rafmacalaba/datause-encoder-data huggingface dataset:
believe that the adopted approach is at this stage more suitable than any fixed algorithm to turn the rich and difficult-to-quantify aspects of policy information into a broadly plausible if somewhat imprecise restrictiveness scores. In Paul Krugman’s words, it has the virtue of being “roughly right rather than precisely wrong.”

# **Weighting Schemes for the STRI Index**

Annex Table A2 below documents the sets of weights used to derive aggregate, country-level STRI scores, _STRIc_ , from basic scores per sub-sector and mode, _s jmc_ . Modal weights sum up to unity within any given subsector, e.g. ‘accounting’ (all subsectors are listed in Table 1). Subsectors are aggregated to the sectoral level, e.g. ‘telecommunications,’ using simple averages. Sector scores are aggregated to the country level using standardized weights based on the constituent services sectors’ share in total services output for an ‘average’ industrialized country. The service sector output shares are taken from Hoekman (1995, p.37/Appendix 1) and scaled so as to make the weights of all sectors covered in the Services Trade Restrictions Database add up to unity. We recognize that services sectors command a different share in total services output in different countries, especially across developing and developed countries, and are at least in part influenced by policy restrictions. As an empirical regularity, the share of financial and business/professional services tends to rise with income whereas retail distribution and, to a lesser extent, telecommunications services occupy a larger share in poorer countries. However, comparability requires the use of one uniform set of weights for all countries. We chose to use the shares for an ‘average’ industrialized country because industrial countries tend to be more open and so shares are less likely to be distorted by restrictions.

**Table A2: Sector and modal weighting schemes used for constructing STRI**

|**Aggregate sectors**|**Subsectors,**<br>**by mode of supply**|**Modal**<br>**weights**<br>)<br>(_j_<br>_m_<br>_w_|**Sector**<br>**weights**<br>_j_<br>_w_|
|---|---|---|---|
|**Banking**|Mode 1:||0.149|
||(1) Deposit acceptance|0.15||
||(2) Bank lending|0.15||
||Mode 3:|||
||(3) Deposit acceptance|0.85||
||(4) Bank lending|0.85||

6
green patents granted. In comparison, high-income countries were granted nearly 1,500 green patents in 2010 alone.

Though small, the ̳importance‘ of green patenting as measured in absolute numbers in developing regions is rising, particularly in EAP and S Asia. This reflects the general rise in patenting by developing countries. Measured by their share in overall patenting per region, green innovations are as prominent in ECA and LAC as in high-income countries, having grown relative to all patents; in this relative sense, green patents have actually declined since the late 1990s in EAP and S Asia and been stagnant across the 2000s (Figure 1, Panel C).<sup>5</sup> But we stress caution in interpreting these trends. For one, these ratios are sensitive to even small changes in the number of patents granted to a few countries. Secondly, scale matters in R&D: even if the relative importance of green patenting is similar to high-income countries, most developing countries have not reached a critical mass of green patenting.

# **2.2 Technologically-sophisticated countries emerging as significant innovators, but limited to a few technologies**

**<u>Figure 2: Increasing Patenting in the most Technological Sophisticated Countries</u>** Number of Green Patents Granted

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40 1996-2000<br>30 2001-2005<br>20<br>2006-2010<br>10<br>0<br>Low-tech Hi-tech Developing High Income (in<br>Developing 100s)<br>Total USPTO grants in OECD Green Technology Areas for three periods. Patents granted to high-income<br>countries are in 100s. Source: USPTO grants in PATSTAT.<br><!-- End of picture text --> Figure 2 suggests that a significant capacity for frontier green innovation exists in a small group of more technologically-sophisticated developing countries. Hence, appropriate green innovation policy is likely to differ between these and other developing countries (though given substantial differences among the technologically more sophisticated countries, appropriate policy would differ significantly between them as well). A group of nine emerging economies (Argentina, Brazil, China, Hungary, India, Malaysia, Mexico, the Russian Federation and South Africa) account for nearly 80 percent of all US green patent grants to developing countries between 2006 and 2010.<sup>6</sup> Moreover, unlike the technologically less sophisticated countries, these ̳emerging‘ economies display a sharp upward trend in green patenting, with their green patent grants more than doubling between 2000-2005 and 2006-2010.

> 5 We have omitted regions where total green patenting is so low that this percentage is a highly volatile, uninformative indicator. Moreover, we have taken 3-year moving averages to smooth out annual fluctuations. 6 We considered indicators of technological sophistication (R&D personnel per capita) as well as the scale of the R&D sector (total R&D personnel) in making this distinction.

5
# **1 Introduction**

The existence of preferential tax rates based on firm size is usually justified on the grounds of providing support for entrepreneurs and small and medium enterprises (SMEs). However, they may also provide the wrong incentives for some firms that may try to remain small in order to take advantage of the special treatment. On the one hand, there exists broad evidence in the economics literature that sizedependent policies (not only tax policy) can affect the growth of firms: providing strong benefits for small firms might discourage them from becoming larger (Bachas, Fattal Jaef, and Jensen 2019; Asatryan and Peichl 2017). On the other hand, it is an empirical question whether any specific size-dependent policy is enough of a deterrent for firms’ growth.

In this paper we study how firms in Lithuania respond to a _notch_ in the corporate income tax (CIT) schedule. The main feature of the CIT in this setting is that firms face two possible tax rates on their profit: a standard 15% rate and a reduced 5% rate for companies with fewer than ten employees and declaring turnover below €300,000. This sudden increase in average tax rates when firms declare slightly higher turnover generates strong incentives to reduce declared turnover. We document the existence of a _static response_ —firms respond to differential tax rates in the short run in order to stay below the threshold—and provide suggestive evidence of a _dynamic response_ —firms bunching in one period are more likely to report lower revenue growth in subsequent periods.

We use novel administrative tax data covering the universe of limited liability corporate taxpayers in Lithuania between 2015 and 2020. The data contain all line items required to file the CIT as well as firm characteristics (e.g., business structure, location, number of employees). Crucially, we are able follow firms across years, allowing us to construct a panel of 600,000 firm-year observations.

In the first part of the paper we document static responses in two dimensions. First, firms respond to the incentives that are present in the tax schedule by bunching below the €300,000 turnover threshold—we see an excess number of firms declaring turnover precisely at that level. However, this is not the only margin that firms have to respond. The tax is designed in such a way that the variable which determines the tax rate (turnover) is not the tax base (profits)—i.e., the amount of _turnover_ (above or below €300,000) determines the fraction of _profits_ that should be paid in taxes. Therefore, firms have more than one option to respond to tax incentives: they can report less turnover to keep a lower tax rate or, if if facing a higher tax rate, over-report costs to reduce profits. We show both responses exist 2
bureaucracy, the availability of information on the local economic situation, quality of life and quality of urban development and safety (IACE, 2019, 2018, 2017, 2016).

- _Proximity to the port (POR)_ : This is a binary variable that indicates the existence or not of a maritime freight terminal within 60 km of the governorate. The proximity to port is a critical determinant for certain FDI decisions particularly for foreign investors with significant share of local production turned to export (INS, 2019).

Finally, we include in the model a dummy variable to capture the behavior of foreign investors in response to the 2011 revolution. This variable is given the value 0 for the year preceding 2011 and 1 for the post revolution years.

# **_4.2. Methodology_**

Using regional annual data from 24 governorates over the period spanning from 2008 through 2018, we start by constructing and estimating a model measuring the impact of a set of determining factors for the attractiveness of FDI . Similar to past research that have provided strong empirical evidence for certain persistence of the FDI across regions and an endogeneity in the relationship between investment inflows and certain macroeconomic determinants, we chose to run a dynamic panel model that accounts for the lagged dependent variable in the explanatory variables (Campos and Kinoshita;2003, Walsh and Yu;2010). This approach, Kinoshita and Campos (2002) and Batana (2011) argue, will allow to incorporate the past FDI inflows as a proxy for the agglomeration effect. In this paper, we follow the same approach by using a GMM model. As pointed out by several authors in dynamic panel models where endogeneity of certain variables is suspected, the results of OLS and fixed effects estimations will be biased upwards and downwards, respectively. To correct for this bias (finite sample bias), the specifications of our model below follow the method suggested by Arellano and Bover (1995) and Blundell and Bond (1998) to estimate the model using the system GMM approach. As suggested by Roodman (2006), we will use two lags for the system GMM estimators, and we will apply the Windmeijer (2005) finite sample correction for standard errors. All GMM estimators use robust standard errors.

The model takes the following general form; FDIi,t = β0 + β1FDIi,t−1 + β2Host Variables + εi,t + ηi
rural Kenya [39]. We believe the fixed nature of start-up and administration costs will further decrease the cost of this intervention if it is implemented over a longer period. As the community becomes more aware of the malaria control activities and changes its health seeking behavior, the intensity of the community mobilization activities could be scaled down, further bringing down total costs.

This study is not without limitations. In traditional rural Indian settings, informal sharing of information is common among the inhabitants of a locality; thus, informational spillovers might have contaminated the control group particularly since the treatment and control villages were often geographically contiguous villages. However, we do not find that outcomes in neighboring treated villages (weighted by distance to the treated village) have a significant impact on outcomes in control villages. This lack of a significant relationship suggests that the results reported above are not contaminated by spillovers. Note that even if spillovers existed, they would have led to a downward bias in the estimated treatment effect since such spillovers would have improved outcomes in control areas. Secondly, while recall bias is not uncommon in community-based surveys, any such bias would have influenced all three study arms in a similar manner. Finally, self-reported preventive behavior may have been biased by social desirability concerns. This type of reporting bias has been observed when contrasting behavior recorded at the health facility and data reported through household survey, with survey data presumed to be the more accurate [40, 41]. Differential program effectiveness observed by district suggests that desirability bias cannot fully account for the program impacts measured here as certain implementers are more effective in achieving outcomes [42]. Nevertheless, any such reporting bias may result in an overestimation of program effects for self-reported preventive behaviors.

18
Appendix Table1. Difference in Individual Characteristics between Beneficiaries and Non-Beneficiaries

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||90,000<=score<br><=110,000|92,000<=score<br><=108,000|96,000<=score<br><=104,000|98,000<=score<br><=102,000|
|Age in 2007|0.345|0.341|-0.001|-0.001|
||(0.117)**|(0.131)**|(0.002)|(0.003)|
|Age 0-5|-0.001|0.000|0.000|-0.007|
||(0.001)|(0.001)|(0.003)|(0.004)|
|Age 5-10|-0.001|0.001|-0.002|-0.002|
||(0.001)|(0.002)|(0.003)|(0.004)|
|Age 10-20|0.001|-0.005|-0.003|0.004|
||(0.002)|(0.002)**|(0.003)|(0.004)|
|Age 20-30|-0.006|0.001|0.003|0.001|
||(0.002)**|(0.002)|(0.003)|(0.004)|
|Age 30-40|<br>0.000|<br>0.000|<br>0.000|<br>0.002|
||(0.002)|(0.002)|(0.003)|(0.004)|
|Age 40-50|<br>-0.002|<br>-0.002|<br>0.002|<br>0.002|
||(0.002)|(0.002)|(0.003)|(0.003)|
|Age 50-60|-0.001|0.001|0.002|0.003|
||(0.002)|(0.002)|(0.003)|(0.006)|
|Age 60-70|0.003|0.006|0.000|-0.009|
||(0.002)|(0.002)**|(0.004)|(0.022)|
|Age 70 +|0.006|0.002|-0.032|0.000|
||(0.002)**|(0.003)|(0.016)*|(0.001)|
|Female|<br>-0.001|<br>-0.088|<br>0.001|<br>0.007|
||(0.003)|(0.011)**|(0.001)|(0.004)|
|Education (years)|-0.118|0.001|0.008|-0.007|
||(0.010)**|(0.000)*|(0.003)**|(0.006)|
|no formal education|<br>0.001|<br>0.010|<br>-0.003|<br>0.001|
||(0.000)*|(0.002)**|(0.004)|(0.002)|
|Primary edu.|0.011|0.003|0.002|0.000|
||(0.002)**|(0.003)|(0.002)|(0.004)|
|Secondary edu.|0.009|-0.002|-0.004|-0.012|
||(0.003)**|(0.001)|(0.003)|(0.019)|
|High school or|||||
|<br>equivalent|-0.003|-0.011|-0.046|-0.001|
||(0.001)**|(0.002)**|(0.013)**|(0.001)|
|above high school|-0.016|-0.076|-0.001|0.005|
||(0.002)**|(0.009)**|(0.001)|(0.004)|
|Education of head (in|-0.090|0.001|0.011|-0.002|
|<br>years)|(0.008)**|(0.001)|(0.003)**|(0.006)|
|Employment|-0.008|-0.041|-0.011|0.019|
||(0.003)**|(0.011)**|(0.004)**|(0.004)**|
|Household size|<br>-0.037|<br>-0.019|<br>0.003|<br>-0.001|
||(0.010)**|(0.003)**|(0.003)|(0.002)|
|Urban|-0.027|-0.010|0.000|-0.003|
||(0.003)**|(0.002)**|(0.001)|(0.001)|
|Region1|-0.015|0.001|-0.002|-0.003|

30
44

# _Energy Policy and Prices_

Domesfic energy prices in Indonesia in the early 1 980s were highly subsidized. Energy subsidies financed by the budget or from special credit programs of the government of Indonesia during 1981-82 amounted to $2.3 billion, an increase of 40 percent over the previous year. The economic cost, i.e., the difference between domestic and border prices of these subsidies was almost double the budgetary cost and nearly 5.4 percent of GDP in the same year.29The first increase in domestic oil products and electricity prices was implemented during 1982-85. This was motivated by the need to reduce the fiscal burden of energy subsidies and to encourage efficient use of energy through pricing that reflected the scarcity of energy resources. Figure 3.1 shows the changes in energy prices implemented during 1981-85.

_Figure 3. 1: Energy Price Changes in Indonesia (percentage p.a in real terms)_ <!-- Start of picture text --> 60%<br>50%<br>40%<br>*30%<br>220%<br>10%<br>0%<br>-10%<br>-20%<br>1981 1982 1983 1984 1985<br>Diesel O Electricity l GasOine Kerosene<br><!-- End of picture text --> <!-- Start of picture text --> 'See World Banlk (1983).<br><!-- End of picture text -->
effect of district-level credit expansion by SCBs. Since the initial impact may also impact the recovery, we also control for the average income change of household _i_ between May 2018 and August 2018: ( _yid,Aug_ 18 _− yid,May_ 18). Finally, we also control for household characteristics (age, caste, religion, and profession).

Table 3 reports the results for income and expenditure recovery. We find that immediate government assistance, as captured by the variable _Reliefd_ , seems to have contributed significantly toward income recovery in all the months from September to December 2018. Specifically, 1 percent higher per capita assistance is associated with roughly 4 percentage points additional income growth between August and December. This is again in line with the hypothesis, providing an additional indication that the fast wage growth was related to reconstruction after the disaster.

Table 3: Allocation of Disaster Response Funds and Household Income Growth

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Sep-18|Oct-18|Nov-18|Dec-18|
|Relief p.c(Log)|4.910<sup>_∗∗∗_</sup>|0.348|3.535<sup>_∗∗∗_</sup>|4.003<sup>_∗∗∗_</sup>|
||(0.783)|(0.945)|(1.049)|(1.100)|
|Income change (Aug-May)|-0.239<sup>_∗∗∗_</sup>|-0.378<sup>_∗∗∗_</sup>|-0.529<sup>_∗∗∗_</sup>|-0.649<sup>_∗∗∗_</sup>|
||(0.030)|(0.030)|(0.029)|(0.029)|
|Controls|Yes|Yes|Yes|Yes|
|N|2112|2045|1993|1975|

Note: Standard errors are clustered at the household level. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001

# **7. Impact on Household Balance Sheet Items**

Examining household balance sheet items, such as borrowing and saving, contributes to a better understanding of how households responded to the disaster. In this section, we use qualitative information on household borrowing and saving, from the CPHS, to estimate linear probability models of the following specification: 28
# **Table 2. Input use in the crop and livestock sectors in Pakistan**

|_By cropping region an_|_d period, 1_|_966-94_||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Sector_**|**_Wh_**|**_eat-mixed_**||**_Wh_**|**_eat-rice_**||**_Crop S_**<br>**_W_**|**_ystem by P_**<br>**_heat-cott_**|**_eriof_**<br>**_on_**|**_Whea_**|**_t-Mungbe_**|**_an_**|**_Al_**|**_l Punjab_**||
||**_1_**|_2_|**_3_**|**_1_**|**_2_**|**_3_**|**_1_**|**_2_**|**_3_**|**_1_**|**_2_**|**_3_**|**_1_**|**_2_**|**_3_**|
|Crop**(hal)**||||||||||||||||
|Fertilizer (kg)|14.1|46|76|12.3|44.8|64.7|18.5|62.3|120.3|6.1|26|49.1|14.1|48.3|86.1|
|Plant<br>|18.2|24.6|23.9|18.9|28.3|13.4|21.3|35.6|70.2|11.1|16.6|25.3|18.3|27.9|40.1|
|protection (%)||||||||||||||||
|Labor (days)|89.5|108.4|84.6|73.6|88.7|69.4|87.2|100.7|65.8|82.4|86.4|62.6|85|98.7|71.1|
|Bullock (days)|74.4|45.6|10.8|72.9|38.1|6.5|85.4|49.6|7.7|83.8|45.8|8|79.6|45.8|8.5|
|Machine (hr)|1.4|6|14.7|1.6|7.1|15.7|1.7|6.4|17.3|0.8|2.6|9.1|1.5|5.7|14.8|
|Water (acre ft)||||||||||||||||
|Tubewell|1.1|1.9|2.5|2.8|5|6|1.5|2.4|2.6|0.6|1.4|2|1.4|2.5|3|
|Canal|4.9|4.6|4.2|2.6|2.5|2.1|5.9|5.4|4.8|3.5|3.2|2.9|4.7|4.3|3.9|
|Irrigated area(%)|81|85.2|84.7|74|85.2|88.6|97.2|98.1|98.4|59.7|61.5|67|81.9|85.1|86.3|
|Crop intensity (%)|113|130|132|132|139|147|121|128|147|104|109|118|117|126|136|
|**Livestock (SAU.l)b**||||||||||||||||
|Labor (days)|35.8|41.5|42.5|32.6|37.8|40.3|33.6|35.6|34.5|30.2|35.2|30.5|33.8|37.9|37.4|
|Feed &fodder(t)|3.19|3.87|4.11|3.5|3.8|4.13|4.07|3.91|3.66|2.07|2.42|2.51|3.4|3.7|3.7|
|Others (Rs)|50.9|60.4|61.9|52.5|62.4|63.3|49.6|58.6|61.9|45.9|54.3|59.5|_50_|59.1|61.7|

1 = Green Revolution 1966-74,2= Intensification 1975-84, 3 = Post Green Revolution, 1985-94

## bStandard Animal Unit

Source: Authors' own calculation 10
for 2010, which were derived by CONEVAL from the 2010 survey and census. Six main results emerge:

1. Combining satellite indicators with household survey data substantially improves the accuracy of municipal poverty estimates relative to estimates using the survey data alone. In the preferred specification, correlation with the benchmark official estimates rises from 0.8 to 0.86 and root mean squared deviation falls 25 percent.

2. Incorporating geospatial predictors greatly improves the precision of the estimates compared with the direct survey estimates. The median coefficient of variation declines from 38.5 to 19.8 in the household level model, which rises to 25 if the mean squared error estimates are adjusted to maintain equal coverage rates across methods. Even after this adjustment, the additional precision achieved by the small area estimation procedure is equivalent to increasing the size of the sample by a factor of roughly 2.4.

3. The household-level model moderately underestimates uncertainty and yields a coverage rate of 77 percent for in-sample municipalities and 83 percent of out-of-sample municipalities. For in-sample municipalities, this is moderately lower than the 86 percent obtained using the direct survey estimates when using the Horvitz-Thompson variance estimator, but much larger than the 39 percent coverage rate when using the standard variance estimator clustered on enumeration areas, which greatly underestimates uncertainty.

4. For all satellite-based estimates, in-sample predictions are substantially more accurate and much more precise than out-of-sample predictions. The correlation between the satellitebased estimates and the official benchmark estimates declines from 0.86 for in-sample municipalities to 0.70 for out-of-sample municipalities, and the median coefficient of variation rises from 19.8 to 33.9.

5. Estimates that combine survey and geospatial data using a household model are more accurate and much more precise than those produced by a sub-area model for in-sample municipalities (correlation of 0.86 vs 0.83, median CV of 19.8 vs. 35.6). The sub-area model estimates, in turn, are more accurate but somewhat less precise than the area-level model estimates for in-sample areas (correlation of 0.83 vs. 0.80, median CV of 35.6 vs 6