rafmacalaba/data-use-mentions-extended huggingface dataset:
ones, we compute the ratio of the net effects of the external variables to the total predicted effect of the shocks. Due to the differences between the predicted change and the observed value, the ratio of the net external effects to the observed change in output growth was also computed. In all the countries, except Colombia, the ratios are well above .5 (Table 9), pointing at the importance of external factors explaining output fluctuations in the subperiod 1998-1999.

Table 9 Effects of external factors on growth as a ratio of the total predicted changes and the observed changes in output growth

||External effects/Total<br>predicted effects|External<br>changes|effects/ Observed|
|---|---|---|---|
|Argentina|.64||.74|
|Brazil|.62||1.29|
|Chile|.66||.77|
|Colombia|.28||.23|
|Mexico|1.3||2.1|

These results coincide with those obtained from the panel exercise with quarterly GDP (Table 2), according to which about 50% of the observed regional slowdown in 19981999 was due to external effects. Colombia is the country where the slowdown is less related to the external factors (Table 9). Brazil and Mexico, in contrast, seem to be the more affected by external shocks. These results support the Joyce-Kamas (1997) study of Mexico and Colombia where they conclude that external factors are the main source of
13

## **IV.** Assessing the Gains from Regulatory Reform

While information on the economic impacts of regulation is limited, there is a fairly comprehensive database in the United States and in some other countries that provides a good indication of the scope for regulatory reform. Moreover, several countries are in the process of developing useful information that would help streamline the regulatory process (see Box 2). Here, we first examine the potential for improving social regulation and then examine the potential gains from reforming economic regulation.

# **Box** 2 **Regulatory Reform in Mexico**

The government of Mexico is now implementing a far-reaching program to carefully examine the country's regulatory structure at the federal, state, and local levels. The aims of the Agreement for the Deregulation of Business Activity include streamlining federal regulation, reducing corruption by codifying regulation, and helping to promote more efficient and effective regulation. The program, while new, has enjoyed some early successes. Recent legislation simplifies administrative procedures, requires a quicker administrative response time, and reduces paperwork for foreign investors. In addition, a series of legal reforms aims to simplify court proceedings and reduce the costs of commercial lending. As a result of these reforms, Mexico City's Superior Court reports that the number of civil trials filed decreased by 24% from 1995 to 1996. Agency-by-agency rule simplification and elimination is also proceeding swiftly. For example, the approval time for a business requiring health, safety, and environmental controls to begin operation has been reduced from an average of over 200 working days to a maximum of 21 working days. Finally, a complete inventory of federal rules in effect are available on the internet. Making such information more easily accessible should help to reduce corruption and compliance costs.

Source: Secretaria De Comercio Y Fomento Industrial (1996)

## **_Social Regulation_**

In the area of social regulation, it is essential to examine the likely impact of individual regulations. Hahn (1996) has compiled the
high temperatures (similar to 2020) with higher precipitation, providing favorable conditions for crop growth. Failure to include weather-related variables may thus bias estimates of the effects of the conflict.

# **3.2 Methodology and results**

To estimate the impacts of concurrent or past conflict on area cultivated with winter and summer crops at the village level, we use data from a panel data set covering 10,770 VCs over 2019–23. Indexing VCs by and years by , the model to be estimated is vv tt v v v −1 v v (1) vv vv YY = αα + ββC + δδC + γγXX + λλ + εε where v is the outcome variable of interest (area covered with summer or winter crops) in village in year ; C v is a vector of conflict indicators (intensity of conflict or area damaged by conflict based on YY vv satellite imagery at different stages of crops’ vegetative cycle based on public sources); v is a vector of tt time-varying variables including weather (number of GDDs) and precipitation at different stages of crop XX development and their squares and, for summer crops, the winter crop area to allow for substitution effects<sup>8</sup> ; are village fixed effects that control for time invariant factors such as agronomic suitability, access to infrastructure or economic opportunities, and quality of local leadership; vv s are time fixed effects; and v αα is a random error term. vv λλ εε Beyond providing an estimate of the effect of contemporaneous conflict ( ), this regression allows testing whether there is persistence in the sense that exposure to conflict in the past affects current production _(δ)_ , ββ at least for one lag. Moreover, to the extent that control for time invariant factors, such as access to infrastructure, soil quality, or village leadership, and vv v as well as for periods before 2022 capture timeαα varying factors not affected by the war, 2022 or 2023 would provide an upper bound estimate of
302|
|Country&year fixed effects|Yes||Yes||
|N|2280||2518||
|Number of countries|145||154||

The third test of robustness is to examine whether the relationship between policy actions and the quality of government economic policy has changed over time. To do this we split the sample at breakpoints from 2000 to 2011, and estimate the main equation (8) for both the earlier sub‐period and the later. The results are reported in full in Annex C, and may be summarized as follows: using the CPIA as the dependent variable, there is a hint of a decline in the coefficient on policy actions (α) at some point 2002 and 2007, but the change is not statistically significant. Using the Economic Freedom Index as the dependent variable, there are fluctuations over time, but these are not statistically significant. Hence, overall, we are not able to conclude that there are significant changes over time in the relationship between development policy operations and the quality of economic policy.

# **7. Discussion**

We have established two ‘stylized facts’ about economic policy lending:<sup>19</sup> policy‐based operations have a positive impact on the quality of economic policy; and the number of policy actions in a given year makes no difference beyond the first. We have also found some tentative evidence that the impact of a policy engagement is strongest in the early years and, while remaining positive, tends to fall in later years.

First we consider why the number of policy actions—beyond the first—does not matter. We surmise that this is because the engagement process or “policy dialogue” is key, not so much

> 19 It is important to note that these findings are limited to economic policy actions, and may or may not be generalizable to policy‐based operations in other areas.

21
how households are able to respond to the intervention. All rounds of the RRMIMP data are used to conduct a household-level panel estimation to compare short- and long-term household effects.

Following Jalan and Ravallion (1998, 2002), the third survey round allows us to employ a dynamic panel estimation, whereby household outcomes _yit_ (such as income or expenditure, for example) depend on the lagged outcome _yit_  1, the household’s current and lagged characteristics _xit_ , project status _pit_ ,<sup>3</sup> and initial (pre-program) geographic factors _vi_ 0 interacted with year _t_ , expressed as follows: The initial (pre-program) community characteristics in 1997, _v j_ 0 , are included to account for factors that could affect program placement, such as agroclimatic features and access to public institutions (e.g., secondary schools, banks, and healthcare facilities) (Jalan and Ravallion 1998; Khandker, Bakht, and Koolwal 2009). We assume that the error term includes a household-specific unobserved effect  _it_ (which may also include unobserved geographic effects) correlated with the regressors, as well as an i.i.d. random component _uit_ , which is orthogonal to the regressors and serially uncorrelated.

If  _it_  _i_ (that is, the unobserved effect is fixed over time), taking deviations from means in equation (1) removes the unobserved effect and yields the following differenced equation: ( _yit_  _~~y~~ i_ )  ( _yit_  1  _~~y~~ i_ )  1( _xit_  _~~x~~ i_ )  2( _xit_  1  _~~x~~ i_ )  ( _pit_  _~~p~~_ )  _vi_ 0( _t_  _<u>t</u>_ )  ( _uit_  _~~u~~ i_ ) (2)

> However, heterogeneity stemming from
# **I.  Introduction**

Recorded flows of workers’ remittances to developing countries have grown from $66 billion in 2000 to more than $100 billion in 2004. Such high levels and rapid growth rates have brought increasing attention to remittances as a potential tool for development. But even these statistics are likely to significantly understate true remittances, as a large share is believed to flow through informal channels. Market observers suspect that informal flows may range from about 50 percent to 250 percent of recorded flows.<sup>1</sup> The primary motivation of this study is to estimate informal remittances for a broad range of countries. To do this, we construct and estimate a model of the determinants of remittances which focuses on the cost of sending remittances through formal channels, the presence of exchange-rate and other economic distortions, migration levels, and other country characteristics. The resulting cross-country estimates, based on macro-level data, are then supplemented with new country-specific micro-level estimates, based on household survey data on remittance receipts.

We define informal remittances as all types of money transfer services that do not involve formal contracts, and hence are unlikely to be recorded in national accounts. Formal channels include money transfer services offered by banks, post office banks, non-bank financial institutions, and forex bureaus and money transfer operators like Western Union and MoneyGram. Informal channels include cash transfers based on personal relationships through business people, or carried out by courier companies, friends, relatives or oneself. In addition, more advanced Hawala and Hundi systems allow faster transfers, relying on a network of agents that transfer money quickly.

> 1 Celent (2005) estimates informal remittances will be 35 percent of total in 2006, AITE(2005) estimates that remittances exceed remittances recorded in IMF statistics by 250 percent.

2
well-documented by anthropologists and sociologists and has been observed among groups all over the world and throughout history, including in northern India, Mediterranean societies, Saudi Arabia, many of the traditional societies of Eurasia and Africa, and the southern United States (Schneider, 1971; Nisbett and Cohen, 1996; Gregg, 2005). In the culture of honor, certain behaviors are regarded as transgressive (that is, challenges to honor), and individuals deter transgressions against themselves by maintaining a reputation for aggressive retaliation (defending their honor). As the anthropologist Joyce Pettigrew (1991, p. 174) writes, the culture of honor “enjoined that revenge be exacted for personal insults and damage to person or property.” As we confirm with a vignette-based survey, the concern for honor is much more characteristic of high castes than it is of low castes, perhaps because low castes have traditionally been denied the right to accumulate wealth and achieve status.

In many ways, the outcome ( _Stag_ , _Hare_ ) fits the bill for the kind of event that would elicit an aggressive, retaliatory response under the culture of honor. A subject that plays _Stag_ is “harmed” by a partner who plays _Hare_ , in the sense that the partner’s choice of _Hare_ imposes on him a loss. The only “honorable” response may be to punish the partner, 3
46 individuals makes it impossible to satisfy the criteria of enough selenium without violating the copper limit (Schneider 2021b).

The least-cost diet metric at the household level could be useful for numerous policy purposes, but we also note that our methods are an initial attempt and further research is warranted. Food prices and item availability are already used in food security early warning systems, incorporating the cost of nutritionally adequate diets could be used to enhance such systems to become more nutrition sensitive. However, where the underlying food price data used for such analyses come from market prices, they may not reflect the prices households face in hyper-local markets nor the option to supplement market availability with own production. Future research should investigate how unit costs from household surveys could be used to estimate more precise least-cost diets, their composition, and the relative contribution of own production to what is available in markets in order to have access to a complete diet. Further, modeling tools such as stochastic optimization could be used to better capture the stochastic nature of the food environment and incorporate uncertainty in the underlying data.

Lowering the cost of nutrient-adequate diets could be achieved through myriad interventions throughout the food system, for example determining food items for investment to increase productivity and reduce cost, guiding safety net transfers to close the affordability gap, or using the food items that emerge in the least-cost diet food basket to guide dietary recommendations for low-income consumers. Although sharing meals is the cultural norm in Malawi, our study has shown that the food items available in rural markets cannot meet the needs of most rural families throughout the year for a diet that is sufficiently nutrient-dense to be shared by the family and meet the needs of all members. Least-cost diets have been used in other countries to determine the amount of public assistance provided to individuals and families to purchase food (Carlson et
4 inform about the possibility of profitable intensification, not on the particular interventions or intervention packages needed to broker it. Limited profitability of intensification has been raised regularly as a potential reason for low technology adoption in SSA (Burke et al., 2017; LiverpoolTasie et al., 2017; Sheahan et al., 2013). Knowing that profitable intensification is possible is a precondition to the design of any intervention package.

The empirical application concerns intensification among smallholder rice growers’ households in Côte d’Ivoire. Rice is an important staple crop for the majority of households in SSA, with rapidly expanding demand following income growth and urbanization continuing to exceed more gradually growing domestic supply. As a result, SSA continues to import around 40 percent of its rice consumption. It accounts for about one-third of the global rice trade. Rice production in SSA remains largely rainfed, including in lowlands. This also holds in Côte d’Ivoire. Lagging production levels have been explained by delays in sowing time due to lack of mechanization, limited access to modern inputs, poor agronomic practices and lack of working capital among the supply chain actors (Komatsu et al., 2022; Niang et al., 2017; Tanaka et al., 2015, Wopereis, et al., 2013).

In particular, baseline data from low-land rice farmers in 60 villages from three different agroecological regions in Côte d’Ivoire<sup>4</sup> are used to investigate the productivity and profitability effects of four different, currently practiced, production technology packages: (a) a traditional production package (TP) involving manual land preparation, without any chemical inputs, (b) a semitraditional production package (STP) including manual land preparation and the use of chemical inputs, (c) a semi-modern production package (SMP) involving animal traction for land

> 4 The data were collected in the context of a World Bank inclusive smallholder rice value chain development pilot (https://www.jobsanddevelopment.org/cote-divoire-country-pilot/)
labor costs only when making losses because re-contracting workers is costly; and they cannot adjust their fixed costs. Implicitly, we assume that firms aim to weather the shock in a way that allows them to scale their production back up quickly at the end of the lockdown. In practice, some of the loss in demand might be permanent, and the losses are likely to occur over a longer span than three or five months, albeit at a lower rate once movement restrictions are eased.<sup>1</sup> While simplistic, these transparent assumptions provide plausible lower bounds of the pandemic’s effect on formal firms and on tax revenue, which can later be complemented with general equilibrium analyses and realized data.

This paper uses for the first time a novel dataset of corporate income tax records across countries assembled by the authors as part of a project to promote the use of administrative tax data from developing countries for research and policy evaluation.<sup>2</sup> Administrative tax data often represent the most recent source of firms’ balance sheets in developing countries, they include the largest firms which are often missing in survey data, and they typically constitute the only panel data of firms. Our dataset contains the entire formal corporate sector of 10 countries at different levels of development, in Sub-Saharan Africa, Eastern Europe and Latin America. While the structure of corporate tax returns differs across countries, we harmonize the

> 1Balla-Elliott et al. (2020) shows that firms expect demand to be a third lower than pre-COVID levels upon re-opening.

> 2See Pomeranz and Vila-Belda (2019) and Slemrod (2018) for a summary of recent studies using administrative tax data.

2
research that has focused on low-income countries has relied largely on two sources of training and validation data: (i) manually-labeled optical imagery (DeFourny et al., 2019; Xiong et al., 2017; Wei et al., 2020),<sup>2</sup> and (ii) ground data collection, including as part of household and farm surveys (Hegarty-Craver et al., 2020; Jin et al., 2017, 2019; Kerner et al., 2020; Lambert et al., 2018; Richard et al., 2017).<sup>3</sup> Our paper is related to earth observation applications that rely on georeferenced survey data to meet model training and validation needs.

> 2 In order to map cropland across the African continent, researchers have manually labeled land cover types for sample points using high-resolution optical imagery as a reference. For example, Xiong et al (2017) created a training data set covering Africa by labeling homogenous crop/non-crop areas with a minimum area of 90 x 90 meters (0.81 ha). Several satellite-based cropland data sets have been created for the African continent (using manually-labeled satellite imagery - see Wei et al.’s (2020) comparison of the available data sets), and at sub-regional scales (DeFourny et al. 2019).

> 3 In order to train and validate remote sensing models for mapping cropland and areas cultivated with maize and nonmaize crops, including cassava and beans, at sub-national areas in Tanzania and Kenya, researchers either have used existing survey data that were collected by non-governmental organizations and government agencies to (Jin et al., 2017, 2019; Kerner et al., 2020) or have conducted new survey data collection to meet model training needs (Richard et al., 2017). Similarly, Lambert et al. (2018) rely on sub-national in situ data collected in Mali for satellite-based crop

2
about possible measurement problems in the objective measures, or at least a prompt for deeper investigation.

One possible subjective indicator of service delivery quality would be reports of problems taken from _international surveys of individual respondents_ . An example is the questions fielded by Gallup in a sample of countries in 2003-2006, which asked about the incidence of problems including poor teaching, overcrowded classrooms, school facilities in poor condition, lack of drugs, and disrespectful staff.<sup>18</sup> Because they are included in general-purpose surveys, these questions will typically not yield detailed information, but may serve as a general barometer of quality of services. A second set of indicators would be the results of _household survey modules_ on availability and quality of services _._ Household surveys could go into more depth than the polling-style surveys, and could produce more rigorously quantitative measures of performance.

# **Conclusion and Next Steps**

In this paper, we have laid out a rationale and framework for constructing indicators of the quality of governance policies and performance in the health and education sectors, as well as the quality of service delivery itself in those sectors. We have also begun to fill in this framework by suggesting candidate governance indicators (both policy and performance) in five key areas—human resources, financing and management, information, provision of critical

> 18 The survey also included questions on provider absenteeism, which could be used under the ―governance performance‖ indicators.

34
about four times more often than do other participants. On the other hand, Allcott and Kessler (2019) report only 2.4% inconsistent choices in a mail-in survey with US energy customers.

Note that Andersen et al. (2008) have argued that choices that look inconsistent simply indicate that the subject is indifferent between a range of options. Complete indifference should be a knife-edge case if the MPL is well constructed and choices are realized, but the subject may still care little about the offered payoffs compared to, for example, the time it takes to go through them carefully, and therefore choose not to incur the attentional 10
L & EC > 750 μS/cm

- Medium Risk: As > 10 μg/L & EC > 750 μS/cm

- Low Risk: As > 10 μg/L & EC > 500 μS/cm

Groundwater risk maps are generated using the above sets of criteria in ‘R’ programming language (R Core Team, 2017). First, raster maps of spatial grid resolution of 2.5-km × 2.5-km for the three hydrological risk variables are generated using the Inverse Distance Weighting (IDW) algorithm in ESRI’s ArcGIS environment (software version 10.3.1). Raster data sets are imported into ‘R’ statistical software environment and the above sets of criteria are applied to individual grid points (total number of grids: 19,613 covering an area of ~148,000 km<sup>2</sup> ).

# **3. Results**

## **3.1 Groundwater risk mapping at the national scale**

8
In a related study, spatial misallocation is shown to have had similarly dramatic costs in terms of output in China and India. Hsieh and Klenow (2007) found that moving to “U.S. efficiency” in terms of the spatial allocation of economic activity would increase total factor productivity (TFP) by 30-50% in China, and 40-60% in India. The output gains would be roughly twice as large if capital accumulated in response to aggregate TFP gains. They further estimate that deteriorating allocative efficiency may have shaved 2% off Indian manufacturing TFP growth from 1987 to 1994, whereas China may have boosted its TFP 2% per year over 1998-2005 by addressing these distortions.

These studies reflect the consensus in the literature: migration and the spatial distribution of economic activity are crucial factors for economic development. For this reason, the very low rates of domestic migration in Uzbekistan – recorded both in official government statistics (Table 1) and the independent survey data used in this study – are cause for concern. The State Statistics Committee (SSC) of Uzbekistan estimates that the migration rate for the 2018 calendar year stood at about .5 percent of the population.

**Table 1: Official Permanent Migration Flows, 2018**

||Arrival|Departure|Net|Total|Arr. %|Dep. %|Net. %|
|---|---|---|---|---|---|---|---|
|National|160|176.2|-16.2|32,639|0.49%|0.54%|0.05%|
|Karakalpakstan|10.8|15|-4.2|1,874|0.58%|0.80%|0.22%|
|Andijan|6.6|8.5|-1.9|3,011|0.22%|0.28%