rafmacalaba/data-use-mentions-extended huggingface dataset:
provide non-social subsidies, expanding farm size beyond optimal levels is a rational response by farmers signaling their disposition to offer bribes in exchange for subsidies (Bulte, Damania, and López, 2004). Land expansion is thus a vehicle to promote subsidies, and knowing that governments are prone to subsidize induces farmers to expand land area. That is, land expansion and subsidies are the cause and effect of each other.

In summary, this paper conceptually justifies and empirically tests three hypotheses: (i) that countries that, ceteris paribus, spend a greater share of rural public expenditures in private goods have lower agricultural per capita income than countries that spend more on rural public goods; (ii) that agriculture in these countries tends to be more dependent on the expansion of the land area rather than on intensification; and (iii) that rural poverty in these countries is worse.

To test these hypotheses we use a newly assembled data set for the period 1985–2000 for the rural sector for several countries in Latin America. These data consist of annual rural public expenditures collected using a consistent methodology across countries and over time. The high degree of detail of these data is sufficient to allow us to aggregate rural expenditures in public goods and in private goods.

The remainder of this paper is organized as follows. First we discuss general conceptual issues, in Section II. In Section III we present the empirical model. Section IV provides an overview of the data used. Section V presents the econometric results. Section VI concludes.

# **II. Conceptual Issues**

The classification of public expenditures into _public_ goods and _private_ goods arises from the conceptual view that governments’ primary responsibility is to, using use taxpayers’ money, supply public goods, including goods and services that are undersupplied as a consequence of missing markets or of market imperfections.<sup>8</sup> The importance of the public versus private good dichotomy is that while the public good tends to be complementary with private investment, the private good
||
|Fraction of female leaders|0.207|0.633***|1.044***|1.189***|1.101***||
||(0.154)|(0.206)|(0.232)|(0.293)|(0.288)||
|Observations|7,726|7,726|7,702|7,725|7,725||
|Outcome mean|0.865|0.814|0.553|0.724|0.752||
|**Panel C: Health worker**|**s and public prog**|**rams**|||||
||Janani|Janani|Kishori|Balika|||
||Suraksha Yojana|Shishu Suraksha|Shakti Yojana|Samriddhi Yojana|||
||(1)|(2)|(3)|(4)|||
|Fraction of female leaders|0.183*|0.121|0.542*|0.387|||
||(0.101)|(0.265)|(0.295)|(0.274)|||
|Observations|7,726|7,726|7,725|7,725|||
|Outcome mean|0.923|0.631|0.495|0.420|||
|State fxed efects|x|x|x|x|x|x|
|District characteristics|x|x|x|x|x|x|

_Notes_ : Data comes from the 2012-13 District-level Household and Facility Survey (DLHS) of India. The columns present 2SLS estimates specified in equations **??** and **??** . We define close elections as those in which victory margin is less that
**2.2 Fact 2: There is considerable heterogeneity in firm performance among these firms.**

There is also tremendous heterogeneity in firm outcomes. Table 2 uses the World Bank Enterprise Surveys to illustrate the variation among firms in three key outcomes that policy interventions often try to affect: employment, firm sales, and whether or not firms export. I break the data down into three size categories: fewer than 10 workers, 10-100 workers, and more than 100 workers.<sup>3</sup> Few of the Enterprise Surveys contain data on firms with less than 5 workers, so the first size category should be considered as encompassing small firms with 5 to 9 workers.

> 1 <u>http://www.gov.mu/portal/goc/cso/ei717/toc.htm</u>

> 2 <u>http://www.gov.mu/portal/goc/cso/ei869/toc.htm. Export-oriented firms are those with an export processing zone</u> certificate and those manufacturing goods for export.

> 3 All numbers reported are for the unweighted data, to illustrate the variation among the sample of firms.  This illustrates the variation in the actual data one might have in using the data that is currently regularly collected on African firms; using the survey weights also gives large heterogeneity.

4
internal instruments to address potential endogeneity challenges. The GMM estimation is executed in three steps: (1) specify the regression equation as a dynamic panel data model; (2) take first differences to remove unobserved time-invariant country-specific fixed effects; and (3) instrument the right-hand-side variables in the first-differenced equation using levels of the series, lagged two periods or more, while ensuring that the model is not overidentified (Blundell and Bond 1998). The model is specified as follows: Where ln[Y]i,t is the logarithm of GDP and Xi,th represents growth determinants, including trade and control variables for i countries over time t, and h is the number of growth determinants where h= 1, 2, ... k. The GMM estimates Equation (5) and identifies inherent dynamic panel bias associated with the correlation between explanatory variables and error terms (Anderson and Hsiao 1982; Nickell 1981).<sup>29</sup> Following standard practice for GMM estimation, this study tests for panel stationarity or presence of unit roots using the Levin and Lin (1992) (LL-test) and the Maddala and Wu (1999) (MW-test) tests. The longer the time series dimension of the panel dataset, the greater the need to test for panel stationarity. In addition, the validity of the results of the GMM estimator can be affected by overidentification of instruments and second-order autocorrelation of the residuals. The system GMM estimator performs uses lagged values of endogenous variables as instruments, the number of instruments can easily explode as the time-series dimension of the dataset becomes longer weakening the test for over-identifying restrictions due to instrument proliferation or overidentification (Roodman 2009). According to Windmeijer (2005), GMM becomes more efficient when the lag length is controlled to use fewer instruments in the estimation. The Sargan or Hansen tests are diagnostic tests designed to establish whether additional instruments associated with the system GMM estimator are valid.<sup>30</sup> Practically, when using the system GMM
believed that consolidation would occur spontaneously as the investment funds, acquiring vouchers from employees, would use them to gain sufficient blocs in privatized enterprises. The table below shows limited evidence of that happening in the past five years-mainly attributable to the increasing acquisitions by Russian holding companies and industrial groups after 1994.

Even in those enterprises in which management-held shares are nominally in the minority, managers have at their disposal a variety of techniques by which their _de facto_ control exceeds their nominal share ownership. Where "outsider" shareholders were fragmented, control was effectively ceded to managers. Employees were the largest category of shareholder in most enterprise put through the MPP, yet many of their shares are non-voting. In addition, managers have often taken to imposing (illegally) bans on the selling of shares to outsiders, placing limits on share ownership, and using implicit threats (dismissal, wage cuts, limitations on access to social assets) on workers who violate these rules. Faced with the prospect of takeovers, managers typically issued new shares (also often illegally) in order to dilute the power of outsiders.

Table **1:** Estimates of Ownership in Russian Enterprises, **1994-199913** (% of Share Capital)

||_1994_|_1995_|_1996_|_1997_|_19991_|
|---|---|---|---|---|---|
|_Employees_|44-56|39-49|26-43|23-40|36|
|_Managers_|9-17|10-17|12-18|12-36|15|
|_Domestic Legal_<br>_Entities_<sup>_2_</sup>|711|20-23|23-25|22-24|23|
|_Domestic_<br>_Individuals_|3-6|9-11|8-12|11-13|16|
|_Foreign Owners_|1-2|1-2|1-2|4-5|8|
|_StateAgencies_|12-20|11-13|9-11|7-14|3|

1. No survey undertaken in 1998.

_2._ Includes investment funds, all other firms (including
on fiscal accounts. In addition, a reform to improve the long-term sustainability of the pension system was approved in 2023. As the post-pandemic recovery went underway, Uruguay found itself as the country with the lowest sovereign spreads of the region, and improved credit ratings from the main credit agencies.

**Figure 3: Uruguay's fiscal outcomes**

a) Fiscal Balance of the non-monetary public sector b) Debt-to-GDP ratio of the non-monetary public sector In percent of GDP In percent of GDP <!-- Start of picture text --> 0%<br>-1%<br>-2%<br>-3%<br>-4%<br>-5%<br>-6%<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022<br><!-- End of picture text --> Source: World Bank database, based on BCU and MEF 3 Marandino and Oddone (2022).

> 4 See World Bank Group (2022) for more details.

7
is bad for the growth prospects of the poor but good for the rich.

This paper disaggregates the inequality-growth relationship in order to address two <mark>empirical questions: Can the effect of overall inequality on future income growth be attributed to IO? If indeed, is this particularly true for the poor, or does it</mark> concern households of all socio-economic classes?

We explore these questions using the IPUMS-USA database, as in van der Weide and Milanovic (2014), since it is the largest individual level database for the United States

> 2 See also Galor and Moav (2004) who have proposed that the impact of inequality on growth changes with the replacement of physical capital by human capital accumulation as a main source of growth along the process of development.

2
institutions including fintech and bigtech companies<sup>13</sup> ; leveling the playing field in interbank markets and moving large (quasi-) government deposits from selected commercial banks to the central bank; deepening capital markets and strengthening institutional investors as a viable alternative to banks’ financial services; and reducing or eliminating switching costs such as through open banking initiatives which democratize the use of bank customer data, and requiring effective disclosure of interest rates and fees by bank to facilitate product comparisons. Stronger competition will not only reduce monopoly rents, but also incentivize banks to innovate and become more efficient.

4. **Facilitate operational and scale efficiencies.** Banks in EMDEs are often relatively small and may operate under legacy business models which prevent them for reaping economies of scale and reducing operating costs which diminishes intermediation efficiency. Indeed, high operating costs are among the most important drivers of interest spreads and lending rates in EMDEs. Policy makers could consider encouraging bank consolidation (with due consideration for the effects on competition and creating “toobig-to-fail” issues), providing enabling policy frameworks for the expansion of financial services through agent networks, and the adoption of digital financial services such as mobile and internet banking as well as the automation of banking services and back-office processes such as risk management and credit scoring. The adoption of financial technologies will also reduce the need for maintaining a costly branch network and allow a reduction in staff costs.

5. **Strengthen bank regulation and supervision.** A strong supervisory approach, underpinned by prompt corrective actions, will prevent the build-up of risks in bank balance sheets and will promote good corporate governance and risk management, as well as compliance with supervisory standards. This increases confidence in the soundness of the banking system which improves intermediation efficiency through downward pressure on funding costs and risk premia.

> 13 For further policy considerations to reap the welfare benefits from financial technologies while mitigating the risks, see the World Bank – IMF Bali Fintech Agenda.

22
disparities may indicate systemic and longlasting challenges faced by the Roma population, which may require the need for tailored antipoverty policies different from those tackling poverty at one point in time, <mark>as efforts should prioritize preventing poverty from becoming entrenched over time.</mark> Differentiating between temporary and persistent poverty is crucial because economic policy should prioritize preventing long-lasting poverty (Biewen, 2014).

**Table 13.** Persistent AROP and material deprivation, Bulgaria, National average and Roma <u>population (2021)</u>

||**Bulgaria**|**Roma**|
|---|---|---|
|Persistent AROP|17.0|60.3|
|Persistent severe material and social deprivation (SMSD)|16.8|62.2|
|Persistent AROP + persistent SMSD|9.0|45.8|

Note: Percentage of individuals (using survey weights). The persistent at-risk-of-poverty indicator considers a person who was poor in the current year (2021) and in at least two of the three preceding years to be persistently poor. Source: Own estimates based on 2021 Bulgaria Panel SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

**Remarkably, Roma individuals constitute a significant portion of those experiencing persistent at-risk-of-poverty (AROP) and severe material and social deprivation (SMSD) in Bulgaria.** Specifically, 28.7 percent of those experiencing persistent AROP, 30.0 percent of those

> 56 Our national estimate of persistent at risk of poverty slightly differs from the estimate published online by Eurostat. Further, note that the persistent AROP rate may be higher than the AROP rate at a given year because persistent AROP is computed using the panel data.

33
may drive some of the results relating self-reported illness and income. Since the effect of the recall period declines with PCE, it influences estimates of the income elasticity of doctor visits, use of medicines, number of illnesses, and amount spent on health care. We cannot decompose the differential reporting of rich and poor into shares contributed by the competing hypotheses, but we do offer evidence of the substantial role played by recall effects. Thus, while differential sensitivity to severity across income groups is an important factor in self-reports, recall bias, awareness of chronic illness and the interaction between the two adds another set of considerations to explain the puzzling empirical regularity of the ―sicker rich.‖ Patterns of health care usage reported here are also problematic for measurements of inequality in access. Standard theories about inequality in health access or the links between health expenditures and per-capita expenditures are consistent with our monthly recall data but harder to reconcile with observed patterns once

> 13 See, for example, the interchange between Murray and Chen (1992,1993) and Riley (1993).

> 14 In the Delhi sample, we examined this possibility and also find substantial evidence of a link between information and the reporting of chronic illnesses (Das and Hammer, 2009).

18
100.0% 60.4% 7.8% 7.3% 22.8%<br>2000s 65.0% 35.0% 100.0% 60.5% 7.2% 7.7% 24.8%<br>Source: Staff Calculations based on World Bank, WDI<br>Contribution to Growth in Aggregate Demand / Supply by Demand Components. By Decades.<br>Period Real GDP Goods and Imports of Services Aggregate DemandSupply / ConsumptionPrivate ConsumptionGovernment Gross Capital Formation Goods and Exports of Services<br>1970s 1.8% 3.9% 5.8% -0.4% 1.0% 1.6% 2.2%<br>1980s 0.6% 0.4% 1.0% 2.2% -0.2% 0.0% -0.3%<br>1990s 1.5% -0.4% 0.9% 2.5% 0.0% -0.4% 0.1%<br>2000s 1.2% 1.4% 2.6% 0.7% 0.3% 0.3% 1.4%<br><!-- End of picture text --> _Note:_ Contributions of each variable to aggregate demand growth are computed multiplying period growth rates by period average share of the variable in total aggregate demand. _Source:_ Authors’ calculation, WDI data, World Bank **.**

# **4.4 Economic Sector Dynamics**

**Togo’s steep economic decline and subsequent stagnation since 1980 has reversed initial trends in structural transformation and has created a domestic economy increasingly dependent on primary activities, mainly at the expense of its tertiary sector (** Figure 12 and Table 2 **).** In fact, primary activities have doubled their share in value added GDP between 1980 and 2009 and represent
<!-- Start of picture text --> R e al G D P a nd p u b lic-s e ct or e co n om ic in fr as tr u c tu ra l i n ve st m e n t<br>(g ro ss) an d fi xe d c ap ita l s toc k (in d ic es, a ll m e asu r ed p e r ca p ita )<br>26 0<br>22 0<br>18 0<br>14 0<br>10 0<br>6 0<br>19 60 1 97 0 1 98 0 199 0 20 00<br>I nf rastr u ctu ra l in vest m en t In fr astr u ct ur al f ixe d c ap ital st ock G DP<br>South A f rica : ind ice s of in fr astr uctur e<br>2 00<br>1 50<br>1 00<br>50<br>0<br>1870 1 880 18 90 19 00 19 10 192 0 1930 1940 1 950 19 60 197 0 198 0 199 0 2000<br>Railw ay lin es G ood s sto ck (r ail) Paved road s<br>Ele ct ricit y Ph on e line s, in c l. m obile<br>Index (1960 = 100) (all values per capita)<br>Index (1990 = 100)<br><!-- End of picture text --> Figure 4: Historical Evolution of Infrastructure in South Africa. Source: Fedderke, Perkins and Luiz (2005).

# **Organization of the Paper**

The paper has three related objectives. First, following previous studies of infrastructure investment needs, we use a panel of 52 countries to estimate robust functions of “demand” for electricity production and telephone lines (both fixed and cellular).
Figure 6. Policy measures during the pandemic <!-- Start of picture text --> A. Policy rates B. Asset purchases in EMDEs<br>Percent World Percent of GDP<br>8 Advanced economies 7<br>EMDEs<br>6<br>6<br>5<br>4<br>4<br>3<br>2<br>2<br>1<br>0 0<br>2010 2012 2015 2018 2021 EAP ECA LAC SAR SSA<br>C. Fiscal measures in response to the D. Fiscal measures in response to the<br>COVID-19 pandemic COVID-19 pandemic, by region<br>Percent of GDP Percent of GDP<br>20 8<br>Additional spending and forgone revenue<br>15 Additional spending and 6 Equity, loans, and guarantees<br>forgone revenue<br>10 Equity, loans, and 4<br>guarantees<br>5<br>2<br>0<br>LICs EMDEs Advanced 0<br>economies EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> Sources: Bank for International Settlements; Haver Analytics; International Monetary Fund; Kose et al. (2021); Kose, Sugawara, and Terrones (2020); Organisation for Economic Co-operation and Development; World Bank. A. Quarterly nominal policy rates. Aggregates are calculated using real GDP in U.S. dollars as a weight. Sample includes 153 countries, consisting of 36 advanced economies and 117 EMDEs. Last observation is 2021Q2. B. Announced or completed purchases (where no announcement exists) relative to 2019 nominal GDP as of November 2020. Bar shows average in each region. Orange whiskers show regional range. Red line shows average of advanced economy programs launched in 2020.

C.D. Country-level data are as reported in October 2021 Fiscal Monitor by the International Monetary Fund. Country groups are weighted
|**10**|**2,079**|**0.00**|**0.00**|**0.00**|**0.00**|**0.00**|
|**Other**|**9**|**133,537**|**0.01**|**0.01**|**0.00**|**0.00**|**0.00**|
|Total|**388**|**2,088,537**|**0.30**|**0.26**|**0.04**|**0.23**|**0.20**|
|**Source: BANSEFI**||||||||

Recent estimates of BANSEFI show that in 2003, membership in popular banking institutions reached 2.9 million persons. If we add the direct clients of BANSEFI, a grand total of 5,378,094 persons benefit from the services of the “Popular Banking Network” or _Red de la Gente._ BANSEFI’s direct clients had an average deposit balance in September 2003 of $120 U.S. dollars.<sup>19</sup> As noted earlier, BANSEFI does not make loans to its direct clients. The average deposit balance suggests that BANSEFI reaches a population with lower average incomes than that of Popular Banking Network.

One of the reasons that BANSEFI serves households with lower levels of income than those served by popular banks is its diversity of financial products. It has, for example, developed a deposit account, similar to the standard account (Tandahorro) at a popular bank that allows small savers to set up a medium-term savings goal with a specified set of periodic deposits to meet that
. Third, there are some indications from Bangladeshi data that wage increases provide compensation for rural wage earners relatively quickly, though there is no evidence that urban workers benefit from any wage response except perhaps in the long run.

These findings potentially point to some important implications. Most importantly, they suggest that international food price movements might be important drivers of national and global poverty trends as well as rural-urban inequality within countries. The data at hand should not be used to draw conclusions on the contributions of higher food prices to global poverty trends, but it is notable that poverty reduction over 2005-2012—a period of relatively high international food prices—was relatively rapid in the developing world. More recently (2013 to early 2016), food prices have plummeted. Time will tell, but the evidence in this paper suggests that declining food prices could have adverse implications for many of the world’s poor by suppressing their most important sources of income: agricultural production and rural wages.

# REFERENCES

Arellano, M., and S. Bond. 1991. “Some tests of specification for panel data: Monte Carlo evidence and an application to employment situations.” _Review of Economics and Statistics_ 58: 277–97.

BBS. 2014. Agricultural Price Series. Dhaka: Bangladesh Bureau of Statistics.

Beegle, K., J. De Weerdt, J. Friedman, and J. Gibson. 2012. “Methods of household consumption measurement through surveys: Experimental results from Tanzania.” _Journal of Development Economics_ 98: 3–18.

19
, the pricing mechanism of sweet sorghum is cost-based pricing involving farmer’s profit; c. Feedstock price is assumed to be the average value from 2007 to 2008, data from Sun et al (2008); d. data from Wei, et al. (2010); e. data from Cai et al (2011); f. non-feedstock cost includes capital costs, fuel costs, operation & maintenance costs, and auxiliary material costs; g. Data from EB (2008); h. data from Han et al. (2010); i. Costs assumed to be the same as those for cassava; j. Investment cost is for the conversion process only (excluding the investment cost to establish a planting base); k. Survey data based on the cassava project of Guangxi Bioenergy Company with total investment as 0.865 billion Yuan.

# **3.1.3 Challenges**

# **Economic supply of feedstocks**

Ensuring low and stable feedstock costs is one of the major challenges facing the development of starch- and sugar-based biofuels in China. Since crops used for biofuels

> 3 st Wholesale price of fuel ethanol is the ex-factory price of 90# Gasoline multiplied with a factor as 0.911 before Mar.1 , 2011, and is the ex-factory price of 93# Gasoline multiplied with the factor of 0.911 after Mar.1<sup>st</sup> , 2011.

8