rafmacalaba/data-use-mentions-extended huggingface dataset:
in these factors, beyond their direct effects for Indian businesses, may also provide beneficial effects from an urbanization and spatial allocation perspective.<sup>13</sup> Our findings suggest that policies that take an inclusionary approach to the urban informal economy may be more successful in promoting local development and managing its strains than those focused only on the formal sector. It is very important for Indian policy makers to recognize that much of the urbanization that is occurring is in the unorganized sector. Moreover, education and infrastructure investments, regardless of original motivation, are primarily operating through the unorganized sector. Going forward, adequate provision of infrastructure is necessary for the informal sector to develop. The more Indian cities recognize this influx and design appropriate policies and investments to support it, the more effective the policy interventions will be. Examples of inclusionary policies are mechanisms to ensure that urban informal livelihoods are integrated into urban plans, land allocation, and zoning regulations; that the urban informal workforce gains access to markets and to basic urban infrastructure services; and that organizations of informal workers participate in government procurement schemes and policy-making processes.

It is something of a paradox that India, among the most densely populated countries in the world, is also among the least urbanized. An important aspect for India’s continued growth is better and deeper urbanization over the next two decades than it has achieved over the past two decades.

# **References**

Amin, Mohammad and Aaditya Mattoo, “Human Capital and the Changing Structure of the Indian Economy,” World Bank Policy Research Working Paper 4576 (2008).

- Baily, Martin, Charles Hulten and David Campbell, “Productivity Dynamics in Manufacturing Plants,” _Brookings Papers on Economic Activity: Microeconomics_ (1992): 187-249.

- Balakrishnan, Pulapre, K. Pushpangadan and M. Suresh Babu, “Trade Liberalisation and Productivity Growth in Manufacturing: Evidence from Firm-level Panel Data,” _Economic and Political Weekly_ , 35(41), (2000): 3679-3682.

> 13 Evaluating
_Sources_ : Own survey of deposit insurers, IADI Survey: Bahamas (2003).

**Bahrain.** ( _Deposit Protection Scheme and Deposit Protection Board, Resolution No. 3_ ) The deposit protection scheme of Bahrain came into effect in November 1993. The scheme has joint administration and ex-post funding. Both resident and non-resident deposits with Bahrain offices of full commercial bank are covered. The coverage is extended to the lesser of BD 15,000 and three quarters of the total eligible deposits of the depositor in the liquidated commercial bank. The scheme extends coverage to both local and foreign currency deposits. The excluded deposits are; government, illegal, inter-bank, deposits of affiliates, shareholders, directors and officers of the banks.

_Sources_ : Bahrain Monetary Agency (2004), Garcia (1999).

**Bangladesh.** ( _Deposit Insurance Fund, Deposit Insurance Ordinance 1984_ ) The deposit insurance scheme of Bangladesh was established in 1984. The system excludes the deposits of domestic and foreign governments, banks and other financial institutions. Deposits in foreign currencies are not covered. All scheduled banks are obligated to be members of the scheme and pay a premium on their deposits at a rate of 0.5%. The system is jointly administered and financed. The agency’s finances are co-mingled within the central bank.

_Sources_ : Asiatic Society of Bangladesh (2004), Garcia (1999), Kyei (1995).

**Belarus.** ( _The Guarantee Fund for the Protection of Deposits by the Population_ ) The deposit insurance system in Belarus was established in 1996 and went through several revisions since then. In 1998, the government promised full guarantees for banks authorized to provide service to government programs. Then they were taken under the supervision of the National Bank of Belarus (NBB) in 2000. In 2001 NBB issued further rules about insurance for ruble and foreign currency deposits in non-authorized banks. As of 2003 the equivalent of USD 2000 (per person per bank) were fully covered under the insurance scheme
other government functions such as infrastructure planning.

However, while governments may desire firms to become formal, firms may find these reasons much less compelling. Registering for taxes will impose additional costs on businesses, lowering profits, and firms may believe they receive few public goods in return. Small firms may not be likely to need some of the purported benefits of formal status (such as bidding for government contracts or exporting) and may face other constraints that prevent them from accessing other benefits such as business banking. And individual firms are unlikely to internalize the societal benefits of their compliance with the rule of law and of their data informing government policy.

> 1 Source: 2004-05 Integrated Household Survey, which shows 93 percent of firms are not registered with the Department of the Registrar’s General (DRG).

2
variables (i.e., lagged endogenous variables in the model) 38. However, unlike in the single equation IV regression where each lagged endogenous variable is used only as the instrument for that endogenous variable, in the system regressions, the entire instrument set for each system is used for all the endogenous variables. In the second approach, we use as instruments only variables within the model that we consider exogenous as well as exogenous variables that are not present in the model 39; this instrument set does not include lagged endogenous variables. Again, these instruments are used for all the endogenous variables within the system.

In a simultaneous equation system. it is quite likely that instruments that are a better match with endogenous variables in one equation -- based on the criteria of a high adjusted R-squared from the first-stage regression and theoretically-correct signs from the second and third stage regressions -- may not be good instruments in other equations<sup>4 0</sup> . Indeed, in the context of our four equation system, the choice of the two instrument sets makes a great deal of difference. Specifically, we find that when lagged endogenous variables are used as instruments, the first-stage adjusted R-squared is consistently higher than when exogenous variables are used as instruments even though they are fewer lagged endogenous variables used as instruments. We also find that lagged endogenous variables are a better match with the growth and size regressions (based on the same

- 38 See Barro and Sala-i-Martin (1995), Barro (I 996a, I 996b) for this approach. Note that lagged endogenous variables are predetermined variables since error terrns are not correlated across decades.

- 39 See Tavares and Wacziarg (1996) and Dollar and Burnside (1996) for this approach.

- 40 We do not base our judgment on the R-squared from the 2SLS or 3SLS as these R-squared cannot be used as model selection criteria.

41
al., 2018), which focus on PMT performance but cannot assess CBT or measure actual performance in practice. Our study also differs from cross-country reviews that are based on actual programs, but not based on primary, micro-level data analysis (Coady et al., 2004; Devereux et al., 2017). Indeed, our methodology allows us to rely on multiple measures of performance that are comparable, and to benchmark our results against potential alternatives (random, geographic or universal targeting). Finally, we provide data on targeting costs, and discuss policy and methodological implications for the design and study of targeted programs in the future.

There are several caveats to our analysis. Our data do not allow us to consider the legitimacy of different targeting methods, or how they affect program impacts on household outcomes. Besides, we do not consider political economy and other issues (Sen, 1993; Devereux, 2016; Duchoslav et al., 2021). All these areas call for further research.

The next section discusses key measurement considerations in analyzing targeting methods. Section 3 describes the Sahel context and reviews the existing literature on targeting methods. Section 4 presents the 4
_Notes:_ *p < 0.1; **p < 0.05; ***p < 0.01. Standard errors clustered on commune level. Quantile regression standard errors are bootstrapped with re-sampling at the commune and household levels. Logit marginal effects estimated with observed values for all other characteristics.

The results with respect to nutrition and anthropomorphic measurements are shown in Table 8. It should be noted that (almost) all the coefficients are statistically insignificant. Consequently, the INDH appears to have had no significantly positive impact on the anthropomorphic measurements or on the nutritional status of the children studied. The only parameter that emerges as significant and positive is the height-for-age parameter in the 2013 wave. It should also be noted that the relationship between the poverty rate of the rural commune where the household is located and the anthropomorphic measurements are not all negative as they ought to be. Given these results, the INDH has not had any positive impacts, either directly or indirectly, on the nutritional status of children living in INDH-targeted communes.

24
0.031|0.097**|0.030|-0.037|0.057|-0.049|
||[0.040]|[0.047]|[0.032]|[0.080]|[0.037]|[0.042]|
|Observations|5,564|7,523|7,464|7,439|6,560|7,457|
|Number of Municipalities/Waves|85/6|112/8|112/8|112/8|112/7|112/7|
|R-squared|0.135|0.051|0.077|0.086|0.078|0.051|
||Panel<br>|C: Individuals Liv<br>|ing in Municipa<br>|lities with Wea<br>|k State Capa<br>|city<br>|
|NTL Density at Municipality (in logs)|0.094***|0.031|0.036|0.187**|0.098***|0.014|
||[0.029]|[0.024]|[0.028]|[0.073]|[0.021]|[0.030]|
|Observations|3,082|4,323|4,209|4,237|3,756|4,302|
|Number of Municipalities/Waves|47/6|64/8|64/8|64/8|64/7|64/8|
|R-squared|0.104|0.036|0.055|0.074|0.055|0.074|
||Panel|D: Individuals Liv|ing in Municipal|ities with Stron|g State Cap|acity|
|NTL Density at
some of their assets. In Table 4.5 we relate the consumption and investment behavior of households in the crisis to their use of bank debt (Credit card, Mortgage). We hereby control for household income, education and employment type. The LITS survey also allows us to control for a range of economic shocks which may have hit the household during the crisis: job loss, income reduction, closing of a family business, or a reduction in remittance flows to the household.

The multivariate analysis presented in Table 4.5 suggests that those households with mortgage debt were more likely to reduce their consumption and investment during the crisis. Households with a mortgage were 3% more likely to reduce their consumption of goods and 8% more likely to cut their use of services and 2% more likely to sell assets. By comparison a job loss by a household member increases the probability of reduced consumption, cut services, and asset sales by 8%, 13% and 2% respectively. Thus at roughly half the impact of the effect of income shocks, the impact of mortgage debt on consumption and investment are sizeable from an economic point of view. Interestingly, we find no impact of credit card use on household consumption or investment. This suggests that many credit card holders may not use them to incur substantial debt.

In line with our analysis for foreign currency firm debt, FX mortgages do not seem to be a source of debt overhang in the household sector. Figure 4.2.b plots the incidence of FX mortgages per country against the depreciation of the local currency against the euro in 2008 and 2009. The figure shows clearly that those countries with the highest incidence of FX mortgages 32
sizes that the impact of disasters does not necessarily increase the prevalence of IPV, even if it is assumed that negative economic shocks lead to increased IPV.<sup>5</sup> Specifically, husbands have the potential to engage in IPV if consumption falls below a critical threshold. However, turbulent disasters have the potential to decrease consumption and to change intrahousehold roles and gender norms, which can in turn also change the critical consumption threshold and further affect the prevalence of IPV (e.g., Panda and Agarwal 2005; Heise 2011; Doss 2013; etc.). Whether a disaster increases or decreases the prevalence of IPV in this setting depends on the change in the consumption threshold relative to the change in consumption.

To more fully investigate the relationship between disasters and IPV, we analyze the impact of a devastating earthquake and a series of aftershocks that precipitated a humanitarian and displacement crisis in Papua New Guinea (e.g., Government of PNG et al. 2018; IOM 2018; etc.). The disaster occurred between the third and fourth phase of a nationally representative survey that collected extensive information on IPV. The initial phases of the survey focused on urban areas and then progressively surveyed more rural and remote regions, and comparing the IPV changes after the third phase in the provinces most strongly affected by the disaster to other regions allows us to estimate the immediate impacts. Several robustness checks illustrate that the changes in IPV following the disaster are likely not being driven by substantial differences between urban and more remote populations across provinces or in regional differences in survey implementation.

The data illustrate two primary empirical findings. First, there was a large decline in the acceptability of IPV in the disaster-affected regions in which the vast majority of the deaths occurred and in which virtually all of the internal displacement was concentrated (e.g., Government of PNG et al. 2018; IOM 2018; etc.). The share of men who thought it was acceptable for a husband to beat his wife declined
The organized manufacturing sector is surveyed by the Central Statistical Organization every year through the Annual Survey of Industries (ASI), while unorganized manufacturing establishments are separately surveyed by the National Sample Survey Organization (NSSO) at approximately five-year intervals. Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. We use the provided sample weights to construct population-level estimates of total establishments and employment at the district and two-digit NIC level. We focus mostly on district and industry variation in our empirical analyses. Districts are administrative subdivisions of Indian states or union territories that are more appropriate spatial units for understanding the urbanization process.

These surveys identify for each establishment whether it is in an urban or rural location. Our study considers changes in urbanization over time, and thus the stability and comparability of this survey question over time are very important. To begin with, the statutory definition of an urban setting during our period of study is:

- (a) All statutory places with a municipality, corporation, cantonment board or notified town area committee, etc., or

- (b) A place satisfying the following three criteria simultaneously:

- i) A minimum population of 5,000; ii) At least 75% of male working population engaged in non-agricultural pursuits; and

- iii) A density of population of at least 400 per sq. km. (1,000 per sq. mile).

This definition has been mostly stable since the 1961 Census. One set of adjustments with the 1971 and 1991 Censuses focused on including ‘outgrowths’ (e.g., railway colonies, university campuses, industrial townships, and residential and commercial complexes) that lay beyond strict town or village boundaries within a combined urban agglomeration concept for the purposes of classifications. A second set of adjustments enacted since the 1981 Census focused on the definition of the workforce and agricultural sector.<sup>4</sup> Among our datasets, the 1989 survey follows the 1981 Census classification, the 1994 and 2000 surveys follow the
these variables for Chile in Table 3.1 reveals an increase in the wage gap between the skilled and the unskilled in the 1990s, and after a short plateau, a fall throughout the 2000s. The relative supply of skilled workers increased over the two decades, although at a slower pace in the 2000s. These patterns of wages and supply are consistent with a strong increase in the demand for skilled labor in the 1990s and a fall in the 2000s.<sup>25</sup> The pattern for the high school-dropout relative remuneration, presented in Table 3.2, is similar to the evolution of the aggregate wage skill premium: it increased slightly over the 1990-2000 decade, and fell significantly (-1.8 per year on average) for the 2000-2009 period. The main difference with the aggregate skilled-unskilled trend is the evolution of the relative supply: workers with secondary education increased substantially for both decades, and the relative change of the high school group with respect to dropouts was larger than the relative increase for the higher skill group. This pattern indicates the presence of an increasing relative demand for workers with secondary education in the 1990s, which counterbalanced the negative effect of the increase in their supply on the corresponding wage premium, and a lower impact of demand factors for this group in the following decade.

The household surveys in Colombia have changed substantially over the period under analysis, posing substantial challenges for the analysis of patterns over time. Table 3.1 and 3.2 report changes in these variables for two episodes for which surveys are comparable to a reasonable degree – 1996 to 2000 and 2001 to 2007. During the first period, the wage skill premium became substantially larger, despite a strong increase in the relative labor supply of skilled workers. This combination suggests a strong increase in the relative demand for skilled labor. In contrast, the 2000s have

> 25 The pattern of a roughly constant wage skill premium and sizeable increase in the relative supply of skilled labor in the 1990s, and the conclusion
other”.<sup>10</sup> Contributors can also provide additional information, such as whether the random assignment was carried out using stratification. We encourage listing out all stratification variables. Stratification and other procedures aimed at improving balance such as re-randomization typically reduce variance but also need to be accounted for in the analysis.

Finally, the section records the _study arms_ . Contributors can give an identifying _name_ to each arm, list the _targeted_ and _actual number of randomization units_ in the arm, and then indicate which intervention(s) this group received (if any: a control group may receive no interventions). This allows users to back out, for example, how many subjects received the same intervention across treatment arms, and (for factorial designs) which intervention combinations are observed in the data.

Some experimental designs are common in the social sciences but less so in clinical trials. We make some recommendations on how to describe these designs using the options provided. One example is randomized phase-in, meaning that the intervention is randomly assigned to start at different times for different experimental arms, and the comparison between the arms (already) receiving the intervention and the arms

> 9For example, the AEA RCT Registry and YARD only have free text fields to describe the intervention(s) and do not between _interventions_ and _arms_ .

> 10Note that we dropped “single group assignment” and “sequential assignment” as these assignment strategies do not create a comparison group and therefore do not constitute an RCT by the common understanding of this term in the social sciences.

12
Next, we analyze poverty rates by demographic category and by displacement status (Table 10). In Ethiopia, the monetary measure shows evidence of large differences in poverty rates between refugees and the host community. For example, while 63% of refugee households with a female household head are monetary poor, only 7% of the female-headed households in the host community are identified as poor by the $1.90 a day measure. This gap between the refugee community and the host community is also large among households headed by men. When turning to the multidimensional measure, a similar pattern emerges, with 55% of refugees in female-headed households being poor in Ethiopia, while only 14% are poor using the multidimensional measure in the host community. These results are consistent in Nigeria among male household heads. Among female household heads, we find no differences between IDPs and non-IDPs using the monetary poverty measure; however, there is a sizable gap between these two groups when using the MPI.

To explore the role of possible disruptions to family structures as part of the displacement, Table 10 also presents descriptive analysis by _de jure_ and _de facto_ head. We find that regardless of the type of female head, IDP households are significantly more likely to be classified as poor according to both the monetary and the multidimensional measures. Another striking finding is that _de facto_ headed households are more likely to be monetary and multidimensionally poor in Ethiopia, while the opposite is the case in Nigeria.

The legal environment, as well as the disproportionate role in caring and housework responsibilities, may act as a disadvantage for households with a relatively larger number of women contributing to the household income. The third set of poverty rates shown in Table 10 explores this hypothesis. In Ethiopia, for example, households where women are contributing more to the household income and those with no earners are the most vulnerable groups in both the refugee and the host community. This pattern is consistent using both measures of poverty. On the other hand, households where most earners are
and NGOs for social protection purposes (cf. Gentilini et al., 2020, 2021; Aiken et al., 2021). CDR have also been shared with the public sector for public health and humanitarian aid applications (Milusheva et al., 2021). Access issues aside, CDR contain private and sensitive data, including phone numbers and location traces. While much has been written about enabling responsible use of CDR for humanitarian response (e.g. de Montjoye et al., 2018; Oliver et al., 2020), to date no consistent privacy standards exist. Informed consent can increase participant agency, but also complicates the implementation logistics. Data minimization may provide a complementary pathway to privacy: as reflected in the feature importances in Table S2, our models rely primarily on only a fraction of the features we — derive from mobile phone data it may therefore be possible to restrict models to features that minimize privacy risk (such as statistics that do not involve contact networks or mobility patterns) without compromising model accuracy. Finally, there may be ways to incorporate differential privacy or other privacy enhancing technologies into a CDR-based targeting system, but such privatization would likely decrease targeting accuracy (Hu et al., 2015).

**Algorithmic transparency and strategic behavior** Using CDR to determine program eligibility may introduce incentives for people to manipulate if and how they use their phone. For instance, while a program that targeted households without phones first might make sense in the context of one-off emergency response, it could not be deployed in equilibrium, as it would introduce undesirable incentives for people to not use their phones. In less extreme settings, we might still expect strategic manipulation of how people use their phones, if they know such behavior is being monitored. These considerations are not unique to CDR, as degrees of manipulation have been documented in social programs that use proxy means tests and other traditional targeting mechanisms (Camacho & Conover, 2011; Banerjee et al., 2018). While complex machine learning