rafmacalaba/datause-encoder-data huggingface dataset:
**-2-**

4. This paper examines the knportance of these rural-urban growth linkages In Indla. It _kiims_ to assess the imiact of agricultural growth on natlonal demand for nonfarm products. In additlon, because growing land scarcity ralses concerns about prospects for rural labor absorptlon, the paper highlights the Impact of agricultural growth on rural nonfarm Incomes and employment.

S. Four major sections address these objectives. The fIrst provides a descriptive overvlew of nonfarm activity In Indla. It examines the Importance, compositlon and location of nonfarm activity as well as general trends over the past 30 years. The second explores the relationship between agriculture and changes In nonfarm activity. After reviewing previous growth linkage studles, It compares nonfarm activity In highand low-productivity agricultural states cross-sectlonally and over time. The third sectlrt estimates the volume of rural nonfarm Income and employment generated by agricultural growth, while the fourth projects patterns of demand for nonfarm goods emanating from alternative agricultural growth scenarlos.

# IL DESCRPTIVE PROFLE OF **NONFAM** ACTrvrrY

# A. kmportance

6. Nonfarm enterprises account for one-th' d of all full-tUne employment In India (Table 1). In large citles, nonagricultural pursuits occupy 95 percent of the workforce, In rural towns, 75 percent, and In rural areas, 20 percent. These proportlons have remained roughly corstant since 1961.

7. Part-time and seasonal employment frequently increase the knportance of nonfarm activity. lnaeed, some nonfarm undertakings In Indla are highly seasonal; 2050 percent of rural manufacturlng enterprlses ooerate only part-time or seasonally (National Sample Survey, 1969). But In the aggregate, measured secondary employment appoars very small. According to the 1981 population census, only 2.4 percent of
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Porter, M. E., & van der Linde, C. (1995). Toward a new conception of the environmentcompetitiveness relationship. _The Journal of Economic Perspectives_ , 9(4), 97-118. <u>http://www.jstor.org/stable/2138392</u> Przychodzen, W., Gómez-Bezares, F., & Przychodzen, J. (2018). Green information technologies practices and financial performance – The empirical evidence from German publicly traded companies. _Journal of Cleaner Production_ , 201, 570-579. <u>https://doi.org/10.1016/j.jclepro.2018.08.081</u> Qi, J., Tang, X., & Xi, X. (2021). The Size Distribution of Firms and Industrial Water Pollution: A Quantitative Analysis of China. _American Economic Journal: Macroeconomics_ , _13_ (1), 151-183. https://doi.org/10.1257/mac.20180227 Rakhshan, K., & Friess, W. A. (2017). Effectiveness and viability of residential building energy retrofits in Dubai. _Journal of Building Engineering_ , 13, 116-126. <u>https://doi.org/https://doi.org/10.1016/j.jobe.2017.07.010</u> Rath, B. N., Akram, V., Bal, D. P., & Mahalik, M. K. (2019). Do fossil fuel and renewable energy consumption affect total factor productivity growth? Evidence from cross-country data with policy insights. _Energy Policy_ , _127_ , 186-199. <u>https://doi.org/10.1016/j.enpol.2018.12.014</u> Ren, H., Tibbs, M., McLauchlan, C., Ma, Z., & Harrington, L. (2021). Refrigerator cost trap for low-income households: Developments in measurement and verification of appliance replacements. _Energy for Sustainable Development_ , 60, 1-14. <u>https://doi.org/https://doi.org/10.1016/j.esd.2020.11.001</u> Roberts, M. B., Bruce, A., & MacGill, I. (2019). Impact of shared battery energy storage systems on photovoltaic self-consumption and electricity bills in apartment buildings. _Applied Energy_ , 245, 78-95. https://doi.org/https://doi.org/10.1016/j.apenergy.2019.04.001 52
Table A28: Robustness of impact on SES index to differential attrition

|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|---|
|||Main|Trimmin|g treated|Im|puting cont|rol|IPW|
|||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1|(Safe Spaces)|-0.02|0.03|-0.06|-0.03|-0.03|-0.02|-0.02|
|||(0.11)|(0.11)|(0.11)|(0.11)|(0.11)|(0.11)|(0.12)|
|T1|+Leaders|0.25**|0.49***|0.08|0.19*|0.22*|0.24**|0.25**|
|||(0.11)|(0.11)|(0.10)|(0.11)|(0.11)|(0.11)|(0.11)|
|T2|(Safe Spaces + IGA)|0.25***<br>(0.09)|0.40***<br>(0.08)|0.15*<br>(0.09)|0.21**<br>(0.09)|0.23**<br>(0.09)|0.24***<br>(0.09)|0.26***<br>(0.09)|
|T2|+Leaders|0.30***<br>(0.10)|0.53***<br>(0.09)|0.13<br>(0.10)|0.24**<br>(0.10)|0.26***<br>(0.10)|0.28***<br>(0.10)|0.30***<br>(0.10)|
|T3|(Safe Spaces + Husbands Clubs)|0.19**<br>(0.09)|0.38***<br>(0.08)|0.03<br>(0.09)|0.11<br>(0.09)|0.15*<br>(0.09)|0.18**<br>(0.09)|0.20**<br>(0.09)|
|T3|+Leaders|0.46***<br>(0.10)|0.56***<br>(0.10)|0.40***<br>(0.11)|0.44***<br>(0.10)|0.45***<br>(0.10)|0.45***<br>(0.10)|0.46***<br>(0.10)|
|T4|(control spillover)|0.12|0.13|0.12|0.12|0.12|0.12|0.12|
|||(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|(0.09)|
|N||3003|3002|3002|3004|3004|3004|3003|
|Con|trol Mean|0|0|0|.002|.001|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

60
2004 to 29 jobs in 2011, the result of declines in both the direct and indirect job intensity of exports.<sup>12</sup> While this is a sizable reduction, South Africa maintained a lower rate of job reduction per unit of gross exports than most other comparator countries. But in spite of this less-dramatic reduction, South Africa remains one of the middle-income countries in the sample with the lowest number of jobs associated with $1 million of exports—either directly or indirectly. That is explained partly by export composition, where the mining and energy sector that dominates South Africa’s exports tends to employ fewer workers per unit of gross exports than other sectors, and partly by the lower job intensity of all sectors but mining and energy (discussed below). We control for this effect in the following section by comparing similar sectors across countries.

On the other hand, South Africa has a higher share of jobs contained in exports relative to total employment than any other comparator country except Thailand (Figure 5 and Figure A2). As a share of total employment, 8% of the workforce is employed directly by exports, and another 15% of the workforce indirectly. Yet this ratio is in decline—in 2004 more than 1 in 4 jobs were directly or indirectly supported by exports, but by 2011 this ratio was just above 1 in 5 jobs.<sup>13</sup>

**Figure 4: Total jobs per $1 million of gross exports 2004-2011**

<!-- Start of picture text --> 1995 2000 2005 2010<br>Year<br>ZAF BRA RUS COL<br>PER THA TUR<br>200<br>150<br>100<br>50<br>Jobs (per $1 mil of gross exports)<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Source: Authors’ elaboration based on LACEX.<br><!-- End of picture text --> 12 The direct number of jobs per $1 million of gross exports fell from 18.3 to 9.1 between 2004 and 2011 and the indirect number of jobs per $1 million of gross exports also fell from 30.5 to 18.6.

13 The direct number of jobs contained in exports relative to total employment fell from 9.9% to 7.9% between 2001 and 2011, the indirect number of jobs contained in exports relative to total employment fell from 16.5% to 15.0%, and the total number of jobs contained in exports relative to total employment fell from 26.4% to 22.9%.

12
and Culture may have their licenses revoked.<sup>13</sup> The fee, compared to Ethiopia's per capita GDP is high- GDP per capita being $122.1 in 2001.'4 The purpose of all such laws is to define a framework for the sharing of information. Sometimes just the act of adopting a law can signify a reduction in the restrictions imposed on information flow. Sometimes the adoption of a law can make people more aware of the value of information (Chongkittavom, 2002). Such laws are one important element in the whole institutional environment affecting information flow.

Adopting a FOI is clearly not enough to ensure that it is effective. Government agencies must be required to publish information and there must be some implementating mechanism for the FOI. For example, in some countries a central commission is charged with ensuring that information gets out to the public as in the case of the Information Commission in Ireland, the Data Protection Inspectorate in Estonia and the Office of the Official Information Board in Thailand,<sup>15</sup> while in Georgia, Bulgaria and Finland this is not the case. Countries vary greatly in the time it takes to satisfy requests for information. In Estonia, Hong Kong, China, and Hungary, the laws specify, that responses to requests must be made before or by the 15th day. In South Africa, the limit specified is 30 days and in Thailand the limit is not specified though it must be within a "reasonable period".

In case requests for information are denied, in most cases, the nature of the appeals process is also specified. Generally, the courts are responsible for oversight: in Canada the final appeal goes to the Federal court. In Ireland there is a review by an Information Commissioner

> 3<sup>3</sup> www.cpj .org/attacksO0/africaOO/Ethiopia.html and www.cpj .org/protests/Olltrs/Ethiopia3I OctOlpl.html '<sup>4</sup> World Bank data.

> 15 Various sources: I)Information Commissioner in Ireland - Freedom of Information Act 1997, Part IV, article 33; obtained from: www.humanrightsinitiative.org 2) Data Protection Inspectorate in Estonia - Public Information Act, RTI 2000, 92, 597, Chapter VI, article 44; obtained from: L1NET 3) Office of the Official Information Board - Official Information Act, B.E. 2540 1997, Section 6; obtained from: www.humanrightsinitiative.org);

13
with transition to a market economy. The group of formerly communist countries presents substantial variation, ranging from the low income economies of Central Asia, to high income Central European countries, which, as members of the EU, tend to have a fully developed market system.

The objective of this study is to shed light on the consequences of corruption for economic performance. Unlike similar _country level_ studies using broad definitions of corruption and institutional quality combined with aggregate measures of economic performance, investigation of the effects of bribery on _firm level_ productivity will allow to be more precise regarding the incentives of economic agents to engage in corrupt behaviour and the consequences this has for productive efficiency. Variables for firm level bribery and productivity can be obtained from the BEEPS database, which, in addition to information on the occurrence of bribing and other aspects of firm operation and performance, allows estimating a measure of total factor productivity (TFP) at the enterprise level.

The need to recur to bribery is often linked to the power of government officials to impose and enforce regulatory requirements on individuals and firms and to exact bribes in the process (see, for example, Djankov et al. 2002). In order to account for this possibility, it is necessary to identify some measure of the power that officials have over firms as enforcers of regulatory requirements. The BEEPS survey offers such a measure at the firm level. It refers to the time that enterprise managers are required to spend complying with government regulations, amounting to a _time tax_ imposed on firms. This may be interpreted as an opportunity cost borne by firms, which, in isolation or in combination with the bribe tax, potentially constitutes a drag on enterprise performance. The availability of a firm level measure for the time spent dealing with bureaucracy offers the opportunity to perform a _direct_ test of the so-called “efficient grease” hypothesis, which is explicitly defined in the literature in terms of bribery helping reduce the _time_ required for some interaction between an economic agent and the state, as in Lui’s (1985) queuing model.<sup>1</sup>

> 1 Use of the time tax as a specific outcome of the institutional inefficiencies linked with corruption distinguishes our paper from other studies, such as Méon and Sekkat (2005) and Méon and Weill (2008). These authors, for lack of more precise country level data explicitly reflecting the time involved in interactions with the state, use generic country level proxies of “governance” to test whether corruption does indeed act as efficient grease.

4
# **1. Introduction**

Recent work by Besedes and Prusa (2007), Brenton and Newfarmer (2007) and AmurgoPacheco and Peirola (2007) shows that export growth in developing countries has been driven predominantly by the intensive rather than the extensive margin of trade. That is, growth comes primarily from existing trade flows rather than from new trade flows. Brenton, Pierola and von Uexkull (2009) show that poorly performing developing countries are not inferior to stronger performing countries with regard to the introduction of new trade flows but experience much lower rates of survival. It is these low survival rates that undermine the expansion of export flows. In their seminal study, Besedes and Prusa (2006b) conclude that for developing countries the key element in achieving higher aggregate export growth is higher survival rates for existing trade flows.

From a practical policy point of view it is important not only to understand the factors driving entry into exporting but also to understand the process by which exports are sustained and export flows grow in volume. Are there factors that are amenable to public policy interventions that impact the chances of survival of a particular exporter or export flow? Do these factors impinge particularly heavily on firms in low-income countries?

This paper makes an attempt at answering these questions. We build on the initial study of the determinants of export flow duration by Besedes and Prusa (2006b)<sup>1</sup> , first, by investigating a novel data set of more detailed product level trade flows comprising a large number of both developing and developed country imports and exports that includes low-income as well as middle and high-income exporters. Second, we pursue a more rigorous empirical approach that examines the reliability of the continuous time proportional hazards model that has to date been unquestioningly used to assess the determinants of the survival rates of export flows (Besedes and Prusa, 2006b; Blyde, 2008). This model is mis-specified in the presence of unobserved heterogeneity, a strong possibility with export data, for example, from the unobserved quality of the management of the underlying exporting firms.

> 1 Blyde (2008) provides another application of the basic approach used by Besedes and Prusa (2006b).

2
13 evidence on Nigerian unofficial exports of manufactured goods to neighboring countries is fragmentary, preventing any definite conclusion. But unlike the early 1980s, Nigerian goods are now competing with other imports in the West Afiican markets. Despite a sharp fail in cocoa prices in the world market, Ghana has more than doubled its exports in the past seven years, with gold exports replacing cocoa as the number one export. Today, at least 20 percent of Ghana's export earnings come from products other than cocoa, gold, and timber, compared with 8 percent a decade ago. Tanzania shows the largest documented rise in non-traditional exports; its unrecorded exports are estimated at about $400-500 million a year. Cote dvloire, Kenya, and Senegal have export bases that are among the most diversified in Africa, but changes during adjustment have been minor and show no persistent trend. Burundi is the only country among the seven to show continuing heavy reliance on coffee; its present diversification efforts, have been negligible.

<!-- Start of picture text --> Cbart VE:L Food Production Per Capita Index average 19794r1100)<br>CPrtm4jnMsperid<br>120 - *A4UIcUtpiW<br>100 __<br>60 -- 1<br>40<br>20.<br>0<br>BEmmd Cstcdlwoiz hMM Kmya Nwip. sait Tmisa<br><!-- End of picture text --> <!-- Start of picture text --> C' 'rt VIklE Export Growth Rate (Volumne)<br>L0<br>-S<br>-10-H<br>-45<br>-20<br>-25<br>-30<br>Bun6 Caft dIIvimze m gri cw Md<br><!-- End of picture text -->
<!-- Start of picture text --> Percentage point<br>0.30<br>~ecercrrttt<br>0.25 --7<br>-7 - -<br>0.20 / 7 ad<br>0.15 v7<br>/<br>0.10 y<br>0.05<br>0) me- -— -_—— — — = = a ee ee ee ee<br>123 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Percentage point<br>0.20<br>0.18 ~ee ee ccc cccte<br>-- 7-7<br>0.16 c<br>0.14 ¢<br>1<br>O12 4<br>0.100.08. l<br>0.060.04 forme<br>0.02 sek<br>00 —_—_—_ -— — —_<br>123 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br><!-- End of picture text --> <!-- Start of picture text --> Percentage point<br>0.00 ——$— —$—<br>~ ~<br>0.02 ~s2o LL ee L e e e ee ee<br>004 XN<br>-0.06 \ N<br>-0.08 ‘<br>\<br>0.10 XN x<br>0.12 ~.<br>014 wee eee eee eee<br>0.16<br>12 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br><!-- End of picture text --> <!-- Start of picture text --> Percentage point<br>0.02<br>.<br>0.00 —_—_——$—_—————<br>“ee een nee eee ---<br>XN —-~---<br>-0.02 XN<br>0.04 ‘<br>-0.06, \ 7 .<br>-0.080.10 \ ‘ se ~2 weeee Ke<br>0.12<br>12 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br><!-- End of picture text -->
**Table 17. Interaction between Oil Prices and Political Stability for Fuel Exporters**

|Dependent variable:|Change in fiscal<br>balance/GDP (%)|Change in total<br>revenues/GDP (%)|Change in total<br>expenditures/GDP<br>(%)|
|---|---|---|---|
|Change in ln (real $ oil price)|0.364<br>(0.04)|11.65<br>(1.58)|7.04<br>(0.85)|
|Political stability|-0.0180|0.0296|0.0384|
|(0 to 100)|(-0.37)|(0.69)|(0.71)|
|Political stability *|0.265|-0.016|-0.195|
|change in ln (real $ oil price)|(1.39)|(-0.12)|(-1.18)|
|Time trend|-0.0.0771**|-0.0436|0.0395|
|(=0 in 2000)|(-2.06)|(-1.31)|(0.96)|
|n|328|336|328|
|No. of countries|33|33|33|
|RMSE|5.05|4.50|4.36|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel >25 percent of total exports. Country fixed effects are included in the regression. RMSE = root mean square error, or sample standard deviation.

# **8. Conclusions**

The evidence does not support the notion that natural resource wealth promotes fiscal indiscipline in general. The permanent income theory suggests that the appropriate response to a resource windfall should depend on how long it lasts. A temporary windfall should be largely saved since the permanent increase in consumption will be small compared with the windfall. The less temporary the windfall is, the larger the proportion that it is appropriate to take in increased consumption.

30
Financial Regulation and Development In this analysis, we want to verify two features: 1) the overall number of branches increases, as the model predicts; 2) new branches are more “rural”. In this case, we focus on verifying the number of branches opened in unbranched towns, as a measure of expansion into rural areas. Because our map explores all geographical features, we can see how many branches are opened in previously unbanked areas and therefore we can rely on this as the most reliably measure of bank ruralization.

For this reason, we run two tests, collapsing our branch-level database to a panel at bank level with months and year. The first test is a typical difference in difference regression where the number of branches and the number of branches in previously unbranched areas, _vity_ , are regressed over bank and month-year fixed effects and the interaction between a policy dummy taking unit value after April 2011, the introduction of the 27% rule, and the big bank dummy variable.

With the second test, we run the same variables over a series of yearly dummies, _ιy_ , which describe the branch accumulation trend for small banks and an interaction of these with the big bank dummy, which describe the differential branch expansion of big banks. Table 2 reports that in line with our expectation, after the policy each big bank opened 10 branches more than the other banks, and they also accelerated rural branching, as big banks opened 2.5 additional branches over small banks in previously unbanked areas. Comparing these values with the mean dependent variable row, these magnitudes are substantial: branches grow for big banks 20% more than smaller banks and rural branches by almost 50%.

Figure 7 further explores this and shows the coefficients for big and small banks by describing their trends. The upper panel reports the evolution of branch installation; it is clear that up until 2012 the trends are indistinguishable, but they start to diverge after the introduction of the policy. Analogously, the lower panel shows a similar story for new branches opened in unbanked locations. Appendix F shows a test of the parallel trend hypothesis, showing that the differences between big and small banks were generally statistically different before the introduction of the policy, while after the introduction of the policy they became in line with the theoretical model.

# **2.2.3 Evidence from Annual Reports**

In this section, we present some supporting evidence concerning the non-negative effect of a shock to _s_ on profits and its positive effect on the number of employees, as a proxy for size. Because these aggregates are collected yearly by banks, we lack the rich within-year variation, which has been exploited in the previous sections. Also, the number of observations becomes a big limitation, because now we only face a panel with five observations at most for each bank. However, we present two tests. The first is a simple difference-in-difference regression, where we verify whether profits 25