rafmacalaba/datause-encoder-data huggingface dataset:
Financial Regulation and Government Revenue Table 4: A Cost–Benefit Analysis of Diverting CBE Lending

|Asset<br>Million Birr|Average Rate|Upper Bound|Lower Bound|
|---|---|---|---|
|Panel A: Curren<br><br>|t Partial Asset Allo<br>|cation||
|(1) Government bonds<br>2.414,03|1.5%|||
|(2) Private sector lending<br>20.365,69|12%|7.5%|16.25%|
|(3) NBE Bills<br>0|3%|||
|(4) Partial revenue|2.480,09|1.563,63|3.345,63|
|Panel B: Counterfac<br><br>|tual Partial Asset <br>|Allocation||
|(5) Government bonds<br>2.414,03|1.5%|||
|(6) Private sector lending<br>6.388,94|12%|7.5%|16.25%|
|(7) NBE Bills<br>13.976,75|3%|||
|(8) Partial revenue|1.222,18|934,67|1.493,71|
|Panel C: Co<br>|unterfactual Reven<br>|ue<br>||
|(9) Revenue|_−_1.257,91|_−_628,96|_−_1.851,92|

_Note:_ This table reports the cost–benefit analysis of diverting private sector lending from the CBE to the purchase of NBE bills. For simplicity, in all panels, we only report a partial balance sheet, which includes only three assets: private sector lending, NBE bills, and Treasury bills. Panel A reports the current composition of the balance sheet, Panel B shows the balance sheet under the assumption that CBE needs to buy all NBE bills using private sector lending resources, and Panel C reports the counterfactual revenue of this exercise.

government bonds for 2.414,029 and private sector lending for 20.365,691 million birr, with the remaining funds dispersed in other assets. In this subsection, we propose a simple counterfactual exercise, in which we calculate the revenue of the CBE when some of its private lending assets are redirected to the purchase of the 13.976,75 million birr of NBE bills imposed on private banks.

From official documentation (Ministry of Finance and Economic Development 2012; National Bank of Ethiopia 2012), we know the average interest rate on all assets reported in the third column of Table 4. Therefore, we can simulate this alternative scenario. In so doing, we are ignoring the cost of liquidating private lending and considering the cost of this measure to be negligible for single firms and aggregate economy. In Panel A, we report the current partial asset allocation between government bonds, private sector lending, and NBE bills. Regarding the returns of this assets, while government bonds and NBE bills present a fixed return of 1.5% and 3% respectively, official authorities report the interest rate on private sector lending for its average, for both upper and lower bounds. For this reason, we can also propose in this case a range of estimates for the CBE revenue. In order to be rigorous, we define the calculated revenue as “partial”, because we are focusing on a subset of CBE assets. In Panel B, we present the experiment in which we fund the NBE bill amount with private sector lending and recalculate the partial revenue. Panel C presents the counterfactual 16
the market or a paved road (that possibly leads to the market), people no longer have the ability to generate as much income, leading to less consumption by the household.

In a final approach it was thought that if the proper distance could be calculated between the households and the markets a better understanding of the relationship between consumption and remoteness could be understood. The survey did provide GPS coordinates for the households, the center of the community, and the common consumer market. However, issues arose when dealing with the GPS coordinates, not all of the coordinates were recorded in the same format or properly. This required cleaning the household coordinates, unfortunately, the time consuming manner of this did not allow for the same to be done for the village or the market coordinates (see Annex 3 for assumptions). As a second best approach, the households were connected to the nearest large city (over 2,000 inhabitants, resulting in 68 cities across the country) and the Euclidean distance was calculated.<sup>17</sup> The reasoning for this approach was that though there may be other markets that are closer, a large city will have all of those markets and will allow consumers and farmers to purchase all of their needs at once.

The distance of each household to the city was then compared to the consumption of the household, providing a more accurate and precise measurement than before. To give an overall picture the households were broken down by quintiles of distance to the city/market, Figure 3. For the overall graph the downward trend is again apparent (for more details see Annex 4). The most important point to note is the large drop in consumption between the first quintile and the second quintile. After 4.5 km from a large city/market the consumption of the household drops greatly, and more than it changes between any other two quintiles after that. This finding is consistent with with Stifel and Minten (2008).

> 17 Estimates of the distance to the markets and the road were given in the community survey, but as noted above the inconsistencies made these measurements unreliable.

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Table 7: Impact on mechanism outcomes by marital status at baseline

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||SRH<br>knowledge<br>(15 yo+)|Gender<br>attitudes|Aspirations|SES|
|T1 (safe spaces)|0.431***|0.246**|-0.174|-0.117|
||(0.162)|(0.122)|(0.110)|(0.157)|
|T1 x Married|-0.211|-0.064|-0.029|0.157|
||(0.169)|(0.126)|(0.188)|(0.141)|
|T1 + Leaders|0.290*|0.144|-0.166|0.416***|
||(0.171)|(0.136)|(0.139)|(0.156)|
|T1 + Leaders x Married|-0.139|-0.104|0.004|-0.283|
||(0.158)|(0.173)|(0.158)|(0.180)|
|T2 (safe spaces + IGA)|0.191|0.203*|0.077|0.425***|
||(0.166)|(0.119)|(0.128)|(0.120)|
|T2 x Married|0.184|-0.065|0.067|-0.280**|
||(0.127)|(0.148)|(0.111)|(0.113)|
|T2 + Leaders|0.154|0.126|0.055|0.361**|
||(0.175)|(0.137)|(0.121)|(0.147)|
|T2 + Leaders x Married|0.049|-0.080|-0.146|-0.096|
||(0.150)|(0.161)|(0.136)|(0.148)|
|T3 (safe spaces + husbands’ clubs)|0.320**|0.287***|0.051|0.296**|
||(0.154)|(0.086)|(0.128)|(0.144)|
|T3 x Married|0.050|-0.107|-0.073|-0.175|
||(0.114)|(0.093)|(0.113)|(0.132)|
|T3 + Leaders|0.298*|0.347***|-0.107|0.497***|
||(0.156)|(0.096)|(0.165)|(0.132)|
|T3 + Leaders x Married|-0.125|-0.245*|0.167|-0.067|
||(0.190)|(0.125)|(0.175)|(0.152)|
|T4 (control spillover)|0.044|0.139*|-0.045|0.176|
||(0.147)|(0.080)|(0.108)|(0.120)|
|T4 (control spillover) x Married|-0.061|-0.163|0.014|-0.099|
||(0.119)|(0.116)|(0.109)|(0.111)|
|T1 + T1 x Mar = 0|.107|.0755|.29|.718|
|(T1 + L) + (T1 + L) x Mar = 0|.19|.761|.265|.318|
|T2 + T2 x Mar = 0|.000851|.143|.215|.133|
|(T2 + L) + (T2 + L) x Mar = 0|.0722|.705|.427|.0139|
|T3 + T3 x Mar = 0|.00187|.0481|.826|.184|
|(T3 + L) + (T3 + L) x Mar = 0|.363|.246|.752|.000904|
|N|2,949|2,999|2,990|2,999|
|Control mean|0|0|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; mar = married; yo = years old; SRH = sexual and reproductive health. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group, for the unmarried then the married sample. All regressions control for the baseline value of the outcome variable, an indicator for being married at baseline, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

26
- Training Workshops for Staff and Country Representatives, sustained through continuing guidance and technical assistance;<sup>14</sup>

- Cost platforms or templates, together with training on those platforms as needed, to assist users in the appropriate application of VFM methods;

- Greater dissemination and promotion of studies that use CEA and CBA methods so that evaluators and authorities become accustomed to their methods, findings, and implications;

- Codification (or even more pointed guidance) of a common set of methods that is sufficiently flexible to allow for reasonable alternatives that can be specified with a discussion of a basis for deviations from the “standard” and the probable consequences. A standard set of defaults could also be used where the user is not sure of the best assumptions;

- Bolstered vigilance on the quality of efficiency calculations in Project Appraisal Documents and Implementation Completion Reports;

- Expectations of sensitivity analysis on deviations from standard methods and assumptions; and

- Consensus on standard outcome measures and specifications for their construction. These may come from measures derived from the Demographic and Health Surveys (DHS), and the Living Standards Measurement Survey (LSMS), for example. Relatedly, the field needs policy-relevant standard numerators for CEA in different sectors, as has been done with the disability adjusted life year (DALY) in health, for example, or months of additional schooling in the Education sector.

If some evaluators opine that demand for VFM from policymakers may not appear to be terribly strong, it is useful to recall that initially, impact evaluations were not in high demand by policymakers either. Instead, the demand for impact evaluation largely came from development funding agencies and actors interested in basic research. The former have become increasingly interested in VFM in more recent times, in part as a result of domestic resource constraints leading to increased scrutiny of the value achieved from the aid budget. As for the academic world, the demand for VFM as exemplified through its coverage in academic journals is still decidedly less strong than for impact evaluations. There is reason to believe that this could change. One of the appeals of VFM is the ability to compare multiple intervention options. As there are more impact evaluations generated around a theme, efficacy comparisons become more feasible—through systematic reviews and other vehicles. Subsequently, the academic conversation around impact evaluations will likely turn to two frontiers: generalizability and efficiency.

> 14 For example, a training program sponsored by the Center for Benefit Cost Study in Education, funded by the Institute of Education Science in the U.S. Department of Education includes five days of intensive, hands-on training in cost analysis with technical training in shadow pricing and sensitivity analysis, all based around an open-source platform for performing cost analysis called CostOut (Teachers College, Columbia University 2016).

26
WPS4274

# **Are Labor Regulations Driving Computer Usage in India’s Retail Stores?**

Mohammad Amin* World Bank

**Abstract** A recent survey of 1,948 retail stores in India conducted by the World Bank’s Enterprise Surveys shows that 19 percent of the stores use computers for their business. In some states like Kerala, computer usage is as high as 40 percent. Using this data we find labor regulation as an important determinant of computer usage. The estimates suggest that when faced with burdensome labor regulations, the probability of using a computer rises by over 36 percentage points for an average store. These findings formally confirm a commonly held but untested view that labor regulation may be responsible for the spread of labor saving modern technology.

Keywords: Labor regulations, Technology, India, Retail JEL: J080, K31, L81, O33, O53 World Bank Policy Research Working Paper 4274, July 2007 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ ________________________

*Enterprise Analysis Unit, World Bank, Washington DC, 20433. Email: <u>mamin@worldbank.org. Phone: (202) 473-1915.</u>
of our variables of interest (e.g. inequality) unless the changes in the real exchange rate drive the model into one of the corner solutions described above (e.g. generate a result in which no firms produce). The starting values of the parameters are chosen to avoid corner solutions for the observed range of real exchange rate values.

The x-axis contains the (exponentially-distributed) phi values. Profits are measured along the vertical axis. Some firms (with low phi values) have profits that are insufficient to cover their fixed costs and they produce zero. Firms that can cover their fixed costs produce positive amounts and fall into two groups. The first group (in the middle of Figure 5a) have higher profits in the domestic market, and therefore produce only for the domestic market. The remaining firms (with high phi values) produce only for the export market.

The second step is to simulate the model using Mexican data. The driving variable is the real exchange rate, defined earlier as the usual Salter-Swan variable of the price of tradable goods relative to the price of non-traded goods. Figure 5b shows the effect on the equilibrium of an increase in the real exchange rate. To calibrate the real exchange rate values, we use Mexico’s price data. Mexico’s INEGI posts monthly consumer price data on-line. The consumer price index data are disaggregated into eight categories. We classify these eight into “tradeable” and “nontradeable” groups. The tradable industries include food, clothing, and furniture. The nontradeable industries include housing, health, transport, education, and other services. We then take the unweighted arithmetic average of these indices for each group. Figure 6 depicts the movements of the two price series over time.

To simulate the effects of the changes in the real exchange rate in the simple heterogeneous-firms Salter-Swan model, we normalize the real exchange rate value in the first period (January 1988) to match the variable _P_ in the model. Subsequent changes in the real exchange rate are then applied to the model, which is then solved for new equilibrium values of the endogenous variables shown in Table 2.

Of the list of endogenous variables listed in Table 2, we are particularly interested in comparing the predictions for the “within” inequality. Since all workers are identical, we define within inequality in the same conceptual way that it is defined earlier in the paper: the dispersion of earnings that remains after the 14
# **3. The poverty and inequality political enlightenment**

To assess the increasing relevance of inequality and poverty among policy makers, we rely on the “dictionary method,” which examines how the frequency of keywords related to these two subjects evolved over time.<sup>3</sup> The choice of keywords for each topic (poverty and inequality) follows Esser and Williams (2013). In the case of poverty, we analyze prevalence of keywords “poverty” and “poor,” while in the case of inequality, we analyze the prevalence of “inequality,” “inequity,” “equity,” and “equality”. Later in the paper we also show the evolution of some related keywords and the results using possible synonyms as robustness checks. We begin by identifying the percentage of speeches that mention one of the keywords at least once. To smooth noisy fluctuations that arise from analyzing yearly data, we pool the speeches by decade. Figure 2 shows the percentage of presidential speeches that have mentioned the keywords each decade over the 1830-2016 period.

**Figure 2. Prevalence of “poverty” and “inequality” in presidential speeches**

_(% of speeches that contain each keyword by decade)_ <!-- Start of picture text --> (a) Poverty (b) Inequality<br>100 100<br>75 75<br>50 50<br>25 25<br>0 0<br>% of speeches % of speeches<br>1830 1850 1870 1890 1910 1930 1950 1970 1990 2010 1830 1850 1870 1890 1910 1930 1950 1970 1990 2010<br><!-- End of picture text --> Note: This Figure shows the percentage of presidential speeches on each decade that contained, at least once, the word “poverty” or “poor” for graph (a), and the word “inequality,” “inequity,” “equity” or “equality” for graph (b). The percentage was calculated by pooling the presidential speeches of 10 Latin American countries by decade. Sse Appendix A for more details about the data.

speeches). The share of words increases to 72% for the 20<sup>th</sup> century (which accounts for 59% of all speeches), and falls to 17.6% for the speeches delivered between 2001 and 2016.

3 These frequencies, provide an indication of the extent to which each text belongs to, or discusses, the subject of interest. For instance, a speech related to the budget would be expected to contain words such as “budget” or “deficit” at higher rates than would a speech discussing advances in medical science. According to this intuition, if the prevalence of a topic in political discourse increases over time, one would expect to see a corresponding increase in the usage of words related to that topic.

5
# **I.  Introduction**

The People’s Republic of China (PRC) has grown from a low-income to a middle-income economy in one generation, benefiting from the 9.5% of gross domestic product (GDP) growth rate during 1978–2005, up from 4% during 1950–1978 (Brandt and Rawski 2008). Although <mark>Young (2003</mark> **<mark>)</mark>** described the PRC’s growth as ―extensive‖ (i.e., the main contribution to growth was the addition of capital and labor), he found that productivity growth increased from 0.5% to 3.8% per year after the reform, and that improvements in productivity accounted for 40% of overall GDP growth from 1978 to 2005. The PRC, in purchasing power parity terms, is converging with Japan (from 37.5% of Japan’s GDP in 1978, to 219% in 2004), and the United States (US) (from 3.2% of US per capita GDP in 1978 to 15.7% in 2005). Moreover, the PRC’s growth was accompanied by dramatic reduction in absolute poverty, which fell from 40.6% of the rural population in 1980 to 4.7% in 2001 <mark>(Ravallion and Chen 2007)</mark> **.** As the PRC’s economy grew, sectoral and spatial transformations took place. Agricultural reforms enabled a mass exodus from agriculture to nonagriculture activities. Aggregate employment in agriculture fell from 69% in 1978 to 32% in 2004. With its increasing integration with the world economy, trade increased from 10% of GDP in 1978 to 22.9% in 1985, to 38.7% in 1995, and to 63.9% in 2005 (Brandt and Rawski 2008). The PRC attracted 40% of all foreign direct investments into developing countries between 1980 and 1995, and took in $70 billion of foreign direct investments between 2004 and 2006 (Fan et al. 2009). The Organization for Economic Cooperation and Development (OECD) estimated that the private sector accounted for 59.2% of the PRC’s GDP in 2003. Accompanying the shift out of agriculture into manufacturing and services was rapid urbanization, rising industrial agglomerations, and increased migration (Cai, Park, and Zhao 2008; Fan 2005; World Bank 2008; Huang and Magnoli 2008). With these structural transformations, the PRC has not only seamlessly integrated into the global markets, but also experienced increasingly integrated domestic goods, labor, and capital markets (Xu 2000, Dong and Xu 2009).

Relying on several excellent surveys contained in Brandt and Rawski (2008) and some recent works that are directly relevant to our topic, we aim to give the reader a succinct summary of key research findings about domestic and international market integration in the PRC. We interpret market integration broadly—the process in which any resource that increasingly flows to its best uses in a larger market is viewed as market integration. We begin by reviewing the evolution of the PRC’s global integration through special economic zones (SEZs) and accession to the World Trade Organization (WTO), and then move on to examine domestic integration in the regional, product, 2
_The Creative Wealth of Nations_ <!-- Start of picture text -->
|| Figure 3 || Printed Music World Exports in 2012 - Trade Value in 1000 USD (Millions of Dollars)<br>Printed Music - Global Exports, 2012<br>$3.0<br>$28.4<br>$31.0<br>Germany<br>United States<br>United Kingdom<br>Other High Income<br>Developing<br>$25.1<br>$23.8<br>Source : WITS Trade Data (UN Comtrade)<br>Note: Nomenclature: H4 | HS 2012 (Selected classification) | Code 4904 -- Music, printed or in manuscript, whether or not bound or<br>illustrated. World exports, nominal US dollars, millions. Sample size ( Reporters): Developing Countries : 22; High Income<br><!-- End of picture text -->

# _Other Aspects of Arts-Related Innovation_

The system of Western music notation cannot capture all the global music traditions. As such, artists in their respective countries could develop their own methods of notation and teach them to the rest of the world. This could be done via such channels as YouTube or other mediums. But in addition to enabling the trade of music scores, arts education could also extend to providing instruction in areas like sound engineering — in a number of developing countries such combinations of arts and technology are rare. These innovations are not new. But they have yet to catch on.

As the Nashville example shows, artistic activities can induce a culture of learning, innovation and entrepreneurship. Indeed, on the features of social dynamics, human capital and productivity, one is tempted to connect such ingenuity to Adam Smith; it is a key pillar to the teeming wealth of nations. For specific policy ideas, recall the section on clusters in the Nashville case study and then consider Part II (on strategic directions for the WBG). The case on the Afghanistan National Institute of Music provides a feasible framework for what the Bank could do with respect to arts education in development. For now, consider the following example on how arts education fuels the economy.

<mark>21</mark>
**28**

# .7: Price incentives Sorghum

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**vs** **_Price_** Guorantee

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# 8: Production Sorghum

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