rafmacalaba/datause-encoder-data huggingface dataset:
legal origin, endowments, ethnic fractionalization) are first order in explaining firm lifecycles, firm-level characteristics are comparable to, and sometimes even larger than institutional factors in predicting size and growth but not productivity. In particular, size at birth plays a key role in predicting variation in firm size and growth since birth over the lifecycle, whereas country factors dominate in predicting variation in labor productivity across the lifecycle.

Using better data from the Indian census of manufacturing firms, and in more careful analysis afforded by a single country setting (India), Ayyagari, DemirgüçKunt, and Maksimovic (2017) show that the founding conditions of a firm, specifically the size at the start-up, are a strong predictor of persistence in firm size over the first eight years of firms’ lifecycle . Start-up size is in turn determined by local institutions. Thus, institutions matter for the selection of firms. The average entrant is smaller with greater financial development, but greater financial development is also associated with higher entry rates. Subsequent to entry, however, during the early lifecycle, large and small entrants do not grow at different rates across states with different institutions or industries with differing reliance on external finance.

In summary, the evidence on the early lifecycle of firms in developing economies highlights the importance of initial conditions such as initial size in forming the blueprint for firms’ relative size and growth. More research is needed to understand how initial size correlates with access to finance and managerial capital in influencing growth.

22
3 for reform, 2nd relates the choices to somie initial conditions of the various reforming countries. In the metaphor of Vaclav Klaus (1990), the Finance Minister of Czechoslovakia, undertaking reform is like playing chess: one needs to know the rules and have a sense of strategy, but it is not possible to plan each specific move at the beginning.

Interdependence between reforming socialist countries is also very important, but the problems posed by the January 1991 reform of the trade and payments institutions of the CMEA will not be addressed at length.3

# II. Initial Conditions for Reform

Recent data on the economies of ESEs and the USSR are presented in Table 1. Estimates of GNP, aggregate or per capita, vary widely, depending on the source and the conversion method, and none can be regarded as accurate.4 The 1988 CIA estimates are well above rates calculated through the exchange rate; the latter are more relevant to the borrowi..g status of actual and potential members of the World Bank.' In any case, income levels in the ESEs appear to be above the averagi of

- 3/ The CMEA is an essentially bilateral system of trade relations, with the Soviet Union being the dominant trading partner of each of the smaller East European countries. Yugoslavia is not a member of the CMEA. Payments among CHEA countries are expected to be made in hard currencies, at world prices, from the beginning of 1991.

- A/ The estimates shown in the row "CMGr Physical Indicators" are close to purchasing power estimates of real incomes. See Fink and Havlik (1989) for discussion of alternative inc.-e estimates.

- 5/ The exchange rate based estimates -- which use actual exchange rates for countries such as Poland and Yugoslavia that have reformed their exchange arrangements, and staff estimates for other countries -- imply that all ESEs would be eligible World Bank (IBRD) borrower. by the income criterion.
scenarios we also compute food price effects as found in recent CGE and PE analyzes which fully modeled biofuel and feedstock markets. Note that PE analyses do not account for all sources of productivity gains and yield effects and tend to generate somewhat higher land and food price effects.

Our results suggest that planned biofuel targets would not cause large impacts on food supply at the global level although the impacts would be significant in developing countries like India and Sub-Saharan Africa. Agricultural commodities such as sugar, corn and oil seeds (as oil), that serve as the main biofuel feedstocks, would experience 1% to 8% price increases in 2020 as compared to that in the baseline due to the expansion of biofuels to meet the existing targets.

Our study shows that land use allocation between forest, pasture and crops, would be significantly altered, and leads to considerable forest and pasture destruction in several countries, especially under enhanced targets. Vast expansion of biofuel does lead to global forest losses of about 26.3 million hectares in 2020 under enhanced targets. Once targets are reached, technical progress eventually would reduce the reliance on land to expand agricultural output and forest land could be regained in the longer run. Within the expanded crop land uses, we obtain large effects in several countries implementing large biofuel targets. The general tendency is to expand land devoted to feedstock crops (sugar crops, coarse grains and, low quality grains for ethanol; oilseeds for vegetable oil for biodiesel). Again, these effects are large in 2020, because they correspond with the expansion phase of the targets. In the longer run, one would expect that the land expansion would recede and productivity gains would reduce the long term use of land induced in the medium term and the short term constraint on food supply would be reduced. Sugar crops and oilseeds expand the most.

We have conducted extensive sensitivity analysis. Various configurations of key parameters have been tried (elasticities of transformation in land, elasticities of substitution in production and fuel use, productivity assumptions, and trade elasticities). The qualitative results for the two scenarios are invariant to major changes in these parameters. The robustness of the results has to do first with the relative small share of farm values in food processing, hence the moderate food price effects. Second, the increase in demand for feedstock can be accommodated given the various mechanisms at play via trade in agricultural commodities, land allocation, and 43
# - 23 -

revenies between levels of government according to predetermined rules. The dominant factors in the latter have been the tax allowance (situado fiscal) and the sales tax transfer (cesion de ventas or IVA). Together, their share in central government RDEs and in central government current revenues grow from 14 to 56Z and 2? to 15Z between 1970 and 1987. The tax allowance is roughly a third larger than the sales tax t-:ansfer, but the latter has been growing more rapidly in recent years.

# 1. The Tax Allowance (Situado Fiscal)

24. The tax allowance was implemented through Law 46 of 1971 which followed from Article 182 of the Colombia Constitution of 1968 which provided that a portion of annual ordinary revenues<sup>13</sup> be allocated among departmenti, national territories, and the Special District of Bogota with 30% to be shared equally among 33 jurisdictions and 70% to be divided on the basis of population shares as they appeared in the 1964 census. Law 46 called for the share to be 13Z, 14%, and 15% in 1973, 1974 and 1975, thereafter increasing by 2% per annum to a maximum of 25% provided that ordinary revenues were rising by at least 15% per year. These 2% increases were never formally implemented and so the sharing formula remains at 15%; as will be shown below, the amounts actually shared have fluctuated around that limit.

25. Law 46 also stipulates that 74% of the amount transferred each year shall be used to fund the operating costs of primary education and 26% to finance those of the health sector. These funds are a major source of finance for primary education and make a significant contribution to the financing of health. The national government retains the right to alter 13/ Ordinary revenues are defined as current revenues not earmarked by legislation for any specific purpose or use.
**Table B7** Treatment-Control Balance at Baseline: Locus of Control and Discount Behavior

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|**Outcome**|**ELA Only**<br>**Control Mean**|**Boys-ELA**|**Supply-ELA**|**No Goal**<br>**Control Mean**|**Goal -**<br>**No Goal**|
|**Panel A. Whole Sample**||||||
|Locus of control|-0.000|0.008<br>(0.093)|0.018<br>(0.102)|0.025|-0.047<br>(0.036)|
|Very confdent can complete task|0.439|-0.019<br>(0.050)|0.035<br>(0.054)|0.459|-0.032*<br>(0.019)|
|Discount factor|0.397|0.052<br>(0.035)|0.012<br>(0.035)|0.423|-0.021<br>(0.015)|
|Chose riskiest option|0.127|0.008<br>(0.021)|0.024<br>(0.022)|0.137|-0.004<br>(0.016)|
|Observations|1,074||3,178|2,313|3,178|
|**Panel B. Balanced Sample**||||||
|Locus of control|-0.000|0.050<br>(0.094)|0.027<br>(0.110)|0.042|-0.054<br>(0.039)|
|Very confdent can complete task|0.439|-0.009<br>(0.053)|0.063<br>(0.059)|0.470|-0.034*<br>(0.021)|
|Discount factor|0.406|0.043<br>(0.037)|0.002<br>(0.035)|0.427|-0.020<br>(0.016)|
|Chose riskiest option|0.114|0.015<br>(0.022)|0.037<br>(0.023)|0.130|-0.002<br>(0.017)|
|Observations|871||2,591|1,900|2,591|

_Notes._ Column 1 shows control means for females in ELA only communitues and column 4 shows control means for females not assigned to the _Goal_ treatment. Columns 2–3 and column 5 shows the difference between means in the corresponding community- or individual-level treatment arms and the corresponding control group means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. Locus of control is an index measured in standard deviation units, generated by standardizing the raw Locus of control score to the mean and standard deviation among females in control communities who were not assigned to the _Goal_ treatment at baseline and at endline separately. Very confident can complete task is a binary indicator for the female reporting being very confident she can complete any task she starts, Discount factor measures the extent to which females discount the future and ranges from .1 to 1, and Chose riskiest option is a binary indicator for the female selecting the riskiest option in a risk game implemented in the field. Standard errors, clustered at the club level, are presented in parentheses below the coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

75
case of new regulations and firn entry and exit. Regulations may affect firm entry and exit in complicated ways, They may impose entry barriers to new finns and impose exit barriers to inefficient (or state-owned) firms. Av "snapshot" of firms at a given point in time (a crosssection) provides limited information on this matter. Only by surveying firms over time and across countries can we begin to understand how new regulations or other policy interventions affect firm entry and exit.

Entry and exit also lead to attrition bias in the sample-another reason to obtain panel data. Properly accounting for both entry and exit of firms is therefore important to understand industry evolution. Attrition bias may arise when firms disappear (exit) because of mergers, acquisition, consolidation, or bankruptcy, or when a firm is included in the sample only if it survives up to the point of interview. Without accounting for attrition, the sole reliance on surviving firms will inevitably bias (usually upward) performance measurements. The additional survey rounds necessary for constructing panel data can help account for this bias by deterrnining which firms have disappeared and why. In addition, panel data can help better understand firn entry.<sup>4</sup> New entrants may differ significantly from surviving firnms. Moreover, a high death rate of firms is not necessarily worrisome if it is accompanied by a high birth rate. To deal with such issues, firm surveys should resample some firms annually and strive to add new entrants.

Surveys should also define industries as precisely as possible and offer detailed informnation of the industry analyzed. When this type of micro data is collected in panel data sets, it becomes possible to compare the technological progress of particular industries in different countries. Such data allow us to better address important unanswered questions, such as why inter-firm dispersion in productivity is greater in industrialized economies than in developing countries (as suggested in Tybout, 1998). A variety of reasons (such as a smaller extent of market and excessive regulation) have been suggested. But there is no consensus among the existing empirical work largely because every data set aggregates industries differently, making comparisons nearly impossible (Tybout, 1998).

> 4 Finding new entrants, however, clearly makes their inclusion easier said than done.

4
|_Table 3 Firms Using Computer Regular_<br>Sectors|_ly by Co_<br>Ghana|_untry (P_<br>DRC|_ercentage i_<br>Tanzania|_n terms of _<br>Uganda|_All Firms_<br>Zambia|_)_<br>Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|68.8|53.7|40.4|56.3|69.1|88.2|62.7|
|**Main Sectors**||||||||
|Manufacturing|66.8|46.0|41.4|42.5|76.8|86.1|60.0|
|Services|70.8|56.3|40.0|60.8|67.0|89.1|64.0|
|**Sectors**||||||||
|Food|72.4|76.5|57.0|56.7|85.9|77.6|71.0|
|Textiles|35.4|0.0|19.3|61.4|88.6|84.9|48.3|
|Garments|37.0|6.6|61.8|40.8|38.2|-|36.9|
|Wood|24.0|40.6|51.1|19.5|40.9|81.2|42.9|
|Publishing, printing, and Recorded media|91.9|90.2|81.0|100.0|100.0|100.0|93.9|
|Chemicals|95.3|79.1|63.9|100.0|100.0|98.5|89.5|
|Plastics & rubber|96.0|100.0|100.0|57.5|79.5|100.0|88.8|
|Non metallic mineral products|77.3|69.7|26.3|94.4|64.4|100.0|72.0|
|Fabricated metal products|45.1|34.9|35.5|17.0|89.5|100.0|53.7|
|Machinery and equipment|-|10.6|0.0|27.0|86.7|100.0|44.9|
|Furniture|48.5|26.4|20.6|31.0|48.6|100.0|45.9|
|Other Manufacturing|87.4|59.3|26.8|97.3|91.2|98.6|76.8|
|Construction|88.1|87.8|26.4|18.7|100.0|100.0|70.2|
|Services of motor vehicles|53.6|77.8|41.0|55.5|90.5|95.4|69.0|
|Wholesale|78.4|78.0|37.8|69.5|57.1|77.8|66.4|
|Retail|59.8|54.4|47.6|57.9|70.4|87.3|62.9|
|Hotel and restaurants|71.2|40.7|34.3|67.4|54.9|91.9|60.1|
|Transport Section|97.7|62.4|52.8|61.9|69.2|100.0|74.0|
|IT|91.1|92.2|-|90.9|100.0|100.0|94.9|

Source: Author’s own elaboration from enterprise surveys _Larger, younger and medium-aged, international traders and foreign owned firms are more likely to use computers._ As Table 4 suggests, larger and medium sized firms are more likely to use computers. Medium aged firms are also more likely to use computers on average, although in Kenya young firms show high computer use and in Tanzania young firms show little computer use. Also, as expected more internationalized firms, via trading or foreign ownership, have larger prevalence of computer use. Firms that are only importers show a higher percentage of computer use than only exporters (see Table 4); although two-way traders are the firms with more computer use in most countries. Overall computer use appears to be quite high among all kinds of Kenyan firms, while computer use is low among Tanzania and DRC firms.

14
# **9. Conclusions and Further Remarks**

The world has witnessed rapid growth in the production and consumption of biofuels over the past several years. Production of fuel ethanol and biodiesel grew by 26% and 172%, respectively, between 2004 and 2006. While high oil prices might have favored this growth, it was mainly driven by policies such as mandates, targets and subsidies catering to energy security and climate change considerations. However, apprehension due to the global food crisis in 20072008 and ambiguity regarding the environmental footprint of biofuels led many industrialized as well as developing countries to reconsider their earlier optimism regarding biofuels and adopt a more cautious approach. They announced that their biofuels program would be redesigned in order to avoid a fuel vs. food conflict. This also led to a shift in focus from first generation biofuels to second generation or advanced biofuels technologies. In addition, the current financial crisis and the drop in oil prices from prior peaks is expected to further retard the growth of biofuels in the near future.

The contribution of biofuels to the escalation of food prices in 2008 and the ensuing food crisis is a point of some contention. Most studies agree that expanded biofuel production, by raising demand for feedstock commodities, does put upward pressure on food prices, but there is considerable variation in estimates of the magnitude of this effect. This estimation is complicated by the presence of several other important drivers of food price, such as oil price, climate variability and currency fluctuation. For the most part, general equilibrium studies tend to find a lower impact on food price from biofuel production since they include the effect of price responses. Second generation biofuels may enable us to cease diverting agricultural commodities fit for human consumption to fuel production and may even enable us to utilize the waste material from agricultural production, but they may still compete for land with food crops.

Despite differences in results and uncertainties of calculations, the literature indicates that greenhouse gas balances are not favorable for all biofuel feedstocks, particularly when cultivation of feedstock causes the conversion of native ecosystems to crop lands directly or indirectly. However, the cultivation of perennial biofuel feedstocks on reclaimed marginal or 37
|||Expenditur<br>_bk08__|e on student<br>_as03E_|Number of<br>teach<br>_T_non_|non-PNS<br>ers<br>_PNS_|Teacher a<br>ra<br>_Teache_|ttendance<br>te<br>_r_attend_|
|---|---|---|---|---|---|---|---|
|VARIABLES||coef|se|coef|se|coef|se|
|D_accredit|Dummy, =1 if school is accredited|-1.335e+06|(1.207e+06)|0.871***|(0.268)|2.312|(2.135)|
|R_admission|Ratio of application / admission|-1.672e+06|(1.865e+06)|-0.343|(0.414)|2.797|(3.305)|
|T_enroll|Total enrollment|38,275***|(6,037)|0.00757***|(0.00134)|-0.0151|(0.0107)|
|T_PNS|Number of civil servant teachers|215,647|(179,504)|-0.170***|(0.0398)|0.213|(0.318)|
|bk05_pc08B|Dummy, =1 if school receives assistance from District or sub-district<br>education staff|-899,001|(1.201e+06)|-0.176|(0.267)|3.522*|(2.128)|
|bk05_pc08C|Dummy, =1 if school receives assistance from Private foundations/donors|-843,199|(3.343e+06)|1.179|(0.742)|-14.25**|(5.924)|
|<br>bk05_ao04G|<br>Dummy, =1 if Principal received training for involving parents and<br>community members in supporting the school|-2.383e+06|<br>(1.518e+06)|-0.119|<br>(0.337)|6.273**|<br>(2.686)|
|Dfemale|<br>Dummy, =1 if school principal is female|-1.256e+06|(1.190e+06)|-0.546**|(0.263)|2.689|(2.101)|
|age|<br>Principal’s age|-152,927|<br>(116,072)|-0.0522**|<br>(0.0256)|0.710***|<br>(0.205)|
|<br>bk09_pj03a|<br>Share of teachers receiving an oral performance evaluation from principal|-21,276|<br>(2.255e+06)|-1.070**|<br>(0.500)|1.370|<br>(3.995)|
|<br>bk09_pj03b|<br>Share of teachers receiving a written performance evaluation from principal|3.341e+06*|<br>(1.943e+06)|0.0514|<br>(0.431)|-1.043|<br>(3.440)|
|bk09_kg03xA|Share of teachers receive training from national government|-1.573e+06|(5.332e+06)|-0.405|(1.184)|16.64*|(9.447)|
|bk09_kg03xB|Share of teachers receiving training province government|5.262e+06|(3.418e+06)|-0.0174|(0.758)|-15.17**|(6.047)|
|<br>bk09_kg03xC|<br>Share of teachers receiving training from district or subdistrict Education<br>Office|-8.056e+06**|<br>(3.318e+06)|-0.109|<br>(0.736)|-1.566|<br>(5.878)|
|bk09_kg04A|Share of teachers receiving training in: Handling classroom management<br>and discipline situations|-3.902e+06|(4.124e+06)|-2.675***|(0.915)|-5.592|(7.306)|
|bk09_kg04B|<br>Share of teachers receiving training in: Planning lessons more effectively|-608,698|(4.981e+06)|-0.196|(1.105)|20.46**|(8.816)|
|bk09_kg04D|Share of teachers receiving training in: Teaching your subject matter or<br>grade level|-1.471e+06|(4.483e+06)|1.650*|(0.995)|-3.291|(7.938)|
|bk09_kg04H|<br>Share of teachers receiving training in: Planning for the allocation of BOS<br>funds|4.733e+06|(4.499e+06)|0.224|(0.999)|15.51*|(7.970)|
|||-1.952e+06|(9.688e+06)|5.305**|(2.146)|42.88**|(17.12)|
|Constant||||||||
|||383||384||384||
|Observations||||||||
|R-squared||0.627||0.346||0.287||

21
# **5 Effect on Wages**

## **5.1 Main Results**

Motivated by the illustrative model predictions presented in Section 2, we study the impact of the tax cut on wages in this section. First, we study the impact on the wages of new entrants by estimating the following regression equation: where _wit_ is the net wage of individual _i_ in May at year _t_ . Note that for wages, we use annual data throughout this section as this is the level of observation available.<sup>31</sup> In our case, _treform_ is 2013.

A key limitation of the regression equation above is that it considers the same proportional wage changes across the entire wage distribution. Nevertheless, given the lump-sum nature of the tax cut, we expect that the proportional increase in wages will be quite small for high wage earners and could be much larger for low wage earners. To take this into account, we assess the impact of the policy by the tax cut rate – the size of the payroll tax cut relative to the wage in the previous year, formally _TCRit−_ 1 = 14 _,_ 500 _/wit−_ 1, where HUF 14,500 is the tax cut amount. This variable goes from 14.5% for low wage earners to zero for very high wage earners, and reflects the percent change in wages that would occur if all of the tax cut were passed through to the worker. Notice that the tax cut rate is calculated for both treated and control workers. For the latter, the tax cut rate reflects the size of the tax cut (relative to their income) that would have been received if the workers were treated.

Then we estimate the following regression: where we interact each term in regression equation (11) with the tax cut rate, _TCRit−_ 1. To calculate _TCRit−_ 1, we need to rely on the previous year’s wage and so we can only estimate this regression for workers who worked in the previous year (incumbent workers).

31 We only see annual income for employment relationships spanning the entire year. This is a common feature of administrative social security data (see e.g. German IAB data).

27
Inequality, Economic Growth and Nonrenewable Resources countries, in which an increase in resource dependence is associated with higher rates of income inequality and lower GDP per capita.

Table 12: Estimated Results from the Non-Recursive Model Specification (resource-rich subsample)

||**IV Fixe**<br>|**d Effect**<br>|**3S**<br>|**LS**<br>|
|---|---|---|---|---|
||**Gini index**|**GDPc**|**Gini index**|**GDP**|
|Nonrenewable resource dependence|-0.175***|0.385**|-0.194***|0.355**|
|Lower income|dropped|dropped|-12.765***|51.841***|
|Lower income* nonrenewable resource|0.121**|-0.960***|0.167***|-0.925***|
|dependence|||||
|High-low education ratio|0.337|-|1.754***|-|
|High-low education ratio square|-0.001|-|-0.030***|-|
|Consumer price index (2005)|-0.001|-|-0.009|-|
|Services value added share|0.114**|-|0.132***|-|
|Manufacturing value added share|0.002|-|-0.078|-|
|Agriculture value added share|-0.157**|-|-0.093|-|
|GDPc|-0.085|-|-0.195***|-|
|Log labor force|-|-30.421***|-|-27.100***|
|Log gross fixed capital formation|-|0.548|-|4.939*|
|Log exports of goods and services|-|9.189***|-|5.571**|
|Log REER|-|4.338*|-|3.613|
|World oil price index|-|0.084***|-|0.088***|
|World metal price index|-|0.027|-|0.011|
|Gini index|-|1.337**|-|1.096*|
|Observations|580|580|580|580|
|R2|0.162|0.229|0.903|0.835|

Note: * ** and *** denote significance at the 10% 5% and 1% levels, respectively.

The subsample analysis appears to confirm the robustness of the full-sample results. Restricting the analysis to resource-rich countries also supports the conclusion that the impact of resource dependence on income inequality and economic growth is determined by country-level characteristics that affect the management of natural resources. The overall conclusions of the analysis are presented in the following section.

15