rafmacalaba/data-use-mentions-extended huggingface dataset:
absolute size of the coefficient. The larger IV coefficient could be due to measurement error in the endogenous variable.

# **<<B>>Falsification test**

To further support the validity of our IVs, in columns 6 and 7 of table 3, we report a falsification test based on the assumption that we should not find an effect of _future_ urban population on rural poverty in the two-stage specifications. On the contrary, if the coefficient of the future urban population is significant, this implies a district-specific trend that is correlated with both rural poverty change and urbanization, which makes the IV estimates inconsistent.

Such a test, however, cannot be directly implemented because the available urban population is lagged two years with respect to rural poverty in the data. Therefore, there is a partial temporal overlap of the two variables, even if the urban population is referred to the subsequent period (e.g., rural poverty in 1983–1993 regressed on the urban population in 1991–2001). Therefore, we use the contemporaneous variable for the years 1981 and 1991 (plus 2001 for the future urban population), and we extrapolate the poverty data for those years using the 38th round and the 43rd round of the NSS survey, conducted in 1983 and 1987, respectively. In this way, we are able to exploit the variation in rural poverty that does not overlap in time with the variation in the urban population. In all other aspects, including the control variables and instruments, the regressions are identical to those reported in column 5 of table 3.

The results from the contemporaneous specification are qualitatively similar to those shown in column 5, with an even larger coefficient (−0.25), which is significant at the 80 percent level (the lower significance is probably due to the interpolation, which adds some noise to the dependent variable). When we substitute the urban 27
low this number, is for a different market and thus is not directly comparable to United States estimates. However, one can use a back-of-the-envelope calculation to provide a more precise elasticity estimate between the two bounds reported by Chang et al. (2014). From Table 1, we find that the median elasticity measured using traded quantities for foreign investors is -0.34, which is 1.62 times larger (in absolute value) than the one we obtain under the assumption that only passive funds rebalance. We can apply this factor to the lower bound elasticity estimate in Chang et al. (2014), to obtain an estimated elasticity of -0.74. Interestingly, in a recent paper, Pavlova and Sikorskaya (2020) estimate, using quarterly portfolio data, for the United States, how many active funds might be behaving as passive when rebalancing as a consequence of changes in Russell indexes. With that strategy, they find an estimated stock demand elasticity for the United States of -0.7, remarkably close to our back-of-the-envelope calculation. Overall, our estimations can be used to back out more precise measures of stock demand elasticities for different markets when data on traded quantities is unavailable.

# **4 Conclusion**

In this paper, we exploit transaction-level data from the Colombian stock market together with episodes of stock additions and deletions from major international equity indexes to shed novel light on the price impact of equity index rebalancings, the behavior of investors around these events, and the elasticity of demand for stocks.

A key takeaway from our analysis is that most large international investors do rebalance their portfolios in the direction of the rebalancing of the indexes they follow. This is consistent with the view that many investors, even when formally active, have ‘implicit mandates’ not to deviate from their benchmark indexes. Instead, arbitrageurs, such as hedge funds, trade very little around these events. As a result, stock demand curves are very inelastic.

14
Policy Research Working Paper 9074

# **Abstract**

Many households in developing countries lack formal financial histories, making it difficult for firms to extend credit, and for potential borrowers to receive it. However, many of these households have mobile phones, which generate rich data about behavior. This article shows that behavioral signatures in mobile phone data predict default, using call records matched to repayment outcomes for credit extended by a South American telecom. On a sample of individuals with (thin) financial histories, our method actually outperforms models using credit bureau information, both within time and when tested on a different time period. But our method also attains similar performance on those without financial histories, who cannot be scored using traditional methods. Individuals in the highest quintile of risk by our measure are 2.8 times more likely to default than those in the lowest quintile. The method forms the basis for new forms of credit that reach the unbanked.

This paper is a product of the Knowledge and Strategy, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dan@bjorkegren.com.

_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
in countries with better contract enforcement: The _Doing Business_ Getting Electricity indicator has a more positive association with firm growth, and the _Doing Business_ Construction Permit indicator has a more positive association with productivity. Provision of electricity and granting of construction permits both require strong contract enforcement. Thus, it is not surprising that these two Doing Business aspects tend to hinge more critically on Contract Enforcement. The _Doing Business_ Getting Credit indicator does not significantly interact with the Contract Enforcement indicator. Overall, there is support for the notion that the de jure indicators of _Doing Business_ are more effective in improving firm performance in countries with better contract enforcement.

# **5. Effect of the business environment on firm performance, foreign versus domestic firms**

Do domestic firms and foreign-owned firms respond differently to the business environment? If so, how? There are reasons why they might respond differently. Foreign firms, for instance, have better access to finance (from foreign owners) and to more advanced technology. They may also have stronger bargaining power with respect to the government: their ability to relocate affects the enforcement of government policies. On the other hand, they may have worse access to information about local markets and demands and preferences of local consumers. We thus assume that domestic and foreign firms respond differently to the domestic BE. In our empirical implementation, we interact the foreign ownership dummy with the BE indicators, while allowing the foreign dummy to have an independent effect on firm performance. Given that interacting all three sets of BE indicators would result in too many coefficients in a single specification (causing multicollinearity), we interact indicators separately from each set of BE indicators. The results are presented in table 7.

Foreign-owned and domestic firms differ not only in their dynamic responses (firm growth) to the BE but in their static response (firm productivity). We conducted an F test that all the interaction terms of the BE indicators with the foreign dummy are jointly zero.The interaction terms are jointly significant only in the regressions of productivity
In this paper, we estimate the total impact of changes in exports driven by foreign demand shocks, rather than tariff changes or import competition, on labor market outcomes including income and employment variables. This direct and indirect export-induced demand has been studied in Goutam et al. (2017) [5]; however, as only employment variables are in focus, many other questions regarding wages and heterogeneity have been left unanswered.

The paper proceeds as follows. Section 2 presents a conceptual framework that allows us to examine the local labor market repercussions of exports, incorporating a supply chain perspective. Section 3 discusses the data used and how we constructed the export exposure analysis using InputOutput linkages. Section 4 describes the empirical strategy and Section 5 presents the empirical results. Section 6 concludes.

# **2 Conceptual Framework**

We apply a standard shift-share approach that assesses the effects of trade shocks on labor markets. Relevant works in the literature include Autor, Dorn and Hanson (2013) and Dix-Carneiro and Kovak (2015) [3][2]. More directly related to exploring the effects of exports on local labor markets are studies by Robertson et. al. (2021) and G ́oes, Lopez-Acevedo, and Robertson (2023) [8] [4].

Unlike the papers above, however, our index in this paper is not one of export exposure, but one of total export receipts exposure. By exploiting the input-output structure of production, we account for both direct and indirect payments to factors of production, and use trade data to move closer to regional production, which is what we would ideally like to observe.

Let _s,d_ be industry index ( _s_ stands for source sector and _d_ stands for destination sector), and let γ _s,d_ denote the intermediate use shares of a good of industry _s_ in the production process of a good of industry _d_ . Under the assumptions of perfect competitive or monopolistic competitive product markets, a constant fraction of total sales will be paid to the
|75th|80|38|95|27|85.5|
|---|---|---|---|---|---|
|25th<br>50th<br>ditional on Using Area|80<br>80|32<br>34.5|83<br>85.7|24<br>26|80<br>80<br>ok up program.|
|SD<br>Hours Con|12.9|10.2|15.9|5.3|5.4<br>firms that to|
|Mean|77.8|35|92.1|24.3|83.5<br>least four|
|Number of<br>Firms Using|71|79|34|19|32<br>missing for at|
|Area|Operational Productivity|Commercial|Labor Productivity|Energy Efficiency|Quality<br>Note: data on hours per area|

34
large farms’ better endowments in terms of soil quality or infrastructure access, it also points towards _prima facie_ potential for spillovers, either through adoption of technology or input use.<sup>10</sup> This is reinforced by the fact that the relationship between yields and farm size is not linear; yields are highest (42, 31, and 42 Q/ha for maize, sorghum, and wheat) for farms in the 10-20 ha range whereas those larger than 20 ha often obtain some 20% less than the top performing group in each crop.

Improved seed and fertilizer have long been identified as key to smallholder productivity in Ethiopia (Krishnan and Patnam 2014). Our data suggest significant variation in their use with differences being most pronounced for improved seed which, for maize, sorghum, wheat and teff, is used by 80%, 42%, 74% and 41% of the largest farms, 52%, 18%, 66% and 85% of those in the 10-20 ha range, but only 25%, 0.4% 7% and 4.9% of smallholders, respectively. Although, with 83%, 45%, and 74% of farms in the largest group who use fertilizer on maize, sorghum, and wheat, compared to 53%, 19%, and 66% in the 10-20 ha category and 39%, 15%, and 73% for smallholders, respectively, the incidence of fertilizer use is higher on larger commercial farms, variation is much less than for improved seed which may be a more binding constraint.

Annual data on small farms for 2004-2014 allow us to describe changes in smallholders’ area, yields, input use, distance to large farms, and exposure to climatic shocks (table 3 panel A). Cultivated area per farmer increased, from 0.89 ha in 2004 to 0.96 ha in 2014 and the fact that more farmers grew maize (46% to 59%), teff (33% to
Overall, more than 750 faculty members of economics departments in developing countries were contacted, covering all World regions: Latin America and the Caribbean, Sub-Saharan Africa, East Asia and Pacific, Europe and Central Asia, and South Asia. This way we were able to collect 171 development economics syllabi from developing countries and were able to extract information on 55 additional development economics courses offered in developing countries from the survey responses. Overall this provides information on 145 undergraduate courses in 54 developing countries, and 81 masters courses in 34 developing countries. A list of the universities from which we obtained the information can be found in the Appendix.

Given the difficulties experienced in collecting information on development economics courses, the reader may be concerned about how representative this resulting sample is. We have two comments on this. The first is that given the lack of information on how development economics is taught around the world, the information we have collected is the best currently available. Second, to the extent that there is a bias from collecting data from the top quarter of universities within countries, and that there is selective responsiveness to our requests for data, we believe this bias will be towards sampling from the best courses taught in developing countries. Therefore any gaps identified are likely to be lower bounds for the extent of these gaps in developing country courses overall.

In order to establish a benchmark against which to compare the courses taught in developing countries, we also asked departments and instructors in the United States for development economics syllabi, focusing only on the undergraduate level given the lack of terminal masters degrees in economics in the U.S. We obtained information on 33 courses, comprising of 16 courses from departments classified in the top-20 economics departments in Repec’s rankings, and 17 courses from departments outside the top-20.

# **4. Results**

This section uses the survey and syllabi data to provide a description of development economics as taught in developing countries, and a comparison of this with the content in top-20 and other U.
*4.2 Baseline results**

Drawing on a sample of around 61,000 rural households in 472 districts of the 18 major states from the nationally representative consumer expenditure survey (CES) collected as part of

> 30Artuc et al. (2010) make a similar point in the context of trade shocks using a dynamic model of costly intersectoral labor mobility. Perfectly segmented labor markets can be viewed as an extreme case, retaining the analytical convenience of a static model.

23
.089***|
|Trade (% GDP)|0.076|0041|-0.024|-0.006|0.019|
|Investment (% GDP)|0.012|0.404*|0.682**|0.720**|0.077*|
|Law and Order|0.035*|0.036*|0.033|0.026|0.042***|
|Observations (All)|62|68|78|79|287|
|_Note:_|||_∗_p_<_|0.1; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

Note: Time-effects are used in the panel specification but country-fixed effects are not. Initial productivity is the average of log productivity over the 10-years in the preceding decade, measured in US dollars at 2010 prices and exchange rates. Productivity growth calculated as the change in average log-productivity between the two decades (10-year average growth). Productivity is assumed to grow at its average rate between 2010-8 for the final year of the decade, 2019. All conditioning variables are lagged decadal averages. Data availability of these covariates affects the sample size in each decade.

# **D Estimating convergence clubs: Commonalities in productivity levels**

The mixture model analysis allows the detection of convergence clubs using snapshots of the crosscountry distribution of productivity levels (Battisti and Parmeter, 2013; Henderson et al., 2008; Gr ̈un and Leisch, 2008). Countries fall into the same convergence club when their productivity levels gravitate toward the same long-term productivity level. In contrast, countries fall into distinct convergence clubs when their productivity levels are pulled toward different attraction points.

28
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We are also interested in the impact of the regulatory and institutional framework on the deposit insurance and systemic risk relationship. The adverse consequence of deposit insurance may well depend on the institutional environment and can potentially be mitigated through bank regulation. For instance, better bank supervision may limit the extent to which banks can engage in correlated risk taking activities in the presence of deposit insurance.

We consider a bank supervisory quality index, which measures whether the supervisory authorities have the power and the authority to take specific preventive and corrective actions such as replacing the management team. This variable comes from the banking surveys conducted by Barth, Caprio, and Levine (2008). The surveys were conducted in the years 1999, 2002, and 2005. Because country level regulations change slowly over time, we use the previously available survey data until a new survey becomes available.

Our dependent variable is bank _i_ ’s systemic risk, whose calculation is described in Section 2.3. The regression specifications and control variables are the same as those used in Section 3.2. Because the full coverage dummy is expected to lead to the most egregious moral hazard problem during good times, we examine whether bank supervision can mitigate such an unintended consequence of full deposit insurance coverage.

Table 6 reports the coefficient estimates. We first partition our sample by the bank supervisory quality index. Column (1) of Table 6 shows the regression results for the subsample where bank supervisory quality is greater than or equal to sample median, and Column (2) of Table 6 presents that the regressions coefficients for the subsample where bank supervisory quality is lower than sample median. We find that, in Column (1) of Table 6, full coverage dummy is associated with lower bank systemic risk during global crisis years and has no adverse impact on bank systemic risk in non-crisis years, when bank supervision is strong. In contrast, Column (2) of Table 6 indicates that, full coverage increases bank systemic risk in all years when bank supervision is weak. Overall
Now consider a direct effect, of magnitude _ρ >_ 0, of mother’s height on child’s height, so _hc_ = _gc_ + _ρhm_ + _ec_ . To avoid unilluminating complications of infinite regress, ignore this mechanism in mothers’ generation, so _hm_ = _gm_ + _em_ .<sup>25</sup> Now mother-child height correlation is which will be greater than in the case without _ρ_ because of the additional link between generations. Therefore, if conditions are such that _ρ_ is quantitatively important in a poor country – perhaps due to rapid change across generations in environmental conditions – but not in a richer country, the intergenerational height correlation could be greater in the poorer country.

Figure 8 plots mother-child height correlations from the Indian and African DHS samples used in this sample, and for the U.S. from the National Longitudinal Study of Youth 1979. NLSY data used span several calendar years; each child’s height is used in the year he or she is five years old. The correlation computed from the NLSY, 0.41, is comparable to other correlations computed from developed country data (Livson et al., 1962). It is also consistent with the formula above, if _ω_ = 0 _._ 5 and _σe_<sup>2issmall.</sup> Two conclusions emerge from the figure. First, the correlation computed for Indian children – although greater than that in the African data – is much below the correlation for U.S. children, suggesting that any _ρ_ , a direct causal effect of mother’s height on children’s height, may not be large. Second, the correlation between mothers’ and children’s height is lower, within both India and Africa, in villages with more open defecation, which is consistent with an important role for _σe_<sup>2andwithMartorelletal.’s(1977)conjecturethat</sup> environmental variation will be more important relative to genetics in poorer places.

> 25Perhaps environmental conditions were so
seasonal gap (8 to 9 percent lower than those found using the dummy variable model, as illustrated through Monte Carlo simulations). Caution is warranted in using dummy variable models in future empirical work to estimate the seasonal gap, especially when less than 15 years of (monthly) price data are available and the peak and trough months are not a priori known.

Second, turning to the findings, food price seasonality in Africa remains substantial (despite the somewhat lower estimates than those reported in the literature). It is also quite diverse across crops, regions and market places. Looking across commodities and countries, the average seasonal gap is 28.3 percent. It is highest for fruits and vegetables (60.8 percent for tomatoes) which are highly perishable and whose production is seasonal, and lowest for eggs and cassava, which are harvested throughout the year and which can be stored in the ground (cassava). Among the cereals, seasonality is highest for maize (33.1 percent), about half this for rice (16.6 percent), which is more irrigated and traded internationally, and around 20‐24 percent on average for millet, sorghum and teff (which store better than maize). These averages hide substantial differences across markets within countries, cautioning against generalizations from non‐representative samples, and highlighting the need for targeted interventions (e.g. when providing better storage facilities).

Third, African seasonal price variability appears substantially higher than what is observed internationally. Looking at both maize and rice, for which there are well‐defined international reference prices, the seasonal price gap is two and a half to three times higher than on the international reference markets. This suggests substantial scope for reduction.

Fourth, price seasonality explains on average about 17 percent of domestic staple crop volatility, rising to 25 percent for maize. Clearly, domestically, there is substantial regularity in price volatility, especially for maize. Internationally, the share is only 6 percent.

Finally, seasonal gaps