rafmacalaba/datause-encoder-data huggingface dataset:
in the past year, coded as zero for firms that do not export, and winsorized at the 99th percentile.

5. **Export innovation (new product-country combination)** : This is a binary variable coded as one if the firm exported to a product-country pair that they had not exported to at all in the past three years, and zero otherwise. Coded as zero for firms that do not export.

6. **Inverse Hyperbolic Sine of Total Export Value in the past year** . This takes the inverse hyperbolic sine transformation of total exports (measured in US dollars), and includes exports coded as zero for firms that do not export.

7. **Inverse Hyperbolic Since of Export Labor Productivity** : This is the inverse hyperbolic sine of the ratio of total export value in the past year (measured in US dollars) to the average number of workers used in the past year (obtained from the PILA database, which has monthly data on formal workers). This is coded as zero for firms that do not export, and winsorized at the 99th percentile.

8. **Standardized export outcomes index** : This index is calculated as the average of the normalized z-scores of primary export outcomes 1 through 7, where each z- score is defined by subtracting the mean and dividing by the standard deviation of the respective outcome.

Our measures of general management and export-specific practices come from survey data collected by Innovations for Poverty Action. Our outcomes are defined as follows:

1. **Export Practices** : The proportion of 15 export-specific practices being used by the firm, coded as zero for firms that close. The 15 practices measured are whether the firm has done any of the following in the past 12 months: received quality certification on export products; planned production with time for external markets; planned resources or training needed for export processes; participated in missions of public agency offerings to learn export quality standards; traveled to external markets to understand consumers there; participated in a trade fair or other industry exhibition for foreign visitors; developed direct marketing strategies to reach international customers; implemented new productive techniques to meet international quality standards; developed a new product with export potential; received distributor information through advertising abroad; received distributor information through an export promotion agency; received distributor information through trade fairs; received distributor information through commercial missions; evaluated the export potential of the company’s products; examined information about customer preferences and consumption patterns in external markets. Figure C1 shows the treatment impacts on each individual component.

51
Table D.7: Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference: Alternative Samples and Specifications - Analysis of Results from Table 4

||||Sector of|Work||||
|---|---|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service /<br>Sales|Agriculture /<br>I<br>Unskilled Manual|deal Number<br>of Children|Ideal Number<br>of Children|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|||Non|-MTI Regio|ns – FPE Only||_Nati_|_onal_|
|||||||_(Separate)_|_(Combined)_|
|||||A. Baseline||||
|�<br>Years of Schoolingizy|0.093|0.059|0.064|-0.048|-0.786|-0.923|-0.902|
||(0.058)|(0.028)|(0.047)|(0.031)|(0.468)|(0.658)|(0.493)|
||[0.107]|[0.033]|[0.169]|[0.116]|[0.093]|[0.160]|[0.068]|
|First Stage F-Statistic|6.06|6.63|6.63|6.63|6.63|6.60|19.37|
|N|13,909|13,755|13,755|13,755|13,789|24,649|24,649|
|||||B. Cohorts: 1968 -|1990|||
|�<br>Years of Schoolingizy|0.076|0.052|0.040|-0.044|-0.661|-0.883|-0.834|
||(0.039)|(0.023)|(0.031)|(0.030)|(0.567)|(0.400)|(0.320)|
||[0.052]|[0.023]|[0.196]|[0.146]|[0.244]|[0.027]|[0.009]|
|First Stage F-Statistic|7.68|7.61|7.61|7.61|7.66|21.26|27.72|
|N|16,465|16,276|16,276|16,276|16,323|29,253|29,253|
|||||C. Cohorts: 1969 -|1989|||
|�<br>Years of Schoolingizy|0.110|0.057|0.058|-0.032|-0.790|-0.839|-0.837|
||(0.056)|(0.029)|(0.042)|(0.030)|(0.737)|(0.565)|(0.440)|
||[0.050]|[0.050]|[0.166]|[0.288]|[0.284]|[0.138]|[0.057]|
|First Stage F-Statistic|6.67|7.20|7.20|7.20|6.76|12.01|26.80|
|N|15,093|14,927|14,927|14,927|14,963|26,832|26,832|
|||||D. Cohorts: 1971|- 1987|||
|�<br>Years of Schoolingizy|0.115|0.081|0.071|-0.049|-1.073|-1.144|-1.110|
||(0.082)|(0.043)|(0.058)|(0.048)|(0.708)|(0.583)|(0.506)|
||[0.158]|[0.060]|[0.221]|[0.305]|[0.130]|[0.050]|[0.028]|
|First Stage F-Statistic|3.47|3.99|3.99|3.99|3.64|7.56|13.80|
|N|13,195|13,045|13,045|13,045|13,084|21,802|21,802|
|||||E. Cohorts: 1972 -|1987|||
|�<br>Years of Schoolingizy|0.158|0.109|0.081|-0.061|-1.087|-0.950|-0.944|
||(0.107)|(0.058)|(0.067)|(0.055)|(1.848)|(0.582)|(0.571)|
||[0.140]|[0.062]|[0.229]|[0.269]|[0.556]|[0.103]|[0.098]|
|First Stage F-Statistic|2.70|3.19|3.19|3.19|2.88|6.56|13.07|
|N|12,863|12,715|12,715|12,715|12,754|21,177|21,177|

A.57
# **<mark>1. OVERVIEW</mark>**

This paper analyzes the relationship between firms’ perceptions of corruption and their experience in dealing with tax officials in the countries of Eastern Europe and Central Asia (ECA), using data from the the EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS). The paper investigates the extent to which experiences with corruption while interacting with tax administration affect general perceptions of corruption. Existing studies have mainly focused on the relationship between the quality of institutions and tax morale or tax compliance (e.g., Torgler & Schneider, 2009), rather than on understanding the role of tax administration-related corruption in the overall perceptions of corruption.

The literature suggests two very different readings of the relationship between firms’ perceptions of taxation and corruption in tax administration. In some instances, firms may prefer to engage in corrupt activities; therefore, the economic benefit of the extracted _“favors”_ may exceed the cost of bribes. For example, bribes could reduce or even help in avoiding altogether paying taxes. From firms’ prospective, corruption - in taxation in particular - may become an efficient business mechanism, especially in countries with deficient institutional frameworks or unclear tax policies (Méon & Weill, 2010).

Alternatively, corruption in taxation is harmful to enterprise growth, harmful to firm operations, and a barrier to efficient economic operations (Fisman & Svensson, 2007; Lambsdorff, 2003; Mo, 2001; Salinas-Jiménez & Salinas-Jiménez, 2007). If so, companies would see paying their taxes as the least costly or least burdensome option, and therefore view corruption as a burden.

These two phenomena may interplay differently depending on the country context, i.e. from firms’ point of view corruption in tax administration may be either (or perceived as) a substitute or a complement to tax liabilities creating benefits or inefficiencies, i.e. the perceptions of corruption are likely to be contingent on the actual practices of tax administration.

Findings presented in this paper provide a provisional support for the second line of reasoning – corruption in taxation as burden, rather than efficiency. Further, regardless of country context, it appears that perceived corruption in tax administration and actual experiences with bribery during tax inspections more than anything else affect the overall perceptions of corruption.

This paper is organized as follows: section 2 reviews and summarizes literatures on corruption and taxation administration that informed data analyses; section 3 then describes the data and method used; section 4 includes hypotheses used to frame analysis of the country-level data 1 | P a g e
# Table 1

Contributions to Annual Rates of Real Output Growth, 1980-88 (exponential growth rates in percent per annum)

||OUTPUT=|CAPJTAL+|LABOR+|MATERIAL+|TFP|
|---|---|---|---|---|---|
|State|848|1.45|0.34|4.31|2.40|
|Collective|16.94|1.96|0.66|9.70|4.63|

Source: JRZ (forthcoming) _Assessing Gabis ih 1Ffie Produicdon Among China's IndustrWa Entrprises,_ by Gary H. Jefferson and Xu Wenyi 17
- 43 -

tests, also reported in Table 3, show considerable parameter stability for equations 2 and 4 during the period. This implies that the coefficient estimates for equations 2 and 4 in table 3 were not significantly different in the 1962-74 period from those in the 1975-86 period. These Chow test results are encouraging since they indicate that the underlying determinants of investment in the economy, or "deep parameters," have been adequately captured. These results from the Engle-Granger procedure will be compared with those from general dynamic modelling below.

# 3.6. Unrestricted Dynamic Estimation

The results of the unrestricted dynamic equations are reported in Table 4. Again, because of the limited number of observations, it is not possible to include several lags of all of the variables.<sup>5</sup> 1 Instead, one lagged difference of each variable was included along with a lagged error correction term that reflects the relationship in the levels:

# ecm = (YK - Ex,)

" This may be an advantage of the Engle-Granger procedure when dealing with small samples. Engle and Granger suggest estirmating the simplest error correction model initially and then considering the effects of lags. Such an approach does preserve degrees of freedom, although the validity of the results hinge on having the correct specification at the first stage. Engle and Granger, 1987.
# **Land Price Effects of Informality, Farm Size, and Land Reform: Evidence from More Than One Million Transactions in Ukraine**

## **1. Introduction**

Land and house prices in high-income countries have increased significantly in real terms since the 1950s (Knoll _et al._ 2017), allowing owners to realize collateral benefits (Kumar and Liang 2024; Zevelev 2021) and take up entrepreneurial activities (Schmalz _et al._ 2017). A growing literature on urban land markets shows that market prices of property are affected by public goods such as connectivity (Ahlfeldt _et al._ 2017), road maintenance (Gertler _et al._ 2024), trees (Han _et al._ 2021), proximity to the coast (Severen and Plantinga 2018), or flood risk exposure (Bosker _et al._ 2019). Data on land prices is instrumental to estimate the cost of agglomeration (Combes _et al._ 2019) and analyze impacts of policies such as urban regeneration (Rossi-Hansberg _et al._ 2010) and low-income housing construction (Asquith _et al._ 2023), density restrictions (Hilber and Vermeulen 2016), building standards (Baylis and Boomhower 2023), disaster risk <mark>(Gourevitch</mark> _<mark>et al.</mark>_ <mark>2023; Hino and Burke 2021) and</mark> insurance subsidies (Garbarino _et al._ 2023).

In rural areas, factor mis-allocation that is not related to endowments (Adamopoulos and Restuccia 2022) has been shown to be widespread (Gollin and Udry 2020) and to negatively affect productivity (Maue _et al._ 2020) and occupational choice (Adamopoulos _et al._ 2022). Measures to improve market functioning such as issuance of transferable certificates (Bu and Liao 2022; Chen _et al._ 2022) or removal of restrictions on land (Chari _et al._ 2021; Chen _et al._ 2023; de Janvry _et al._ 2015) or labor (Giles and Mu 2018; Zhao 2020) have been shown to reduce mis-allocation.

Yet, although digital and remotely sensed data would allow to assess market functioning and put in place regulations to limit exercise of market power (Balmann _et al._ 2021) in near real time, rigid restrictions on land market operation remain in place in many countries (Shifa and Xiao 2023; Vranken _et al._ 2021). Use of geo-coded price data to study impacts of how land was acquired (Allen and Leonard 2021), surveyed (Libecap and Lueck 2011), assigned (Smith 2024), or can be transferred (Dippel _et al._ 2024) has largely been limited to historical cases in the US.

This paper uses more than 1 million transactions in lease and sales markets from Ukraine, a country that adopted far-reaching reforms including opening of land sales markets and land price reporting in 2020/21, to explore the scope for using land price data to make inferences on land market functioning that could inform policy in a more contemporary setting. It shows that linking administrative data to remotely sensed ones to identify fields as basic units of agricultural production within a farm can provide insights on (i) impacts of market liberalization on prices and structure of land markets at the local level; (ii) effects of 2
<!-- Start of picture text --> RCEP, ESTIMATED INCREASES IN EXPORTS RCEP, ESTIMATED INCREASES IN IMPORTS<br>Australia, 26.7 , Australia, 28.1<br>Brunei Darussalam, Brunei Darussalam,<br>0.2 0.1<br>China, 202.6 Japan, 144.0 China, 196.5 Japan, 138.5<br>Malaysia, 24.4<br>Malaysia, 24.8<br>New Zealand, 4.4 New Zealand, 4.6<br>Southeast Asia, 0.6 Singapore, 7.4 Southeast Asia, 0.5 Singapore, 7.8<br>Viet Nam, 10.0 Viet Nam, 9.9<br>India, 80.4 Korea, 42.5 Thailand, 1.4 India, 81.4 Korea, 42.4 Thailand, 1.5<br>Indonesia, 7.7 Indpnesia)\7.9<br>Laos, 0.3 Philippines, 7.9 Cambodia, 0.7 Philippines, 7.7<br>Cambodia, 0.7 aos, 0.3<br><!-- End of picture text --> <!-- Start of picture text --> FTAAP, ESTIMATED INCREASES IN EXPORTS FTAAP, ESTIMATED INCREASES IN IMPORTS<br>Russion0Federation, aaa\ 38.9Chile,~s a a6Mexico,Australia,30.9 26.5 Russion 76.7Federation,Canada, 40.4 Chile, 2.7erMexico, af ___Brunei31.3 Darussalam, 0.4<br>runei Darussalam, 0.4 ustralia, 27.8<br>Japan, 213.4 Japan, 208.5<br>Malaysia, 57.9<br>China, 544.2 Malaysia, 56.9 Sno<br>New Zealand, 7.4 ~~ New Zealand, 7.8<br>\ gg\_ Singapore, 14.6 \‘\ singapore, 16.2<br>\Viet Nam, 41.5<br>\Viet Nam, 41.2<br>USA, 254.3 Indonesia, 24.5___ USA, 288.8<br>Indonesia, 24.1 _——<br>Philippines, 25.4 9 ghailand, 18.3 Philippines, 24.9 |thaitand, 18.3<br>Korea, 79.8<br><!-- End of picture text -->
more precise measure of GVC involvement, but it is limited to a small sample of countries (41) and time coverage is limited to 1995-2011.<sup>9</sup> The empirical approach is based on the standard augmented gravity, which has been widely used to assess the impact of PTAs on trade flows (for a seminal paper, see Baier and Bergstrand, 2007). Differently from the standard approach, which uses a “dummy” to identify the presence of a trade agreement between a country pair, Osnago, Rocha and Ruta (2016) use the three measures of the “depth” of PTAs discussed in Section 2 (“total depth”, “core depth” and “PCA depth”). As standard in the modern gravity literature, the regressions include a set of fixed effects and controls for various determinants of bilateral trade.

Signing deep agreements has a large and positive impact on GVC trade. Figure 5 offers a summary of the regression results found in Osnago, Rocha and Ruta (2016). Specifically, the figure reports the estimated coefficients of the impact of PTA depth on GVC trade. The first histogram reports the coefficient estimated using the measure of trade in value added, while the second histogram reports the estimated coefficients for trade in parts and components. For completeness, I report the data for each of the three measures of depth. The analysis shows that adding a provision to a PTA increases bilateral trade in parts and components by 1.5 percent and re-exported value added by 0.4 percent. This means that signing the deepest PTA in the sample doubles trade in parts and components and increases re-exported value added by about 22 percent.

> 9 Data on trade in parts and components come from COMTRADE, while the data on trade in value added are based on the decomposition by Wang et al. (2016) and come from WIOD.

9
Table D.3. Effects of Guerrilla Territorial Control on Main Outcomes Using Conley Standard Errors

|_Panel A: Conley Stan_|_dard Errors (0.5 Kms)_|||
|---|---|---|---|
||Night Light Arcsine|Wealth Index|Years of Education|
||(2013)|(2007)|(2007)|
||(1)|(2)|(3)|
|Guerrilla control|-0.186***|-0.121***|-0.279***|
||(0.0242)|(0.0343)|(0.103)|
|Observations|3,652|3,630|3,637|
|_Panel B: Conley Stan_|_dard Errors (2 Kms)_|||
|Guerrilla control|-0.186***|-0.121**|-0.279**|
||(0.0278)|(0.0482)|(0.129)|
|Observations|3,652|3,630|3,637|
|_Panel C: Conley Stan_|_dard Errors (4 Kms)_|||
|Guerrilla control|-0.186***|-0.121**|-0.279**|
||(0.0344)|(0.0566)|(0.142)|
|Observations|3,652|3,630|3,637|
|Bandwidth (Km)|2.266|2.266|2.266|

_Note:_ The table presents the results of equation 1 using Conley standard errors. The unit of observation in all Columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. We use the algorithm of Calonico, Cattaneo and Titiunik (2014) to set the bandwidth and weight using a triangular kernel. Conley standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

78
(ii) the condition for a positive optimal brain drain ( dm<sup><u>dI</u></sup> s<sup>> 0forms= 0)is</sup> (iii) and the condition for an interior optimal brain drain ( dm<sup><u>dI</u></sup> s<sup>< 0forms= 1)is</sup> In these conditions, variables zs, z0 and z1 are country-speci...c and their distribution can be computed using the Gaussian kernel density estimator. The ratio<sup><u>�2</u></sup> �1 results from our estimation. Using the point estimates from our baseline regressions both in an unbalanced and balanced sample (column (3) of tables 1, 2, 3, 4 for the unbalanced sample, and columns (1), (3), (5) of table 5 for the balanced sample), we can simulate these three conditions.<sup>25</sup> To save place and given that we are mostly interested in the ...rst of these conditions, we do not present simulation results for the last two.<sup>26</sup> Figures 2a to 2d show the kernel distribution of 11��mh<sup>�zsandverticallinesat</sup> values of<sup><u>�2</u>the4indicators(thicklinesfortheunbalancedsample;thinlines</sup> �1<sup>for</sup> for the balanced sample). As can be seen from the ...gures, the marginal e¤ect of the skilled emigration rate is positive for almost all of the countries (the kernel distribution is completely to the left of the thresholds, except for CL in a balanced sample).<sup>27</sup> However, when we calculate 90 percent con...dence intervals (dashed lines) for the vertical lines at values of<sup><u>�2</u>eachofthe4indicators(bothinthebalancedand</sup> �1<sup>for</sup> unbalanced samples<sup>28</sup> ),<sup>29</sup> this result turns out to be not signi...cant since the bulk of the kernel distribution lies between the upper and lower bounds of the con...dence interval.

> 25For the simulations, we consider the unbalanced sample of 91 countries as in column (3) of tables 1 and 2. Data considered refer to year 2000 from Barro and Lee (2001) and Defoort (2008). When Barro and Lee (2001) data on human capital are missing for year 2000, the human capital data from Defoort (2008) are considered (this happens for 11 countries).

> 26 They are available from the authors upon request.

> 27The few "losers" (negative marginal e¤ect) are all with extremely high brain drain rates.

> 28For EFW indicator, we consider only the unbalanced sample as in the balanced sample both �1; �2 are not signi...cant and there are too few observations.

> 29 <u>2</u> Con...dence intervals of<sup><u>�</u>computedusingtheStatacommandnlcom.Calculationsfollows</sup> �1<sup>are</sup> the delta method, a method based on Taylor series expansions used for deriving variance approximations and con...dence intervals for transformed variables.

25
ket frictions may also highlight policies for private sector development that are not explicitly climate-focused but which carry large benefits for climate adaptation and mitigation. For instance, lowering tariffs on energy efficient appliances could help firms make investments that lower production costs and emissions while also reducing exposure to energy market volatility. Mainstreaming climate in ongoing discussions about private sector development may uncover unexpected tools for building climate resilience through economic policy.

Finally, we require more evidence on the effectiveness of different adaptation strategies in practice. Generating such evidence presents multiple empirical challenges. For one, assessing the effectiveness of adaptation strategies requires measuring the variance in outcomes over time as firms experience a series of weather realizations. The increasing variability of weather realizations under climate change makes this particularly challenging. Adaptations that are effective over the next ten years may prove less effective as weather shocks become increasingly severe. Another challenge is assessing the effectiveness of different adaptation strategies in general equilibrium. For instance, switching to suppliers less exposed to climate risk may be an effective strategy in theory, but as entire sectors attempt to make their supply chains more resilient, prices for climate-protected suppliers may rise dramatically. This, in turn, could lead to consolidation and reduced competition. Future work on adaptation will have to tackle the empirical challenges of increasing variability of weather realizations and adaptation in general equilibrium to better inform climate policy.

# **References**

- Abeberese, Ama Baafra (2017). “Electricity Cost and Firm Performance: Evidence from India”. _The Review of Economics and Statistics_ 99, 839–852.

- Adhvaryu, Achyuta, Namrata Kala, and Anant Nyshadham (2020). “The Light and the Heat: Productivity Co-Benefits of Energy-Saving Technology”. _The Review of Economics and Statistics_ 102, 779–792.

35
Table 1: Descriptive statistics on land and labor

||Mean|Std. Dev.|
|---|---|---|
|Have primary usage rights to land|81.9%||
|Land owned with ofcial rights (hectares)|10.5|24.5|
|Gini coefcient of land owned|0.52||
|Ratio of land share to population share of main ethnic group|1.2||
|Land-labor ratio (hectares land per active worker)|2.76|7.39|
|Participates in land transfers|45.2%||
|Land transfer (hectares)|1.04|2.83|
|Land transfer (% of donors’ land endowment)*|37.6%|0.307|
|Number of working adults|5.4|10.4|
|Participates in labor transfers (within the village)|54.4%||
|Labor transfer (days)|5.3|15.4|

* Conditional on transferring out land. Excludes observations where the donor is also a receiver. Source: Author’s own calculations on the Gambia Networks Data 2009.

82 percent of farming household have some _primary_ usage rights to land and their average land holding is 10.5 hectares per household (see table 1). The distribution of primary usage rights is highly unequal, however, with some households having less than 0.5 hectares and others more than 100 hectares. The Gini coefficient of primary land rights, averaged over the 50 villages, is 0.52 and the share of land in the hands of the 10% most land-rich households is 34% on average. This unequal distribution is partly a result of the process of village settlement – which, in the case of much of sub-Saharan Africa, tend to be relatively recent.<sup>17</sup> According to African customs, village founders acquire primary usage rights over the land they clear. Upon their death, these rights are passed on to their descendants. As the village grows by attracting new members from other lineages and ethnic groups, descendants of the village founders tend to retain rights over more of the village land.

The unequal ownership of land is reflected in the social structure: members of the founder lineages have intermarried and tend to belong to the same ethnic group. Therefore, owners of much of the land tend to come from the same ethnic group and to all be related to each other: a village’s main ethnic group typically own more land than its share of the population – the ratio between the two exceeds 1 on average (see table 1). Similarly, households in the village’s largest kinship group on average own more than three times more land that households outside that group. There is therefore much scope for increasing allocative efficiency by transferring secondary land usage rights from descendants of the village founders to relative newcomers.

17