rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 11: Benchmark and crisis projection: GDP growth from 2011, annualized growth**

<!-- Start of picture text --> 8%<br>6%<br>4%<br>2%<br>0%<br>-2%<br>-4%<br>-6%<br>Benchmark Crisis Benchmark Crisis<br>baseline year year 1* year 2*<br>(2012)*<br>GDP Primary Industry Commerce Services<br>Annualized growth (percentage)<br><!-- End of picture text --> Source: WB staff calculations based on data from BCU and ECH 2011. *Change with respect to 2011 Similar to the 2001/02 crisis, under the crisis scenario, all the sectors are expected to be affected negatively with the exception of the primary sector. The resulting annualized unemployment rate would be 13.4 percent higher than the rate in 2011 due mainly to the contraction of the industry and commerce sectors that in 2011 represented 21 and 27 percent of the employed population, respectively<sup>5</sup> . In the absence of a crisis, activity, employment and unemployment rates are not expected to change significantly in the first year, but by year 2 the unemployment rate is predicted to decrease at an annual rate of over 1 percent.

**Figure 12: Benchmark and crisis projection: labor growth from 2011, annualized growth**

<!-- Start of picture text --> 18%<br>15%<br>12%<br>9%<br>6%<br>3%<br>0%<br>-3%<br>Benchmark Crisis Benchmark Crisis<br>baseline year year 1* year 2*<br>(2012)*<br>Activity rate Unemployment rate Employment rate<br>Annualized growth (percentage)<br><!-- End of picture text --> Source: WB staff calculations based on data from ECH 2011 *Change with respect to 2011

> 5 Own calculations using 2011 _Encuesta Continua de Hogares._

12
makers – such as employment and revenue growth – are significantly different for marketing training relative to finance training, and occur in a manner consistent with the mechanisms we outline.

A key distinguishing feature of our study compared with previous literature is the intensity of the underlying business training program. The program included 10 weeks of high quality and focused training with approximately eight hours per week of face-to-face classroom time and additional application exercises and e-learning sessions for both marketing and finance, delivered by a local business development and training organization.<sup>2</sup> Such an intense and practically relevant intervention might help entrepreneurs overcome the inertia that is inherent whenever there are preexisting habits and methods of engaging in business. Indeed, McKenzie and Woodruff (2016) conclude that the reason most business training studies struggle to find effects is that they offer fairly short training courses that fail to improve business practices, and hence have limited power for measuring impacts on sales and profits.

We measure outcomes at two intervals after the trainings, at six months (midline) and again at 12 months (endline). The data were collected using a novel electronic survey tool that was designed specifically for this study to measure key outcomes such as sales, costs, and profits using triangulation aids for anchoring across different measures and allowing for real-time adjustments to further improve the precision of estimates.<sup>3</sup> Our results show strong positive and statistically significant improvements in profits for both finance and marketing groups 12 months after the trainings were delivered. The medium term results at the 6 month interval show only modest improvements in profits, suggesting that business training requires an incubation period of several months for firms to apply and test their new knowledge before improvements in firm outcomes are realized.

> 2 Please see www.thebusinessbridge.org

> 3 Anderson and Zia (2016) specifically test the measurement methodology of anchoring and adjusting revenues, costs, and profits against simple recall and calculated estimates (reported sales minus reported costs). The analysis finds the anchoring and adjusting method significantly improves precision, with smaller sample variation, over the other methods.

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Policy Research Working Paper 8627

# **Abstract**

This paper estimates the profitability of fertilizer and hybrid seeds in Uganda, using agronomic evidence on the yield returns to inputs from experimental fields, as well as output price data from local markets between 2000 and 2012. The results suggest that the returns to fertilizer are positive across the entire price range for beans, maize, and matooke and positive for the top 75 percent of prices for coffee. Commonly available improved seed varieties for maize and beans increase gains by 32 percent on average. However, accounting for the quality of the inputs available to farmers in the market, the sizable positive returns become negative for most of the price distribution, possibly explaining the low adoption of inputs in Uganda. The paper also examines the impact of other factors that could affect input adoption, by using a relatively long panel data set spanning 12 years. The analysis finds evidence that enhanced access to economic markets and past weather conditions have small effects on input use, and positive correlations between the use of extension services and knowledge and input use.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at cmejiamantilla@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Road Infrastructure and Economic Development Some Diagnostic Idicators <!-- Start of picture text --> by<br>Cesar ueiroz<br>and<br>Surhid Gautam<br><!-- End of picture text -->

# Table of Contents

|Introduction|2|
|---|---|
|How Roads Influence Development|3|
|Sources of Data|3|
|Cross-Section Analysis|4|
|Time-Series Analysis of U.S. Data|6|
|Comparison of Cross-Section and llme-Series Analyses|7|
|Comparison of Road Supply in the World Economies|7|
|A Discussion of Causality|9|
|Conclusions|10|
|References|12|
|Figures|16|
|Table|25|
|Appendix|27|
outcomes, as well as being possible to define systematically across the data sets used here. In each case the variable of interest is regressed on dummy variables for quintiles (with the poorest quintile as the reference group) and dummy variables for age and gender. Figures 1 to 5 display, for each country and for each economic status measure, the predicted average outcome for each quintile at the means of the other correlates.

Figure 1 displays inequalities in the school participation rate among youths seven to 19 years of age and in the completion of grade six among youths 15 to 19 years of age.<sup>19</sup> In Brazil, for example, 67 percent of seven to 19 year olds in the poorest per capita expenditure quintile are in school as compared to 97 percent in the richest quintile—as predicted from the multivariate regression that controls for age and gender. In contrast, 64 percent of youths in the poorest principal component quintile (using all indicators) are in school, as compared to 98 percent in the richest quintile.

In general for the two education outcomes, the pattern is similar across all countries: the different economic status measures yield extremely similar rich-poor gaps in outcomes. The difference between the richest and poorest quintiles in school participation and sixth-grade completion are statistically significantly different from zero for all countries and for all economic status measures. The rich-poor gaps tend to be larger when using the asset indices than when using per capita expenditures.

Figures 2 and 3 display inequalities in the use of health services—figure 2 is limited to children under five years of age while figure 3 is limited to people 16 and older. The top panel refers to the proportion in each quintile that has visited a medical provider in the past month, and the bottom panel refers to the proportion that has visited a private sector provider.<sup>20</sup> The results are more variable than those for education. Economic status gradients are similar regardless of the asset index used, but there is less of a systematic pattern in the comparison between per capita expenditures and the asset indices. For example, the rich-poor gap in the proportion of children obtaining medical care is smaller using asset index quintiles than per capita expenditure ones. This difference is statistically significant in Brazil, South Africa, Uganda, and Vietnam.

> 19 Statistical significances for the estimates underlying figures 1 to 5 are in the tables in Appendix 3. 20 Consistent with Dow (1996) these estimates do not condition on self-reported health status which may be systematically related to socio-economic status.  We do not study self-reported illness directly because of the potential problem of biased self-reporting: if the poor are less likely to recognize illness—perhaps because the implicit cost of doing so is high—then it is unclear what the economic status gradient in self-reported illness actually represents.  This issue is discussed in Butler, Burkhauser, Mitchell, and Pincus (1987); Deolalikar (1998), Sindelar and Thomas (1991), and Strauss and Thomas (1996).

20
- 30 -

lead to rising food consumption and imports. Indeed, Zambia, where cheap food policies have been in place for 40 years, has the most urbanized populaion in Africa.

Table 6. Regression euation for Sub-Saharan Africa's food imports

|Dependent variable:<br>variable|food imports<br>Coefficient|t-value|
|---|---|---|
|Constant|13.3|(3.3)**|
|Urban population|0.29|(10.7)**<br>|
|Real exchange rate|0.06|(2. 1)*|
|Import capacity|0.40|(21.0)**|
|Weather|-59.3|(-23.7)**|
|Trend|1.09|(7.4)**|

Degree of freedom = 487

# Source: World Bank and FAO.

* indicates significance at the 95 percent level.

** indicates significance at the 99 percent level.

Note: pooled cross-section time-series GLS method was used.

An additional ambiguity iia this result is the extent to which urbanizadon has affected domestic food production as a result of a reduced agricultural labor force. Outmigration of agricultura **l** abor may result in slower growth in production relative to overall population growth. The upward pressure of prices for domestic foods that might result would further shift demand toward imports. If, however, producer food prices are controlled, or if cheap food imports or food aid keep producer prices low, the price signals necessary to stimulate growth in domestic food production for an increasingly urban population may not ensue. To the extent that this is occurring, some of the increase in food imports may be seen as a supply problem, but one whose origins lie in rural to urban migration and the policies which have encouraged it.

The estimated coefficient for the real exchange rate implies that exchange rate policies have contributed to the growth in Africa's food imports. There are several ways in which overvalued exchange rate will contribute to food imports. The most direct way is through its effect on the relative prices of imported versus domestically produced foods, many of which are nontraded or have significant natural barriers between domestic and international prices. Thus when exchange rates become overvalued imported food prices will fall relative to domestic prices leading to a shift in consumption toward imported food. In addition, overvalued currencies are usually accompanied by trade and exchange restrictions (in order to stem the excess demand for foreign exchange reserves). This may have the added effect of reducing productivity in domestic food production in cases where food production relies on imports such as fertilizer, fuel and farm machinery. In some cases, howeve , the accompanying import restrictions may also apply to some imported foods. If effective this would have the opposite effect of reducing food imports. The existence of such contradictory effects may explain the lower significance level for this variable.

The coefficient for import capacity is positive and significant indicating an income effect, oa that changes in aggregate purchasing power are positively associated with changes in food imports. As described above the vcry large increases in Nigeria's import capacity coincided with the doubling of
firm characteristics could have a non-linear impact of how constraining the same objective condition is reported to be (e.g. large firms may report the same number of outages as more constraining than a small firm).

Overall, the analysis shows that subjective rankings are significantly correlated with objective measures – taken from within the survey or outside sources. Variations in reported constraints thus do correspond to differences in conditions on the ground.

Firm characteristics also matter in determining reported constraints. However, the effects of size and performance are not to raise or lower overall levels of constraints. Rather, expanding and contracting firms both report higher overall levels of constraints than stable firms. Firm characteristics affect the _relative_ importance of different constraints; so which issues matter does depend on who you are and how you are doing. But the evidence indicates this reflects differences in objective conditions experienced by firms, _not_ how they translate those conditions into rankings of constraints. Thus, contracting firms that complain about electricity are doing so because they are experiencing more outages. There is little evidence that the same number of outages is reported as more or less constraining based on performance or size. This implies that endogeneity remains a concern in using firm based indicators to explain firm performance, but the source of endogeneity is from firm characteristics on the business environment they face, rather than whether the measure of the business environment used is subjective or objective.

The paper is organized as follows. Section 2 discusses the concerns raised in the literature about using subjective rankings. Section 3 describes the Enterprise Survey data used in this analysis. Section 4 examines how well subjective conditions reflect 5
# **Appendix Table A1: Baseline summary statistics by business practices at baseline**

||Below<br>Percenta|median<br>ge of business p|Above<br>ractices used at|median<br>baseline|
|---|---|---|---|---|
||Control<br>Mean/SD<br>(1)|Treatment<br>Mean/SD<br>(2)|Control<br>Mean/SD<br>(3)|Treatment<br>Mean/SD<br>(4)|
|_Panel A: SEBRAE-PR data (2017/2018)_|||||
|Used SEBRAE service during the year b|efore the interve|ntion|||
|Any type of service|0.151|0.179|0.266|0.194|
||[0.360]|[0.384]|[0.445]|[0.396]|
|Material|0.093|0.161|0.228|0.157|
||[0.292]|[0.368]|[0.422]|[0.364]|
|Training|0.070|0.060|0.114|0.078|
||[0.256]|[0.237]|[0.320]|[0.269]|
|Business practices used (%)|0.295|0.264*|0.709|0.717|
||[0.151]|[0.152]|[0.146]|[0.135]|
|_Panel B: RAIS data_|||||
|Number of employees (2015)|3.953|3.301|3.785|4.275|
||[3.850]|[3.678]|[3.225]|[5.519]|
|Average wage (minimum wages, 2015)|1.580|1.604|1.615|1.641|
||[0.465]|[0.496]|[0.522]|[0.573]|
|Average employee age (2015)|34.216|35.011|33.873|32.670|
||[8.824]|[9.317]|[8.677]|[7.639]|
|Share of male employees (2015)|0.453|0.574**|0.440|0.395|
||[0.413]|[0.407]|[0.406]|[0.384]|
|Share of employees with college or|0.055|0.050|0.092|0.102|
|higher (2015)|[0.160]|[0.165]|[0.218]|[0.226]|
|Firm age (2018)|12.884|13.217|12.392|11.841|
||[9.092]|[10.053]|[11.046]|[8.651]|
|Commerce sector|0.500|0.485|0.456|0.449|
||[0.503]|[0.501]|[0.501]|[0.498]|
|Service sector|0.372|0.327|0.405|0.452|
||[0.486]|[0.470]|[0.494]|[0.498]|
|Starting capital (USD)|9228.393|6778.421|10374.000|9638.463|
||[22576.267]|[9777.351]|[17413.359]|[19118.309]|
|N|86|336|79|345|

Notes: This table shows baseline summary statistics using administrative data from SEBRAE-PR, for 2017 and 2018, and RAIS, for 2015, the latest available year at the start of the study. Starting capital comes from the Federal Tax Authority (RFB). Columns 1 and 2 displays means and standard deviations for control firms and treatment firms using below the median percentage of business practices at baseline (50%), grouping all four different treatments together. Columns 3 and 4 display means and standard deviations for firms with percentage of business practice above the median at baseline. Material denotes receipt of written or recorded material on business practices, either during a visit to a SEBRAE service center or to SEBRAE's website. Training includes classes, consulting, and networking events. Business practices are only available for 846 observations, since recording errors led to missing observations for firms in one week. Stars refer to differences in means of the treatment group to the control group in each subsample. ***, **, and * indicate significance at the 1, 5, and 10 percent level.
development in the model is to occur in the periphery where undeveloped but developable land is still plentiful. In fact, according to the data between 2000 and 2005, the aggregate housing floor area in the peripheral zone grew by 47.65%, depleting an estimated 10.0% of the periphery’s land stock that was available for development in 2000. The housing in the peripheral area is newer and more desirable and has higher density than the older housing in the core.

**TABLE 1: Core versus periphery land, housing and densities (Beijing,** **_circa_ 2005)**

|**Zone**|**Total**<br>**land**<br>**(sq-km)**|**Developable**<br>**vacant land**<br>**(sq-km)**|**Land in**<br>**housing**<br>**(sq-km)**|**Housing**<br>**Units**|**Housing**<br>**floor area**<br>**(mill. m-sq)**|**Floor,**<br>**per ho**<br>**unit (m**|**land**<br>**using**<br> **-sq)**|**Gross**<br>**structure**<br>**Density**|
|---|---|---|---|---|---|---|---|---|
|_Core in 2000,2005_|93.39|0|39.62|760,000|49,498,880|65.13|52.14|1.25|
|_Periphery_<br>_in 2000_|1275.93|424.28 (*)|89.10|1,876,286|144,473,981||||
|_in 2005_|1275.93|284.63(*)|131.56|2,770,370|213,318,490|77.00|47.49|1.62|
|_construction 00-05_<br>_(% change 00-05)_||42.46<br>(10.00%)|+42.46<br> (+47.65%)|+ 894,084<br>(+47.65%)|68,844,509<br>(+47.65%)||||

Table 2 presents the distribution of a random sample of workers in 2005.<sup>4</sup> The displayed distribution is by three aggregated modes of commuting which are non-motorized (walking and bicycling), public motorized (bus or subway) and private automobile; and by the location of the commuter’s residence and job as core or periphery. The data in Table 2 shows that a little more than 79% of the jobs are located in the periphery and only a little more than 20% in the core. 25% of the residences are in the core and thus the core has a higher gross residential density than a gross job density. 15% of the commutes are in the “reverse commuting” direction from residences in the core to jobs in the periphery, while 10% are from the periphery to the core. 64% of the commutes originate and terminate within the periphery.

From these descriptions of the data, Beijing does not resemble much the monocentric city typically studied by urban economists. Its core area, while similar in size to an American CBD, is about equally residential as it is commercial, and contains a much higher percentage of the total residential population than most American CBDs now contain. However, since a lot of income growth is expected in China’s future and there is a lot more room for car ownership to increase, it is reasonable to assume that the periphery will develop fast (as it did in the 2000-2005 period) and that as these trends continue, many core residents may gravitate increasingly to peripheral housing over time.

> 4 This survey was conducted by the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Science, in Beijing, during August and September of 2005. The primary benefit of this sample is a pair of matched records, indicating where each worker works and lives. We are grateful to Siqi Zheng who provided this data with financial support from the World Bank.

7
then, which has solidified its place among the group of upper-middle income countries (World Bank, 2017).

This economic growth process is by itself quite intriguing, and raises a number of policyrelevant questions. The egalitarian economic model as exemplified by Russia deems equality of income distribution for everyone as its highest priority, and indeed operated based on this ideal principle. But did inequality increase after Russia changed into the market economy model, which is driven by the opposite motto of free-for-all competition? Figure 1 suggests that inequality, in fact, even went down from a Gini coefficient of 0.47 in 1994 to that of 0.31 in 2015 (dashed line). Clearly, this trend appears counter-intuitive and leads to other questions. Was the trend in the short term similar to those in the medium term and in the longer term? Would poorer households suffer from less income mobility and lag even further behind richer households? If yes (or no), what are the magnitudes of the gaps between the rich and the poor? What were the factors that are associated with upward (or downward) mobility, or no mobility? These questions are pertinent not just for Russia but for other developing countries in a similar transition process as well.<sup>3</sup> We aim in this paper to shed light on these policy-relevant questions. More broadly, we also aim to provide a comprehensive picture of welfare mobility and inequality for Russia over the past two decades, and we attempt to do so from new angles. First, we focus our analysis on lowerincome population groups rather than those in higher income categories. For a transitional country

> 3 Notably, several centrally planned economies that have been undergoing a similar transition process to a market economy, such as China, Cuba, the Lao People’s Democratic Republic, the Democratic People’s Republic of Korea, and Vietnam, may particularly benefit from Russia’s experience. Economies with heavy government subsidies such as the República Bolivariana de Venezuela may likely share certain features with Russia’s previous central economic model.

3