rafmacalaba/datause-encoder-data huggingface dataset:
increase in the volume and types of data available to inform their development plans, track performance, and improve government accountability to the public. New global entities, such as the Global Partnership for Sustainable Development Data and the SDSN Thematic Research Network on Data and Statistics, fostered collaborations between official and nongovernmental data producers. The expectations for official statisticians to abide by open data policies also increased.

As a result of these initiatives, measures of statistical capacity, openness, and funding modestly increased.<sup>1</sup> The expanding requirements on NSSs to tap new data sources, find ways to make data more accessible, and expand their measures of development outpaced capacity improvements, however, reinforcing the need for NSSs to continually engage with users—particularly state actors—to ensure that capacity improvements are made in the areas vital to fostering better development outcomes.

The UN High-Level Group for the Modernization of Official Statistics identifies trends, threats, and opportunities to modernize NSSs. For more than a decade, it has overseen the development of standards and models for describing statistical production processes, organizational structures, and metadata (UNECE 2020b). It has provided guidance on communication and relationship management with users, integrating data from varied sources, big data, and managing partnerships and human resources. Operations in many high-performing NSSs now conform to its standards and guidelines and develop a culture of experimentation and innovation.

# **_Measures of Statistical Capacity_**

Statistical capacity is difficult to measure because it is only partially revealed by achievements or other observable characteristics of the NSS. A system may have the capacity to produce goodquality data but not have yet done so, or it may no longer have the capacity to produce good data despite having done so in the past.

> 1 The Statistical Capacity Building Indicator (established by the World Bank in 2004) increased by about 2 percent between 2004 to 2017 (World Bank 2020b). The Open Data Inventory Index, which assesses the coverage and openness of official statistics, increased by 9.4 points between 2016 and 2020 (ODW 2020). Total funding commitments to statistics more than tripled between 2014 and 2017, rising from $214 million (0.14 percent official development assistance) to $689 million (0.34%) (PARIS21 2019). There is also some anecdotal evidence that domestic resources in low-income countries increased in recent years (GPSDD 2019).

5
adoption caused by land and labor market failures therefore complements this literature, by both imposing less structure and leveraging our plot-level discontinuity in access to irrigation as an exogenous labor- and input-complementing productivity shock.

This paper is organized as follows. Section 2 describes the context we study and our sources of data. Section 3 presents our estimates of the impacts of irrigation in Rwanda. Section 4 presents our model of adoption of irrigation in the presence of market failures. We implement tests of constraints to adoption and labor market failures suggested by the model in Section 5, and experimental tests in Section 6. Section 7 concludes.

# **2 Data and context**

## **2.1 Irrigation in Rwanda**

We study 3 hillside irrigation schemes, located in Karongi and Nyanza districts of Rwanda, that were constructed by the government in 2014; a timeline of construction and our surveys is presented in Figure 1. Rainfed irrigation in and around these sites is seasonal, with three potential seasons per year. During the main rainy season (“Rainy 1”; September - January), rainfall is sufficient for production in most years. In the second rainy season (“Rainy 2”; February - May), rainfall is sufficient in an average year but insufficient in dry years. In the dry season (“Dry”; June - August), rainfall is insufficient for agricultural production for seasonal crops. Absent irrigation, agricultural production in these sites consists of a mix of staples (primarily maize and beans) which are cultivated seasonally and primarily consumed by the cultivator, as well as perennial bananas which are sold commercially;<sup>10</sup> most farmers adopt either a rotation of staples, fallowing land in the dry season, or cultivate bananas.

Irrigation in these schemes is expected to increase yields by reducing risk in the second rainy season and enabling cultivation in the short dry season. As the dry season is relatively short, cultivating the primary staple crops is not possible for households not robust to the presence of unobserved heterogeneity across plots or measurement error (Gollin & Udry, 2019).

> 10Staple rotations also include smaller amounts of sorghum and tubers, while there is also some cultivation of the perennial cassava, along with other minor crops. In our data, maize, beans, or bananas are the main crop for 85% of observations excluding horticulture.

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following the crisis. While flows to advanced economies collapse and remain fairly low, flows to emerging and developing countries rebound and increase across all sectors. An important difference in flows by sector is in the evolution of debt inflows to governments. Across all country groups, governments see an increase in debt inflows precisely when private flows collapse, with an especially large and sustained increase for developing nations relative to their private flows. Advanced-country central banks also see a small increase as private flows collapse.

Panels (d)-(i) plot portfolio debt and other investment debt flows. They reveal that the increase in inflows for governments comes primarily in the form of bonds, with the exception of developing country governments, which also see an increase in other investment debt funding (i.e. loans). Advanced economy corporates also have a significant share of their inflows coming in the form of portfolio debt. Although emerging market banks and corporates see an increase in bond flows in the wake of the GFC, the aggregate pattern of their flows is driven primarily by other investment debt. Advanced country banks get the lion’s share of capital inflows prior to 2008, the majority of which is in the form of other investment. However, they see consistent negative net inflows for several years following the GFC, reflecting the deleveraging of these institutions. Developing country banks and corporates are also primarily receiving inflows in the form of other investment debt.

Much of the increase in emerging-market private debt after 2008 is attributable to a few large EM. Foremost among these is China, whose debt inflows are shown in Figure 4. China has poor sector coverage in the BOP data, so much of the measured effect is derived from our data filling series. Both bank and corporate inflows increase substantially, but bank in21
### and storms.

Theoretical literature on the economic effects of natural disasters has been gradually evolving. Marto et al. (2018) develop a model that explores the macroeconomic impact of a major natural disaster. Their study focuses on debt sustainability concerns that arise from the need to fully rebuild public infrastructure over the medium term and compares policies, such as building adaptation infrastructure and fiscal buffers, with the post-disaster support provided by donors. Burns et al. (2021) develop a macrostructural model for Jamaica that embeds climate damages from natural disasters and considers different risk management strategies to address them. When it comes to DSGE models, Keen and Pakko (2011) develop a model to investigate the appropriate monetary policy response to natural disasters like Hurricane Katrina. Fern ́andez-Villaverde and Levintal (2018) compare different solution methods for computing the equilibrium of DSGE models with rare disasters, given large nonlinearities that could be triggered by these events. Strulik and Trimborn (2019) examine the welfare effects in the aftermath of natural disasters. Similarly, Cantelmo et al. (2019) study the macroeconomic outcomes and welfare implications in disaster-prone countries and policies to mitigate them. Most of the papers focus on the channel in which disasters destroy durable goods or productive capital. Gallic and Vermandel (2020), on the other hand, build and estimate a DSGE model with a weather-dependent agriculture sector to investigate the adverse weather implications in New Zealand.

The contribution of this paper is twofold. First, it provides evidence of the fiscal implications of natural disasters in a broad sample of countries using different specifications and the disaster measure that is based on physical intensity. As pointed out in Felbermayr and Groeschl (2014), EM-DAT and similar databases comprise disaster intensity measures that are a function of economic development. Reported monetary damages of disasters are higher in richer economies, while human costs are usually lower. As a consequence, this is likely to lead to biased estimates. Second, the paper provides a theoretical explanation of the obtained results by looking at the propagation of the shock that affects agricultural productivity. I add Gallic and Vermandel’s (2020) agriculture sector framework to an otherwise standard Real Business Cycle (RBC) model (Kydland and Prescott, 1982; King et al., 1988; King and Rebelo, 1999), extend it with a more detailed fiscal sector, and calibrate the model to resemble the economy of Bangladesh - a country particularly sensitive to frequent weather-related natural disasters.

# **3 The Empirical Framework**

## **3.1 Data**

– The main variable in the analyses the measure of disaster intensity, comes from the ifo GAME database (Felbermayr and Groeschl, 2014). It is based on primary geophysical 4
beliefs—there would be common knowledge.<sup>39</sup> In contrast, cultural psychologists and sociologists take it for granted that society is partly “inside us”: socioeconomic factors affect the mental models that influence how we process information (e.g., Markus and Kitayama 2010). This section will present evidence in support of the view that society shapes who we are. In particular, the evidence suggests that the social environment shapes cognition. But first we present a simple model.

# 5.2  A simple model, with three examples

A model helps illustrate the difference between the analysis here of the social determinants of _perception and cognition_ and the analysis in the previous section on the social determinants of _preferences_ . We have to introduce the concept of _perception_ , denoted P. Individuals’ perceptions of their “real experiences” are affected by social stimuli, S. As before, we focus on one aspect of their experiences, the actions of others **.** What individuals see and how they interpret what they see (Was it aberration? How was it motivated? What does it indicate about others?) are all socially determined. The perceptions (which may themselves be described by a vector of beliefs) determine the individuals’ behavior. Thus, now we write the actions of all individuals, **a** , as In the steady state, a social equilibrium is one in which As before

## (5.3)  Z( **p, a)** = 0.

Obviously, the range of possible equilibria and the scope for policy interventions have increased enormously by taking into account that people respond to their perceptions rather than to the objective actions. If there is some way we can change perceptions, we can change behavior—and possibly the equilibrium—without doing much else.

Traditionally, we have had a hard time empirically distinguishing this model from the previous one, since we do not typically observe perceptions but only actions. But recent research (some of which

> 39 There is an important literature explaining why individuals may in fact “agree to disagree” (a survey is in Bonanno and Nehring 1997).

33
Source: Authors’ own estimate based on NSSO and census data.

# **3.4 Social Groups and Female Labor Force Participation**

It is evident that membership in certain social groups affects a woman’s ability to participate in the labor force. Households belonging to marginalized communities are known to have lower levels of income compared to others, thus producing a negative income effect on women’s participation. Moreover, the literature indicates that women in households belonging to upper caste groups are discouraged from engaging in paid work due to the long-standing stigma attached to women’s paid work (Boserup, 2008). To analyze the effect of social groups on the FLFP rate in India, we must first look at the demographic composition of the female population, searching for any effect on the FLFP rate that occurs due to demographic changes in the composition of social groups. The composition of various social groups was largely stable during the period of 1993-94 to 2011-12 (Figure 5). Scheduled castes (SC) and tribes (ST)<sup>5</sup> had jointly comprised approximately 30 percent of the population in rural areas, and about 20 percent in urban areas (Figure 5).

**Figure 5: Composition of Social Groups in Female Population**

<!-- Start of picture text --> 71.4 68.2 67.9 83.8 78.5 78.4<br>16.2 16.4 16.0<br>12.4 15.5 16.1 10.1 6.1 13.67.9 13.08.6<br>1993-94 2004-05 2011-12 1993-94 2004-05 2011-12<br>Rural Urban<br><!-- End of picture text -->

> 5 Through constitutional provisions, the Government of India specifically recognizes a number of historically marginalized social groups, including those from various caste and tribal communities. These groups are referred to as scheduled castes (SC) and scheduled tribes (ST). In addition, the Government of India recognizes certain groups belonging to other specially defined socially and educationally backward classes, also known as Other Backward Classes (OBC).

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source of variation in the timing of the first collective action by SHGs. Table 4 overviews the evolution of both the creation of SHGs and their activity over the election periods.

Table 4: The creation and activity of SHGs by election period

|||Election<br>|mandate:<br>||
|---|---|---|---|---|
||1992-1996|1997-2001|2002-2006|2007-2011|
|% of wards in which at least one SHG has|0.0|42.6|100.0|100.0|
|been created|||||
|% of wards in which at least one SHG has<br>started undertaking collective actions<br>(conditional on a SHG being present)|0.0|28.3|68.1|92.9|

_Source_ : Authors’ analysis based on data described in the text.

The first SHGs were created during the 1997-2001 election period and, by the end of the 20022006 period, all wards had at least one SHG. As mentioned before, on average, SHGs that undertake collective actions, do so for the first time after three years of weekly meetings. Therefore, it is not surprising that most SHGs start undertaking collective actions during the mandates 2002-2006 and 2007-2011.

We will instrument _SHG active_ by whether or not a SHG was created in the ward during the relevant time period. Our approach leads to the following first stage regression: where _SHG created_ is a dummy that takes value 1 if SHGs were present in ward _j_ during the mandate of WM _i_ . We use the parameters to predict whether SHGs undertake collective actions, and estimate consistent estimators for: The assumption underlying this analysis is that the timing of the creation of SHGs is uncorrelated with pre-existing differences in the tendency to intervene for issues across wards, after controlling for time-varying WM characteristics, time-invariant ward characteristics and a com31
# **A1.3 Standard and Poor's DRI/McGraw-Hill (DRI)**

DRI is an economic consulting and information company which provides data, analysis, forecasts and expert advice to strategic planners, business and financial analysts, and policy makers. It was founded in 1973 as a unit of Standard & Poors and is based in the United States. As of May 1999, DRI can be contacted at: http://204.151.55.106/index.htm.

In 1996, DRI launched the Country Risk Review (CRR), a quarterly publication providing country risk assessments to international investors. A first draft of the risk ratings in this publication are produced by country analysts, who then submit their preliminary assessment to regional review committees charged with analyzing and challenging these assessment. The global risk service committee evaluates the reviewed assessments to ensure quality and crosscountry consistency. The country analysts then produce the final country risk review. In this paper we use data from the first quarter of 1998 version of the Country Risk Review.

The CRR assesses the relationship between country risk and its effects on the profitability of investments. For each country, DRI identifies a number of "potential sources of risk", specifies measurable "risk events", measures how probable those risk events are, and assesses the seventy of impact that each outcome would have. Based on these considerations, DRI produces a risk score for each country.

The CRR identifies a total of 33 "immediate risk events" and 18 "secondary risk events" for 106 developed and developing countries. Immediate risk events are classified into policy risks (tax, and non-tax), and outcome risks (price, and non-price). Secondary risk events are classified into domestic political risks, extemal political risks, and economic risks. These risk events are described in Table A1.3.

For each risk event, DRI produces a short run and a long run risk rating. These ratings provide subjective estimates of the likelihood that a particular risk event will occur within one and five years respectively. DRI follows a methodology to ensure that the five year forecasts are consistent with the short-term forecasts. Although these indicators nominally measure the likelihood of future changes in govemance concepts, in practice the long-run ratings provide good measures of the current levels of govemance.<sup>18</sup> This judgement is based on a private communication with Farid Abolfathi, Director of the Country Risk Review. In response to our request of advice as to whether to use DRI's risk ratings on corruption as a proxy for current levels of corruption he replied: 'Our approach focuses on the risk of increases in corruption, but our experience has been that these are highly correlated to corruption levels, particularly in a sample with large variance. Finally, if you are mainly interested in using our numbers as a proxy for corruption levels in a cross section of countries, I recommend using the five-year ones." 32
growth and wider access of financial institutions for saving, credit, and insurance should help even poor individual **s** pread their lifetime incomes over thei- lifetime needs. Furthermore, a richer society can be expected to be willing and able transfer more resources to those in need whether by stats intervention, by private charity, or by intra-family transfers. However, neither of these indirect potential connections between higher incomes and poverty were, in fact, manifested in ways that helped the poor very much in the 19th century. Access to formal capital market institutions did not always develop rapidly enough to replace disappearing "traditional" means of income support for the poor. And during early industrial revolutions in the 19th century laissez faire policies often served to reduce or even remove state interventions which had previously transfered resources to the poor. We shall have more to say about this in Section IV.

There are four ways that poverty might actually increase during early industrial revolutions, all of which will get some attention in this essay. First, the living standards of the poor may fall and inequality rise in response to technological events driving industrial revolutions. Second, the cost of living facing the poor may rise more dramatically for exactly the same reasons, eroding their living standards in ways which conventional income statistics may fail to capture. Third, early industrial revolutions may undermine both the earning potential of secondary unskilled workers and the secondary earning sources of primary unskilled workers. Fourth, modern economic growth may, in Amartya Sen's language, erode traditional entitlements which serve as safety nets in pre-industrial societies. Let us dwell a moment on each of these.

The most important way that early industrialization might raise poverty, or at least inhibit its eradication, is if rising inequality is associated with early industrialization. Ve bave a name for this hypothesis -- the

-4-
|P002494|1989|1993||6 Closed|Somalia|
|---|---|---|---|---|---|
|P002494|1989|1994||8 Closed|Somalia|
|P002494|1989|1995||8 Closed|Somalia|
|P002495|1988|1988||0 Closed|Somalia|
|P002495|1988|1989||3 Closed|Somalia|
|P002495|1988|1990||5 Closed|Somalia|
|P002495|1988|1991||7 Closed|Somalia|
|P002495|1988|1992||8 Closed|Somalia|
|P002499|1990|1990||2 Closed|Somalia|
|P002499|1990|1991||3 Closed|Somalia|
|P002499|1990|1992||4 Closed|Somalia|
|P002499|1990|1993||4 Closed|Somalia|
|P002499|1990|1994||5 Closed|Somalia|
|P002499|1990|1995||5 Closed|Somalia|
|P002532|1985|1986||0 Closed|Sao Tome and Principe|
|P002532|1985|1987||0 Closed|Sao Tome and Principe|
|P002532|1985|1988||0 Closed|Sao Tome and Principe|
|P002532|1985|1989||5 Closed|Sao Tome and Principe|
|P002532|1985|1990||8 Closed|Sao Tome and Principe|
|P002532|1985|1991||10 Closed|Sao Tome and Principe|
|P002532|1985|1992||12 Closed|Sao Tome and Principe|
|P002533|1987|1988||0 Closed|Sao Tome and Principe|
|P002533|1987|1989||3 Closed|Sao Tome and Principe|
|P002533|1987|1990||6 Closed|Sao Tome and Principe|
|P002533|1987|1991||10 Closed|Sao Tome and Principe|
|P002533|1987|1992||15 Closed|Sao Tome and Principe|
|P002533|1987|1993||1 Closed|Sao Tome and Principe|
|P002535|1992|1992||3 Closed|Sao Tome and Principe|
|P002535|1992|1993||7 Closed|Sao Tome and Principe|
|P002535|1992|1994||8 Closed|Sao Tome and Principe|
|P002535|1992|1995||11 Closed|Sao Tome and Principe|
|P002535|1992|1996||13 Closed|Sao Tome and Principe|
|P002535|1992|1997||14 Closed|Sao Tome and Principe|
|P002535|1992|1998||16 Closed|Sao Tome and Principe|
|P002535|1992|1999||18 Closed|Sao Tome and Principe|
|P002535|1992|2000|6/30/2000|20 Closed|Sao Tome and Principe|
|P002535|1992|2001|6/29/2001|22 Closed|Sao Tome and Principe|
|P002536|1987|1988||0 Closed|Sao Tome and Principe|
|P002536|1987|1989||7 Closed|Sao Tome and Principe|
|P002536|1987|1990||8 Closed|Sao Tome and Principe|
|P002536|1987|1991||11 Closed|Sao Tome and Principe|
|P002536|1987|1992||12 Closed|Sao Tome and Principe|
|P002538|1990|1990||1 Closed|Sao Tome and Principe|
|P002538|1990|1991||3 Closed|Sao Tome and Principe|
|P002538|1990|1992||6 Closed|Sao Tome and Principe|
|P002538|1990|1993||8 Closed|Sao Tome and Principe|
|P002538|1990|1994||10 Closed|Sao Tome and Principe|