average of Brazil; the middle class was defined as individuals with an income between the per capita incomes of Brazil and Italy; rich are those individuals with incomes at or above the average income in Italy.

(3)Thresholds of Brazil and Italy are annual per capita incomes (2000 PPP) of US$3,914 and US$16,746.

Source: Authors’ calculations.

How much of the expected increase in the global middle class is attributable to the economic performance of China and India? Figure 12 divides the global middle class into citizens from China, India and the rest of the World (RoW). In 2000 only 13.5 percent of the global middle class were Chinese nationals and no Indians belonged to this group.<sup>18</sup> By 2030 citizens from China and India had a combined shared of 44 percent of the global middle class, with the great majority (38 percent) being Chinese, in fact half of the total 740 million new entrants into the global middle class will be Chinese nationals.

# **Figure 12 Chinese and Indian weight in the global middle class**

> 18 It is quite likely that in reality some Indians are within the middle and high income ranges, nevertheless by the way the Indian Household Survey data is being collected, _outliers_ (high income citizens) are not captured at all.

20