rafmacalaba/datause-encoder-data huggingface dataset:
distortions) in firm _i_ and period _t_ the empirical model, in the spirit of Leon-Ledesma and Christopolous (2016), is: Dit =  +  1 BNDit +  2 (BNDit * FDit ) +  3 FDit +  Zit + ui +  t +  it (8) Our variables of interest, BNDES credit use (BNDit) and financial dependency (FDit), enter interacted with each other in our empirical model. Marginal effects (and the contribution) of BNDES credit on conditional expected distortions would be given by  1 +  2 FDit. This could create difficulties in the use of the Foster decomposition formula as discussed in the original paper. But note that a simple manipulation of equation (7) yields the equivalent expression for the contribution of explanatory variable _j_ to the regression dependent variable variance: Cov(fj,Y)/V(Y) where fj = Xj bj Y|X1,..,Xj. (10)

This is the population equivalent of the simple regression coefficient of fj explained by Y. We expand the formula so that the share of the variation of a dependent variable explained by an interaction variable is given by: fj = Xjbj + (Xk*Xj) bk.

Public credit use and financial dependency may be associated with size, a time variable dimension. To partially control for this confounding variable, we use lagged size measures (value added and revenues) as Z variables in equation (8) above.

# **5. Empirical results**

We present capital distortions variance and output distortions variance in Table 1. We find a high level of dispersion for both capital and output, with capital distortions variance larger than output distortions variance. This is consistent with international patterns and with previous findings for Brazil in the manufacturing sector.<sup>10</sup>

**Table 1: Distortions standard deviation – selected years**

|Year|ln(1+kit)|ln(1-yit)|
|---|---|---|
|2003|1.72|0.88|
|2008|1.66|0.90|
|2013|1.74|0.93|

Source: authors’ calculations based on IBGE data.

Figure 1 presents a visual representation of the distortions in the manufacturing sector. Scales are not standardized across graphs, but it is possible to see the larger capital distortions variance, with the dispersion increasing over time. Results on the effects and interactions between credit use for financing investment (our financial dependency variable) and

> 10 See Vasconcelos (2017).

13
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE 2003–08, all countries of the group experienced economic growth, although the averages varied by country. Overall, ECOWAS countries grew at a rate of 5 percent during the period, and infrastructure contributed one percentage point to that growth. As in other regions of Africa, most of that growth can be traced to the introduction of mobile telephony (figure 1.1a). At the same time, deficient power infrastructure held back economic growth in West Africa by 0.1 percentage point per capita per year.

**Figure1.1a  Infrastructure’s contribution to economic growth, 1995–2005**

## a. Infrastructure‘s historic contribution to growth, 1995–2005

<!-- Start of picture text -->
1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>b. Infrastructure‘s potential contribution (% GDP per capita per year)<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>North Africa West Africa East Africa Southern  Central Africa<br>Africa<br>Potential impact  of  better<br>infrastructure on growth per capita<br>Additional percentage points  of per capita economic growth North Africa West Africa East Africa Southern A Central Afri<br><!-- End of picture text -->

Source: Calderon 2008.

However, infrastructure could potentially contribute much more to economic growth than it has in the past (figure 1.1b). Simulations suggest that if West Africa‘s infrastructure could be upgraded to the level of the best-performing country in Africa (Mauritius), the impact on per capita economic growth would be on the order of 5 percent. While all areas of infrastructure—ICT, power, water, and transport—need to be upgraded, improvements in power could affect growth by more than 2.5 percent.

# Why regional integration matters

With its large number of small, isolated economies, ECOWAS‘s economic geography is particularly challenging. Of the 15 member countries, three are landlocked, 8 have fewer than 10 million people, 11 have a gross domestic product (GDP) of less than $5 billion per year, and 6 rely on transnational river 11
The **epidemic context** , in particular, drives community responses and the types of activities undertaken. In Sub‐Saharan Africa community responses are often described in terms of PLHIV and OVC support, whereas in Asia, examples tend to highlight responses driven by group most at risk of HIV infection, such as sex workers and MSM.

Community responses have also evolved as the epidemic has evolved. NGOs working on AIDS issues in Brazil focused initially on fighting social exclusion and pressuring the government to be more responsive. NGO leadership subsequently was instrumental in creating the legislation to mandate nationwide HIV testing of blood donations, and NGOs created home‐care programs, established HIV support groups, and launched preventive efforts. In Zimbabwe, the Families, Orphans and Children Under Stress (FOCUS) program has evolved from an initial focus on home‐based care and awareness raising to encompass support for affected children. As treatment has become more widely available, community organizations have increased their involvement in treatment‐related activities or changed the emphasis of their work. For example, in South Africa, the Treatment Action Campaign has expanded its work on treatment access to include mobilizing community involvement in treatment education and treatment adherence.

Responses are also characterized by **location** . A mapping of community responses (M’Zamani et al. 2007) in three settings in South Africa ‐a large urban township, a small town, and a rural area ‐found very few organizations operating in the rural area. In the rural area, community response, instead, emerged as outreach conducted by churches and home care provided by a local NGO. In contrast, there were 67 and 104 organizations in the town and urban township, respectively, involved in a range of activities. These included support groups, home‐based care, spiritual support, condom distribution, and provision of transport to clinics for PLHIV.

Another review of a subset of faith‐based organizations in a South African database (Birdsall 2005) found that the nature of services provided by FBOs depended on the setting. FBOs in rural areas were less involved in food programs but more involved in condom distribution than FBOs in urban areas, and a higher proportion of FBOs in urban areas reported involvement in care and support. Most FBOs in both rural and urban areas provided services to PLHIV, OVC, and HIV‐positive mothers, but in urban areas FBOs were more likely to also provide services to substance abusers, sex workers, and street children.

Finally, community responses have been driven by peer‐learning facilitated by networks, such as Grassroots Organizations Operation Together in Sisterhood (GROOTS) International, Huairou Commission, Shack/Slum Dwellers International and others.

20
# 1.0 Introduction

For researchers estimating the effect of inequality on growth, China would seem a promising “laboratory.” Since the start of economic reform in the early 1980s, it experienced plenty of both: Per capita income has grown nearly 8 percent annually, while the Gini coefficient rose from 0.28 to 0.39 (Ravallion and Chen, 2007). There was also significant within-China variation in growth and inequality at the local level (Benjamin, Brandt, and Giles 2005). Experience with cross-country aggregate data, however, demonstrates that estimating a robust correlation, let alone a causal relationship between inequality and growth, faces major empirical challenges, some of which stem from less than ideal data.<sup>1</sup> Better data alone, however, cannot solve the causality problem. Kuznets (1955) suggests the opposite chain of causality, from growth to changes in the income distribution; and almost certainly, unobserved heterogeneity is a potential factor, with inequality reflecting other factors that drive growth. Finally, even if we can estimate a “reduced form” effect of inequality on growth, it may still be impossible to identify the channels through which it matters.

In this paper we use the post-reform experience of rural China to determine whether local inequality impeded the growth of household incomes. We are able to address some of the methodological problems that plague cross-country data. By using a repeated, consistently applied household survey we avoid some of the measurement problems endemic in the cross-country setting. At least compared to international variation, the relative similarity of local institutions across villages in China also permits a cleaner isolation of the impact of inequality from other unobserved factors. At the same time, there are sufficient spatial differences in institutions and inequality that exploring the experiences of rural households scattered across villages can inform us about the potential channels by which inequality affects income growth. In particular, we can distinguish between two broad classes of explanations that have been offered as to why inequality affects growth: imperfect factor markets, including credit, or growth-inhibiting institutions.

The foundation of this paper is a rich panel that tracks household incomes from early in the reform period (1987) to nearly the present (2002). The data allow us to link the detailed trajectories of household income to initial village and household conditions. Our question is simple: Controlling for a rich set of covariates, did higher village inequality dampen household income growth? There are several

> 1 The potential problems are numerous: cross-country heterogeneity of data and measurement standards, problems of aggregation, measurement error of key variables, short time-series’ of inequality and growth, and less-thancomparable estimates of inequality.

_Inequality and Growth in Rural China_ , Page 1
Since we are using a new data set, we believe it is useful to look at some simple statistics and compare them to official statistics of the IMSS. We do this comparison in table 1, although it should be noted that we made no attempt to replicate the precise methodology used in the generation of these official statistics. In fact, we do not know the precise methodology used by the IMSS although we are using the same raw data.

The first employment figures in table 1 are official IMSS statistics on _cotizantes_ on December 31 of each year.<sup>9</sup> _Cotizantes_ are employees who pay social-security taxes or for whom social-security taxes are paid. Of the official statistics we have found, we suspect that these statistics use the definition that most closely matches our definition of all individuals who receive positive salaries. Unfortunately, however, we could not find statistics on _cotizantes_ before 1992.

(Table 1 here) The second set of employment observations presents our calculations of employment on December 31 of each year. The figures match up fairly well, although the net-growth percentages in our data tend to be slightly smaller than in the official statistics. The third set of statistics gives our counts of “jobs” which will correspond to our worker- and job-flows statistics. The difference between the statistics on jobs and employment is that one employee may have more than one job.

Our data represent all sectors of the Mexican economy, but, as an additional check, we also compared our 1993 average employment in manufacturing with the 1993 average total employment in the 1993 Mexican Industrial Census. One would expect the majority of employees in manufacturing to be formally registered, implying that manufacturing employment registered with the IMSS should be similar to manufacturing employment recorded in a 5
<!-- Start of picture text --> El Salvador<br>"caro"<br>100<br>80<br>60<br>40<br>20<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Figure 2: Results for 10 days during the study period for the “caro” keyword in El Salvador. The dark line is the average. The gray bands are minimum and maximum observed values for that month over the study period.

8
about 8 percent of Kenyan exporters export to their main industrial market (the United Kingdom), since 58 percent of Kenyan enterprises export, this represents over 4 percent of Kenyan enterprises. The poor performance of Ethiopian exporters in regional markets probably reflects regional difficulties that have prevented Ethiopian enterprises from developing export partnerships with firms in neighboring countries (e.g., in Eritrea, Somalia and Sudan).

# **II.2 Barriers to Trade**

Many different factors affect firms’ propensities to export. Since the cost of transportation will vary between sectors, enterprises will be more likely to export in some sectors than they will be in others. Similarly, enterprise size is likely to be important—there are fixed costs associating with exporting that larger enterprises will find it easier to bear. Finally, transportation costs will vary between countries. Although exporting will generally be more difficult for enterprises in landlocked countries, it is important to note that most of the small and medium enterprises included in the investment climate surveys export to neighboring countries (including neighboring landlocked countries) rather than to more distant markets.

Another factor that might affect export performance is the existence of policy-induced barriers to trade. Export taxes are the most direct policy-induced barrier. In practice, however, export taxes—which were mostly levied on primary production in any case—have become far less important in recent years. For example, Senegal eliminated export taxes in the mid-1980s and Uganda abolished export taxes in the mid-1990s.<sup>6</sup> In addition to export taxes, tariffs and non-tariff barriers on imports might also affect exports. By increasing the cost of imports, they will divert resources towards import substituting activities, discouraging exports. In addition, if firms find it more costly to import intermediate and capital goods, this will increase production costs. Although exporters are often entitled to reimbursement of import duties under duty drawback schemes, these schemes are often complex and are administered inefficiently (Collier, 1998). Tariffs might also discourage exports, if

> 6 Mbaye and Golub (2003) note that Senegal had few export taxes other than on groundnuts and gold and that these were eliminated in 1985.  Similarly, Grenier _et al_ . (1999) note that export duties had become uncommon by the mid-1990s in Tanzania.  Milner _et al_ . (2000) note a similar point for Uganda.

6
9 _Figure 3. Real quarterly farm gate FOB, CE and producer cocoa prices (1990 US$/mt)_ <!-- Start of picture text --> 3000<br>2500 P|oducer CE +FOB<br>2000<br>1500<br>500<br>83 83 84 85 86 86 87 88 89 89 90 91 92 92 93 94 95 95 96 97<br><!-- End of picture text -->

_Source:_ Authors' calculations.

_The income benefit offorward sales_ A second benefit claimed for forward sales is that they allow exporters a forward premium. From the mid 1980s through the mid 1990s, the world market was in relatively abundant supply. With stocks carried forward, forward prices have generally been higher than prices for spot or nearby deliveries 9. This implies that producing countries could indeed receive higher prices by selling forward, i.e., the existence of a positive premium.

The forward premium is not necessarily positive, however. Futures prices may indeed be either upward (downward) biased, in that they might fall (rise) as maturity approaches. Gilbert (1997) argues that, if short hedging (selling futures to hedge) outweighs long hedging (buying futures to hedge), speculators will tend to be net long (bought futures), and the futures price (of the same contract) must rise over time (as it approaches expiration or delivery) to give a hedging profit. Where long hedging dominates, speculators will be net short and futures prices will fall towards maturity.

Gilbert (1997) analyzed London cocoa market data from 1989 through 1996. He found that the average November price of the December futures contract was 5.2 percent lower than its February average, 4.7 percent lower than its May average and 3.3 percent lower than its August average. This suggests an upward biased futures price consistent with long hedging. 10 The comparable figures for March were 4.4 percent higher in February, 3.3 percent higher in May and 2.3 percent higher in

> 9 The forward price is based on the futures price for the relevant month plus or minus a quality premium. Nearby refers to the closest to delivery futures contract which may mean for immediate delivery or for delivery in a month or two. For practical purposes, a nearby contract is very close to being a spot contract.

> '° However, Gilbert (1997) notes that hedgers have usually been net short, suggesting that speculators have consistently lost money in the cocoa market over this period.
7

# **4. The global cost of cotton subsidies and tariffs**

What is the cost of current distortions to cotton markets or, equivalently, what would be the effects of eliminating all cotton subsidies and import tariffs, as called for by African cotton-exporting countries as part of the WTO’s Doha Development Agenda? Given the extent of subsidies to cotton production and exports, and of tariffs on cotton imports as of 2001 (modified as described in the previous section), we estimate using the GTAP model that their removal would boost global economic welfare by $283 million per year,<sup>5</sup> and would raise the price of cotton in international markets by an average of 12.9 percent.<sup>6</sup> The price rise ensures that all cotton-exporting countries would benefit, while net importers of cotton would be worse off, as shown in the right-hand columns of Table 1.

What is striking about the welfare effects is their distribution among developing countries (Table 2 and Figure 1). Especially noteworthy is the relatively large benefit bestowed on Sub-Saharan Africa, of $147 million per year. About two-fifths of that would go to the Cotton-4 and another one-fifth to other West African countries. This is driven by an estimated increase in Sub-Saharan African cotton output and net farm income of nearly one-third, and in the real value of the region’s cotton exports of more than 50 percent. By contrast, cotton output and exports would fall by one-quarter in the United States and would halve in the EU (middle columns of Table 1). That would raise

> 5 Of course if textile and clothing tariffs also were removed, global welfare would increase far more: by an extra $6.8 billion per year, according to our GTAP model results.

> 6 This is almost identical to the 12.6 percent claimed by Brazil using a model developed by FAPRI (FAPRI 2005) and close to the 10 percent estimated by Sumner (2006, p. 282), which is also the simple average of the studies surveyed by Baffes (2005, p. 122) although the range reported by Baffes is up to 30 percent. When we alter the GTAP trade elasticities to the larger ones used in the World Bank’s Linkage model (see van der Mesbrugghe 2005), our estimate of 12.9 percent falls to 11.9 percent.
- Shi, Y., X. Gao, D. Zhang, and F. Giorgi, 2011. Climate change over the Yarlung ZangboBrahmaputra River Basin in the 21st century as simulated by a high resolution regional climate model. Quaternary International 244: 159-168.

- Shilpakar, Raja Bhai, N.M. Shakya, and A. Hiratsuka, 2009. Impact of climate change on snowmelt runoff: A case study of Tamakoshi basin in Nepal. International Symposium on Society for Social Management Systems, SSMS2009, Kochi, Japan, Internet Journal, SMS09-124.

- Shrestha M.S., 2008. Impacts of floods in south Asia. Journal of South Asia Disaster Study 1(1): 85–106.

- Shrestha, A.B, and L. Devkota. 2010. Climate change in the Eastern Himalayas: Observed trends and model projections; Climate change impact and vulnerability in the Eastern HimalayasTechnical Report 1, Kathmandu, Nepal. ICIMOD. ISBN 978-92-9115-153-0.

- Shrestha, A.B., C.P. Wake, J.E. Dibb, and P.A. Mayewski, 2000. Precipitation Fluctuations in the Nepal Himalaya and Its Vicinity and Relationship with Some Large Scale Climatological Parameters. International Journal of Climatology 20:317–327.

- Shrestha, A.B., C.P. Wake, P.A. Mayewski, and J.E. Dibb, 1999. Maximum Temperature

- Trends in the Himalaya and Its Vicinity: An Analysis Based on Temperature Records from Nepal for the Period 1971-94. Journal of Climate 12: 2775-2786.

- Shrestha, A.B., M. Eriksson, P. Mool, P. Ghimire, B. Mishra, and N.R. Khanal, 2010. Glacial Lake Outburst Flood Risk Assessment of Sun Koshi Basin, Nepal. Geomatics, Natural Hazards and Risk 1: 157-169.

- Singh, P., and L. Bengtsson, 2004. Hydrological Sensitivity of a Large Himalayan Basin to Climate Change. Hydrological Processes 18: 2363-2385.

- Singh, P., and N. Kumar, 1997. Impact Assessment of Climate Change on the Hydrological Response of a Snow and Glacier Melt Runoff Dominated Himalayan River. Journal of Hydrology 193: 316-350.

- Singh, P., and S.K. Jain, 2002. Snow and glacier melt in the Satluj River at Bhakra Dam in the western Himalayan region. Water Research 47: 93-106.

- Singh, P., S.K. Jain, and N. Kumar, 1997. Estimation of snow and glacier-melt contribution to the Chenab River, western Himalaya. Mountain Research and Development 17: 49-56.

43
|Receive income from renting crop land||(42.92)<br>124.408<br>(86.78)|
|---|---|---|
|Constant|656.87|912.588*|
||(383.38)|(213.68)|
|Observations|1552.00|1395|
|R-squared|0.14|0.302|

Source: Authors’ estimation using SLIS 1999-2000.

Notes: Robust standard errors in parentheses. Omitted variables include female household heads, farms that rely on irrigation only, households with no land tenure, households planting multiple crops, selling in informal markets, households not receiving subsidy, households not receiving income from renting capital and household heads not born in the current location.

27
Figure 1 <!-- Start of picture text --> ¢ D,<br>é<br>P,MC a“ D, C<br>Ful<br>raI P1<br>1 '<br>Bo! !<br>i} ! I<br>i} 1 \<br>1 i i<br>i} ! I<br>i} 1 \<br>fit I!1 i}] I1<br>i} 1 \<br>Qi Q2 43<br><!-- End of picture text -->
Renfew, A. 1992. _ESAP and Health_ . Mambo Press: Harare.

Rosenzweig, M. and K. Wolpin. 1993. “Credit market constraints, consumption smoothing, and

- the accumulation of durable production assets in low-income countries: investments in bullocks in India”, _Journal of Political Economy_ , 101:223-45.

- Rosenzweig, M and H. Binswanger. 1993. “Wealth, weather risk and the composition and profitability of agricultural investment _”, Economic Journal_ , 103:56-78.

- Scoones, I., C. Chibudu, S. Chikura et al. 1996. _Hazards and Opportunities.  Farming_

- _Livelihoods in Dryland Africa.  Lessons From Zimbabwe._ London:  Zed Books

- Toulmin, C., R. Leonard, K. Brock, N. Coulibaly, G. Carswell, and D. Dea. 2000.

- “Diversification of livelihoods, evidence from Mali and Ethiopia”, Research Report 47, Institute of Development Studies, Brighton, UK.

- Townsend, R.M.  1995. “Consumption insurance: an evaluation of risk-bearing systems in lowincome economies”, _Journal of Economic Perspectives_ , 9(3):85-102.

- Udry, C. 1990. “ Credit markets in Northern Nigeria: Credit as insurance in a rural economy”, _World Bank Economic Review_ , 49(30):251-69.

- Udry, C. 1994. “ Risk and insurance in a rural credit market: An empirical investigation in Northern Nigeria.” _Review of Economic Studies_ , 61(3):495-526.

- Udry, C. 1995. “ Risk and saving in Northern Nigeria”, _American Economic Review_ , 85(5):1287300.

- Zimmerman F. and M. R. Carter. 1996. “Dynamic portfolio management under risk and subsistence constraints in developing countries”, Agricultural and Applied Economics, Staff Paper Series, University of Wisconsin-Madison.

16