rafmacalaba/datause-encoder-data huggingface dataset:
2000; the results imply a sizeable nine percentage point drop in the consumption poverty rate at poverty lines around 600 yuan. However, this is not true at lower and higher lines, where impacts over the two time periods agree fairly closely.

For all of the above impact estimates, the counterfactual is the absence of the SWP. There is an alternative counterfactual of interest, namely the absence of direct participation in any anti-poverty program, including the government’s programs. For identifying this counterfactual we can use those households in non-SWP villages who did not participate in any other program; this applied to 69% of the households in non-SWP villages. So we repeated the above calculations dropping those who recorded any direct participation in other programs. (The balancing tests passed comfortably.) The impacts for 2000 were similar to those above. However, the long-run impacts on mean income and consumption were larger. For example, the simple _DD_ estimate of the impact on mean income in 2004 rose to 125 yuan per person (as compared to 45 yuan in Table 5) although this fell to 99 yuan when we corrected for selection bias using PS weighting. Nonetheless, the impacts relative to this alternative counterfactual were still not significantly different from zero; for example, the t-ratio on the simple _DD_ for mean income was 1.47, which dropped to 1.13 with PS weighting.

_5.3 Heterogeneity in impacts_ We tested for differences in impacts according to the initial values of income, education and ethnicity.<sup>35</sup> The score-weighted _DD_ ’s were not significantly different for any of our outcome variables when we stratified by education or ethnicity. However, we found a notable difference when stratified by initial income (above or below the median), with significant longer term gains for the low-income group. When we interacted income with education we found that the longer-term gains were strongest for the relatively well educated (at least junior high school) amongst the low-income households, as can be seen in Table 6.

The heterogeneity in returns suggests that a different assignment of the loans would have increased overall impact. The household participation rate was slightly higher for the group of relatively poor but well educated households; 61.1% of this group in

> 35 We distinguish Han Chinese from all other ethnic minorities.  The ICR points to concerns about how well ethnic minorities were reached by the SWP (World Bank, 2003).

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Therefore, the basic structure of economic activities in the city is not substantially affected by the variable we use to instrument enforcement, although (as we will show in the next section) the structure of the labor market will see some changes.

Finally, we show that the instrument cannot predict past values of the main variables of interest in this paper: city level informality (the share of workers not paying social security), the unemployment rate, inequality (Theil index), and the poverty rate measured in 1980. In sum, this table shows that the instrument is both theoretically plausible and empirically credible.<sup>29</sup> Table 3 reports estimates for the coefficient on the instrument, and the average marginal effect of distance on enforcement. The relevant F-statistic measuring the strength of the first stage relationship is shown at the bottom of each column. Since we are using a large set of controls, for transparency we report three different specifications. In the first column we regress enforcement (measured by the log number of inspections per firm in the city) on distance to the nearest labor office (measured in travel time), its interaction with the number of labor inspectors per firm in the state (the instrument), and state fixed effects. In the second column we add distance to the state capital and its interaction with state inspectors per firm. In the third column we present the full specification. Across columns, the marginal effect of distance on enforcement is negative, and the coefficient on the interaction of distance and inspectors in the state is positive, showing that the effect of distance is smaller in states with more inspectors. The coefficient on this interaction is similar across columns, and the F-statistic is always high so there is no concern with the instrument being weak (Stock and Yogo, 2003).

# **6. Empirical Findings**

## **6.1 Aggregate Labor Market Outcomes**

Table 4 reports least squares (panel A) and instrumental variables (panel B) estimates of the effect of log inspections per firm in the city on the share of informal workers (defined as those without a work permit and the self-employed, constructed from the Census), the head count

> 29 One could be concerned that the interaction term we emphasize is hard to identify in small samples, and that the findings in table 2 are mainly due to imprecision. This is not likely to be the case. In appendix table A4 we show not only the coefficient on the interaction of distance and state inspectors, but also the coefficients on the interactions between distance and all the other state level variables, which we use as control variables in table 2. The only interaction term that is systematically unrelated with the dependent variables in table 2 is the one we use as an instrument for enforcement.

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_Vigt_ : The value of exports of HS-4 digit product _g_ from firm _i_ in Indonesia to Japan in month _t_ ; _Iig,_ 0: The index of competition between Chinese exports and those of Indonesia in product _g_ for Indonesian firm _i_ in Japan in the initial time period (2014); _EUSD,t_<sup>_China_:Exchange</sup> rate between Chinese yuan and USD, expressed as yuan/USD; _NTMExposurei_ 0 _,t_ : The measure of NTM exposure of the firm _i_ selling product _g_ in month _t_ , computed on the basis of the firm’s initial composition of the import bundle. We will describe the construction of the Chinese competition index and the NTM exposure variable in detail below.

Following Mattoo et al. (2017), we formally define the competition index that each firm faces vis-a-vis China in equation 2 below.

_EXig′,_ 0<sup>Measures the importance of HS-6 product</sup><sup>_g′_in the total exports of the HS-4 product</sup> _EXig,_ 0<sup>:</sup> _g_ exported to Japan by the Indonesian firm _i_ in the initial time period (time 0, which corresponds to 2014 in our analysis); _MSg_<sup>_China′_</sup> _,_ 0 : China’s share of HS-6 product _g_<sup>_′_</sup> in the total imports of _g_<sup>_′_</sup> by Japan in 2014. The share of each HS-6 in an HS-4 product ( _EXEXiig,g′,_ 00<sup>)is</sup> weighted by China’s share of product _g_<sup>_′_</sup> in the total imports of _g_<sup>_′_</sup> by the destination country ( _MSg_<sup>_China′_</sup> _,_ 0 ) and summed over all HS-6 products _g_<sup>_′_</sup> within an HS-4 product _g_ to arrive at a measure of Chinese competition index at the Indonesian firm _i_ and HS-4 product _g_ level. We follow Mattoo et al. (2017) in constructing this index at the HS-4 level using dis-aggregated product data at HS-6 to reflect the fact that too narrowly defined products, such as HS8, may not accurately reflect the effects of Chinese competition while too broadly defined categories such as HS-2 may confound these effects.

Intuitively, according to this measure, if the HS four-digit product, let’s say TVs, consists of only two items, color TVs and non-color TVs, the measure is simply the share of Chinese imports in Japan’s imports of each type of TV, weighted by the importance of each type of TV in firm _i_ ’s TV exports to Japan. If the firm faces no competition from 14
that households vary in their marginal returns to investment, and reallocation across households in general equilibrium may therefore have first order impacts on efficiency, in addition to the usual distributional consequences. Buera et al. (2021) estimate a model of microfinance in general equilibrium, in which entry and exit of and reallocation across heterogeneous entrepreneurs are triggered by microfinance and its impacts on equilibrium wages, while Breza & Kinnan (2021) provide evidence that equilibrium impacts of microfinance on wages are large. As a complementary approach, recent work has decomposed the general equilibrium impacts of microeconomic shocks on aggregate welfare into a partial equilibrium effect (which I estimate), and an allocative efficiency effect (Baqaee & Farhi, 2020; Bau & Matray, 2020; Sraer & Thesmar, 2020); under these approaches, the impacts of financial inclusion on allocative efficiency depend crucially on its impacts on reallocation towards high marginal return entrepreneurs.

# **4 Consumer surplus from savings and microfinance**

In this section, I apply the framework in Section 2, and approach discussed in Section 3.1, to estimate consumer surplus from savings and microfinance. I build on three randomized control trials (Duflo et al., 2006; Karlan & Zinman, 2018, 2019) which introduce experimental variation in interest rates for retirement savings accounts in the United States (Section 4.1), commitment savings accounts in Philippines (Section 4.2), and microfinance loans in Mexico (Section 4.3), respectively.

## **4.1 Retirement savings (Duflo et al., 2006)**

### **4.1.1 Context and experimental design**

I leverage experimental variation in retirement savings match rates studied by Duflo et al. (2006) to estimate the saver surplus from retirement savings. The experiment was implemented with H&R Block, a large tax preparer in the United States, for a single retirement savings product, the X-IRA.

**Retirement savings product** I briefly summarize the description of the retirement savings product from Duflo et al. (2006), and incorporate it into the model in Section 2. At the time households enrolled in the experiment prepare their taxes with H&R Block, they are offered the opportunity to make a one-time deposit into an X-IRA with their forthcoming tax refund, with a minimum deposit of 300 USD. The deposit into the X-IRA may or may not be tax deductible, and households may or may not face tax penalties for withdrawing

23
but may not raise the general population above a new subsistence equilibrium. Lastly, the poverty impacts of climate events can be severe and may persist for generations.

# **Climate Change Mitigation and Poverty**

Thus far we have focused our attention on the poverty impacts of climate change, potential adaptation strategies which the poor may adopt, and the ways in which the policy environment shapes the adaptation process. However, as the global community focuses increased attention on climate change mitigation, the potential for such mitigation efforts to have an impact on poverty increases. We argue here that the poverty consequences of policies to mitigate climate change could potentially rival in importance the poverty impacts of the climate change itself.

Forestry and agriculture account for roughly one-third of global greenhouse gas emissions (IPCC 2007, figure 2.1) thus offering tremendous potential to contribute to climate change mitigation programs. Furthermore, at high carbon prices (e.g., $100 per ton CO2 equivalent), the combined mitigation potential of agriculture and forestry is greater than that of any other individual sectors in the economy (World Development Report 2010, 25). Indeed, Golub et al. (2009) estimate that, in the next two decades, these sectors could account for as much global abatement as would be attained from reduced fossil fuel consumption based on a global carbon tax of $28 per ton CO2 equivalent.

Much of this low cost abatement is due to avoided deforestation in the tropics. This has focused considerable international attention on programs for Reducing Emissions from Deforestation and Degradation (REDD). Indeed, a handful of wealthy nations committed a $3.5 billion down payment for REDD -- with much more to follow -- at the recent climate summit in Copenhagen (The Economist, Dec. 17, (Anon. 2009b)). As it happens, much deforestation occurs 37
# **Visualizing Uncertainties,**

# **or how Albert Hirschman and the World Bank disagreed on project appraisal and development approaches**

## **Michele Alacevich**<sup>∗</sup>

Keywords: Economic history; Project Appraisal; Uncertainty; Project Evaluation; Development Economics JEL codes: B20, B31, N00, O20 sector board: Economic Policy

> ∗ Columbia University, Email: ma2944@columbia.edu.

> I am grateful to Jean-Jacques Dethier, Massimo Di Matteo, Valentina Kalk, Chris Kobrak, Philip Scranton, Judith Tendler and Christopher Willoughby for their extremely valuable comments and insights on an earlier version of this paper. I am also indebted to the Minda de Gunzburg Center for European Studies, Harvard University, where an early draft of this paper was written; the staff of the World Bank Group Archives; and the Princeton University Library, Department of Rare Books and Special Collection at the Seeley G. Mudd Manuscript Library, Princeton, for precious assistance during archival research.
# **6. Consolidation as an Instrument for More Efficiency and Stability**

Kenya’s Vision 2030 seeks to facilitate the transformation of the banking sector to bring in fewer, stronger, and larger banks. The higher capital levels in banks are expected to create a vibrant and globally competitive financial sector that will create jobs and also promote high levels of savings to finance Kenya’s overall investment needs. What does theory and international experience tell us about the relationship between market structure, stability and efficiency?

Theoretical models have made contrasting predictions on the relationship between bank concentration, competition and stability.<sup>21</sup> On the one hand, bank concentration may enhance profits and therefore lower bank fragility. High profits provide a buffer against adverse shocks and increase the franchise value of the bank, reducing incentives for bankers to take excessive risk. In addition, proponents of this ‘concentration-stability’ view argue that larger banks can diversify better so that banking systems characterized by a few large banks will tend to be less fragile than banking systems with many small banks (Allen and Gale, 2004). Further, few large banks might be easier to monitor than many small banks. On the other hand, proponents of the concentration-fragility view argue that market power might result in higher interest rates, which in turn provides incentives to borrowers to take higher risks (Boyd and de Nicoló, 2005). Second, advocates of the concentration-fragility view argue that (i) relative to diffuse banking systems, concentrated banking systems generally have fewer banks and (ii) policymakers are more concerned about bank failures when there are only a few banks. Based on these assumptions, banks in concentrated systems will tend to receive larger subsidies through implicit “too-big” or “too important to fail” policies that intensify risk-taking incentives and hence increase banking system fragility (e.g., Mishkin, 1999). Further, having larger banks in a concentrated banking system could also increase the contagion risk, resulting in a positive link between concentration and systemic fragility.

> 21 See Carletti and Hartmann (2003) for an in-depth literature survey and Allen and Gale (2004) for an excellent exposition on the different theoretical mechanisms that can lead to contrasting relationships between competition and stability. See Beck (2008) for an overview of the recent empirical literature.

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**Figure 1: Distribution of extreme poor, non-poor bottom 40% and non-poor top 60% (2011, 2030)**

<!-- Start of picture text --> 2 0 1 1 2 0 3 0<br>C . T o p 60 % , n o n -po or C . T o p 6 0 % , n on -p o o r<br>A 2 A 2<br>B . B o tto m 4 0 % , n on -p o o r<br>B . B o tto m 4 0 % , n o n -p o o r<br>A 1 . P oo r<br>A 1 . P o o r<br>0 2 0 0 0 4 0 0 0 6 0 0 0 8 00 0 0 2 00 0 4 0 0 0 6 00 0 8 00 0<br>P o p u la tio n (m illio n ) P o pu la tio n (m illion )<br>1 0 0 10 0<br>8 0 80<br>6 0 60<br>4 0 40<br>er c e n tile ( w ith in c o u n tr y)<br>P P e rc e n tile (w ithin c o u n tr y )<br>2 0 20<br>0 0<br><!-- End of picture text --> Note: This chart is inspired by Beegle et al (2014), updated with our data for 2011 and 2030 (baseline, distribution-neutral growth). The area of the figure represents the total world population (in millions). The horizontal axis is the cumulative population of the world (from poorest to richest) and the vertical axes shows the percentiles within each country. The three shaded areas represent three groups in the world. Area A (A1 and A2) represents the World’s extreme poor, 14.5% of the total area. Area B represents the total population which is in the bottom 40% of their respective countries but not classified as extreme poor. Area C represents the total population which is in neither in the bottom 40%, nor among the extreme poor. The graphs assume zero extreme poverty in high income countries.

In this paper, we use a similar comparison to explore different scenarios for growth in the bottom 40%. We define the shared prosperity premium as the difference between the growth in the mean ( ) and growth in the bottom 40% ( 40 ). This premium can be negative or positive, and can be expressed as mm γγ γγ 40 (1) mm= γγ −γγ Growth in the mean ( ) can be written as a weighted sum of growth among the bottom 40% ( 40 ) and growth of the top 60% ( 60 ), where the weights are the respective income shares in the initial period γγ γγ ( 40 , 60 ): γγ ss ss 40 40 60 60 (2) γγ= ss ∗γγ + ss ∗γγ Using the fact that 40 60 and the definition for (1), we can rewrite (2) to obtain an expression 1 for 60 : 60 ss = 1 −ss60 ~~�~~ (also see Equation 4). Figure 2 shows how the growth rate of the top 60% mm varies with their income share for a given value of γγ γγ = γγ+ mm�1 − ss . It is clear that as a result of fixing the growth rate of the bottom 40% above the growth rate of the mean (i.e. ), we impose a lower growth rate on mm the top 60%. As can be seen in Figure 2, this growth shortfall by the top 60% declines with their income mm> 0 5
The third challenge is to distinguish between general investment promotion (information provision, image building, help with red tape etc.) and tax incentives. There is a high correlation between IPA existence and tax incentives, which unfortunately prevents us from separating the two effects with confidence.

# **3.3 Data**

Our data on investment promotion activities come from the 2005 Census conducted by the World Bank’s Research Department in cooperation with the Foreign Investment Advisory Services, the Multilateral Investment Guarantee Agency and the World Association of Investment Promotion Agencies. An electronic survey was sent out to all national investment promotion agencies around the world. After several weeks reminder e-mails were sent out, and after some more weeks phone calls were made to increase the likelihood of response. As the survey forms came in, the data were carefully checked for inconsistencies and missing information. Then new rounds of phone calls were made to clarify inconsistencies and complete the data. The survey was sent out in December 2005, and by April 2006 most of the information was complete. The survey form gave uniformity needed for comparison across countries, while the information collected through the phone calls provided guidance on interpretation of the responses. This comprehensive process yielded responses from 109 national investment promotion agencies. Additionally, we found detailed information on the activities of the Austrian IPA, which did not respond to the Census, on its website. The sample covers countries across all geographic regions as well as all income levels. Eighty-one of the responses received were from developing countries. The sample also includes additional 31 countries that we regard as very likely to not have an investment promotion agency. These were identified by their absence in different directories of IPAs, lack of websites, by confirmation of national embassies/other national public institutions or by consultations with World Bank country economists.<sup>10</sup> A potential concern is that high quality agencies are overrepresented in the sample due to selfselection. We cannot rule out this possibility completely, but a glance at our sample reveals a wide representation of countries across all income groups and regions. Also our experience from collecting the data suggests the opposite. Some developed countries were among the hardest to

> 10 The actual number of countries included in the empirical analysis depends on the availability of other variables included.

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tradition fostered by the UK Treasury. Only in May 1997 has the newly elected labor party government of Prime Minister Tony Blair assured the Bank of England a free hand in its pursuit of price stability. Such independence may still on occasions be compromised as the Chancellor of the Exchequer still retains a presence on the board of directors as a voting member. New Zealand and France (unitary states) have lately recognized the importance of central bank independence for price stability and have granted independence to their central banks. The 1989 Reserve Bank Act of New Zealand mandates price stability as the only function of the central bank and expressly prohibits the government from involvement in monetary policy. The People’s Bank of China, on the other hand, does not enjoy such independence and often works as a development bank or as an agency for central government “policy lending” and in the process undermines its role of ensuring price stability (see World Bank, 1995 and Ma, 1995). For monetary policy, it has only the authority to implement the policies authorized by the State Council. The Law of the People’s Bank of China, 1995, article 7 states that its role is simply to “implement monetary policies under the leadership of the State Council” (see Chung and Tongzon, 2004).

For a systematic examination of this question, Huther and Shah (1996) relate the evidence presented in Cukierman, Webb and Neyapti (1992) on central bank independence for 80 countries to indices of fiscal decentralization for the same countries. Cukierman et al. assess independence of a central bank based upon an examination of 16 statutory aspects of central bank operations including the terms of office for the chief executive officer, the formal structure of policy formulation, the bank’s objectives as stated in its charter, and limitations on lending to the government. Huther and Shah (1996) find a weak but positive association between fiscal decentralization and central bank independence confirming our a priori judgment that central bank independence is strengthened under decentralized systems. Table 1 uses a cross section of 40 countries for the period 1995-2000 to provide econometric analysis of the impact of fiscal decentralization on central bank independence. The results confirm the positive impact of fiscal decentralization and federalism on central bank independence.

Increases in the monetary base caused by the central bank’s bailout of failing state and non-state banks represent occasionally an important source of monetary instability and a significant obstacle to macro economic management. In Pakistan, a centralized federation, both the central and provincial governments have, in the past, raided nationalized banks. In Brazil, a decentralized federation, state banks in the past made loans to their own governments without due regard for their profitability and risks causing the so called $100 billion state debt crisis in 1995. Brazil, nevertheless later dealt with this issue head on with successful privatization of stateowned banks in late 1990s and through prohibition of government borrowing from state banks or 6