rafmacalaba/datause-encoder-data huggingface dataset:
And are there virtuous cycles under which business formalization leads to greater labor formalization and vice versa? The analysis calls for the use of instrumental variables to control for business and labor formalization being both dependent and explanatory variables. We use proximity to SUNAT as an instrument for business formalization, and the proportion of firms within a region (state) that registers their workers in the Electronic Payroll Registry as an instrument for labor formalization. The results support a pragmatic view of the formalization process, in which at each step firms weigh the advantages and disadvantages of formalizing. We find that business formalization favors labor formalization but not the opposite, which is consistent with the view that business formalization is a lighter and less costly process that can be more easily reversed.

Taken together, the results show that small entrepreneurs may find it more feasible to formalize gradually. At each step, they weigh the relative costs and benefits of formalization, choosing the most cost-effective path to business (often) or labor (less often) formalization—but rarely both at the same time. Small entrepreneurs are also ready to revert to informality if, after making the transition, they realize that the costs outweigh the benefits. Such a nuanced approach to the formalization process provides useful insights for the design of effective formalization policies. Given the propensity of small entrepreneurs to formalize gradually and take diverse pathways toward formality, approaches that focus on only one side of formalization are prone to failure. Successful policies not only need to be broad in scope and tackle the many obstacles affecting business and labor formalization; they also need to understand the specific constraints of each entrepreneur and develop better tailored solutions.

The paper unfolds as follows. The next section discusses the related literature; section 3 provides the context for SME formalization in Peru; section 4 discusses the methodology and data; section 5 presents the empirical analysis; and section 6 concludes.

# **2. Related Literature**

Over the past decade, several studies have assessed the effects of various policy interventions to promote business and labor formality. Studies on policies to promote business formality delve primarily into efforts to increase registration at government entities, particularly the tax authority. Studies on policies to promote labor formalization delve into efforts to increase registration of workers in the payroll, pension, and health systems. We explore the emerging results of both areas of research.

5
Policy Research Working Paper 7764

# **Abstract**

To estimate the impact of weather on rural income changes over time, this study combines data from the panel subsample of the latest Vietnam Household Living Standard Surveys 2010, 2012, and 2014 and gridded weather data from the Climate Research Unit. The analyses show: (i) crop cultivation, livestock management, forestry and fishing activities, and agricultural wages remain important income sources in rural Vietnam—especially for poorer households; (ii) rural communes are exposed to substantial inter- and intra-annual weather variation, as measured by annual, seasonal, abnormal, and extreme weather conditions and weather events; and (iii) these types of weather variation are indeed related to income variation. In particular, warmer temperatures and heat extremes can have negative income effects in all climate contexts and for all socioeconomic groups and most income activities. Only staple crops, forestry, and fishing seem to be less sensitive to hotter conditions. The effects of rainfall are more difficult to generalize. Some findings indicate that more rainfall is beneficial in drier places but harmful in wetter places. Interestingly, the incomes of poorer households seem to be negatively affected by wetter conditions, while those of wealthier households are more impacted by drier conditions. An increase in rainfall levels and flood conditions between 2012 and 2014, which were relatively wet years, is related to reduced income growth between these two years. Altogether these findings suggests that greater attention has to be paid to making rural livelihoods more resilient to weather variation which, is very likely to increase because of climate change.

This paper is a product of the World Bank Environment and Natural Resources Global Practice Group and the Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank work on “Climate Change and Poverty in Vietnam.” It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at unarloch@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **INTRODUCTION**

Despite the fact that official development assistance for health has increased dramatically over the last few years, there has been limited improvement in health outcomes in low and middle income countries.<sup>1, 2, 3</sup> One reason is that health worker productivity and morale is low, often plagued by absenteeism.<sup>4</sup> One promising intervention to improve worker productivity is pay for performance (P4P),<sup>5-9</sup> which provides incentives in the form of bonuses to providers for improvements in utilization and quality of care indicators. P4P can affect health care in two ways; first by incentivizing providers to put more effort into specific activities, and second by increasing the amount of resources available to finance the delivery of services. Despite the promise of P4P, there is little rigorous evidence on its impact in middle and low-income countries, and none that separates out the effect of incentives from increased resources.<sup>10</sup> This study examines the impact of P4P on maternal and child health services in Rwanda, one of the poorest countries in the world.<sup>11</sup> Rwanda has made remarkable progress in improving health since the 1994 genocide.<sup>12</sup> This study uses data produced from a prospective quasiexperimental evaluation design nested into the P4P program rollout. Not only is this among the first rigorous evaluations of P4P in a low-income setting, it is the first to isolate the impact of P4P incentives from the associated increase in resources.

# **METHODS**

In 2005, based on positive field reports from pilot NGO P4P schemes, the Government of Rwanda (GoR) decided to implement a national P4P scheme to supplement primary health care centers’ input-based budgets with bonus payments based on the quantity and quality of key services.<sup>13</sup> The scheme pays for 14 maternal and child healthcare output indicators (Box 1).

2
3 especially illuminating<sup>1</sup> and the keywords in the EconLit metadata are freely chosen by authors and unsurprisingly are highly heterogeneous. We therefore used our own topical classification system, and assigned a topic to a subset of our records manually. We aggregate our metadata to the author, institution and journal levels, showing which of each have published the most, and which of each have had the greatest impact in terms of citations. We show publication counts for both ―country of origin‖ and ―country studied‖. We present frequency distributions of the words used in titles and abstracts and keywords. We also show how the subtopic focus has changed over time and varies across the top institutions.

Two previous exercises are worth mentioning, although neither comes close in terms of comprehensiveness and ambition. Rubin and Chang (2003) undertook a bibliometric analysis of health economics articles over the period 1991-2000 using EconLit metadata. They also report author rankings (using number of articles and number of pages) but in most other respects their analysis is much more limited, covering a shorter time period, excluding citations, using the limited JEL topic classification system, and omitting many of the cuts of the data that we present. Also of note is the exercise Culyer undertook as background for his four-volume collection of 78 articles on health economics (Culyer, 2006). This was based on a survey of teaching members of the International Health Economics Association (iHEA) in 2004 that asked for their student reading lists, the objective being to ensure representation in the 2006 collection of those articles most frequently cited for student reference by people teaching the subject. His e-mail poll of iHEA yielded usable responses from 112 members and 164 articles that were included on at least two reading lists. We use this source as basis for making some intertemporal comparisons between health economics then (2004) and now (2011), and also for comparing the frequency of citations in general (in 2011) with literature recommended by specialist health economics teachers (in 2004).

The rest of the paper is organized as follows. Section II presents our methods, including the bibliometric methods used to analyze citations and the classification of health economics subtopics. Section III introduces our data. Section IV presents our findings, and section V our conclusions.

> 1 They comprise: (a) Health: General, (b) Analysis of Health Care Markets, (c) Health Production, (d) Health: Government Policy; Regulation; Public Health, and (e) Health: Other. Recently two categories have been added: (f) Health and Inequality, and (g) Health and Economic Development.
12 total growth rate than the<sup>other alternative procedures. According to</sup><sup>**RLSGR,**</sup> Brazil's per capita GNP grew<sup>at an annual rate of 4.33 percent during the</sup> 1970-87 period but LSGR and<sup>IWGR3 procedures showed the growth performance of</sup> only 2.84 and 2.58 percent, respectively during the same period.

India's growth performance was<sup>considerably</sup> better in the 1980s than that in the 1970s. Accordingly,<sup>IWGR3 which gives higher weights to growth</sup> rates in the most recent period<sup>should show a higher growth performance. This</sup> indeed is the case. Bangladesh's growth rate varies from<sup>-0.4 percent to</sup> 1.07, depending on which procedure<sup>is used.</sup> Table 2 summarizes the characteristics of the countries which had negative per capita GNP growth<sup>rates during the period 1970-87. It can be</sup> seen that the number of countries<sup>which experienced negative growth rates,</sup> vary from 24 to 35, depending on<sup>which procedure is used to compute growth</sup> rates. RLSGR yields the smallest<sup>number of such countries while IWGR3 the</sup> largest number. The difference<sup>between the two procedures is that one gives</sup> higher weight to the growth rates at<sup>the beginning and another at the end of</sup> the period. This observation suggests<sup>that the overall growth performance of</sup> developing countries deteriorated during the 1980s.

The above analysis clearly demonstrates<sup>that inferences concerning the</sup> economic performance of countries<sup>can vary substantially</sup> with respect to the procedure employed. It is, therefore,<sup>of considerably importance to know</sup> which computational procedure is<sup>the most appropriate one. The next two</sup> sections provide an evaluation of<sup>the alternative procedures from the welfare</sup> point of view.
THE IMPACT OF INFRASTRUCTURE SPENDING IN SUB-SAHARAN AFRICA

# **Cameroon > Nonproductive investments > Macro results**

||||Reduction in<br>h bli|Nonprodu|ctive invest<br>I|ment<br>Fi|I|
|---|---|---|---|---|---|---|---|
|||||||||
|Variable|Definition|Reference|<br>expenditure|VAT|<br>duties|<br>aid|<br>tax|
|Ym|Aggregate household income|5,409,614.00|–0.19|0.25|0.35|0.62|0.20|
|EV|Equivalent variation||–0.19|0.23|0.32|0.65|–1.68|
|s|Wage|1.00|–0.43|0.56|0.78|1.38|0.43|
|yg|Government income|1,500,596.00|0.31|6.36|6.36|6.36|6.36|
|g|Total government expenditure|656,249.00|–6.56|7.27|7.27|7.27|7.27|
|ye|Firm income|2,118,815.00|0.50|–0.64|–0.89|–1.58|–0.49|
|It|Total private investment|1,213,585.00|0.66|–0.86|–1.16|–2.01|–1.69|
|GDP|GDP|6,395,129.00|0.00|0.00|0.00|0.00|0.00|
|e|Nominal exchange rate|1.00|0.35|0.35|–1.92|–6.81|–0.06|

# **Cameroon > Nonproductive investments > Sectoral results**

|Variable|||Reduction in|No|nproductive|||
|---|---|---|---|---|---|---|---|
||Sector|Base|other public<br>expenditure|VAT|Import<br>duties|Foreign aid|Income tax|
||Crop agriculture|108,8074.00|0.41|–0.72|–0.87|–1.17|–1.48|
||Export agriculture|419,908.00|0.71|–0.47|–0.60|–0.79|–0.44|
|Va|Mining and gas|623,479.00|0.03|–0.02|–0.10|–0.29|–0.01|
|Value added or|Industries|1,266,964.00|0.57|–0.85|–0.60|–0.37|–0.26|
|output|Construction|108,698.00|7.78|5.62|5.24|7.14|6.88|
||Private services|2,265,916.00|0.21|–0.56|–0.52|–0.61|–0.64|
||Public services|622,090.00|–4.55|4.34|4.19|4.56|4.53|
||Crop agriculture|1.00|–0.26|0.59|0.74|0.84|–0.01|
||Export agriculture|1.02|0.05|1.09|1.67|1.08|–0.07|
||Mining and gas|1.04|0.46|0.40|0.71|–6.74|–0.06|
|pq<br>Market prices|Industries|1.11|0.21|2.31|2.65|–0.94|–0.06|
||Construction|1.00|0.34|2.01|2.24|–0.20|0.26|
||Private services|1.03|–0.13|1.11|0.73|0.47|–0.19|
||Public services|1.00|–0.27|0.96|1.13|0.84|0.27|
||Crop agriculture|1.00|0.09|–0.36|–0.34|–0.12|–1.45|
||Export agriculture|1.00|0.98|–0.39|–0.42|–0.20|–0.44|
|r<br>|Mining and gas|1.00|0.51|–0.04|–2.84|–8.70|–0.05|
|Rental rate of<br>capital|Industries|1.00|1.01|–1.56|–0.73|0.44|–0.22|
||Construction|1.00|8.91|7.35|7.12|10.09|8.76|
||Private services|1.00|0.00|–0.57|–0.27|0.12|–0.87|

48
_Africa’s Macroeconomic Story_ downside losses in exchange for upside gains. On the other side, governments should be willing to forego upside gains and limit downside losses to ensure that adverse outcomes do not undermine debt sustainability. The contingent nature of the sovereign’s liability complicates debt sustainability assessments. Public disclosure of accurate, timely information, including analysis of contingent liabilities, would help ensure that the risks are well understood by official and private sector creditors.

Volatile resource revenues complicate fiscal planning but also provide scope for precautionary saving. Countries undergoing a major expansion in the resource sector will need to reorient the macroeconomic policy framework to address the volatile nature of resource revenues and absorptive capacity constraints. Technical assistance can play a valuable role in helping countries make the transition in a pre-emptive manner.<sup>27</sup> Monetary and fiscal policy coordination is vital to ensure that domestic spending can be absorbed without fueling inflationary pressures and that an overvalued currency does not undermine the competiveness of non-resource tradable sectors. Initiatives to promote diversification of the export and production base merit a high priority in the policy agenda.

Governance also plays a pivotal role. Building sound institutions is essential to instill investor confidence in the rule of law and protection of property rights, a major concern for long-term equity investments. Major legal and regulatory reforms are needed to ensure that extraction rights maximize resource revenues. Developing capacity for public financial management and achieving compliance with the Extractive Industries Transparency Initiative would help ensure that resource revenues are monitored in a transparent manner.

Foreign asset accumulation provides a mechanism to decouple resource revenue flows and domestic spending. Resource revenues saved during expansions and spent during contractions would enhance macroeconomic stabilization and also improve the efficiency of public investments. Channeling spending to contractionary periods would reduce factor costs and mitigate capacity constraints. Holding financial assets in foreign currency implies that more export receipts are exchanged for domestic currency during contractions and less during expansions, alleviating

> 27 For a comprehensive review of how the macroeconomic policy framework can be tailored to meet the special needs of resource-rich developing countries, see IMF (2012c, 2012d, 2012e and 2012f).

38
using the 2001 Nepal Census I compute the percentage of international migrants from a Village Development Committee (VDC)<sup>6</sup> as an instrument for migration in 2010-2011 as in (3). Following is the equation for migration decisions.

where _Mi,j_ is an identier that individual _i_ lives in household _j_ with or without a migrant, **_Xijc_** is a vector of controls as dened in equation (1). _Zijc_ , dened in (3), is an exogenous instrumental variable that must satisfy as good as randomly assigned, the rst stage, and the exclusion criteria conditions. The 2001 migration decisions are likely to be random to the 2011 labor supply and migration decisions as migrants from a community in 2001 would not have anticipated the community's labor market conditions in 2011, which satises as good as randomly assigned condition. However, migration networks provide information about the economic opportunities at the destination, potential costs, and might reveal migrations' impact on their family's wellbeings to the community, which might inuence other community members' migration decisions (rst stage). Similarly, as long as a decade lagged unobserved community characteristics do not inuence individual's labor supply in 2011, the 2001 community level migration shares are unlikely to feature in the 2011 labor supply equations. Hence, the instrument is likely to aect the outcome only through the endogenous variable, satisfying the exclusion criteria condition. Following is the instrument.

where _MIG_ 2001 _,c_ is the total number of people living abroad in 2001 from a VDC _c_ and _POP_ 2001 _,c_ is the population of the VDC _c_ in 2001. It is important to note that the IV estimates are likely to be greater than the OLS estimates as the instrumental variable approach identies the causal impact of treatment on outcome only on the compliers; that is, IV can recover only the local treatment eect (LATE) (Angrist, 1991).<sup>7</sup>

> 6Village Development Committee (VDC) is the lowest level of administrative unit. Similar to a municipality, it is responsible for the proper use and distribution of state funds and local level service delivery. Depending on the size, it may represent a single community or multiple communities. It is divided into 9 subdivisions called wards and currently there are 3,276 VDCs in Nepal.

> 7 OLS assumes treatment to have homogeneous eect. It estimates an average slope, which, in reality, might not be true as there are likely to be heterogeneous responses to the treatment. Let _y_ be a potential outcome, _d_ be a

9
**TABLE 7 REGRESSION RESULTS**

# **REGRESSION 1: DETERMINANTS OF ABATEMENT EFFORT**

|Variable|Variabl**e**<br>Coeffic nt<br>Coficn|Standard Error<br>]t<br>StnadEroI||_P>_**it!**|
|---|---|---|---|---|
|PRESSURE|9.67|4.13|2.34|0.03**|
|FORMREG|3.36|.95|3.53|0.00**|
|EMP|.0069|.0028|2.49|0.02**|
|EMP<sup>2</sup>|-1.04e-06|4.12e-07|-2.51|0.02**|
|COMPETE|3.48|2.02|1.72|0.10*|
|SOE|-12.79|4.57|-2.79|0.01**|
|CLEAN|-6.89|3.60|-1.91|0.07*|

Number of observations = 22; adj R' square = 0.8

*Significant at 10% confidence level.

**Significant at 5% and 1% level.

|REGRESSION 2: **DETERMINANTS OF ABATEMENT EFFORT**

|l<br>Variable|Coefficient|I<br>Standard Error||It<br>|<br>P> it|
|---|---|---|---|---|
|COMPETE|5.71|1.56|3.64|0.00**|
|Ln PCI|6.74|2.71|2.48|0.02**|
|LOCI|-11.94|2.73|-4.36|0.00**|
|EMP|.0041|.0021|1.95|0.07*|
|EMP<sup>2</sup>|-5.69e-07|3.0Oe-07|-1.83|0.09*|
|FORMREG|2.99|1.55|1.92|0.07|
|CONS|-47.21|17.51|-2.69|0.01**|

Number of observations = 20; adj _RW_ 0.7520: F6 ,3 = 10.6

*Significant at 10% confidence level.

**Significant at 5% and 1 % level.

24
Table S.23: Impacts on children’s educational achievement - Math: complementarity <u>(OLS)</u>

|Dependent variable:|||Math: GPA|z-scores|||
|---|---|---|---|---|---|---|
|_X_:|No. of||Schoo|l-level ratio|of||
||students<br>in each|no. of<br>teachers|no. of<br>blackboards|no. of<br>benches|no. of<br>fans|no. of<br>toilets|
||school-|to|to|to|to|to|
||grade|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|no. of<br>students|
||(a)|(b)|(c)|(d)|(e)|(f)|
|**December in 2013**|||||||
|All products|-0.062|-0.280|-0.087|-0.201|-0.081|-0.099|
||(0.144)|(0.209)|(0.179)|(0.272)|(0.056)|(0.090)|
|One product|0.278|0.160|0.208|0.186|-0.059|-0.247**|
||(0.168)|(0.294)|(0.185)|(0.275)|(0.072)|(0.109)|
|All products _× X_|0.000|9.840|2.330|0.365|9.327***|14.852|
||(0.003)|(8.425)|(8.381)|(0.558)|(3.057)|(19.291)|
|One product _× X_|-0.007**|-10.176|-15.387|-0.618|-5.285|42.326*|
||(0.004)|(12.191)|(9.600)|(0.575)|(6.503)|(23.927)|
|No. of students|-0.006|-|-|-|-|-|
|in each school-grade|(0.007)||||||
|R-squared|0.396|0.391|0.391|0.391|0.393|0.391|
|No. of obs.|849|850|850|850|850|850|
||(g)|(h)|(i)|(j)|(k)|(l)|
|**December in 2014**|||||||
|All products|-0.523***|0.264|0.004|-0.572**|-0.096|-0.209|
||(0.178)|(0.331)|(0.256)|(0.268)|(0.097)|(0.154)|
|One product|-0.065|0.529|0.134|-0.161|0.048|-0.221|
||(0.268)|(0.342)|(0.247)|(0.395)|(0.096)|(0.150)|
|All products _× X_|0.008***|-15.263|-5.505|1.075*|-1.258|27.739|
||(0.003)|(12.771)|(13.381)|(0.627)|(3.131)|(29.134)|
|One product _× X_|0.002|-20.467|-5.047|0.455|-2.238|68.518**|
||(0.004)|(13.163)|(13.439)|(0.849)|(5.679)|(32.891)|
|No. of students|-0.001|-|-|-|-|-|
|in each school-grade|(0.004)||||||
|R-squared|0.167|0.165|0.162|0.165|0.162|0.168|
|No. of obs.|845|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

68