rafmacalaba/datause-encoder-data huggingface dataset:
**1 Introduction**

In 2000, the Government of India launched the program known as _Pradhan Mantri Gram Sadak Yojana_ (hereinafter PMGSY), under which all of India’s habitations with populations exceeding 500 persons (250 in hilly and desert areas) were to enjoy an all-weather road connection by 2015. At that time, about 170,000 habitations were eligible; at this time of writing, somewhat over 40 percent have their road. By the end of 2010, accumulated expenditures had amounted to about US$14.6 billion, and it is estimated that a further US$40 billion will be required to complete the program by 2020 (World Bank, 2010). By any measure, PMGSY is a very large undertaking.

The aim of the program is to draw all of India’s villages into the mainstream, especially in three domains. First, with improved connections to markets, villagers should face more favorable prices for inputs and outputs, which will raise their incomes and sharpen their incentives to cultivate more intensively, pursue new activities and invest in new methods. Secondly, by reducing the time spent traveling to and from school (and in the rainy season, by making the trip actually possible), the provision of an all-weather road should improve the attendance not only of pupils, but also of their teachers, thus promoting the formation of human capital. Thirdly, by likewise improving the villagers’ access to timely treatment, especially in the event of accidents and bouts of acute sickness, the connection should lower morbidity and mortality. Improvements in these domains will, if realized, reduce poverty and increase productivity in the short and the long run.

The effects of investments in rural roads on villagers’ well-being, broadly interpreted, is hardly a new topic, but it has received considerable attention in the past decade or two, as governments and aid donors have sought to find efficient ways of improving rural welfare. Beginning with the sub-continent, two well-known studies are Binswanger _et al._ (1993), which is largely concerned with output and investment in rural India, and Fan _et al._ (2000), which addresses welfare more directly, in that it also investigates the effects of government spending on rural poverty. Rather closer to the present paper in the type of evidence and methods employed is Khandker _et al._ (2009), which analyzes two panels of data, comprising 42 and 18 villages, respectively, in connection with Bangladesh’s Rural Roads and Market Improvement and Maintenance Projects, which were undertaken in the late 1990s. The salient findings conform to the expectations outlined above. Transport costs were much reduced, output and net prices were boosted, as were agricultural wages and per capita household consumption. Boys’ and girls’ schooling rose, too. The distribution of consumption changed favorably, or at 2
**_5_**

Researchers typically define TFP growth as a residual: real per capita **GDP** growth minus real per capita capital growth times capital share in the national income accounts, which is commonly taken to be between **0.3** and 0.4. Thus, simply **by** using better capital data, we obtain more accurate measures of TFP growth. Moreover, aggregate studies of TFP growth frequently ignore human capital accumulation. In contrast, we use both the Mankiw **(1995)** and the Bils and Klenow **(1996)** specifications to control for human capital accumulation. Thus, we obtain three improved measures of TFP to examine the impact of banking sector development on productivity growth. Finally, this paper also uses an improved measure of banking sector development. We measure banking sector credits to the private sector relative to **GDP.** This measure more carefully distinguishes who is conducting the intermediation, to where the funds are flowing, and we more accurately deflate financial stocks than past studies [e.g., King and Levine 1993a,b]. Finally, we check our results using the King and Levine **(1993** a,b) and Levine and Zervos **(1998)** measures of financial intermediation after extending their sample periods and deflating correctly.

We find that banks exert a strong, causal impact on real per capita **GDP** growth and per capita productivity growth. Using both the pure cross-sectional instrumental variable estimator and system dynamic-panel indicator, we find that higher levels of banking sector development produce faster rates of economic growth and total factor productivity growth. These results are robust to alterations in the conditioning information set and to changes in the measure of banking sector development. Thus, the data are consistent with the Schumpeterian view that the level of banking sector development importantly determines the rate of economic growth **by** affecting the pace of productivity growth and technological change.

Turning to physical capital growth and savings, the results are more ambiguous. We frequently find a positive and significant impact of banks on the rate of capital per capita growth. Nonetheless,
# **5. The impact of registering for taxes on profits**

We now turn to estimation of the impact of a NIT on firm profits. We focus on profits as the dependent variable for two reasons. First, from the point of view of a firm deciding on whether or not to register, the impact on profits is a key determinant. Secondly, whilst from a social welfare point of view one would also like to know whether formality improves productivity, estimation of firm productivity is much more complicated than profitability, and relies heavily on assumptions that are unlikely to be credible with the types of firms surveyed here (see the critique in Katayama, Lu, and Tybout, 2006).

The basic estimation equation of interest is, for firm _i_ : ' ' ' ln ( _profits_ ) _i_ = α + β _NITi_ + δ _X i_ +γ _Z i_ + θ _Li_ + ε _i_ (2) where _NITi_ is a dummy variable taking value one if firm _i_ has a tax identification number, _Xi, Zi,_ and _Li_ are owner, firm, and locational characteristics respectively. We instrument the tax identification number using log distance to the tax office. We employ two methods of estimating (2). The first is two-stage least squares, which has the advantage of not imposing distributional assumptions on the error term. The second method is maximum-likelihood estimation (MLE) of a treatment effects model, which takes account of the fact that _NIT_ is a binary variable, and jointly estimates a probit equation for _NIT_ along with equation (2).<sup>7</sup> In both cases we estimate the equation with and without controls for firm size.

Columns 1 and 2 of Table 4 provide the MLE estimates and columns 3 and 4 provide the 2SLS estimates. The estimates show very large, and highly significant, increases in profits from registering for a tax identification number. The dependent variable is log profits, so the MLE estimate in column 2 suggests that firms which obtain a tax identification number have 86 percent higher profits. The 2SLS estimates are larger, but not significantly so. The MLE estimates are significant at the 1 percent level, and the 2SLS estimates at the 5 percent level. Thus according to these estimates, firms earn much larger profits after registering.

> 7 This was carried out using the treatreg command in STATA.

- 13 -
Table S.6: Using Selection on Observables to Assess the Bias from Unobservables (a) CNDD

|Dependent variable|Set of controls|_R_<sup>_CNDD_+</sup><br>_i_<br>(1)|_D_<sup>_CNDD_+</sup><br>_i_<br>(2)|_S _<sup>_CNDD_+</sup><br>_i_<br>(3)|
|---|---|---|---|---|
|Total Consumption|No control|-0.37|9.76|-1.21|
||Lag|2.04|-19.12|4.96|
|Consumption expenditures|No control|-2.27|-3.99|-2.10|
||Lag|2.08|12.50|-11.19|
|Consumption from stocks|No control|-0.88|0.53|-1.03|
||Lag|-0.75|-11.31|4.57|
|Nonfood spending|No control|-1.13|62.60|-12.58|
||Lag|0.98|14.39|-21.85|
|TLU|No control|-3.58|0.15|-1.63|
||Lag|233.01|-0.24|23.18|
||(b) FNL||||
|Dependent variable|Set of controls|_R_<sup>_FNL_</sup><br>_i_<br>(1)|_D_<sup>_FNL_</sup><br>_i_<br>(2)|_S _<sup>_FNL_</sup><br>_i_<br>(3)|
|Total Consumption|No control|1.49|2.26|-1.98|
||Lag|6.35|35.00|9.95|
|Consumption expenditures|No control|-10.06|2.29|-1.15|
||Lag|-42.94|10.53|43.23|
|Consumption from stocks|No control|-2.77|6.96|-6.22|
||Lag|-8.11|-6.28|16.79|
|Nonfood spending|No control|-23.82|29.99|-4.00|
||Lag|-16.11|33.16|1538.95|
|TLU|No control|3.55|-4.82|-2.41|
||Lag|22.13|-12.45|-60.60|

_Notes:_ Authors’ analysis based on data described in the text. Each cell of the table reports ratios based on the coefficient corresponding to the respective column: column (1) is the ratio for _returnees_ , column (2) for _demobilized_ and column (3) for the spillovers. There are two lines per outcomes. The first is the restricted model without any control, and the second is a regression that includes the lagged dependent variable. For each outcome, each regression is based on the same sample size is restricted; andand includes province fixed eαˆ _F_ when the regression the full set of controls.ffects. Denote αˆ _R_ the coefficients on the demobilization variables (The reported ratio is calculated as: _R_ , _D_ andαˆ _R_ αˆ− _<u>F S</u>_ αˆ _F_ ) when the model<sup>.Negative ratios</sup> reflect higher coefficient estimates when the full set is included relative to the restricted model, hence decreasing the relative role of unobservables in driving the effect.

41
a priority relative to combating poverty overseas. Parliamentarians, voters and interest groups in the donor country may favor earmarking aid more narrowly, so it can potentially be delivered in ways that support job creation or other commercial benefits to the donor country. This argument leads to our third hypothesis:

**Hypothesis 3** When donors face increased unemployment, they tend to invest in small trust funds rather than large trust funds.

Unemployment rates (in percent) for all bilateral donors are taken from the World Bank’s (2014c) World Development Indicators (WDI).

Some contexts are characterized by increased burden-sharing benefits as well as increased preference homogeneity, particularly when a core set of donors has committed to act upon a certain challenge. For instance, when a topic is discussed at the level of the G8 and commitments are made, one can infer that the issue requires some common action and that there is some commonality of interests among most of the G8 member states.

An example of a trust fund emanating from a G8 summit was the Global Agriculture and Food Security Program (GAFSP) of the World Bank. At the L’Aquila summit in 2009, G8 leaders expressed their concern about hunger and poverty caused by soaring food prices, lack of investment, and the burgeoning global financial crisis. Recognizing the "urgent need for decisive action", they promised to "[...] partner with vulnerable countries and regions to help them develop and implement their own food security strategies, and together substantially increase sustained commitments of financial and technical assistance to invest in those strategies" (G8 2009). A number of international organizations including the World Bank attended the summit and endorsed the statement of the G8. In total, leaders pledged USD 22 billion for food security over three years. USD 800 million were ultimately committed to GAFSP, which was established one year after the L’Aquila summit (G8 2010).

This example demonstrates that issues adopted at G8 summits typically imply a strong consensus among the G8 leaders. This alignment of preferences may catalyze joint funding for initiatives addressing common-priority development problems.

**Hypothesis 4** Trust funds related to topics discussed at the level of the G8 should lead to small or even large multi-donor funds rather than single-donor trust funds.

We measure trust funds’ relevance to the G8 by donors’ commitments at preceding summits. Specifically, for each trust fund, we count the number of its sectors for which pledges were made at the G8 summit in the year prior to trust fund activation.

The salience of donor cooperation is even more evident when it comes to addressing global public goods such as climate change or the spread of communicable diseases. Activities in these areas benefit all donors and often require a large common effort to move things forward. Hence cooperation among many donors appears as the most effective form of intervention:

**Hypothesis 5** A contribution to global public goods will be most attractive in the form of large multi-donor trust funds.

13
poverty at the national level, (1) predicting changes from the past distribution is superior to predicting poverty rates directly, and (2) conditional on knowing real GDP per capita, publicly available remote sensing data carry little additional information.

COVID-19 illustrates both the promises and pitfalls of nowcasts. On the one hand, the pandemic caused an unprecedented temporary stop in the production of household surveys leading to an important gap in timely data and an important role for methods that circumvent the availability of primary data sources. On the other hand, the nature of the shock casts doubts on whether models trained on historical data work in a radically changed environment. Partially due to this uncertainty, the country-level nowcasts we produce should not be understood as a substitute for country-level nowcast exercises. When the objective is to nowcast poverty for a single country, often relying on other methods, such as microsimulation tools, or more granular data will be superior as they can be tailored to country-specific contexts. Yet as those methods are hard to implement consistently across many countries, the present exercise can be attractive when the objective is to compare poverty across a range of countries.

# **2 Method**

We will employ several methods to predict poverty around the world. In this section we outline the methods we use, their advantages and disadvantages, and other important methodological choices. We will focus on three distinct issues: (1) The target variable being predicted and (if not poverty rates directly) how poverty rates are obtained from these predictions, (2) the algorithms used to generate the predictions, and (3) the evaluation criterion used to judge predictive performance. Throughout this paper, when we refer to predicting poverty rates, we are using the international poverty line of $1.90 per day (Ferreira _et al._ , 2016).

## **2.1 The target variable**

When deciding how to predict poverty, it seems intuitive that the target variable to predict should be the poverty rate in each country. Yet, such a method ignores that we have prior poverty estimates from most countries which we could predict changes from. Behind these prior estimates are a full distribution of welfare which might contain relevant information – for example by distinguishing the near poor and the rich. Utilizing some of this past information, we will work with 5
# consumption level is equal to his current disposable income.<sup>8</sup>

In each period a worker devotes a fixed amount oftime to one of the following two activities: working in the formal sector or working in the informal sector of the economy. Our informal-sector data in the empirical section are taken from the self employed and we will therefore call a person working in the informal sector a self-employed worker. In each period, a worker, regardless of his current employment status, receives an idiosyncratic shock determining the relative attractiveness of employment versus self-employment ("taste-shock", change in expected payoff to selfemployment). After observing the shock realization, an employed worker makes a quit/stay decision and a self-employed worker makes a search/no-search decision. Whereas an employed worker automatically becomes self-employed when deciding to quit a job, a self-employed worker who decides to search for formal-sector employment receives ajob offer only with probability less than one.

The Appendix Al discusses the Bellman equation associated with the worker's decision problem and analyzes the resulting optimal decision rule. The optimal decision rule gives rise to a quit-ratefunction, q. = q(w,,w,p;T,b),where q, standsforthequitrateexperiencedbyfirm i, _w1_ for the (growth-adjusted) wage paid by finn i, w the average (growth-adjusted) wage, p for the probability of finding (formal sector) employment when not employed, X for the tax rate on formalsector labor income, and _b_ for the average (growth-adjusted) pecuniary benefits from self employment.

Let _q(w,p;t,b) i q(w,w,p ;_ T,b) _be the economy-wide quit-rate fimction when all 8ln a sense, this assumption renders the model classical rather than neoclassical. It is mainly made for tractability reasons since it trivially deterrnines the wealth distribution of workers (no wealth). Without this assumption the wealth distribution is in general non-trivial and has to be computed as part of the equilibrium, except when there is complete consumption insurance. The assumption of restricted capital market participation is also made in Danthine and Donaldson (1990,1995) for the same tractability reason. Kimball (1994) does not treat capital accumulation and therefore does not deal with wealth effects. Phelps (1994) emphasizes wealth effects, but nowhere develops a complete general equilibrium model with endogenize wealth distribution. We hope to dispense with this assumption in future work.

_5_
and road quality using the latest machine learning models using satellite imagery. Our main finding is that there is indeed a statistically significant reduction in the incidence of violent events recorded by the UCDP after road rehabilitation projects are completed. The improved quality of transport infrastructure thus has a beneficial effect on conflict dynamics. However, this effect is relatively short-lived as the positive impact of roads on the number of violent incidents reduces significantly after some time, as road quality decreases due to a lack of maintenance. Both findings are mutually reinforcing and suggest that, for the empirical case at hand, the quality of the road network indeed has a dampening effect on violence, with violence decreasing just after road rehabilitation and increasing again as roads gradually deteriorate over time.

Assuming these findings turn out to be externally valid (i.e., they hold for road rehabilitation projects in other conflict-affected contexts), the policy implication is that investments in road rehabilitation have a ‘peace dividend’ but that such a peace dividend is perishable, decreasing as road roughness increases with time. This aligns with qualitative studies that have suggested that road projects in tropical, conflict-affected settings only have temporary effects on road quality (Bachmann et al. 2022b) and, therefore, never durably impact conflict dynamics (Schouten 2022). If such investments are to deliver _sustained_ peace, investment should be made in _durable_ roads, prioritizing more resilient road types or investing in road maintenance (and preferably both).

While the impact of roads on violence has intuitive appeal, we suggest several mechanisms through which they interact. Is it that roads facilitate the deployment of government forces and, thereby, the re-establishment of state authority in areas previously under rebel control? Do improved roads facilitate mistrustful communities to interact peacefully through commercial exchange? Does increased trade incentivize stationary banditry by conflict actors, thus leading to a reduction in violent extractions? Or is there an entirely different reason that violence decreases when roads are more passable? We provide preliminary responses to such questions. The positive impacts of road projects are significant for one-sided violence using UCDP data, and for violence against civilians and battles using ACLED data. These findings suggest that not all forms of violence are evenly affected by road projects. Second, higher mining revenues affect differently the impact of roads on violence across the types of minerals. The completion of road projects when revenues from artisanal gold mining increase tends to increase the likelihood of violence while the opposite is true for large cobalt mining activities.

# **References**

**Ali, Rubaba, Alvaro Federico Barra, Claudia N Berg, Richard Damania, John D Nash, and Jason Russ** , “Infrastructure in Conflict-Prone and Fragile Environments: Evidence from the Democratic Republic of Congo,” World Bank Policy Research Working Paper 7273, The World Bank, Washington, DC 2015.

29
Table 9: Country income group - Size heterogeneity in effects of all and essential SPS and TBT provisions Panel A: High-income countries

||Export valu|e of agricultural products|Export val|ue of any product|
|---|---|---|---|---|
|Prov:|All SPS|Essential SPS|All TBT|Essential TBT|
|1<sup>_st_ </sup>tercile _×_ Prov|0.859<sup>_∗∗∗_</sup>|1.099<sup>_∗∗∗_</sup>|0.848|1.029|
||(0.147)|(0.164)|(0.843)|(0.691)|
|2<sup>_nd_ </sup>tercile _×_ Prov|0.342<sup>_∗∗∗_</sup>|0.282<sup>_∗∗_</sup>|0.593|0.709|
||(0.128)|(0.120)|(0.922)|(0.813)|
|3<sup>_rd_ </sup>tercile _×_ Prov|0.302|0.235|0.517|0.121|
||(0.186)|(0.227)|(0.811)|(0.824)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster|ij|ij|ij|ij|
|N|888246|888246|2442665|2442665|

Panel B: Low and middle-income countries

||Export valu|e of agricultural products|Export val|ue of any product|
|---|---|---|---|---|
|Prov:|All SPS|Essential SPS|All TBT|Essential TBT|
|1<sup>_st_ </sup>tercile _×_ Prov|0.689<sup>_∗∗∗_</sup><br>(0.260)|0.833<sup>_∗∗_</sup><br>(0.390)|0.845<sup>_∗∗_</sup><br>(0.332)|1.617<sup>_∗∗_</sup><br>(0.787)|
|2<sup>_nd_ </sup>tercile _×_ Prov|-0.0434|-0.153|-0.0698|-0.380|
||(0.301)|(0.387)|(0.202)|(0.471)|
|3<sup>_rd_ </sup>tercile _×_ Prov|-0.125|-0.206|-0.233|-0.971|
||(0.303)|(0.356)|(0.241)|(0.722)|
|fxed-efects|fkj fkt|fkj fkt|fkj fkt|fkj fkt|
|cluster<br>N|ij<br>480787|ij<br>480787|ij<br>2797370|ij<br>2797370|

_Notes_ : PPML estimation is used. Standard errors clustered by origin-destination presented in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels, respectively. Provision measures are normalized between 0 and 1. The specifications control for tariffs, GDP of the destination, and PTA depth.

39