rafmacalaba/datause-encoder-data huggingface dataset:
The interpretation of responses to managers as to the importance of tax rates for the operation of their business faces similar difficulties as the interpretation of the importance of access to finance, and partly for similar reasons. Just as managers do not take account of the social benefits of a financial system that constrains access to finance by aiming to screen out poor projects, they also do not take account of the social benefits arising from government spending funded by the taxes they pay.

We suggest that the way to interpret the responses of managers on tax rates is that they point to the costs imposed on firms if public inputs are supplied at the cost of higher taxes than necessary. Tax rates are very highly ranked as a constraint by managers in virtually all countries (irrespective of their level of development) but it does not follow that it is a priority everywhere to cut taxation. A high ranking is probably better interpreted as indicating that policies to reduce tax rates while holding other aspects of public infrastructure provision constant (for instance, by improving administrative efficiency) would improve firm performance.

For the public input constraints, we use the survey answers to get at the private cost to the firm of inadequate or unreliable public inputs or burdensome regulation. It is not possible to use micro data of this kind to uncover whether tax rates are a key constraint to firms – firms are unlikely to take into account the public inputs that are paid for by taxation when responding to the question.

# **4.  Are job-creating firms in the South Asia Region also high-performance firms?**

The analysis in this paper relates to formal-sector firms in 8 South Asia Region (SAR) countries: Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan and Sri Lanka. The data were collected in a series of surveys over the 10-year period 2000-2010. Tables 1 and 2 present the number of firms and the median permanent employment by country and year. Firms in the three most-populous countries – India, Bangladesh and Pakistan – account for 80% of the total sample. Most of the firms surveyed are SMEs; median permanent employment is 19 persons. This is true of the individual surveys as well; the exceptions are the surveys in Bangladesh and Sri Lanka in 2002 and 2003, where permanent employment in the median firm exceeded 12
**Table A7: Country-Specific Proportion of Sibling Correlations Explained Explained by Intergenerational Transmission Estimates (1990 Cohort)**

|**Country**|**Prop.**|**S.E.**|**N**|**Country**|**Prop.**|**S.E.**|**N**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific||||Sub-Saharan Africa||||
|Cambodia|0.659|0.020|1751|Benin|0.809|0.031|1915|
|Indonesia|0.774|0.023|4540|Burkina Faso|0.727|0.095|241|
|Philippines|0.810|0.026|4301|Burundi|0.639|0.033|2484|
|Vietnam|n.a|n.a|n.a|Cameroon|0.871|0.032|1502|
|||||Chad|0.677|0.024|1058|
|Europe & Central Asia||||Congo, Rep.|0.728|0.061|291|
|Albania|0.762|0.080|1115|Cote d'Ivoire|0.732|0.078|154|
|Armenia|0.555|0.084|428|Ethiopia|0.679|0.023|1935|
|Kyrgyz Republic|0.501|0.159|304|Gabon|0.775|0.095|249|
|Tajikistan|0.587|0.053|1744|Ghana|0.796|0.066|577|
|Turkey|0.701|0.075|742|Guinea|0.753|0.062|949|
|||||Kenya|0.877|0.041|2568|
|Latin America & Caribb|ean|||Lesotho|0.817|0.070|593|
|Bolivia|n.a|n.a|n.a|Liberia|0.699|0.048|909|
|Brazil|n.a|n.a|n.a|Madagascar|n.a|n.a|n.a|
|Colombia|0.706|0.022|3729|Malawi|0.745|0.027|2178|
|Dominican<br>Republic|0.838|0.093|540|Mali|0.699|0.034|1332|
|Guatemala|0.722|0.019|2690|Mozambique|0.759|0.055|279|
|Haiti|0.629|0.021|2144|Namibia|0.823|0.115|279|
|Peru|0.738|0.028|2645|Niger|0.705|0.056|308|
|||||Nigeria|0.693|0.011|5396|
|Middle East & North Af|rica|||Rwanda|0.715|0.043|1362|
|Egypt, Arab Rep.|0.656|0.025|2934|Senegal|0.792|0.069|354|
|Jordan|0.543|0.023|5047|Sierra Leone|0.597|0.039|1139|
|||||South Africa|0.592|0.072|578|
|South Asia||||Tanzania|0.711|0.052|1213|
|Afghanistan|0.561|0.017|4857|Togo|0.837|0.073|410|
|Bangladesh|0.718|0.025|1712|Uganda|0.704|0.037|1710|
|India|0.686|0.004|72192|Zambia|0.741|0.027|2011|
|Nepal|0.640|0.032|1317|Zimbabwe|0.721|0.054|518|
|Pakistan|0.762|0.013|5583|||||

_Notes_ : This table presents the estimated share of the intergenerational component using the Bingley and Cappellari (2019) method for each of the 53 developing countries in the Demographic and Health Surveys (DHS) using the 1990s birth cohort sample.

45
fertilizer application rates that also affect yields<sup>1</sup> (Offodile, 2010; Akighir and Shabu, 2011; Adedeji et al., 2014). This paper uses this rich national representative panel data set with plot level information (The LSMS-ISA) to provide empirical evidence on the profitability of fertilizer use for maize production across Nigeria, addressing the endogeneity of the input use decision. We exploit panel data estimation techniques to estimate expected profit maximizing quantities of applied nitrogen for maize production across agro ecological and market conditions. Then we compare these expected optimal rates (adjusted to account for the riskiness of fertilizer use for smallholder farmers) to the actual rates used by maize farmers. We explore if fertilizer use pays at current prices and if not always, under which circumstances it does.

Thus, this paper addresses two gaps in the literature. First we are able to more consistently identify the yield response to fertilizer application by accounting for unobserved time invariant household characteristics likely to affect fertilizer application and yields. Second we are able to address a key gap in the fertilizer use literature which believes fertilizer use is low in SSA, even though it is profitable. As a result of this assumption, the literature generally looks to other constraints to its adoption, such as financial market imperfections (credit/insurance/savings), knowledge, or lack of demand and thus the realization of economies of scale on the supply side (agro-dealer network), or lack of access to markets to sell the produce, but these all link again to profitability issues. This paper rather focusses on the profitability of fertilizer use as a likely explanatory factor for observed fertilizer use rates.

The rest of this paper is organized as follows: Section 2 describes fertilizer use generally, and within major maize producing farming systems across Nigeria while Section 3 presents our conceptual framework and empirical methods. We present the production function estimates, marginal (and average) products of applied nitrogen and the analysis of the profitability of nitrogen application for maize across various categorizations in section 4.Section 5 concludes.

# **2. Fertilizer use across Nigeria**

Since the 1940s, Nigerian governments have generally perceived that fertilizer use in the country was low. By the 1960s, population density had started rising and the government became increasingly concerned about farmers’ awareness of fertilizer’s benefits (Whetham 1966), and

> 1 Liverpool-Tasie (2014) is the only paper found in Nigeria while Sheahan et al (2013) and Xu et al (2009) are examples for maize in Kenya and Zambia respectively.

3
business, insurance companies and banks. Establishing a subsidiary in a low tax country gives them the opportunity to develop tax avoidance strategies. It is indeed difficult for any one country to claim the right to tax the holding company if its operations have taken place in multiple markets at the same time. A typical example is when filing tax returns in a high tax country, a multinational company claims that it has earned as little profits as possible. Instead, it tries to attribute as much profit as possible to its operations in lowtax countries by arranging "transactions" between its subsidiaries in the two countries, and setting the "transfer price" of those transactions so that it has the desired effect on profits. Multinationals can also adjust the timing of their dividend repatriations from foreign subsidiaries (see Hines and Hubbard (1990)). In practice, such strategies may explain the success of tax havens countries in attracting subsidiaries of "global" companies and the expenses made by multinationals on economists and accountants to justify their transfer prices that suit their tax needs. Still, very little is known about the magnitude of such international tax evasion and how much do they affect tax revenues across countries (see for some preliminary evidence, Grubert, Goodspeed and Swensson (1993) or the Economist (2000)).

# B. Tax Instruments Used by Governments

Governments have several tax instruments that they can use to attempt to influence the effective tax rates and the location decision of multinational companies. 8 The literature has traditionally focused on the instruments linked to the corporate income tax such as tax holidays and tax allowance. Of course, these instruments are of no help to an unprofitable company and, therefore, other forms of incentives have also been widely used around the world. Exemptions from custom duties or local indirect taxes (generally to targeted sectors) do exist in many countries, even though their use has been restricted in most international and bilateral trade treaties. Outright grants are used in many industrial countries but rarely in the developing world because of their upfront costs.

> 8 For example, an effective tax rate in the United States of about 25% at the end of 1994 was produced by a 38% corporate tax rate combined with no investment tax allowance, depreciation rates on buildings of 4.4% and 18.6% on machinery, and an number of other assumptions about inflation, interest rates (interest

11
Another caveat is that many countries passed stimulus packages to help the population survive the pandemic—both physically and financially. Several of these stimulus packages contained provisions supporting renewables.<sup>10</sup> We lack systematic, high-quality data about these stimulus packages, which presumably resulted in increased renewables capacity, and are thus unable to assess their effectiveness. In practice, we would expect a time lag between the fund allocations and project completion: The funds might be used now for expansion of renewables electricity projects, but it would take a few years to get these projects completed. We hope to be able to examine this issue in future research, relying on more precise data and a longer pandemic or post-pandemic sample period.

# **References**

Abu-Rayash, Azzam, and Ibrahim Dincer. 2020. “Analysis of the Electricity Demand Trends amidst the COVID-19 Coronavirus Pandemic.” Energy Research & Social Science 68 (October): 101682. https://doi.org/10/ghbqqp.

Agdas, Duzgun, and Prabir Barooah. 2020. “Impact of the COVID-19 Pandemic on the U.S. Electricity Demand and Supply: An Early View From Data.” IEEE Access 8: 151523–34. <u>https://doi.org/10.1109/ACCESS.2020.3016912.</u> Alberini, Anna, Giuseppe Prettico, Chang Shen, and Jacopo Torriti. 2019. “Hot Weather and Residential Hourly Electricity Demand in Italy.” Energy 177 (June): 44–56. <u>https://doi.org/10.1016/j.energy.2019.04.051.</u> Andreoni, Valeria. 2021. “Estimating the European CO2 Emissions Change Due to COVID-19 Restrictions.” Science of The Total Environment 769 (May): 145115. <u>https://doi.org/10/gkfj7x.</u> Bao, Rui, and Acheng Zhang. 2020. “Does Lockdown Reduce Air Pollution? Evidence from 44 Cities in Northern China.” Science of The Total Environment 731 (August): 139052. <u>https://doi.org/10/ggxfj5.</u>

> 10 See https://www.natlawreview.com/article/sun-also-rises-congress-votes-to-stimulate-renewable-energy- <u>efficiency-carbon</u> for a discussion of such provisions in the US.

21
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**_Table 5: The relationship between night work and women’s economic opportunity_**

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|**Dependent Variable**|**Female Top Manager**|**Log**|**of Total Female Emplo**|**yment**|
||**All firms**|**All firms**|**Manufacturing &**<br>**Construction**|**Services (excl.**<br>**Construction)**|
|Female employees allowed to work<br>night shifts(Y/N)|0.216|0.490**|1.343***|0.461**|
||(0.192)|(0.192)|(0.507)|(0.204)|
|Log of age of firm|-0.146|-0.155|-0.227|-0.152|
||(0.092)|(0.163)|(0.145)|(0.192)|
|Log of size|-0.068*|0.467***|0.512***|0.438***|
||(0.040)|(0.099)|(0.143)|(0.127)|
|Part of a larger firm (Y/N)|-0.121|0.344|-0.649|0.411**|
||(0.143)|(0.164)|(0.666)|(0.185)|
|Offers formal training (Y/N)|-0.140|0.177|0.086|0.215|
||(0.089)|(0.147)|(0.325)|(0.178)|
|Top manager experience in sector<br>(years)|0.009|0.012|0.018*|0.013|
||(0.009)|(0.014)|(0.011)|(0.017)|
|Exports 10% or more of sales (Y/N)|-0.089|-0.445*|-0.242|-0.607*|
||(0.108)|(0.266)|(0.437)|(0.312)|
|Foreign ownership (Y/N)|0.043|0.241|0.571**|0.227|
||(0.105)|(0.285)|(0.228)|(0.442)|
|Has checking or savings account<br>(Y/N)|0.271**|0.185|-0.063|0.181|
||(0.121)|(0.179)|(0.237)|(0.205)|
|Has a line of credit or loan (Y/N)|0.067|-0.063|-0.018|-0.128|
||(0.128)|(0.274)|(0.446)|(0.332)|
|Experienced losses due to crime (Y/N)|-0.023|-0.079|-0.018|-0.081|
||(0.093)|(0.288)|(0.245)|(0.354)|
|Constant|0.388**|0.059|-0.520*|-0.675|
||(0.180)|(0.411)|(0.308)|(0.493)|
|Number of observations|214|213|79|134|
|R2<br>Notes: All models included sector (ISIC<br>are presented in parentheses;***p<0.0|0.300<br>2 digit) fixed effects as we<br>1,**p<0.05,*p<0.1. Sam|0.480<br>ll as location (w<br>pling weights are|0.729<br>ithin country) fixed effe<br>used throughout.|0.429<br>cts. Standard errors|

Notes: All models included sector (ISIC 2 digit) fixed effects as well as location (within country) fixed effects. Standard errors <u>are presented in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Sampling weights are used throughout.</u> 18
and much more if one focuses on time changes rather than the absolute value of overall global inequality. The second reason to ignore within country inequality is that, in view of available data and methods for incidence analysis, there is practically no way to take into account the within country distributional impact of global redistribution mechanisms studied in this paper.

The objective of this paper is to analyze how direct transfers of aid as well as international policies that limit trade opportunities and possibly flows of labor and capital affect the international distribution of income. One of the international redistribution channels is the allocation of foreign aid by high-income donors to developing country recipients. To the extent that official development assistance (ODA) is in grant form or can be made equivalent to pure grants, these transfers can be thought of, in the short run, as pure redistribution of global income.

Trade is another area of international policy we consider in this paper. A successful implementation of multilateral trade liberalization via the Doha Development Round would increase the world’s and many countries’ GDP, with interesting distributional consequences. While recognizing that most of the gains from multilateral trade liberalization would arise from the lowering of trade barriers between developing countries, we concentrate here on the redistribution effects of merchandise trade reform by high-income countries in order to compare these effects to those of ODA. Using World Bank’s computable general equilibrium (CGE) Linkage Model (van der Mensbrugghe 2005), we analyze the international redistribution of income that would result from fully liberalizing merchandise trade by highincome countries.

2
**_Table 18: Balance across screening participants (male)_**

||**T**|**otal**|**Co**|**ntrol**||**Movie**|||**MFB**|||**Movie + M**|**FB**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|**N**|**Mean**|**N**|**Mean**|**N**|**Mean**|**P-value**|**N**|**Mean**|**P-value**|**N**|**Mean**|**P-value**|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|_Personal characteristics_||||||||||||||
|Age of respondent|349|37.08|78|35.23|87|36.68|0.376|86|37.47|**0.095***|93|38.71|**0.013****|
|Married|352|0.75|79|0.71|88|0.72|0.998|87|0.80|0.152|93|0.77|0.331|
|Widowed|352|0.00|79|0.00|88|0.01|0.339|87|0.00||93|0.00||
|Single|352|0.24|79|0.29|88|0.27|0.872|87|0.20|0.152|93|0.23|0.331|
|Muslim|352|0.38|79|0.33|88|0.48|**0.038****|87|0.43|0.205|93|0.30|0.695|
|Christian|352|0.61|79|0.66|88|0.52|**0.057***|87|0.56|0.213|93|0.69|0.678|
|English|352|0.81|79|0.77|88|0.83|0.399|87|0.83|0.374|93|0.81|0.584|
|Igbo|352|0.29|79|0.29|88|0.26|0.615|87|0.25|0.582|93|0.34|0.461|
|Yoruba|352|0.68|79|0.68|88|0.70|0.721|87|0.75|0.367|93|0.59|0.214|
|Other ethnicitiy|352|0.03|79|0.03|88|0.03|0.721|87|0.00|0.137|93|0.06|0.226|
|_Education_||||||||||||||
|No completed school education|352|0.04|79|0.03|88|0.03|0.721|87|0.06|0.306|93|0.04|0.531|
|Primary school education|352|0.20|79|0.20|88|0.22|0.770|87|0.22|0.804|93|0.17|0.611|
|High school diploma|352|0.57|79|0.62|88|0.53|0.277|87|0.51|0.139|93|0.61|0.922|
|Diploma|352|0.09|79|0.05|88|0.13|0.162|87|0.09|0.307|93|0.08|0.513|
|Graduate school|352|0.10|79|0.10|88|0.09|0.863|87|0.13|0.613|93|0.10|0.922|
|_Household characteristics_||||||||||||||
|Household (HH) size|348|4.39|78|4.06|88|4.43|0.207|86|4.34|0.356|91|4.70|**0.038****|
|Number of children below 12 in HH|342|1.30|77|1.38|87|1.20|0.301|85|1.24|0.467|88|1.36|0.949|
|Number of dependents in HH|346|2.95|77|2.87|88|3.07|0.555|86|2.80|0.839|90|3.02|0.646|
|<br>Number of dependents outside the HH|329|1.92|75|2.52|83|1.87|0.200|80|1.63|**0.065***|86|1.78|0.114|
|_Business characteristics_||||||||||||||
|Months in operation|350|104.27|79|109.81|87|107.10|0.943|87|91.21|0.208|92|109.64|0.991|
|Has a savings account|352|0.67|79|0.70|88|0.68|0.895|87|0.62|0.309|93|0.71|0.848|
|Keeps written financial records|352|0.41|79|0.42|88|0.33|0.297|87|0.47|0.491|93|0.43|0.871|
|Operating inside main market|352|0.17|79|0.18|88|0.11|0.193|87|0.16|0.781|93|0.22|0.537|
|Number of employees|352|1.81|79|2.05|88|1.90|0.700|87|1.68|0.399|93|1.66|0.352|
|Business experience inyears|352|10.98|79|11.18|88|9.99|0.241|87|11.55|0.761|93|11.24|0.963|

*** p<0.01, ** p<0.05, * p<0.1

42
||||**[1]**|**[2]**|**[3]**|
|---|---|---|---|---|---|
|**Deficit districts**|**Regime**||**REGIME I**|**REGIME II**|**REGIME III**|
|**Rural north**|constant|_γ0 _|0.427|0.440|0.133|
||||(12.9)|(9.8)|(3.2)|
||fs|_γ1_|-0.148|0.200|-0.070|
||||(3.6)|(2.8)|(0.1)|
||trend|_γ2_|-0.00128|-0.00042|0.00171|
||||(3.5)|(0.8)|(3.6)|
||dbuy x fs<br>|_νj, γj.fs_|yes|yes|yes|
||R<sup>2</sup>||0.1445|0.2164|0.1435|
||chi<sup>2</sup>(parameters)||50.2 (6)|82.0 (6)|49.8 (6)|
||# of observations||297|297|297|
|**Rural central **|constant|_γ0 _|0.472|0.256|0.272|
||||(14.9)|(6.8)|(5.6)|
||Fs|_γ1_|-0.196|0.038|0.126|
||||(4.7)|(0.8)|(1.9)|
||trend|_γ2_|-0.00084|0.00096|-0.00013|
||||(2.7)|(2.6)|(0.3)|
||dbuyx fs<br>|_νj, γj.fs _|yes|yes|yes|
||R<sup>~~2~~</sup><br>||0.1202|0.0638|0.0622|
||chi<sup>~~2~~</sup> (parameters)||53.4(8)|26.6(8)|25.9(8)|
||# of observations||391|391|391|
|**Rural south**|constant|_γ0 _|0.534|0.306|0.159|
||||(15.5)|(6.7)|(2.4)|
||fs|_γ1_|-0.190|-0.008|0.197|
||||(3.0)|(0.1)|(2.4)|
||trend|_γ2_|-0.00168|-0.00023|0.00192|
||||(5.7)|(0.6)|(5.0)|
||dbuy x fs<br>|_νj, γj.fs_|yes|yes|yes|
||R<sup>2</sup>||0.1968|0.0806|0.1137|
||chi<sup>2</sup>(parameters)||114 (12)|40.9 (12)|59.9 (12)|
||# of observations||467|467|467|
|**Urban **|constant|_γ0 _|0.537|0.118|0.375|
||||(16.3)|(3.1)|(9.2)|
||Fs|_γ1_|-0.238|0.277|0.120|
||||(4.6)|(4.6)|(2.4)|
||trend|_γ2_|-0.00073|0.00261|-0.00188|
||||(2.1)|(6.2)|(4.4)|
||dbuyx fs|_νj, γj.fs_|yes|yes|yes|
||R<sup>~~2~~</sup><br>||0.2036|0.2001|0.1312|
||chi<sup>~~2~~</sup> (parameters)||74.9(6)|73.3(6)|44.2(6)|
||# of observations||293|293|293|

The table reports correlations of regime probabilities with a constant, a food shortage dummy, and a trend variable. REG I represents integrated markets and efficient trade, REG II represents integrated markets without trade or with trade at a loss, and REG III represents nonintegrated markets with unexploited and profitable arbitrage opportunities. Regime probabilities are calculated assuming κ=1 (see equations (7) to (9)). Estimation is by Seemingly Unrelated Regression. Values of regime probabilities are never at the lower limit (0) or upper limit (1); therefore, there is no need to control for censoring of the dependent variable. Absolute z-statistics are given in parentheses (.) below the coefficient.

The results in table 3 indicate that the average probability is between 43 and 54 percent for regime I (integrated markets with positive trade flows), between 12 and 44 percent for regime II (integrated, without trade or with loss-making trade), and between 13 and 38 percent for regime 27
# _Variables in the “Small Model” (Largest Availability)_

**Inflation** is measured as the log change of _v_c/q_c_ (household consumption at current national prices / household consumption at constant national 2017 prices) from the national account module of PWT10.0, which has larger availability than inflation data from WDI. We add 1 to this variable to avoid negative numbers and then take logs to obtain _linflation_na_ .<sup>15</sup>

The **real exchange rate** , _lrer_ , is calculated as the log of the GDP price level (at purchasing power parity) over the nominal exchange rate: _pl_gdpo/xr_ , both taken from PWT10.0. Since _xr_ is measured as national currency/US$, an increase in _lrer_ reflects a real appreciation, which is expected to negatively affect output and growth through various channels (e.g., Rapetti, 2019; Levy-Yeyati et al., 2013). We also calculate (absolute) deviations of the real exchange rate from a HP-filtered trend, _mad_lrer_deviation_ , which is included in the data set as alternative, but not included in our models.

**Trade openness** is conventionally captured as the sum of exports and imports over GDP. To account for higher trade shares of smaller countries and higher trade in times of higher demand from abroad, irrespective of trade policies, we calculate a _trade_resid_ as the residual of a regression of exports and imports over GDP on log of exporter population and global GDP, which acts as demand shifter. We then take the log of that residual which first requires transformation to positive values. This is done as: _ltraderesid_ = ln(trade_residmin(trade_resid)+0.01). All respective variables are taken from PWT10.0.

**Infrastructure** is captured by an index, as often is the case in the literature (surveyed in Välilä, 2020). We therefore combine information on phone lines, mobile phones, internet coverage, availability of secure internet connections, and access to electricity from WDI. Since they have different sample coverage, we use the most available series (phone lines) to predict missing values of the second-most available (mobile phones), use those to predict missing values in the next series, etc. Each of those five updated infrastructure series is then Pearson standardized (i.e., transformed to mean 0 and standard deviation 1). Their weights in the final infrastructure index are the number of available original observations at each year, divided by the total number of original observations from all five series in the same year.

> 15 This shift transformation is consistent with the earlier approach in Araujo et al. (2016) and most common in the literature. We are aware of potential problems and that there are possible alternatives which researchers are free to explore using our data set. See also Karakaplan et al. (2020).

-8-