rafmacalaba/datause-encoder-data huggingface dataset:
Mountford, A. and H. Uhlig. 2009. “What Are The Effects of Fiscal Policy Shocks?” Journal of Applied Econometrics 24(6): 960-992.

Nickel, C., and A. Tudyka. 2014. “Fiscal Stimulus in Times of High Debt: Reconsidering Multipliers and Twin Deficits.” Journal of Money, Credit and Banking 46(7): 1313–1344.

OECD. 2009. OECD Economic Outlook: Interim Report. March. Paris: Organisation for Economic Co-operation and Development.

Perotti, R. 1999. “Fiscal Policy in Good Times and Bad.” Quarterly Journal of Economics 114(4): 1399–1436.

Perotti, R. 2008. “In Search of the Transmission Mechanism of Fiscal Policy.” In NBER Macroeconomics Annual 2007, Volume 22, edited by D. Acemoglu, K. Rogoff, and M. Woodford, 169–226. Chicago: University of Chicago Press.

Perotti, R. 2011. “Expectations and Fiscal Policy: An Empirical Investigation.” IGIER Working Paper 429, Bocconi University, Milan, Italy.

Ramey, V. A. 2011. “Identifying Government Spending Shocks: It’s All in the Timing.” Quarterly Journal of Economics 126(1): 1–50.

Riera-Crichton, D., C. A. Vegh, and G. Vuletin. 2015. “Procyclical and Countercyclical Fiscal Multipliers: Evidence from OECD Countries.” Journal of International Money and Finance 52: 15–31.

Romer, C. D., and D. H. Romer. 2010. “The Macroeconomic Effects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks.” American Economic Review 100(3): 763–801.

Sá, F., P. Towbin, and T. Wieladek. 2014. “Capital Inflows, Financial Structure and Housing Booms.” Journal of the European Economic Association 12(2): 522–546.

Spilimbergo, A., S. Symansky, and M. Schindler. 2009. “Fiscal Multipliers.” IMF Staff Position Note 09/11, International Monetary Fund, Washington, DC.

Sutherland, A. 1997. “Fiscal Crises and Aggregate Demand: Can High Public Debt Reverse the Effects of Fiscal Policy?” Journal of Public Economics 65(2): 147–162.

Towbin, P., and S. Weber. 2013. “Limits of Floating Exchange Rates: The Role of Foreign Currency Debt and Import Structure.” Journal of Development Economics 101: 179–194.

Zubairy, S. 2014. “On Fiscal Multipliers: Estimates from a Medium Scale DSGE Model.” International Economic Review 55(1): 169–195.

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**Figure 12. Backward and Forward Integration to Manufacturing GVCs by Sources and Destinations of Imports and Exports of Intermediate Inputs: Africa, Non-Oil Resource Rich Group of Countries (1995 vs. 2015)**

<!-- Start of picture text --> 100<br>90<br>85<br>80<br>70<br>65.5<br>60<br>50<br>40<br>35<br>30 29.4<br>20<br>10<br>5.9<br>0<br>-10 West Europe East Asia China India USA Germany<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

**Figure 13. Backward and Forward Integration to Manufacturing GVCs by Sources and Destinations of Imports and Exports of Intermediate Inputs: Africa, Oil Exporting Countries (1995 vs. 2015)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>54.3<br>50<br>40 40.4<br>30<br>20<br>10<br>0<br>-10 West Europe East Asia China India USA Germany<br>-20<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

32
include a balanced government budget For a general Keynesian policy analysis see, for example, Meade and Vines ( 1988).

15. The increase in the price of nontradables is exacerbated when production of nontradables exhibits increasing returns to scale (as in the case of natural monopolies, for example). The drop in demand for nontradables following the initial price rise would lead to an additional increase in the price of nontradables In many transition countries this upward price spiral and a decline in output followed the cut in the subsidy to utilities until a declining price elasticity of demand or price regulation stopped the process.

16. See Mundell (1967) and Fleming (1962).

17 The relative price is not altered if the disturbance is only monetary. As Bruce and Purvis ( 1985) explain, with trade balance as a condition in the long run and an endogenous money supply, the real exchange rate does not change in the long run, and the prices of nontradables and tradables prices move to the same extent as the exchange rate, _J)'v J'J) d_ 1 8. For a comprehensive comparison of the macroeconomic policies related to subsidy cuts during transition, see World Bank (1996).

19. Government spending needs to be restricted to nontradables for fiscal policy to effectively boost employment. For a detailed explanation, see Helpman (1977).

20 As noted before, this effect is greater the greater are the price and substitution elasticities. With respect to devaluation, this condition is rephrased by lHelpman ( 1977) who shows that the trade account improves if and only if the effective relative size of the nontradables sector exceeds the marginal propensity to consume the nontraded commodity. In the case considered, the nontradables sector is large enough to accommodate the increase in the expenditure share on 23
20 level, as well as at the level of individual states.<sup>23</sup> A recent study applies the small area estimation methodology used here to estimate poverty at the local level in three states of India in 2004/5 (Gangopadhyay et al, 2010). The study confirms that in West Bengal, Orissa and Andhra Pradesh the poverty-city size gradient observed from NSS data also emerges from estimates derived out of the small-area estimation procedure (Table 10). Thus, at least in India, the finding of a poverty-city size gradient is robust to alternative empirical methods. This provides some support to the claim that the findings reported in preceding sections are not driven by our reliance on small-area estimation techniques.

# **8. Conclusions**

Using highly disaggregated poverty map data from eight countries drawn from all six regions of the developing world, we have shown evidence of a common – although not universal – inverse relationship between poverty and city size. In all countries in our sample, poverty is both more widespread (higher FGT(0)) and deeper (higher FGT(1)) in very small and small towns (those with a population below 100,000) than in large or very large cities (those with a population greater than 0.5 million). Metropolitan poverty, in particular, is considerably lower than poverty in other urban areas in all countries in our sample, except for Kenya. Dominance analysis of cumulative distribution functions indicates that the basic pattern is generally robust to the choice of poverty line.

Neither is it true that, because of sheer population size, most poor people in these countries live in large cities. In fact, in all eight countries, a majority of the urban poor live in medium, small or very small towns. In four of the eight (Albania, Brazil, Sri Lanka and Thailand), a majority of the urban poor live in towns smaller than 100,000 people.

The greater incidence and severity of consumption poverty in smaller towns is compounded by similarly greater deprivation in terms of access to basic infrastructure services, such as electricity, heating gas, sewerage and solid waste disposal. This pattern is not absolute. It does vary by type of service and across countries. Access rates seldom

> 23 World Bank (2010) also shows that the pattern of differential per capita access to public services across city size categories is skewed in India, with small towns faring more poorly than large cities.
with a discussion of the Nafin factoring program in Mexico, which is an example of “reverse factoring”, a factoring technology can succeed in weak business environment.

# **2.  The Mechanics of Factoring**

In factoring, the underlying assets are the seller’s accounts receivable, which are purchased by the factor at a discount. The remaining balance is paid to the seller when the receivables are paid to the factor, less interest and service fees. For example, most factors offer sellers financing up to 70% of the value of an account receivable and pay the remaining 30% – less interest and service fees – when payment is received from the buyer. In general, financing is linked on a formula basis to the value of the underlying assets, e.g., the amount of available financing is continuously updated to equal a percentage of available receivables.

An important feature of the factoring relationship is that a factor will typically advance less than 100% of the face value of the receivable even though it takes ownership of the entire receivable. The difference between this advance amount and the invoice amount (adjusted for any netting effects such as sales rebates) creates a reserve held by the factor. This reserve will be used to cover any deficiencies in the payment of the related invoice.<sup>1</sup> Thus, even in non-recourse factoring there is risk sharing between the factor and the client in the form of this reserve account.

Factoring can be done either on a “non-recourse” or “recourse” basis against the factor’s client (the sellers). In non-recourse factoring, the lender not only assumes title to

> 1 The reserve account represents a liability of the factor to its client.  In effect, the client has extended contingent credit to the factor, which exposes the client to risk.  As a result, if the factor becomes insolvent the client will become a general creditor of the factor and will be exposed to a potential loss up to the amount of the reserve.  Thus, from the client’s perspective the reputation and creditworthiness of the factor may be an important consideration.

3
# Poorest Tercile  (39)

Figure 4 Average distance of zero trade flows, Richest and Poorest Terciles. Richest Tercile (39)

## **Average Distance (km, unweighted)**

**Average Distance (km, unweighted)**

<!-- Start of picture text --> 85 00<br>8000<br>7 5 00<br>M=0<br>7 000<br>M>1<br>6 5 00<br>6 000<br>Number of observations (balanced=4,797)<br>1 00%<br>9 0%<br>80%<br>7 0%<br>6 0%<br>5 0%<br>M>1<br>4 0%<br>M=0<br>3 0%<br>2 0%<br>1 0%<br>0%<br>1970 1975 1980 1985 1990 1995 2000 2005<br>1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text -->

# Poorest Tercile (39)

<!-- Start of picture text --> 85 00<br>8000<br>7 5 00<br>M=0<br>7 000<br>M>1<br>6 5 00<br>6 000<br>1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> Richest Tercile (39)

**Number of observations (balanced=4,797)**

<!-- Start of picture text --> 1 00%<br>9 0%<br>80%<br>7 0%<br>6 0%<br>5 0%<br>M>1<br>4 0%<br>M=0<br>3 0%<br>2 0%<br>1 0%<br>0%<br>1970 1975 1980 1985 1990 1995 2000 2005<br><!-- End of picture text --> Note: 5-years periods over 1970-2004 and a 2-years period 2005-2006 _Source: authors’ calculations on data from UN-COMTRADE and WITS_ 17
23 shock to MEXDIS I has a stronger permanent effect on both variables than a shock to POLDIS (even though the residuals on MEXDISI are more volatile).

From the analysis of the _impulse response function_ we can reach another important conclusion: on the basis of the estimated coefficients of the Vector Error Correction model, a one standard deviation innovation to MEXDIS has a permanent effect on POLDIS of 35 bs. pts. (out of 91 bs. pts.) or of 44 bs. pts. (out of 118 bs. pts.) depending on the sample period - in both cases 38% of a shock to MEXDIS has a permanent effect on POLDIS.

# Response to One S.D. Innovations

<!-- Start of picture text --> Response of MEXDIS1 to MEXDIS1 Response of MEXDIS1 to POLDIS<br>120 120<br>100 100<br>80- 80<br>60- 60<br>401 36 40 29<br>201 20<br>20 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>-20 2 7 12 17_22 27 32 37 42 47 2 7 12 17 22 27 32 37 42 47<br>Response of POLDIS to MEXDIS1 Response of POLDIS to POLDIS<br>34 3<br>3232<br>30 3<br>28 28 27<br>26 , . , , , , , , , , , . 26 .... ......<br>2 7 12 1722 2732 37 42 47 2 7 12 17 22 27 32 37 42 47<br><!-- End of picture text --> _Figure 9_ The forecast error variance decomposition reported in figure 10 shows that: 1) the long run forecast error on MEXDIS I is due for 70% to innovations to MEXDIS I and for _30%_ to innovations to POLDIS; 2) the long run forecast error on POLDIS is due for _61%_ to innovations to MEXDIS I and for the remaining 39% to innovations to
Evans, and Luque 2011). But more than 11,000 employees did not meet the minimum criteria to be eligible for the bonus.

In the security sector, there is also evidence supporting the results. Milagres de Assis (2012) analyzes the effect of Minas Gerais’ result-based management in the security sector; more precisely, on results achieved by the Military Police ( _Polícia Militar_ ), the Investigative Police ( _Polícia Civil_ ), and the Fire Department. Survey data and in-depth interviews with policemen, firemen, and senior managers reported by the author show that the establishment of goals and financial incentives created profound changes in these agencies. Increases in productivity were especially sizable in the Investigative Police. In the Fire Department, the new incentive structure nudged the institution to conceive new strategies, and generated important productivity gains, and improvements in service delivery outcomes. However, Milagres de Assis (2012) also warns that the strong focus on the number of police investigations in the Military Police led to a reduction in their quality. The absence of obstacles made it possible for some police officers to reclassify crimes to improve their departments’ statistics in the key indicators that were being monitored. In sum, results agreements and financial incentives are very powerful and may generate positive or negative results, depending on the way they are implemented.

# _Perception Data_

In addition to having increased political support from the population, performance management appears to be associated with greater satisfaction rates in the security and education sectors. In this section, we draw on data from the AmericasBarometer and the System of Social Perception Indicators (SIPS) of the Brazilian Institute of Applied Economics Research _(Instituto de Pesquisa Econômica Aplicada or_ IPEA _)_ . These two surveys ask questions related to citizens’ perceptions of different public policies, including the three sectors of focus in this paper, to a representative sample of households. Because the surveys are relatively new, it is not possible to compare changes over time. However, we can weigh how states using performance management get rated against other states.

The level of citizens’ trust in the Military Policy, for example, is considerably higher in the states of Pernambuco and Minas Gerais, when compared with their respective regional averages (Figure 12 and Figure 13). Ceará and Bahia also have above-average ratings in this question. The difference of over ten percentage points is statistically significant in both cases.

26
Rural poverty fell in recent years but it is still larger than urban poverty. More than one-third of Pará’s poor population lives in rural areas, with limited access to basic infrastructure and services. The rural poor are primarily smallholders and informal wageworkers that depend on a diverse strategy of income-generating activities in which subsistence production predominates. These small farmers lack titles to obtain access to credit, modern production technology, basic infrastructure to store harvests to take advantage of cyclical price fluctuations, technical assistance to improve productivity, and organized marketing facilities. Family income is therefore highly variable and there is little opportunity for saving. They have very few assets, including education and documents, such as birth certificates, and, therefore, often very vulnerable.

A multi-pronged approach is called for to reduce rural poverty. The differing characteristics of the smallholders and wageworkers suggest that a poverty reduction strategy needs to provide multiple paths out of poverty tailored to the heterogeneous cross-section of poor rural households. A national study for Brazil<sup>7</sup> suggests that this will involve at least a three-pronged approach aimed at: (i) small farm sector intensification, (ii) improved employment opportunities in dynamic commercial agriculture, (iii) growth of the rural non-farm sector and, provision of safety nets for those “trapped” in poverty. The recommended measures include: improving human capital endowments; reforming the land, labor, and financial markets; enhancing research and extension; improving the supply of public goods and services; pricing and trade policies; and, transfer programs. One important first step would be to ensure that everybody have documents, which give children access to school and healthcare, poor parents access to conditional cash-transfers, and old people access to pensions and benefits.

In order to increase land productivity and labor-intensive farming, it is necessary to facilitate the movement toward farming medium-sized land holdings, in part via facilitating land rentals and sharecropping arrangements.<sup>8</sup> This can be done by providing more secure titles to land and by the revision of the land legislation so as to secure longer-term tenancy arrangements, resolution of disputes regarding interpretation, and enforcement of land rental arrangements. The impact of such a program would be greatly enhanced by simultaneous adjustments of the labor code and of the land tax system. Labor laws have had an anti-sharecropping bias. In this context, the experience with the Rural Leasing Exchange in the Triângulo Mineiro contains useful lessons that are worth considering for Pará.

A more direct way of improving farm productivity and revenues is via the communitybased approach to land reform. Under this approach, beneficiary groups negotiate directly with potential sellers of suitable properties, and then obtain financing for the purchase of the land and complementary sub-projects and receive technical assistance.

> 7 See World Bank 2001a.

> 8 ibid.

35
for each institution and loan type by creating a dummy equal to one if the amount outstanding of either the DNI or RUC associated with a loan applicant increases by any amount. The immediate post-application period allows six months for loans to be processed and disbursed and provides some time for applicants to potentially shop around with other financial institutions for other loan offers. For a placebo test, we define an analogous outcome measuring whether each loan applicant took out a new loan from each financial institution in the six months immediately preceding his or her loan application with our partner bank.

We also purchased Equifax credit scores for the month when the SME applied for the loan from our partner bank. Here, Equifax included a dummy variable indicating whether this score was primarily based on their credit history, i.e. a “thick file,” or on demographics and other sources, such as the Peruvian tax authority (SUNAT), i.e. a “thin file”.<sup>4</sup> Our sample includes 1,517 thick file borrowers and 366 thin file borrowers.

We utilize two measures of credit market competition: the log of per capita NBFI lending at the district level, and the per capita number of NBFI branches at the district level. We focus on NBFIs as our results indicate that the increase in loans for thin-file applicants comes from NBFIs, not banks. We note that as our partner bank operates in only eight districts, this is a fairly coarse measure of competition. Total NBFI lending for February 2012 was obtained from the Peruvian Bank Supervisor (SBS) and the 2012 population size from the Human Development Index of the UNDP.

Though we do not directly observe the profits that lenders derive from each loan applicant, we compute an imperfect measure of profits from new loans at the applicantinstitution level. If the applicant does not have a new loan with a given financial institution six months after the loan application, profits are defined to be zero. If they have a new loan within six months of the loan application, we measure the size of this loan as the sum of all loan balance increases in the six months. The size is set to zero for applicants with no new loans. We obtain the interest income by multiplying the size of the loan by its “typical” interest rate. Typical interest rates are reported by the SBS for each financial institution

> 4 For individuals with “thin files”, Equifax calculates credit scores based on several variables, including whether they have a co-signer, their income level, and recurring payments, such as tuition, rent, and electricity.

8