rafmacalaba/datause-encoder-data huggingface dataset:
the outcome. The optimal bonus therefore does not depend on the nature of production and cost complementarities since the employee will internalize them when choosing the optimal level of effort. As a result, the only way to assess complementarities in production and disutility cost is by offering a bonus scheme that only rewards one task.

# 4 Data

Data used in the empirical analysis come from multiple sources. Survey data were collected between January and February 2005, prior to the announcement of the bonus, and in June 2006, the last month of the intervention. These surveys asked each FA about his or her demographic and household characteristics, current employment conditions and work history, along with his or her level of motivation for working with NRSP.

These two rounds of survey data are supplemented with administrative data from NRSP, including the monthly employee records with the employment status of each FA, salary and bonus information, and the name of FU or village branch where the FA worked. NRSP also provided us with a monthly record of COs managed (or co-managed) by each FA from June 2004 to June 2006. This FA-CO panel helps us construct the monthly portfolio of COs for each FA during the 10 months before and the 15 months of the bonus intervention.

FA’s performance was tracked using two administrative datasets. Data on loan disbursements and recovery are obtained from NRSP’s Management Information Systems (MIS) database. The MIS digitally records all loans taken and repaid by all borrowing CO members by installment. A total of 5,364 unique COs appear in the MIS database during the 25 months that overlap with the FA-CO panel. Of them, 4,404 COs (82.1 percent) were managed by the 162 FAs who were working for NRSP at the time of the study.<sup>13</sup> Out of the 4,404 COs managed by the study-sample FAs, 4,008 COs show loan activity at least once during these 25 months, and have 5.81 active borrowers each month with repayment or disbursement in 14 out of the 25 months, on average.

Information on social mobilization efforts is obtained from the Monthly Progress Reports (MPRs) submitted each month by each FA for the COs managed or co-managed by him or her. This report includes information on meeting attendance, member savings, and loans approved and denied during the meeting. The MPRs data are available for the 15 months when the bonus was implemented. These data were verified by a supervisor through random visits to a subset of scheduled

> 13The rest of the COs were managed and formed by FAs hired after the bonus intervention began in March 2004. In our analysis, we focus on the FAs who were already working with NRSP prior to the bonus intervention because any differential selection of new hires on their characteristics/quality across the different experimental arms could confound the results. While financial incentives have also been shown to affect performance through differential selection into working in an organization (B ́o et al., 2013; Deserranno, 2016), the main focus of this paper is to understand the incentive effects on performance due to task complementarities.

14
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- 10 -

The perception of greater risk associated with annuities could lead potential annuitants to demand a risk premium, also shifting the demand curve to the left.

The aggregate flow supply of annuities is shown as the downward sloping curve in Figure 4. The aggregate supply is downward sloping because the annuity rate is the basic cost of issuing new annuity contracts. An increase in annuity rates relative to the interest rates on financial assets implies a reduction in intermediation spreads and profit margins for annuity providers, and a loss of enthusiasm in issuing new annuity policies. A general increase in interest rates for the same annuity rate implies an increase in spreads and profit margins and would lead to a supply expansion—in Figure 4 the aggregate supply would shift to the right. Changes in market structure resulting in greater industry concentration and increased monopoly power would lead to a contraction in the aggregate supply—in Figure 4 the aggregate supply would shift to the left.

**Figure 4: Supply and Demand for Annuities**

<!-- Start of picture text --> 10.0<br>Da<br>8.0<br>6.0<br>4.0<br>Sa<br>2.0<br>0.0<br>13000 20000 27000<br>Number of New Annuity Policies<br>Annuity Rate (in % p.a.)<br><!-- End of picture text --> Market equilibrium is illustrated in Figure 4 with an annuity rate of 4 percent and 20,000 new annuities issued within one year (these are roughly the figures for 2004). A general increase in interest rates would lead to a contraction in demand and an expansion in supply, with ambiguous effects on the flows of new annuities, but producing an unambiguous increase in annuity rates. This simple framework also allows the analysis of the impact of other variables on the annuity rate. For example, a general portfolio shift from government bonds to higher yield corporate bonds could result in an expanded supply of annuities and a higher annuity rate. Providers would expand supply and increase the annuity rate if they could extract an increase in the risk-adjusted return. This would be possible if the yield on corporate bonds contained a liquidity premium. If annuitants perceive an increase in risk and demand a risk premium, the aggregate demand would contract, reinforcing the increase in the annuity rate. Again, the effect on the new flows of annuities is ambiguous, but the impact on the annuity rate is unambiguously positive.

The equilibrium depicted in Figure 4, where the market is cleared by one annuity rate, admittedly simplifies the structure and organization of annuity markets. First, the market comprises several types of annuities and rates. Second, adverse selection may restrict severely the overall size of the market, and for some risk segments there may not be a market clearing annuity rate. In a scenario of high and volatile interest rates the market
This finding suggests that the theories that argue that financial development can help the poor may have some bite. The balance-sheet size of even a well-developed banking system will be adversely affected by inflation, as is repeatedly confirmed in the literature. Allowing for this either by including inflation as an additional regressor, or by substituting the residual in a regression of credit on per capita GDP and inflation<sup>9</sup> , takes account of this. In both cases credit remains highly significant (Equations E and F, Table 1).

Political-institutional characteristics can affect financial development as they can affect other aspects of economic conditions. As a robustness check we included each of the omnibus governance variables included in the World Bank’s database (Kaufman, Kraay and Zoido-Lobatón, KKZ 1999; Kaufman, Kraay and Mastruzzi 2003) as additional regressors. None were significant at the 95% level, and the credit variable retained its significance (Equations A-E, Table 2). An alternative way of attempting to ensure that hidden politico-institutional characteristics are not biasing the results is to include regional intercept dummies. Although these are collectively significant, they do not much alter the size or significance of the main variables (Equation F, Table 2).

(Using the higher poverty threshold of $2 a day gives broadly similar results, though not quite as strong an effect of credit; see Tables 3 and 4).

So much for the banking dimension to finance. How about capital markets? Their development has been shown to be equally important for growth. Do they also contribute to a greater lowering of poverty than implied by their effect on growth? The answer is that apparently they do not. Adding stock market capitalization and or market turnover to the basic equation does not significantly alter fit or the other coefficients and the new variables are not significant. Nor does bank concentration appear to be a significant contributor (Table 5).<sup>10</sup> The empirical correlation which we have detected is suggestive, rather than conclusive evidence that emphasizing financial development is benign in regard to poverty in that it is more likely to reduce poverty than the average pro-growth initiative. But the analysis is at too aggregate a level to be fully convincing. Additionally, the ways in which financial development is being measured, based mainly on size, are clearly rather weak ones.

# 4. **The pitfalls in measuring financial development with banking depth – country cases**

The focus so far here, as in much of the early literature, has been on banking depth as the main measure of financial development. But when one considers the likely channels through which a more developed financial system likely helps promote growth and reduce poverty, it becomes evident that, though useful and readily available, banking depth is unlikely to be a wholly reliable summary indicator. Indeed, financial

> 9 This auxiliary regression is reported in Table 7.

> 10 Once again the $2 a day threshold gives similar results (not shown).

8
# **Figure 9: Information on ratings**

## **a. Banks required by supervisors to have external credit ratings (% of respondents answering Yes)**

**b. Mean percentage of the top ten banks rated by international credit rating agencies (%)**

Source: Waves 4 and 5, BRSS.

Note: Off-shore financial centers are excluded from the calculations. DEV are developing countries. The number of countries for which we have a response for the specific item/question for both rounds appears in parentheses. * next to the number of countries represents a statistically significant difference in the mean values of a regional group in the two reference years at the 10% level using a two-tailed test.

Bank supervisory reporting also contributes to market discipline as it has an indirect influence on banks’ behavior and increases the information set available to market participants when it is publicly available (Flannery, 2001). Availability of information on enforcement actions and fines and settlements resulting from non-compliance with regulations provide information both on compliance of individual banks with regulations and on the extent of bank supervisory agency forbearance. Information on enforcement actions is made public in 41% of high-income OECD countries according to BRSS round 5, up from 37% in BRSS round 4 (Figure 10, Panel A). However, except in MENA where the figure increased to 43%, that ratio actually declined or stayed relatively low and unchanged for the other developing regions from BRSS round 4 to BRSS round 5, though these changes over time were not statistically significant except in LAC. The share of countries that require banks to publicly disclose all fines and settlements also increased for all regions except EAP, ECA, and MENA. However, for all the country groupings the changes were not statistically significant (Figure 10, Panel B). In general, the data from BRSS round 5 show no substantial improvement in the quality and availability of information to market participants and the broader public, especially for developing regions.

18
<!-- Start of picture text --> Areenting 1980-2010 Boliviz: 1978-2001 Brazil: 1960- 2010 Chile: 1970- 2002<br>0 3 1 0 5 1 0 35 1 0 5 1<br>=)3 Colombia: 1873- 1885 Costa Rice: 1963-2011 Dominicen Republic: 2002-2010 Ecuador: 1690-2010<br>Ea : : : :<br>3= : .; : : ; ;<br>EA rr rr a | rs<br>0 5 1 0 3 1 o 5 1 o 3 1<br>F |<br>ij<br>= Jemaica: 1982-2001 Rbzico: 1960-2010 Nicsreguz 1971-2005 Pensme: 1960- 2010<br>|:<br>=oo as | a | rrr re. | rs eS |<br>4 0 3 i 0 5 i 0 3 i 0 5 1<br>uw<br>Peru: 1855 - 2007 Uruguay. 1965-2011 Wenecuela:1971 - 2001<br>—_ nn a eT<br>0 3 1 0 3 1 o 35 1<br>Share of population<br>SS First year || Second year<br><!-- End of picture text -->
Table 3: Regressing on SPEI Shocks (Dry or Wet)

|||_Dependen_|_t variable:_||
|---|---|---|---|---|
|||log(WSF inc|rement + 1|)|
||(1)|(2)|(3)|(4)|
|SPEI shock (wet or dry)|0.10<sup>_∗∗∗_</sup>|_−_0.05<sup>_∗∗∗_</sup>|_−_0.004|_−_0.04<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Controls|✓|✓|✓|✓|
|Year FE||✓|✓||
|Grid cell FE|||✓||
|Country_×_ year FE||||✓|
|N|478,379|478,379|478,379|477,424|

_Notes_ : Constant for model (1) was estimated but is not shown. Controls include distance to international border, average annual temperature, the area of each grid cell covered by land, the baseline number of urban pixels in each grid cell in 1985, a non-lagged measure of drought, as well as drought that is only lagged temporally or spatially. Standard errors are clustered at the grid cell level. FE = fixed effects. Statistical significance:<sup>_∗_</sup> p _<_ 0.1;<sup>_∗∗_</sup> p _<_ 0.05; _∗∗∗_ p _<_ 0.01.

Finally, we use several alternative distance-based spatial weight matrices to allow for the possibility that the spillover effects of drought operate at larger or smaller distances than we assume in our baseline approach. We first implement a Rook contiguity method that defines two polygons as neighbors as long as the polygons share at least two boundary points, i.e. at least a small boundary segment. This produces a smaller number of neighboring cells because each cell now has on average four neighboring cells. Second, we opt for a distance-based approach where we calculate spatial weights for all cells that are within 200 km of the destination cell.<sup>12</sup> On average, this produces 59 neighbors per cell, meaning that we are able to capture spillovers caused by drought over substantial distance. Regardless of which method we use, we continue to find substantively and similar results - remains correlated with our urban statistically drought positively dependent variables in most of our models. See Appendix E for regression tables.

> 12While the distance of 200 km is somewhat arbitrary, it represents roughly double the ambit assumed by our baseline approach.

17
and 29 percent for 2001 and 2004, respectively. The relative poverty risk for the employed and those not in the labor force is broadly unchanged. Those out of the labor force still constitute the largest share of the poor, followed by the unemployed, the informally employed, and the formally employed.

There are substantial differences between men and women in the way the labor market mediates their welfare outcomes. For example, the predominance among the poor of individuals who are out of the labor force is driven in large part by women. Among women, about two-thirds of the poor are out of the labor force (Figure 7). Together with the unemployed, those out of the labor force account for over 80 percent of poor women. In contrast, only about 28 percent of the men are out of the labor force. Together with the unemployed, those out of the labor force account for a little over 50 percent of poor men.

Informal sector workers face high and increasing relative poverty risk. Relative to formal sector workers, informal sector workers have a higher incidence of poverty and the disparity in relative poverty risk has widened over the last four years. In particular, the disparity in poverty headcount grew between 2001 and 2004, with formal sector workers experiencing decreasing poverty risk over time (the poverty headcount dropped from 12 to 9 percent) while informal sector workers experienced increasing poverty risk (the poverty headcount rose from 16 to 19 percent).

In general, there is no necessary relationship between informal employment and poverty, especially in transition economies where formal sector employment itself may be characterized by a degree of uncertainty of earnings (in the form of wage delays, for example) and where informal employment may, in contrast, be associated with highreward entrepreneurial activities. In the case of BH, however, there are strong reasons to think that informal sector activities are associated with lower productivity and greater inequality of outcomes, as data on the inequality of wages, for example, would suggest. The _Country Economic Memorandum_ (CEM) (World Bank 2005a) also argues that firms in this sector face limits on their ability to grow and obstacles to exporting and integration into European production chains. In addition, transition probabilities (Table 3) suggest that the flows from employment to either inactivity or unemployment are much higher in the informal sector than in the formal sector, especially among women.

Low education is associated with higher poverty risk, but the risk-education relationship is broadly unchanged between 2001 and 2004. There is some increase in risk for those with vocation and high school education and some decrease among those with college or higher education.

15
Policy ReseaRch WoRking PaPeR 4339

# **Abstract**

Many countries spend significant resources on investment promotion agencies in the hope of attracting inflows of foreign direct investment. Despite the importance of this question for public policy choices, little is known about the effectiveness of investment promotion efforts. This study uses newly collected data on national investment promotion agencies in 109 countries to examine the effects of investment promotion on foreign direct investment inflows. The empirical analysis follows two approaches. First, it tests whether sectors explicitly targeted by investment promotion agencies receive more foreign direct investment in the post-targeting period relative to the pre-targeting period and non-targeted sectors. Second, it examines whether the existence of an investment promotion agency is correlated with higher foreign direct investment inflows. Results from both approaches point to the same conclusion. Investment promotion efforts appear to increase foreign direct investment inflows to developing countries. Moreover, agency characteristics, such as the agency’s legal status and reporting structure, affect the effectiveness of investment promotion. There is also evidence of diversion of foreign direct investment due to investment incentives offered by other countries in the same geographic region.

This paper—a product of the Trade Team of the Development Research Department—is part of a larger effort in the department to understand the determinants and consequences of foreign direct investment. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at bjavorcik@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Is there such thing as middle class values?**

**Class differences, values and political orientations in Latin America**<sup>**1**</sup>

Luis F. Lopez-Calva Jamele Rigolini World Bank World Bank Florencia Torche New York University JEL D3, D7, O1, Z1 Keywords: Middle class, income, values, political orientations.

> 1 The authors would like to dedicate this paper to the memory of Gonzalo Llorente, who unexpectedly left us during its writing. They would also like to thank participants at the _Summer Initiative for Research on Poverty, Inequality and Gender_ (SiRPiG), and at the Workshop on _Socio-Economic Mobility and the Middle Class in Latin America_ for helpful comments and discussions. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.