rafmacalaba/datause-encoder-data huggingface dataset:
To provide evidence on that question, in Figure A9 we show, for different horizons, what is the share of firms declaring growth of €20,000 or more. The orange line shows that, on a one-year period, those declaring revenue in the interval €280,000300,000 are much less likely to declare growth above that level than other firms with lower or higher revenues—meaning that they are more likely to stay below the threshold. On a five-year period (2015-2020), however, that effect disappears: firms starting below the threshold are no less likely to have grown enough to surpass the threshold providing beneficial tax rate. This is additional evidence that, while the notch seems to have very meaningful effects in the short run, in the medium-run it seems to be less of a stark deterrent to the growth of all small firms.

# **5 Conclusion**

This report studies how legal entities in Lithuania respond to the existence of differential tax rates based on firm size by leveraging administrative microdata on the universe of limited liability corporate taxpayers. Corporations in Lithuania face an increase in the average tax rate when they declare turnover above €300,000—this notch in the tax schedule creates a sudden increase in average tax rates from 5% to 15%. The use of preferential tax treatment targeted towards entrepreneurs and small and medium enterprises as the one described above is usually justified on the grounds of providing support to firms in their early stages<sup>11</sup> . However, this type of policy may also provide the wrong incentives for firms that may take advantage of the special treatment by remaining small in order to pay lower taxes. We document a clear response of firms to this policy in the short run, while this effect weakens as we extend the time horizon.

In the short run, firms respond to the incentives that are present in the tax schedule by reporting turnover such that they remain below the threshold and by overreporting costs. Firms reduce their reported turnover by up to €32,000 to avoid facing the higher statutory tax rate. This bunching response could be driven by two different underlying behaviors: real production decisions and/or misreporting. However, an additional finding suggests it is mostly misreporting. We document that higher tax rates on profits encourage firms to overreport their true costs and therefore small firms declare higher pre-tax profit margins than large firms. Absent the change in tax policy, there is no reason to believe that firms that are slightly larger should have systematically different profit margins. Therefore, this discontinuity in firms’ costs at the revenue threshold is more compatible with misreporting

> 11See for example Piza (2018).

14
**The 1997 STPS study** had results that were roughly similar to those of the previous study.  In 1993 (in most cases, prior to program participation), CIMO enterprises on average had lower rates of capacity utilization, labor productivity, pay, job retention, use of quality control, sales, and levels of production as compared to the control group.  By 1995, however, the tabulations showed:

- A higher proportion of CIMO firms had some form of organizational change (80 percent versus 51 percent) than in the control group.  These changes included reorganization of teamwork, quality control circles, and client orientation.

- More CIMO firms provided training, and of those in training, a greater proportion relied on external training providers.

- A higher proportion of CIMO firms introduced changes in their fabrication methods and production processes.

- Between 1993 and 1995, a higher proportion of CIMO firms introduced quality control systems so that by 1995 there was no difference in levels of quality control use between the two groups.

- Over time, production and sales of both groups declined in real terms because of adverse economic conditions due to the tequila crisis.  However, the rate of decline in value added between 1994 and 1995 was slightly higher in the pilot group (16 percent) as compared to the control group (11.6 percent).

# **B. Post-Program Impact Measurements Using Regression Models**

Following the tabular comparisons, both studies used regression models to estimate the impacts of program participation on final outcomes (wages and productivity) for possible use in cost-benefit analyses. Despite the availability of three years of pre- and post-participation data, neither study fully exploited the panel information on enterprises. Instead, both studies essentially estimated cross-section regressions – for 1993 in the first study, and for 1995 in the second study – to explain post-program differences in **productivity levels** between the treatment and control groups, controlling for firm attributes such as size and industrial sector.

The first STPS study estimated a model relating labor productivity (value added per worker) in 1993 to an extensive range of explanatory variables and a CIMO indicator variable, with a value of 1 if an enterprise was in the treatment group, and equal 0 otherwise. Of particular interest was whether the estimated coefficient of the CIMO indicator variable was positive, which would indicate that program participation was associated with higher labor productivity. Instead, the estimated coefficient was negative. The model also sought to identify 7
operating for a full year. As also mentioned above,<sup>27</sup> our definition of a new firm is thus a firm that for the first time files a tax return for a full year in any given tax year. For tax year 2011, these are firms that registered in 2010. If we use 2011 revenue data to analyze the effect on formal firm creation, we are thus studying whether additional firms were created in 2010 due to the reform. The government passed the reform in September 2010 and the reform was publicly announced in July 2010, so it is possible that firms registered due to the reform in 2010. We also use revenue data for 2012 to measure whether the reform led more firms to register in 2011.<sup>28</sup> Figures 15a and 15b show the corresponding RDD graphs around the GEL 30,000 cutoff in 2011 and 2012. The number of new firms registered with the tax authority is clearly higher just below the cutoff in 2011 than just above the cutoff. The pattern in the 2012 data is not as clear, although the graph also shows a small jump at the cutoff.

<!-- Start of picture text --> Figure 15a: Number of new firms around GEL Figure 15b: Number of new firms around GEL<br>30,000 cutoff – 2011 data (post-reform) 30,000 cutoff – 2012 data (post-reform)<br>Local Avg. + Polynomial in GEL 100 Intervals from Cutoff Local Avg. + Polynomial in GEL 100 Intervals from Cutoff<br>10000 20000 30000 40000 50000<br>2011 Declared Revenue<br>10000 20000 30000 40000 50000<br>2012 Declared Revenue<br>40 40<br>30<br>30<br>20<br>20<br>10<br>Number of new firms in 2010<br>10<br>Number of new firms in 2011<br>0<br>0<br><!-- End of picture text --> As previously shown, there was no statistically significant jump in number of new firms at the GEL 30,000 cutoff in the pre-reform period (2008 and 2009), implying that we can use basic RDD analysis to estimate the effect of the micro tax reform. Table 8 shows regressions following Equation 1. The specifications in Columns 1 and 2 use a relatively narrow window of revenue around the cutoffs, whereas the specification in Column 3 uses a wider range of observations. The specifications in Column 1 and 2 include a linear function of revenue as a control variable since, in the relatively small intervals, 27 See supra note 9.

28 Another way of dealing with this measurement issue would be to annualize 2011 revenue declared by firms that were created in 2011. For example if a firm registered in April 2011, implying that their declared revenue is for 9 months in 2011, we could divide this by 9 and multiply by 12 to annualize the revenue. However, this method does not allow for seasonal variation and would probably lead to inaccurate estimates. Robustness checks using this approach give results that are overall similar, but tend to be noisier.

24
particularly evident for individuals born between 1945 and 1959, i.e., those who would have been of secondary school age at that time. Similarly, females born between 1950 and 1954 and who would have been of tertiary school age during the 60. also show a significant increase in post-secondary schooling.

Table 3 Highest **Level** of Education Attended By Birth Cohort and Sax (percent of sample)

|Birth cohort|1925-39|1940-44|1945-49|1950-54|1955-59|1960-66|
|---|---|---|---|---|---|---|
|Females|||||||
|Never attended|43.0|36.5|28.1|19.7|12.6|<br>8.3|
|Primary|40.7|42.5|39.6|37.1|32.2|<br>27.6|
|Secondary|12.5|15.0|22.7|28.0|38.3|<br>46.5|
|Post-secondary|<br>3.8|6.0|9.7|15.2|16.9|<br>17.6|
|Hales|||||||
|Never attended|15.2|10.8|8.5|3.9|2.6|<br>3.3|
|Primary|57.9|53.8|42.7|34.1|25.2|24.0|
|Secondary|18.5|19.9|32.4|36.4|45.1|<br>54.6|
|Post-secondary|<br>8.4|15.5|16.4|25.6|27.1|<br>18.1|

29
_Central America and NAFTA_ _7_ from Nicaragua, provided of course that changes in the labor force do not make these countries more similar.

25. Another feature of the 1988 data that is worth pointing out is that both Mexico and Costa Rica have much higher ratios of capital per worker than the other Central American countries with the exception of Panama. If the model is correct, Mexico and Costa Rica are more likely to have production of manufactured goods. Indeed this conforms well with reality, to which we now turn.

# **Trade Dependence Profiles for Central America**

26. In this subsection we discuss trade dependence profiles based on the commodity aggregates that are discussed in greater detail in the full report (Section IV.B). The classification includes two raw materials aggregates (Petroleum and Raw Materials), four crops (Forest Products, Animal Products, Tropical Agriculture and Cereals), and four manufactures (Labor-intensive, Capital-Intensive, Machinery and Chemicals.) In terms of input intensities, these four manufactured aggregates are ordered by physical capital intensities, but chemicals is generally more intensive in human capital than is machinery. These four manufactured products form a ladder of development which many countries seem to follow, beginning with exports of apparel (LAB), then moving on to textiles and iron and steel (CAP), and finally to machinery (MACH) and chemicals (CHEM).

27. Net exports per worker of these ten aggregates in 1965 and 1988 for El Salvador, China, Costa Rica, Honduras, Mexico, Nicaragua, Panama and the U.S. are 2 reported in Table E.1 and illustrated in Figures E.3, to E.6. Countries in Table E. 1 are ordered by their net exports per worker of the labor-intensive manufactures aggregate. Care should be taken in comparing the charts because the vertical scales are different. The choice of scales facilitates a comparison of the comparative advantage but it obscures the overall level of trade dependence which is most accurately summarized by the average absolute net export figures reported in the last column of Table E.1.

28. Generally Central America is a region with a heavy dependence on exports of tropical agricultural products. A very important but more subtle feature of the table is growing comparative advantage in labor-intensive manufactures. Although only Costa Rica in 1988 had positive net exports of these labor-intensive products, the other countries in the region over this time reduced their levels of imports and seem on the verge of becoming net exporters. We argue below that this trend toward exporting labor-intensive manufactures will continue if and only if Central America experiences reasonable levels of growth in GDP per capita.

29. The 1988 trade patterns and the 1988 factor abundance triangle (Figure E.2) conform rather well with the three-factor Heckscher-Ohlin model described in the 2 Data assembled by Ligang Song (1993) and in Leamer (1984).
**Attrition and Take-up Tables**

Table A1: Attrition

|||Attriter<br>midline survey|
|---|---|---|
|T1|(safe spaces)|0.021|
|||(0.041)|
|T1|+ Leaders|-0.063*|
|||(0.035)|
|T2|(safe spaces + IGA)|-0.063<br>(0.041)|
|T2|+ Leaders|-0.091***<br>(0.030)|
|T3|(safe spaces + husbands’ clubs)|-0.098**<br>(0.042)|
|T3|+ Leaders|-0.016|
|||(0.043)|
|T4|(control spillover)|0.002<br>(0.031)|
|T1|= T1 + L|.0621|
|T2|= T2 + L|.496|
|T3|= T3 + L|.128|
|T1|= T2|.0927|
|T2|= T3|.496|
|T1|= T3|.0166|
|T1|+ L = T2 + L|.427|
|T2|+ L = T3 + L|.0964|
|T1|+ L = T3 + L|.328|
|N||3,785|
|Co|ntrol mean|.238|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, OLS regression with standard errors in parentheses clustered at the locality level. IGA = income-generating activity; OLS = ordinary least squares. The dependent variable for column (1) is a dummy for attrited individuals at the follow-up survey. The p-values of the difference tests between the treatments are in the second part of the table. This regression controls stratification fixed effects.

34
another in the six major landlocked states<sup>8</sup> . The difference in the timing of the impact reflects the structure of interstate economic transactions in a country of wide regional diversity (figure 3).

The decline in exports had an immediate negative impact on the coastal states, leading to a sharp deceleration of growth to 6.5 percent from 10.0 percent and higher in the previous three years. Growth deceleration was less steep at the national level, because growth in the landlocked states accelerated in 2008 to 8 percent, up from 7 percent the previous year. The negative impact of the global crisis caught up with these states only in 2009, when growth declined by a relatively mild 0.5 percent.

**Figure 3 Economic Growth in Coastal and Landlocked States of India, 2005–11**

<!-- Start of picture text --> Real annual growth (percent)<br>12<br>Major coastal states<br>11<br>10<br>9 All states<br>8<br>7<br>6 Major landlocked states<br>5<br>4<br>2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> _Source:_ Central Statistical Organization, Government of India.

_Note_ : Figures are based on gross state domestic product at constant 2004/05 prices.

The landlocked states have a disproportionately large share of agriculture and production of nontradable commodities in their output (where “nontradable” refers to India’s external trade). Growth in these states is driven more by national demand than by export demand.

> 8 The eight major coastal states are Andhra Pradesh, Gujarat, Karnataka, Kerala, Maharashtra, Odisha (Orissa), Tamil Nadu, and West Bengal. They account for 47 percent of the population and 58 percent of national GDP. The six major landlocked states are Bihar, Haryana, Madhya Pradesh, Punjab, Rajasthan, and Uttar Pradesh. They account for 40 percent of the population and 27 percent of national GDP.

27
-4-

functions on the public sector increases exponentially the chances of failure and poor performance.

4. The change in development strategy also requires as a prerequisite a modernization and an improved performance of the political process. This point is seldom mentioned but it is crucial; otherwise, the new development strategy will fail.

The analysis has the Latin American reality as background, but is based on the broader experience of the World Bank in this area. The tone of the paper is normatiie; its purpose is to highlight the importance of .he four points stated above.

# II. Has the Pendulum Swug Too Far?

Much has been written and discussed about the role of the state from philosophical, political and economic points of views. Interesting arguments have been developed to help define the functions that shou'ld belong to the public and to the private sectors. However, we are not interested in those debates in this paper. Our purpose is to explore what a more private sectororiented strategy means in terms of the sRecific roles that should be played by the public sector. For these purposes, it is sufficient to accept the conventional wisdom regarding the role of the state. This is summarized in the World Bank's World Development Report (WDR) of 1988. The report states that a pragmatic approach, balancing the arguments of both the public interest
to generate meaningful improvements in long-run human capital and that sustainable management of forests should be part of the menu of interventions targeting stunting in rural communities.

The rest of the paper is organized as follows: section 2 explains the data sources and the process of combining the health outcomes with the geospatial information to create a rich micro database, section 3 describes our empirical strategy. Section 4 analyzes the results, and conducts falsification and robustness checks to demonstrate that our results are driven by the water channel. Section 5 concludes.

# **2. Data**

Analyzing the impact of upstream forest cover loss on downstream health outcomes requires combining household surveys with high-resolution spatial data. We use data from DHS and link the spatial coordinates of the household clusters with geospatial data on forest cover (Hansen et al., 2013), upstream and downstream watersheds (Lehner et al., 2008) and climate (Hersbach et al., 2018).

## **2.1. Demographic and Household Surveys (DHS) Data**

The DHS Program, sponsored by the United States Agency for International Development (USAID), provides technical assistance for the implementation of nationally representative, stratified, two-stage cluster sample household surveys that collect data on population, health, and nutrition for over 90 developing countries around the world. We focus our analysis on 91 surveys representing 46 developing countries<sup>2</sup> for the 2000-2020 period. To be included in our sample, each DHS survey must georeference clusters (shown in Figure 1), so that we can match the health outcomes and household characteristics to the environmental and climate data. Following Herrera et al. (2017), we restrict the sample to rural communities, as they are directly dependent on watershed conditions, and focus only on households that have either always lived in their current location or have migrated to their current location before the child was conceived (the rural non-migrant sample). This ensures that health outcomes are measured in the same location of conception and birth where exposure occurred. Given that the health impacts of low water quality are most significant for young children, we restrict the sample further to ages 0 to 5 years, yielding a final sample of 0.7 million children.

> 2 The 46 countries that are used in our analysis were selected based on the following criteria: countries that provide GPS coordinates for each cluster (this rules out most DHS surveys, as only 60 countries provide geo-referenced data), countries that surveyed individuals between 2000-2020, and countries that provide migration data. For the list of countries included in our DHS sample, please refer to Table A1.

5
35

**Table 6: Regression on private sector attitudes, and wanting to work**

||(1)<br>Okay to work in<br>private|(2)<br>Okay to work in<br>private|(3)<br>Prefer Private|(4)<br>Want to work|
|---|---|---|---|---|
|Social empirical|0.0262**||0.00389|0.0574***|
||[0.0104]||[0.0121]|[0.0199]|
|Social normative|0.0517***||0.00649|0.0188|
||[0.0106]||[0.0123]|[0.0196]|
|Personal Belief||||0.0526**|
|||||[0.0224]|
|Expectation of counterpart|0.132***||0.0280**|0.0383*|
||[0.0107]||[0.0125]|[0.0210]|
|Social empirical index||0.0397***|||
|||[0.0109]|||
|Social normative index||0.0298**|||
|||[0.0133]|||
|Expectation of counterpart|||||
|index||0.0918***|||
|||[0.0134]|||
|Female|0.104***|0.104***|-0.00715|0.0908**|
||[0.0209]|[0.0219]|[0.0243]|[0.0412]|
|Age|-0.000209|-8.00e-05|-0.00263*|-0.00650***|
||[0.00133]|[0.00140]|[0.00154]|[0.00241]|
|Yrs of Education|-0.000681|-0.00139|-0.0113***|0.0123***|
**Figure 3: Potential land availability vs. potential for increasing yields, developing countries**

<!-- Start of picture text --> MOZ SDN<br>Type 3 B EN ZAR A GO Type 4<br>RWA MWI T ZA Z MB C OG<br>GHA<br>BDI N GA<br>SLV ROM UKR<br>RUS<br>POL TKM<br>LTU<br>KAZ URY<br>MEX<br>PHL<br>BRA ARG<br>PAK IND<br>HUN<br>CHL C OL<br>SVN PER<br>GUY<br>Type 1 VNM Type 2<br>CHN<br>MYS<br>EGY<br>JOR<br>-10 -5 0 5<br>Suitable relative to cultivated area (in logarithms)<br>1<br>.8<br>Yield Gap .6<br>.4<br>.2<br><!-- End of picture text --> _Note:_ Dashed lines indicate average yield gap and 50<sup>th</sup> percentile for relative suitability. Source: Deininger _et al._ 2011 32
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Maintaining the SADC’s regional backbone, once completed, would cost a significant amount. Although the bulk of the regional infrastructure spending needs relate to new investment, there is also a significant ongoing need for maintenance spending. This amounts to a total of $410 million per year—the lion’s share for regional road networks.

Regional spending needs in the SADC, though only 0.6 percent of the regional gross domestic product (GDP), weigh heavily on some countries (figure 5.1). The Democratic Republic of Congo has the most daunting spending requirement at almost 14 percent, followed by Mozambique at around 4 percent. The requirements for the Democratic Republic of Congo, which is a relatively small and fragile economy, are untenable, making it unlikely that the country will be able to deliver its portion of the regional backbone without some external or cross-border funding arrangements. The spending needs in the Democratic Republic of Congo are driven by the large export-oriented hydropower projects that the country would need to develop before it could assume its natural role as a power exporter for the region. The spending needs of the remaining countries pale in comparison to those of the Democratic Republic of Congo, but even for a country like Zambia, devoting 2 percent of GDP to regional infrastructure would be a significant challenge.

**Figure 5.1 Spending on regional infrastructure as a share of GDP**

<!-- Start of picture text --> 16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>Spending Needs as a % of GDP 0%<br>Mauritius Seychelles Lesotho South Africa Botswana Angola Malawi Tanzania Madagascar Swaziland Namibia Zimbabwe Zambia Mozambique Congo DRC<br><!-- End of picture text --> _Source:_ Derived from Carruthers and others 2009, Rosnes and Vennamo 2009 and Mayer and others 2009. _Note:_ GDP = gross domestic product.

Looking at regional spending needs as a percentage of historic spending on infrastructure presents an even more improbable picture for some countries. The SADC countries would have to devote between 1 and 192 percent of their existing infrastructure spending to meet regional needs. Figure 5.2 expresses each country’s regional spending need as a percentage of existing infrastructure spending. (Information on existing spending is available for only a subset of countries.) This analysis identifies a group of countries (Lesotho, South Africa, Botswana, Malawi, Madagascar, and Tanzania) that could meet their regional 60