rafmacalaba/datause-encoder-data huggingface dataset:
30 obtained from the fixed effect estimates. The marginal productivity of improved seeds is equal to the exponent of the fitted value in the production function (using column 4) multiplied by the coefficient of the variable ―Log (improved seeds +1) in kg‖ and divided by the value of ―improved seeds +1‖. The marginal productivity of inorganic fertilizer is equal to the exponent of the fitted value in the production function (using column 4), multiplied by the summation of the coefficient of the variable ―Log (inorganic fertilizer +1) in kg‖ and the multiplication of the coefficient of the variable ―Log (inorganic fertilizer +1) in kg*%land irrigated‖ and the variable ―%land irrigated‖, and divided by the value of ―inorganic fertilizer +1‖.

**Table A2-1:Results** **<u>of the production function estimation</u>**

||Log (crop|income)|Log(maize r|ice income)|
|---|---|---|---|---|
||OLS|FE|OLS|FE|
|Log (land owned +0.1) in ha|0.259***|0.250***|0.208***|0.207***|
||(0.050)|(0.070)|(0.047)|(0.060)|
|Log (adult equivalent)|0.026|-0.005|0.065|0.070|
||(0.071)|(0.141)|(0.078)|(0.143)|
|Log (agr assets +1) in 1000Tsh|0.050**|0.012|0.034*|0.008|
||(0.019)|(0.032)|(0.019)|(0.034)|
|Log (livestock +1) in 1000Tsh|0.105***|0.119***|0.047**|0.059**|
||(0.018)|(0.025)|(0.019)|(0.025)|
|Log (traditional seeds +1) in kg|0.168***|0.168***|0.170***|0.160***|
||(0.027)|(0.034)|(0.030)|(0.037)|
|Log (improved seeds +1) in kg|0.147***|0.077*|0.200***|0.117**|
||(0.032)|(0.044)|(0.038)|(0.049)|
|Log (organic fertilizer +1) in kg|-0.026**|-0.007|-0.009|0.012|
||(0.011)|(0.013)|(0.013)|(0.014)|
|Log (organic fertilizer +1) in kg*%land|0.050**|0.015|0.032|-0.009|
|irrigated|(0.020)|(0.025)|(0.026)|(0.028)|
|Log (inorganic fertilizer +1) in kg|0.064**|0.071**|0.160***|0.107**|
||(0.025)|(0.032)|(0.029)|(0.034)|
|Log (inorganic fertilizer +1) in kg*%land|0.012|-0.007|0.060|0.132**|
|irrigated|(0.049)|(0.054)|(0.060)|(0.065)|
|Gender of household head|0.136|0.350**|0.154|0.393**|
||(0.100)|(0.173)|(0.106)|(0.186)|
|Education of household head|0.030**|0.017|0.042**|0.016|
||(0.012)|(0.021)|(0.013)|(0.023)|
|Percentage of illiterate household|-0.179|-0.467|0.066|0.292|
|members|(0.246)|(0.375)|(0.221)|(0.368)|
|Age of household head|0.002|0.006|-0.003|0.000|
||(0.002)|(0.005)|(0.002)|(0.005)|
|Dependency ratio|-0.032|0.193|-0.027|0.065|
||(0.135)|(0.194)|(0.144)|(0.197)|
|Whether plant maize|0.007|0.122|0.957***|0.904***|
||(0.087)|(0.104)|(0.100)|(0.119)|
|Whether plant rice|1.505***|0.483|2.439***|1.308|
||(0.338)|(0.668)|(0.365)|(0.822)|
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6. Niger’s road indicators**

|**Indicator**|**Unit**|**Low-**<br>**income**<br>**countries**|**Niger**|**Middle-**<br>**income**<br>**countries**|
|---|---|---|---|---|
|Total road network density [1]|km/1,000 km<sup>2</sup>of land area|132|13|318|
|Classified road network density|km/1,000 km<sup>2</sup>of land area|88|11|278|
|GIS rural accessibility|% of rural pop. within 2 km of all-season road|25|15|31|
|Household survey rural accessibility|% of rural pop. within 2 km of all-season road|34|37|63|
|Classified paved network condition|% in good or fair condition|72|67|86|
|Classified unpaved network condition|% in good or fair condition|53|58|65|
|Classified paved road traffic|AADT|1,131|387|2,451|
|Classified unpaved road traffic|AADT|57|31|107|
|Primary network overengineering|% of primary network paved with 300 AADT or less|30|53|18|
|Primary network underengineering|% of primary network unpaved with 300 AADT or more|13|23|20|

_Source_ : AICD Road Sector Database. _Note_ : [1] Includes the classified and unclassified network. GIS = geographic information system; AADT = average annual daily traffic.

Traffic levels are extremely low, making it difficult to justify heavy road engineering. Traffic in the classified paved network, at 387 vehicles per day, is only one-third of the average 1,131 vehicles in LICs and one-eighth of the average 2,451 vehicles in MICs. The volume of traffic in Niger’s unpaved network is also low—31 vehicles per day on average, versus 57 vehicles for comparable LICs. Niger has not been sensitive to this reality: around 53 percent of its primary network is over-engineered (that is, it is paved even though it has traffic levels of under 300 vehicles per day; table 6).

The spread of population over a wide geographical area, along with the requirement for international road connectivity, poses a challenge to road financing. At an estimated 6 cents per liter for diesel and super gasoline, the Niger fuel levy is in the middle of the Sub-Saharan Africa distribution. But the fuel levy is set well below the 26 cents per liter needed to cover the maintenance costs arising from wear and tear of the network by road users and is sufficient to finance only 34 percent of routine maintenance (figure 7). The estimated optimal fuel levy for maintenance in Niger is one of the highest on the continent after Ethiopia and Chad, reflecting the geographical characteristics of the country.

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- 4 -

horizontal axis indicates RD costs and the private discounted rents from the innovation (Q).

Figure 2 <!-- Start of picture text --> B +<br>Q (B,T)<br>B (RD)<br>0 //<br>la<br>00<br>y X RD.<br><!-- End of picture text --> It is assumed that the productivity of RD can be approximated by B(RD) function which eventually has decreasing returns. Whether there is a segment of increasing returns in the B(RD) function is not clear from the empirical literature. As a matter of fact, Nordhaus developed his model based on empir cal researchp which showed continous decreasing returns to RD (Nordhaus, 1969, p. 80). It _is_ also noted that RD is an Activity whose output is very uncertain but as I will note below, recent developments have incorporated this feature into the model.

On the other hand, under the assumption of linear demand and cost functions, the rent schedule Q(B,T) is also linear and is a function of the cost reduction and the length (T) _of_ the patent. As the patent life increases, the Q function rotates to the right but because additional benefits
firms awarded with low corruption-risk GPP contracts have on average a 5.9 percent higher TFP and a 19 percent higher labor productivity.

The results are similar when we use the share of GPP contract values in firms’ total public procurement income as a measure of GPP. Specifically, firms with a higher share of GPP in their total public contract value have a higher labor productivity than fully privatelyowned firms awarded with non-GPP contracts that operate in the same sector and have a comparable firm size and age.

Taken together, the findings suggest that GPP enhances the efficiency of resource allocation in the economy by helping to channel public resources to more productive firms relative to same sector competitors. Consistent with H3, at least a significant part of this effect is explained by the positive interaction between GPP and the lower risk of corruption. In other words, the positive impact of GPP on the application of more competitive public procurement practices in Bulgaria helps allocating public resources to more productive firms relative to competing firms offering the same product (i.e., that operate within the same 4-digit sector).

_Table 6. The relation between green procurement, corruption risk in procurement, and firm productivity among firms awarded with public procurement contracts._

|||||Depend|ent variable<br>|:<br>|||
|---|---|---|---|---|---|---|---|---|
||TFP<br>ols|TFP<br>ols|TFP<br>Ols|TFP<br>ols|VA/L<br>ols|VA/L<br>ols|VA/L<br>ols|VA/L<br>ols|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|GPP dummy|.048|.059**|||.145**|.190**|||
||(1.51)|(2.37)|||(3.33)|(4.91)|||
|CRI mean||-.012||||.092**|||
|||(-0.48)||||(3.85)|||
|GPP dummy *||-.119||||-.251**|||
|CRI>0.5||(-1.39)||||(-2.34)|||
|GPP share|||.023|.038|||.090|.145**|
||||(0.66)|(1.32)|||(1.53)|(2.77)|
|CRI mean||||-.012||||.086**|
|||||(-0.96)||||(3.65)|
|GPP share *||||-.118||||-.284**|
|CRI>0.5||||(-1.36)||||(-2.37)|
|4-digit sec FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Size, Age, SOE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.813|0.813|0.812|0.812|0.537|0.538|0.535|0.535|
|Adjustd R2|0.810|0.810|0.810|0.810|0.530|0.531|0.528|0.528|
|Obs total|22,137|22,137|20,831|20,831|26,618|26,618|24,991|24,991|

_Source_ : Firm census panel data from 2010-2018 matched with public procurement transaction data. _Note_ : 25
**Figure A.3: Average Middle-School CLASS Scores by County**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2 Dianbai<br>1 Lianjiang<br>0 Wuhua<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

Figure A3 shows Dianbai again standing out in almost all areas.

# **A.2 By teacher type (new, backbone candidate, backbone)**

Moving to the teachers themselves, differences between three types of teachers were examined: new teachers, teachers who had been identified as backbone candidates, and backbone teachers. Backbone teachers are those who have been formally identified as leaders among their fellow teachers, often holding open classes and having demonstrated many years of professional distinction. For the purposes of this section, those teachers that have been identified as potential backbone teachers will be referred to as “candidates”. Table A.1 summarizes the average ages and teaching experience of the three teacher types. There are 12 teachers for each type.

**Table A.1: Average and Range of Age and Teaching Experience by Teacher Type**

||**Age (Range)**|**Teaching Experience**|
|---|---|---|
|**New**|30.7 (26-38)|7.8 (1-19)|
|**Candidate**|36.7 (29-44)|15.5 (7-25)|
|**Backbone**|38.0 (28-47)|15.6 (4-25)|

Table A.1 shows that on average, new teachers are the youngest and have the least experience, while backbone teachers are the oldest and most experienced. The ranges, however, show that the picture is somewhat more complicated, with significant overlap existing in ages and experience between the different categories of teachers.

Figure A.4 shows the scores for each type of teacher, along with the overall scores for all teachers. As seen in other breakdowns above, the differences in scores are not very large among the three different categories.

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# **1 Introduction**

During economic crises, governments often try to help struggling firms by providing financing with the idea of increasing their chances of survival and a faster recovery. In doing so, they face different trade-offs. One is the need to quickly reach broad coverage, at the expense of distributing untargeted assistance to firms that do not need help or are too risky. Another trade-off is that although these policies might be beneficial in sparing firms from collapse in the short term, they can increase the overall indebtedness of the private sector, affecting debt overhang, repayment rates, financial stability, and fiscal cost in the longer term.

The aggregate consequences and risks of government-induced credit injections hinge critically on how debt is actually distributed throughout firms. This depends on the incentives and reactions of borrowers and lenders and on different factors that can augment or mitigate micro and macro risk-taking. On the demand side, forces can help channel funds to those firms with the highest marginal returns to capital. But limited liability could also generate demand from firms with higher default risk. On the supply side, when banks have only partial insurance from the government for the credit they grant, they will tend to lend to firms that limit their risk-taking. But other reasons (such as changes in the cost of borrowing and lending) can also affect their decisions. Understanding which incentives and conditions matter in credit markets and their quantitative consequences on the aggregate equilibrium outcome is challenging and important and also requires comprehensive micro data.

In this paper, we study how credit and risk are distributed across the whole economy when market participants face different incentives, under a massive new policy during a systemic shock. We exploit a large-scale public credit guarantee program (henceforth credit program) established by the Chilean government to support firms during the COVID-19 pandemic. This allows us to study empirically and analytically the drivers and consequences of the distribution of “crisis credit” (credit during crisis times), which has implications beyond crises. To be able to conduct the analysis, we obtain unique and detailed information on the behavior of all types of firms and banks and the overall environment where economic agents operate.

Our main results show a sharp distinction between the micro and macro effects. At the micro level, there is a substantial expansion of indebtedness both within firms and across different types of firms. The credit expansion is characterized by “adverse selection” on the extensive margin (selection into the program) and the intensive margin (increase in debt) by 1
TABLE 3: Impact of changes in public expenditure on changes in a child’s nutritional level and height by area

|_Dependent Variable_:||Z-score (hei|ght for age)||Height|(in cm)|
|---|---|---|---|---|---|---|
|||Pa|nel||Pa|nel|
||Whole<br>sample OLS|Whole<br>sample IV|Rural IV|Urban IV|Rural IV|Urban IV|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Total public spending|-0.052|0.041|-0.078|0.238|0.050|0.825|
||(0.058)|(0.068)|(0.029)**|(0.127)*|(0.224)|(0.328)**|
|Other controls|YES|YES|YES|YES|YES|YES|
|N|1,902|1,902|688|1,214|692|1,218|
|R<sup>2</sup>|0.344||||||

_Source_ : Young Lives Study Round 1 and 2, ENAHO 2002, 2006 and Ministry of Economic and Finance Budgetary Expenditure for 2000–6.

_Note_ : The estimations include regional fixed effects, individual fixed effects, and time fixed effects. Controls: child’s age, predicted child’s health, number of children under 5 years of age in the household, availability of drinking water, household size, wealth score, rice prices in the community, population size, a dummy for living in the highlands, a dummy for being in an urban area, a poverty index, and the level of regional debt payments. For estimations (5) and (6), I include as an additional control the lag value of height. Spending measured in 100 million soles. The instrument for the IV estimation is the amount of unanticipated regional mining revenues. Robust standard errors clustered by region-year in parentheses, * significant at 10%; ** significant at 5%; *** significant at 1%.

30
# **Table A - 1 : Time for Customs Clearance of Imports by Port of Entry**

|**Selected PAFTA Countries**|**Air Freight**|**(days)**|**Sea Freig**|**ht (days)**|**Road Tran**|**sport (days)**|
|---|---|---|---|---|---|---|
||**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|**Range**<sup>**(*)**</sup>|**Average**<sup>**(*)**</sup>|
|**Egypt**|2.0 - 7.0|2.0|3.0 - 9.0|3.0|2.0 - 5.0|3.0|
|**Jordan**|2.0 - 3.0|2.0|3.0 - 15.0|3.0|2.0 - 5.0|3.0|
|**Lebanon**|2.0 - 6.0|2.0|2.0 - 6.0|2.0|1 - 5.0|2.0|
|**Morocco**|Less than 1 - 1.5|1.0|1.0 - 5.0|2.5|1 - 2.0|1.0|
|**Saudi Arabia**|2.0 - 10.0|5.0|4.0 - 27.5|6.0|2 - 4.0|2.5|
|**Syria**|2.0 - 3.0|2.0|2.0 - 5.0|3.0|2 - 3.0|2.0|
|**Tunisia**|2.0 - 4.0|2.0|3.0 - 8.0|6.0|2 - 4.0|2.5|
|**UAE**|Less than 1 - 1.5|1.0|1.5 - 3.5|2.0|1.0 - 2.0|1.0|
|**Yemen**|2.0 - 4.0|3.0|4.5 - 5.0|5.0|1.0 - 3.0|2.0|
|**Total Countries (2008 Survey)**|**2.0 - 5.0**|**2.0**|**3.0 - 8.0**|**5.0**|**1.5 - 4.5**|**2.0**|
|**Total Countries (2001 Survey)**|**2.0 - 5.0**||**2.0 - 10.0**||**1.0 - 3.0**||

(*) Ranges represent lower and upper values using Tukey's hinges (lower and upper quartiles) which are standardized by avoiding the bias of extreme values; Average is the arithmatic average of lower and upper values (Tukey's hinges), for more details, see <http://support.spss.com/>.

21
implement an objective and replicable approach to mass valuation, applying it routinely in practice requires legal amendments to valuation and tax codes. Such amendments should include provisions for a gradual expansion to non-agricultural urban land by integrating data on land and buildings that are currently managed in separate registries, in line with the roll-out of a unified address registry.

Second, the discrepancies between NMV and market-based values identified here imply that a transition from NMV to market values based on mass appraisal as a basis for taxation may change specific local communities’ receipts or taxpayers’ bills. Making provisions for a gradual transition, including the ability to set local tax rates within a wide band, will be important to maintain fiscal stability for local governments and mitigate backlash by taxpayers. In fact, war-induced drops in land prices may make transition towards a market-based land valuation easier and put in place a buoyant system to help recovery.

The rest of the paper is organized as follows: Section 2 discusses global background and principles of transparent property valuation as a basis for own source revenue generation by local governments. Section 3 links to the Ukrainian context by documenting the country’s journey towards establishing a land market and improving land valuation and remaining challenges. Section 4 describes the empirical model, discusses results from estimation of land values based on publicly available data as well as predicted prices for the 7 million parcels of commercial agricultural land. Section 5 concludes with policy implications.

# **2. Putting property taxes in context**

Immovable property taxes are rather simple to administer; target users of local government services with limited spillover across jurisdictions; are less likely to distort decisions by economic actors than taxation of production factors with a higher elasticity due to its immobile base; and as land and real estate is owned by more affluent individuals, are progressive. To capture this potential, it is important to have a complete tax base, an automated process for valuation that is responsive to market conditions, credible enforcement with minimal exemptions, and appropriate assignment of revenue.

# **2.1 Conceptual evidence in global context**

As land is immobile, taxing it allows to raise revenue to finance local public goods in a way that is less distortive (Schwerhoff _et al._ 2020) and, if the rich hold more of their wealth in land, more progressive (Bonnet _et al._ 2021) than taxes on mobile factors such as capital and labor. Property taxes increase owners’ incentives to use land effectively (Oates and Schwab 1997) and discourage speculation (Norregaard 2013), an issue that is important as in all countries for which data are available, prices for land and property grew strongly in real terms since the 1950s (Knoll _et al._ 2017). Property taxes also encourage compact urban development (Ermini and Santolini 2017; Song and Zenou 2006) rather than sprawl.

3
Aw; = W278 + AVez299 + AVG22, + AZo9 + ATi22, + Arie, + Au? Ap; = Yo2F + AYP Z299 + AYP2? + AvEzoog + Argzk, + Arezh. + Au? where Aw; = Wi,01 — Wi,99, Api = Pi,01l — Pi,99, and Au; = Ui,ol — Ui,99- These regressions characterize which individuals, in terms of initial condition and shock exposure, became “richer” and “more powerful” between mid-1999 to end 2001. The right-hand side variables differ to equations 2 and 3 in five regards. First, v represents exposure to shocks and changes since 1999. Second, in addition to distinguishing explanatory factors according to type of characteristic (violence v, basic b, economic e, social s, and community c), variables are indexed with regard to being time-invariant (indicated by subscript ”. and pre-determined (indicated by subscript ”99”). As discussed below, in addition to the shock measures z?, the estimation includes pre-determined basic (zg and economic (299 variables, and time-invariant basic (z?”), social (zj_), and community zf variables. Third, the TLSS does not provide complete recall information on all time-varying x variables of equations 2 and 3. The z variables in equations 4 and 5 are therefore typically a subset of the x variables of equations 2 and 3. Fourth, the random errors u;9; and U;,99 are differenced, which may reduce correlation with them and the observable determinants. 4° Fifth, equations 4 and 5 give direct estimates of the changes in the coefficients from 1999 to 2001. They indicate whether the fundamental transformation led to an increase (Ay > 0 or decrease (Ay < 0 in the returns to characteristics. Individuals with high values of a characteristic for which Ay is positive (negative) are winners (losers) of the systemic changes since the referendum on independence.

As before, the discussion of the estimation results will be guided by hypotheses on Timor-Leste’s recent history. The analysis of Section 3 suggested that welfare gains were highest for individuals who ranked low in 1999. Hypothesis 4 Welfare winners have low initial economic resources: <!-- Start of picture text --> Ow; <0.<br>OZ§99<br><!-- End of picture text --> contrast, Section 3 indicated that most population groups benefited from 49 Differencing also removes any individual-specific fixed-effects like risk aversion or other time-invariant character traits.
where the subscript indicates the partial derivative with respect to the corresponding argument. It simply means that the efficient allocation should equate the marginal productivity of public capital in both sectors. Similarly, we can derive the relationship that characterizes the political equilibrium allocation: We thus see that the political equilibrium will be efficient only when the groups have the same marginal productivity of private capital. Broadly speaking, the equilibrium will be inefficient as long as the groups differ in terms of productivity. We can also assess the bias that will result in this equilibrium, still in the sense defined in text. If the technologies have constant returns to scale, the marginal productivities will be homogeneous of degree zero, an we can express the terms in (C.1) and (C.2) as functions of the private-public capital ratios. Suppose, without loss of generality, that group 1 has a larger marginal productivity of private capital than group 2 in the equilibrium allocation. We <u>ψ</u><sup>1</sup> <u>1</u><sup><u>(k</u></sup> <u>1</u><sup>∗,a∗</sup> <u>1</u><sup><u>)</u></sup> thus have ψ<sup>1</sup> 2<sup>(k</sup> 1<sup>∗, a∗</sup> 1<sup>)<</sup> ψ<sup>2</sup> 1<sup>(k</sup> 2<sup>∗,a∗</sup> 2<sup>)ψ</sup> 2<sup>2(k</sup> 2<sup>∗, a∗</sup> 2<sup>).</sup> In order to achieve equality, we must increase<sup><u>k1</u>to</sup><sup><u>k2</u>sothattheLHSwillincrease,andtheRHS</sup> a1<sup>relatively</sup> a2<sup>,</sup> will decrease (assuming that both types of capital are complements). It follows that the political equilibrium will be biased towards group 2, which is precisely the one for which private capital is less productive in the efficient allocation. This is precisely the manifestation of the comparative advantage intuition that was stressed in our model.

# 8 References

ACEMOGLU, Daron. "The Form of Property Rights: Oligarchic vs. Democratic Societies". Cambridge, Mass.: MIT (mimeo), 2003.

ACEMOGLU, Daron, AGHION, Philippe & ZILIBOTTI, Fabrizio. ”Distance to Frontier, Selection, and Economic Growth”. Cambridge, Mass.: NBER (Working Paper 9066), 2002.

ACEMOGLU, Daron & ROBINSON, James A.. ”Why Did the West Extend the Franchise? Democracy, Inequality, and Growth in Historical Perspective”. Quarterly Journal of Economics, v. CXV, n. 4, pp. 1167-200, 2000.

AGHION, Philippe & BOLTON, Patrick. ”A Theory of Trickle-Down Growth and Development”. Review of Economic Studies, v. 64, pp. 151-72, 1997.

ALESINA, Alberto F. & RODRIK, Dani. ”Distributive Politics and Economic Growth”. Quarterly Journal of Economics, v. CIX, n. 2, pp. 465-90, 1994.

ATKINSON, Anthony B.. ”Bringing Income Distribution in from the Cold”. Economic Journal, v. 107, pp. 297-321, 1997.

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