rafmacalaba/datause-encoder-data huggingface dataset:
with H the terminal period, β the discount rate and<sup>_c_</sup> _itT_<sup>the consumption of tradables by country</sup><sup>_i_. It</sup> is assumed that utility is separable between consumption of tradables and non tradables (which may be then be omitted from the analysis as they do not contribute to risk sharing), and that the _T_ endowment process for tradables ( _yit_ ) follows a random-walk with drift of the type: with<sup>_z_</sup> _i_<sup>a standard Brownian motion process (with mean and variance respectively equal to 0 and</sup> _dt_ ). In the absence of risk pooling consumption would just be equal to the endowment, with a resulting utility, at time 0 , equal to: On the other hand, when risk pooling is present, the variance of endowments, denoted by σ _TW_ will typically be smaller. In this case, it can be shown that the welfare gain, measured as the (permanent) percentage increase in expected consumption yielding an equivalent increase in welfare, is equal to: with _d_ σ _T_ 2 = σ _TW_ 2 − σ _T_ 2 the change (decrease) in the variance of consumption growth, ~~μ~~ = μ − 5.0 γσ _T_ 2 the risk adjusted growth rate of consumption, _r_ the risk-free interest rate, and the other symbols have a clear interpretation.

To compute the welfare gains of our LAC countries according to equation (12) a few assumptions have been made.

First, and most importantly, we suppose that although LAC countries may not achieve, by means of international risk sharing, the aggregate rate of growth of consumption of the reference area, be it LAC or OECD, they can at least get the same variance of aggregate per capita consumption growth. 2 Therefore, we have replaced σ _TW_ by the variance of per capita consumption growth in the LAC 18
# **Table 5d—Trade Flows by Trading Partner of Rwanda (in percentage)**

|||||I|mports|||||||||Exports|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Kenya|Tanzania|Uganda|COMESA|SADC|USA|EUR|China|ROW|Kenya|Tanzania|Uganda|COMESA|SADC|USA|EUR|China|ROW|
|**Business Services**|||||||||||||||||||
|Air Transport|0.1|0.0|0.0|0.4|0.8|1.2|52.0|0.4|45.2|0.0|0.1|0.0|0.5|0.9|13.8|39.8|3.2|41.7|
|Communication|0.3|0.0|0.0|1.7|0.0|2.5|53.9|3.0|38.6|0.2|0.0|0.0|0.6|0.5|11.6|56.2|1.8|28.9|
|Insurance|0.0|0.0|0.0|0.4|0.0|14.8|50.7|2.0|32.1|0.0|0.0|0.0|0.7|0.7|27.4|24.9|5.0|41.3|
|Business Services nec|0.0|0.0|0.0|0.2|0.3|13.4|53.5|2.3|30.3|0.0|0.0|0.0|0.6|1.1|11.7|31.7|2.1|52.7|
|Financial Services nec|0.2|0.0|0.0|1.7|1.9|0.0|0.0|8.5|87.7|0.0|0.0|0.0|0.6|1.0|12.8|24.9|2.0|58.6|
|Transport nec|0.1|0.1|0.1|2.8|1.2|9.9|34.5|5.0|46.4|0.0|0.0|0.0|0.4|0.5|25.0|42.1|3.2|28.8|
|Water Transport|0.1|0.1|0.0|1.2|1.0|2.1|44.4|8.2|42.9|0.0|0.1|0.0|0.5|0.7|12.4|40.4|3.4|42.5|
|**Dixit-Stigliz Goods**|||||||||||||||||||
|Chemicals Mineral and Metal Products|19.1|3.4|28.6|1.8|3.1|1.1|24.1|3.1|15.6|4.4|0.1|13.7|10.1|0.2|3.1|15.0|3.5|50.0|
|Energy and Minerals|41.0|0.2|1.5|4.3|0.3|0.2|1.0|1.7|49.8|0.1|0.0|0.2|0.0|0.4|21.8|31.2|9.6|36.7|
|Food Products|14.3|6.4|34.6|9.5|7.4|7.6|8.6|0.1|11.5|66.9|0.0|0.3|0.8|0.1|8.7|6.6|0.5|16.2|
|Petroleum and Coal Products|15.1|0.0|0.8|0.2|0.1|1.3|2.1|0.2|80.1|0.0|0.0|0.0|93.3|0.0|0.8|4.2|0.2|1.5|
|Other Manufacturing|5.6|0.5|1.4|2.3|5.1|2.6|43.9|12.9|25.7|0.6|0.5|4.4|9.5|1.8|13.3|23.4|1.4|45.1|
|Textile and Apparel|20.3|1.8|9.8|2.0|2.7|1.2|9.1|9.0|44.2|0.0|0.0|1.1|6.3|0.2|5.1|40.5|8.5|38.3|
|Wood and Paper Products|27.2|0.8|12.8|5.8|6.2|1.0|28.6|4.8|12.9|0.3|0.2|12.5|4.3|0.2|51.2|16.7|0.6|14.0|
|**CRTS**|||||||||||||||||||
|Agriculture and Forestry|7.2|5.9|62.9|2.8|4.3|2.0|2.6|0.5|11.8|1.5|0.0|0.6|2.7|8.5|10.1|52.2|0.6|23.9|
|Other Services|0.3|0.0|0.1|0.8|1.0|33.1|32.8|3.4|28.5|0.1|0.0|0.0|1.0|1.0|39.7|20.3|1.2|36.6|
|Trade|0.0|0.1|0.0|0.3|0.5|5.4|48.9|9.7|35.1|0.0|0.0|0.0|0.2|1.5|2.6|49.8|12.8|33.1|
|Utilities|0.0|0.0|1.5|0.2|1.7|4.2|60.5|4.1|27.6|0.0|0.0|0.0|0.0|0.2|5.2|63.3|1.3|29.9|

# **Source:** GTAP 8.1 data set.

62
positive outcome and counter the risk of an early and incorrect decision based on faulty analysis and evidence.

# _Value Added and Applicability_

The model in section III was designed specifically to illustrate the trade-off between timeliness and rigor and to allow an exploration of the merits of provisional assessments. The intention was to cut away the tangle of real-world complexity and context-specificity to expose the essential tension between meeting professional research standards and serving policy makers in a timely manner. This analytical approach departs from the reliance on case studies that has to date dominated research on the research-policy link. The strength of case studies lies in their ability to delve into the rich detail of specific examples, but they are not easily generalizable and usually lack analytical rigor. The model presented above builds on, and brings analytical confirmation to, the results of existing, casespecific research by demonstrating analytically the general merit of two findings that have emerged from observation in a variety of different circumstances: first, successful research-policy episodes are often characterized by early interaction between researcher and policy maker; and second, short, focused inputs are usually the most effective means of communication. The value added of the model lies primarily in its rigorous demonstration that these two elements, when combined in what we have called provisional assessments, do indeed yield outcomes superior on average to those yielded by the traditional approach of waiting until the research is complete in all respects before sharing results with policy makers.

The framework also offers an approach to thinking about the interaction between policy-makers and researchers in an orderly way. For it is flexible enough to be more widely applicable than its formal structure might at first suggest. For example, the initial exchange between researcher and policy maker could lead to
Page 17 BOX: 5

# Women's Economic Incentives

A study of rice production in Cameroon found evidence of household production decisions that resulted in suboptimal production of the cash crop and failure to maximize income. This could be explained by the compensation women received for their labor. Revenues from the sale of rice were traditionally controlled by men, although women were expected to contribute their labor. Women derived income from the sale of subsistence crops, the returns to which were considerably lower than for rice. Wives were willing to work on rice only when they were compensated for their labor according to the market value of their product, or at least well above what they could earn on subsistence crops. Otherwise they chose to work on subsistence crops, even though this kept the family's total income below the potential maximum. This demonstrates the shortcomings of assuming a "unitary household." The bargaining model, reflecting who controls what, may be more appropriate. It also indicates the potential cost to productivity of denying women the fruits of their own labor.

# Source: Jones 1985

A study in Guatemala found that in three villages that produced vegetables for an agribusiness venture, different structures of female participation, incentives, and labor remuneration had different effects on production and output quality. In the first village, where women were not involved, the project suffered for lack of labor. In the second village, where women were involved as laborers (taking time from income-earning activities as market vendors) but were remunerated for their efforts only indirectly (through checks made out to their husbands), project performance was better than in the first village but less than good. In the third village, where women were paid directly in cash for their efforts, product yields and quality were by far the best. Thus the level and quality of production appear to reflect not only women's participation in the project but also the compensation they receive and their control over it.

Source: Carloni 1987
||[0.22]|[0.23]|[0.22]|[0.21]|
|---|---|---|---|---|
|Female headed household|0.09|0.05|0.10|0.10|
||[0.29]|[0.22]|[0.30]|[0.29]|
|Household size|7.40|6.48|6.99|7.83|
||[3.60]|[2.53]|[3.27]|[3.91]|
|IDP|0.07|0.12|0.14|0.02|
||[0.25]|[0.32]|[0.35]|[0.12]|
|Observations|1500|540|300|660|

Note: Standard deviations are in brackets. _Data source_ : Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

_Table 2. Common variables between the SWIFT and the SEVAT model_

||**(1)**<br>**Iraq**<br>Mean[SD]|**(2)**<br>**Kurdistan**<br>Mean[SD]|**(3)**<br>**North**<br>Mean[SD]|**(4)**<br>**Center-South**<br>Mean[SD]|
|---|---|---|---|---|
|Home: not owned by HH|0.27|0.30|0.29|0.26|
||[0.45]|[0.46]|[0.45]|[0.44]|
|Household size|7.40|6.48|6.99|7.83|
||[3.60]|[2.53]|[3.27]|[3.91]|
|Employment rate (emp/hhsize)|0.22|0.21|0.21|0.23|
||[0.15]|[0.15]|[0.14]|[0.15]|
|Water: no public network tap|0.08||0.05||
||[0.27]||[0.22]||
|Observations|1500|540|300|660|

Note: Standard deviations are in brackets. _Data source_ : Rapid Welfare Monitoring Survey (SWIFT) 2017-18.

The PPMT formulae for each region corresponding to the 2016 VM model and the new SEVAT models are reported in Table 3 and Table 4 respectively. The coefficients, weights, are estimated using equation 1. Given the small set of covariates, it is not surprising that the _R_ -squared for both sets of models are relatively low - below 30% for both the Kurdistan and Center-South models and about 50% for the North. While employment rate is positively correlated, all the other variables in the old models are negatively correlated with log per capita consumption apart from the female headed household and IDP variables in the Center-South model, which are positively correlated (Table 3). In the new models, household size and not owning a house have a negative association with consumption; employment variable, in contrast, has a positive association. Surprisingly, not having access to public network appear to be a positive predictor of a household’s measure of welfare (Table 4). These pseudo coefficients are applied on the appropriate beneficiary registration database to understand what proportion of the existing humanitarian caseload would be eligible for the government’s cash transfer program.

_Table 3. Pseudo-PMT for the 2016 VM model_

|**(1)**|**(2)**|**(3)**|
|---|---|---|
|**Kurdistan**|**North**|**Center- South**|

9
ability of withholding information: before that period, withholding declarations were annual and filed on paper, while after that they became monthly and digital. For those reasons, we do not have access to withholding declarations before 2011. This limits our ability to observe important sources of income at the top, including all capital income and wage income for individuals whose only source of labor or mixed income is wages (and therefore do not file income tax declaration). Below we discuss in more detail how we proceed to impute incomes at the top of the administrative data for those years.

# **3.2.2 Corporate Income Tax & Shareholder Registry**

One key conceptual contribution of the Distributional National Accounts (DINA) program is to use a broader measure of income to compute inequality, considering not only income flowing directly to households but also to corporations and the government, under the assumption that eventually households are the final beneficiaries of all income generated in an economy (Alvaredo _et al._ , 2020, Zwijnenburg, 2019). One large component of national income not flowing to households is undistributed corporate income. The importance of considering income retained at corporations at the household level is illustrated by the significant changes in measurement of inequality and income share in the US around the 1986 tax reform, when incentives to retain income in corporations substantially changed (Nelson, 2016, Smith _et al._ , 2022).

In close collaboration with the tax authority in Honduras, we have created a comshareholder for all active in the We more prehensive registry firms country. provide detail in Appendix B and explain briefly here the available data. We use a series of administrative records to recover shareholders and their participation in each corporation, and then process the data to deal with inconsistencies and produce a final dataset at the firm-by-shareholder level, including the participation of each shareholder. Whenever direct shareholders are corporations, we apply an iterative process to recover individual shareholders and their shares through up to five levels of ownership. This is a static dataset, reflecting the most updated data available to the tax authority as of early 2023.

We then use information on corporate income tax (CIT) declarations to compute firms’ profits every year and assign the resulting profits to their shareholders using our static shareholder registry. In recent years, we observe approximately 30,000 corporations filing income taxes and over 50,000 identifiable domestic shareholders to whom we assign their share in corporate profits.

7
In a recent influential paper on the United States, Brynjolfsson et al. (2020) argue that in the “discordance between high hopes and disappointing statistical realities, one of the two elements is presumed to be somehow wrong.”<sup>3</sup> However, there are good reasons to be optimistic about the contribution of new technologies, including digital business solutions, to productivity and jobs. These technologies are general purpose technologies (GPTs) that have broad cross-sectoral applications (Jovanovic and Rousseau 2005; Helpman and Trajtenberg 1996). Brynjolfsson, Rock, and Syverson (2017), Syverson (2017), and Brynjolfsson et al. (2020) argue that GPTs have an impact in the economy after firms make the necessary complementary investments or organizational changes needed to take advantage of them. Yet the productivity gains from these investments or restructuring processes do not materialize immediately; it takes time to discover, develop, and implement them (Bresnahan, Brynjolfsson, and Hitt 2002).

Nonetheless, emerging evidence from advanced economies provides room for optimism. Recently, Gal et al. (2019) document that digital adoption in an industry is associated with productivity gains at the firm-level in 20 countries in the European Union and Turkey. Two earlier literature reviews by Syverson (2011) and Draca, Sadun, and Van Reenen (2006) concluded that there is a positive and significant association between ICT and productivity. These findings are, however, in contrast with recent evidence by DeStefano, Kneller, and Timmis (2018) for the United Kingdom, who show that ICT causes increases in firm size (captured by either sales or employment) but not productivity.

While evidence for developing countries is scarce, Hjort and Poulsen (2019) find positive effects of the arrival of internet on firm-level productivity in Africa. World Bank research on Argentina, Brazil, Chile, Colombia, and Mexico concludes that digital technology adoption argument, (Bloom et al. 2020) document that it takes progressively more researchers to generate a unit of TFP. The second hypothesis is related to measurement issues associated with the supply of digital products or services for which the price paid by consumers is zero. Consequently, these transactions are not captured in the data (Mokyr 2014; Hatzius and Dawsey 2015; Byrne, Fernald, and Reinsdorf 2016). However, this hypothesis was challenged by evidence indicating that the size of the productivity slump was unrelated to the spread of digital technologies across countries (Syverson 2017).

> 3. Brynjolfsson, Rock, and Syverson (2017) refers to artificial intelligence, but the argument is equally applicable to other types of general purpose technologies such as digital technologies.

6
The latter are a) land size, which for a farming population is likely to correlate strongly with income; b) distance to the nearest road, which may be taken as an indicator of the wealth or income of a village as a whole; and c) altitude, which in Ethiopia correlates strongly with the productivity of land, as well as the value of forest growing in the surrounding areas (the three measures are uncorrelated).<sup>10</sup>

**Table 2:** Income and wealth

|||I|||II||
|---|---|---|---|---|---|---|
||_α_|_β_|_σ_|_α_|_β_|_σ_|
|land size|-0.015|-0.060***|0.004|-0.069**|-0.067***|0.016|
||(0.018)|(0.019)|(0.008)|(0.030)|(0.024)|(0.016)|
|distance to road|0.006|-0.021|0.007|0.009|-0.027|0.008|
||(0.018)|(0.023)|(0.008)|(0.023)|(0.029)|(0.011)|
|altitude|0.052***|0.080***|-0.037***|0.062**|0.021|-0.018|
||(0.016)|(0.024)|(0.006)|(0.029)|(0.027)|(0.015)|
|literate|0.013|0.066|0.004|0.001|0.105*|0.025|
||(0.034)|(0.045)|(0.013)|(0.051)|(0.055)|(0.017)|
|middle school|0.052|0.115*|-0.002|0.102|0.154*|-0.011|
||(0.053)|(0.070)|(0.019)|(0.079)|(0.080)|(0.020)|
|own business|-0.044|0.073|-0.032|-0.145|0.159|-0.002|
||(0.097)|(0.147)|(0.054)|(0.097)|(0.175)|(0.066)|
|female|-0.105|0.215**|0.026|-0.074|0.180**|-0.037|
||(0.066)|(0.089)|(0.028)|(0.070)|(0.083)|(0.024)|
|age|-0.007|0.052***|-0.014***|-0.008|0.105***|-0.015|
||(0.018)|(0.020)|(0.005)|(0.032)|(0.035)|(0.010)|
|unmarried|0.025|-0.196**|-0.009|0.030|-0.307***|0.066|
||(0.075)|(0.084)|(0.021)|(0.130)|(0.097)|(0.042)|
|Region fxed efects|✓|✓|✓|✓|✓|✓|
|constant|0.674***|0.631***|0.184***|0.678***|0.634***|0.124***|
||(0.041)|(0.040)|(0.014)|(0.072)|(0.074)|(0.017)|
|Subjects|493|493|493|254|254|254|
|_LL_|_−_12_,_338_._49|_−_12_,_338_._49|_−_12_,_338_._49|_−_6_,_430_._57|_−_6_,_430_._57|_−_6_,_430_._57|

Standard errors in parentheses; *p<0.1, **p<0.05, ***p<0.01 Continuous independent variables are entered as z-scores In regression I we look at the three main income proxies, while controlling for basic demographics such as education, sex, age, and marital status. We find land size to be highly correlated with risk preferences, with larger land ownership being associated with higher risk tolerance<sup>11</sup> as indicated by a smaller _β_ parameter, as we hypothesized. We also find higher altitudes to be related to

> 10The productivity of land decreases with altitude for several reasons. Rainfall is generally scarcer at high altitudes. This effect that is confounded by stronger winds, which tend to dry out the top soil. Furthermore, land at high altitude is often inclined, which means that water drains quickly and soil is easily eroded, leading to worse soil quality and hence lower agricultural productivity. And finally, the lower temperatures lead to slower growth of crops.

> 11Risk tolerance is defined in behavioral terms (Wakker, 2010); i.e. Peter is (strictly) more risk tolerant than Randy iff Peter’s certainty equivalent for a given prospect is (strictly) larger than Randy’s.

17
household would be less likely to pay bribes, and would pay lower bribes conditional on bribing. Our empirical results later contradict both of these implications: the households in heavy rainfall areas are not less likely to pay bribes, or they do not pay lower bribes.

## Rainfall and Psychology of Corruption

In an interesting analysis of the e¤ects of poverty on crime in 19th century Bavaria, Germany, Mehlum et al. (2006) use rainfall as an instrument for the price of rye. They point out that a potential objection to the exclusion restriction imposed on rainfall is that it may a¤ect the ‘mood’ of a prospective criminal and thus can exert a direct e¤ect on their outcome variable, violent crime. A similar direct e¤ect of rainfall on the propensity to bribe for education seems less plausible. But to be as clinical as possible, we exclude contemporaneous rainfall, i.e., the year of the NHSC survey 2010, and use the average rainfall over the period 2000-2009.

## Institutions and Rainfall

With respect to potential di¤erences in institutions between heavy rainfall and other areas, note that the relevant institutions we are interested in are those that deal with law and order. While historical rainfall di¤erences across di¤erent countries may have a¤ected settler mortality through disease environment and thus institutional development, a point emphasized by Acemoglu et al. (2001), it is di¢ cult to see how it can be relevant for variations within a given country. Also, we use region ...xed e¤ects, so the rainfall variations within a region are used for identi...cation. This implies that if there are any regional di¤erences in institutions of law and order, they are mopped up by the ...xed e¤ects.

# Validity of the Identi...cation: A Falsi...cation Test

Here we present evidence from a falsi...cation exercise that builds on the observation that rainfall should not a¤ect income signi...cantly in large cities because they do not rely on agricultural activities. This is similar to the falsi...cation test used by Bruckner and Ciccone (2011) in their analysis of window of opportunity for democratic change. In our case, if rainfall does not a¤ect income, and the identifying assumption that rainfall a¤ects bribes only through income is valid, then if we regress propensity to pay bribes and the amount of bribes on the rainfall based instrument directly in a sample of households living in the large cities, the instrument should not 20
# **AN AGENDA FOR THE FUTURE**

The previous sections have provided an overview of informality in Indonesia, both in terms of existing data sources and empirical evidence. This final section summarizes the remaining gaps and outlines an agenda for the future.

# **Data gaps**

# _Informal sector_

We have identified a number of existing data sources that can be used to study informality in Indonesia. In terms of the informal sector, the IMK Survey is the most viable source of data for a number of reasons. First, its coverage of both micro and small enterprises allows it to capture more than 90 percent of all firms in Indonesia – most of which are informal in nature. Second, the sampling methodology ensures that the survey is representative of the broader MSE sector, which is critical for the generalizability of findings. Nevertheless, the survey has its own limitations. By virtue of its coverage, it excludes medium and large firms, which could also operate informally. This means that relying solely on the IMK survey will underestimate the true size of the informal sector. Moreover, medium or large informal firms may represent a different segment of the informal sector with very different motivations (e.g., rational exit) and characteristics. Finally, the survey contains a limited set of questions that can be used to understand the reasons behind firm informality, or the barriers they face to formalization. Consequently, the survey needs to be complemented with other data sources to provide a deeper and more comprehensive analysis of the informal sector.

Given the limitations of existing firm-level datasets in Indonesia, there is a case to be made for new data to be collected. Ideally, the new survey should include questions of a more qualitative nature, such as the benefits gained from operating informally, linkages with the formal sector, perceived costs or barriers to formalization, and the use of government services. Existing enterprise surveys can be used to inform the exact framing of these questions. For example, the World Bank Enterprise Survey asks firms to identify their challenges to doing business based on a 21