rafmacalaba/datause-encoder-data huggingface dataset:
help improve smallholders’ resilience to climate shocks, either via learning about (crop-specific) moisture saving technology or via support (e.g. with tractors) to create moisture conservation structures, we estimate where _Yikt_ is the log of the yield of crop _k_ and **_R_** _it_ is a vector of indicator variables specifying, for each pair kebele _i_ and year _t_ , whether rainfall was above or below the long-term mean by less or more than one standard deviation using the pixel-level data on precipitation discussed above,. The elements of **_β_** _1_ will then indicate if small farms closer to large ones be more able to maintain yields in the presence of rainfall shocks.

# **3. Descriptive evidence on large and small farm performance**

Data on the country’s main cereals highlight striking, though commodity-specific, differences in yields and input use between large and small farms. Limited presence of non-farm employment and rainfall variability. Maps illustrate the expansion of large farm presence over time. As this provides options for interaction and exchange of technology, it makes positive technology spillovers via informal interaction plausible and justifies efforts to try and identify other types of externalities from large farm formation in our data.

# **3.1 Comparing large and small farmers’ productivity and input use**

Table 1 provides data on large farms overall and for our four crops. Mean commercial farm area is 266 ha, with wheat farms largest (552) and sorghum and teff (235 and 238 ha) ones smallest. About 25% were established during the 2007/08 peak, some 22% each in 2002-2006 and 1992-2002, and some 15% and 8%, respectively in 2009/10 and 2011-13. Compared to 47% of farms overall, 68% and 61% of large teff and maize farms were established after 2007 with a corresponding shift in the level at which negotiations were held. With 55 ha per permanent employee, the average farms employs has less than 5 permanent workers; wheat being the least and teff the most employment intensive crop in our sample.<sup>9</sup> With 37% on average, the share of those reporting payment of lease fees ranges from 73% for wheat to 30% for sorghum. While only 6% overall, the share of farms who access credit is 15% for teff but only 5.6% for sorghum. The fact that some two-thirds of large farms (59% for sorghum, 73% for wheat, 80% for maize, and 88% for teff) claim to provide benefits to surrounding small farmers further justifies exploration of spillover effects.

Table 2 provides information on area cultivated, yield, fertilizer and seed use, for the four cereal crops of interest (maize, sorghum, teff, and wheat) based on 2013/14 data for smallholders as well as commercial farms of different sizes. Lack of data on input quantities and prices makes productivity comparison difficult but in all crops except teff, smallholders’ yields (27.1, 21.3 and 21.9 Q/ha for maize, sorghum, and wheat, respectively) are 75% of or less of those by commercial farms. While part of this difference may be due to

> 9 As flower farms are not included in our sample, coffee is the most employment intensive crop among large farms.

9
of PE activity in Central and Eastern European countries and suggest that higher GDP growth should attract more PE capital. Similarly, we predict that there will be more opportunities for private capital deals in growing industries and countries due to higher demand for external capital from growing companies. Hence, our first hypothesis is that there is more buyout activity in countries and industries with better macroeconomic conditions. To test this, we use GDP per capita growth and unemployment as measures of country-level economic conditions, and changes in overall industry employment and capital expenditures as measures of industry-level economic conditions and explore if they are significantly related to buyout capital invested in a country (industry).<sup>8</sup> Previous studies have shown the importance of stock markets (Black and Gilson, 1998; Jeng and Wells, 2000) as well as credit markets (Green, 1998; Hellman et al., 2004) for the development of active VC markets. Aizenman and Kendall (2012) investigate the factors that affect the market for international VC investments and find that the presence of high-end human capital, a better business environment, and deeper financial markets are crucial. We expect active stock and credit markets to also be important for buyout investments for several reasons. Demirguc-Kunt and Levine (1996) show that countries with better-developed stock markets have better-developed banks and nonbank financial intermediaries, suggesting that stock and credit markets complement each other. Similarly, Beck and Levine (2002) find that it is the overall financial development that spurs industry growth and having a bank-based or market-based financial system does not matter per se. As such, active stock and credit markets measure the level of financial development for a country, which likely proxy for other factors that could also lead to

> 8 As robustness, we also use other macroeconomic condition variables such as the interest and inflation rates, but do not find any significant relationships with those variables. Our other results remain unchanged if we include those as additional controls in our specifications.

9
|Can borrow money from at least one<br>or multiple persons in case of emer-<br>gency||||0.668**|0.548*|
|---|---|---|---|---|---|
|||||(0.302)|(0.303)|
|Did not receive remittances in the past year||||||
|Received remittances in the past year||||0.883***|0.988**|
|||||(0.316)|(0.418)|
|Constant<br>0.886***|2.005|1.163***|1.477|0.923|0.566|
|(0.169)|(2.012)|(0.340)|(1.968)|(2.056)|(0.385)|
|Observations<br>393|393|393|393|393|393|
|Prob > F<br>0.170|0.297|0.201|0.293|0.00376|0.0168|

_Table 30: Probability of having recovered after two years, as a function of various housing characteristics and magnitude of losses. N.B. Logistic regression is run after matching the data with CEM._

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Did not lose more than 10% of||||||
|household expenditure due to the||||||
|flood||||||
|Lost more than 10% of household ex-<br>penditure due to the flood|-0.954***|-1.014***|-1.002***|-1.041***|-1.106***|
||(0.367)|(0.366)|(0.381)|(0.379)|(0.386)|
|Log of real per capita expenditure||-0.176||-0.113|-0.107|
|||(0.195)||(0.205)|(0.217)|
|Main income source: Monthly salary||||||
|Main income source: Casual labor|||-1.014***|-0.997***|-0.912**|
||||(0.380)|(0.383)|(0.394)|
|Main income source: Hawking|||0.0395|0.0160|-0.0101|
||||(0.668)|(0.669)|(0.690)|
|Main income source: Remittances|||0.125|0.111|-0.516|
||||(0.548)|(0.549)|(0.614)|
|Main income source: Stable business|||-0.150|-0.161|-0.184|
||||(0.285)|(0.285)|(0.291)|
|Main income source: Other|||-0.217|-0.226|-0.646|
||||(0.618)|(0.618)|(0.654)|

Does not anyone to borrow money from in case of emergency 55
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- 33 -
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 10: Comparison of Liberia’s power tariffs against various cost benchmarks**

<!-- Start of picture text --> US$ cents per kilowatt-hour<br><!-- End of picture text --> Source: Derived from Eberhard and others 2009.

Imported power is an attractive option in the medium term for Liberia, but it will require development of the transmission network to link with Cote d’Ivoire and Guinea. Cote d’Ivoire is the largest power exporter in the West Africa Power Pool today, supplying Burkina Faso and Guinea with 1.8 terrawatthours, which is much more than Liberia’s total estimated grid demand in 2020 of 1.2 terrawatt-hours. Furthermore, Cote d’Ivoire fully honored its power export contracts during the recent crisis period, and it has therefore earned a reputation as a reliable supplier. Cote d’Ivoire has a very strong track record of harnessing independent power projects to privately finance new gas-fired plant in a relatively short period and is starting a new round of investment in power generation. Exploratory discussions with Cote d’Ivoire could therefore help Liberia to identify feasible trading possibilities. In the long term, Liberia could also import power from Guinea, which has abundant low cost hydropower and an export potential of 17.4 terrawatt-hours. Currently, however, Guinea must overcome many technical, financial, and political obstacles if it is to fully realize its power export potential.

In addition to importing power from neighboring countries, Liberia may be able to integrate major planned power generation investments by the mining sector into national power development plans. It is estimated that over the next 30 years, Liberia’s mining concessions will require 840 megawatts of power generation capacity, for which they will provide financing. Plans are already underway for private development of the first few hundred megawatts. Both the country and the concessionaires may benefit from coordinating these investments so as to obtain higher economies of scale and create surplus capacity for public supply. To do so it will be necessary to have a clearer picture of the power generation plans of 20
Table 6 Continued <u>from previous page</u>

||Pa|nel A: Dumm|y DTA vari|able|Pane|l B: Continu|ous DTA va|riable|
|---|---|---|---|---|---|---|---|---|
|ISO3<br>(1)|FDI <br>inw. FDI<br>(2)|lib.<br>outw. FDI<br>(3)|FDI and<br>inw. FDI<br>(4)|trade lib.<br>outw. FDI<br>(5)|FDI<br>inw. FDI<br>(6)|lib.<br>outw. FDI<br>(7)|FDI and <br>inw. FDI<br>(8)|trade lib.<br>outw. FDI<br>(9)|
|PAK|5.96|0.13|6.63|0.51|7.67|0.14|8.09|0.32|
|PER|13.33|0.42|14.26|0.74|12.90|0.39|13.50|0.65|
|PHL|10.72|0.77|11.59|1.78|7.72|0.59|8.25|1.23|
|POL|1.01|0.52|1.94|1.44|1.36|0.77|1.95|1.29|
|PRT|0.86|0.26|1.67|0.87|1.17|0.38|1.69|0.72|
|QAT|0.16|0.01|0.72|0.02|0.16|0.01|0.51|0.01|
|ROM|0.19|0.01|0.95|-0.01|0.21|0.02|0.67|0.00|
|RUS|0.24|0.29|1.13|1.06|0.26|0.31|0.82|0.70|
|SAU|0.18|0.03|0.87|0.12|0.19|0.04|0.63|0.07|
|SDN|0.06|0.00|0.26|0.00|0.06|0.00|0.17|0.00|
|SER|0.23|0.00|1.04|0.00|0.24|0.00|0.74|0.00|
|SGP|16.96|3.47|17.89|5.02|13.62|2.82|14.18|3.99|
|SVK|0.91|0.01|1.81|0.08|1.21|0.02|1.79|0.06|
|SVN|0.88|0.06|1.69|0.10|1.19|0.08|1.69|0.11|
|SWE|0.96|0.46|1.73|1.23|1.31|0.67|1.79|1.12|
|SYR|0.11|0.01|0.52|0.09|0.12|0.01|0.37|0.03|
|THA|10.72|1.56|11.62|3.22|7.78|1.23|8.32|2.34|
|TKM|0.06|0.01|0.33|0.07|0.07|0.01|0.21|0.04|
|TUN|0.14|0.01|0.67|0.20|0.15|0.01|0.46|0.10|
|TUR|0.23|0.05|1.06|0.70|0.24|0.05|0.75|0.33|
|TZA|0.19|0.00|0.87|0.04|0.20|0.00|0.63|0.01|
|UKR|0.16|0.02|0.81|0.28|0.17|0.03|0.56|0.11|
|USA|2.07|72.87|3.01|87.52|2.18|70.94|2.78|83.34|
|UZB|0.12|0.00|0.55|0.04|0.13|0.00|0.39|0.01|
|VEN|1.26|0.24|2.12|0.32|1.51|0.30|2.06|0.33|
|VNM|10.65|0.11|11.39|0.37|7.99|0.09|8.43|0.26|
|ZAF|0.24|0.02|1.10|0.14|0.25|0.02|0.78|0.08|
|ZWE|0.12|0.00|0.59|0.00|0.13|0.00|0.41|0.00|
|World|2.77|72.87|3.67|87.52|2.79|70.94|3.36|83.34|
|Lib-Countries|3.00|72.87|3.69|87.52|3.01|70.94|3.38|83.34|
|ROW|0.54|0.01|0.98|0.00|0.64|0.01|0.70|0.00|

Notes: This table reports results from our counterfactual analysis. Column (1) gives the iso3-country codes. Panel A reports results based a uniform change in the bilateral FDI frictions between all countries that have signed a DTA with investment provisions that is based on our estimate of 0 _._ 295 . Columns (2) and (3) report the percentage changes in inward and outward FDI, respectively, when DTAs change FDI frictions, while in columns (4) and (5) in addition to the change in FDI frictions DTAs also change trade frictions. Panel B reports results based on the estimates on the continuous depth variables. Columns (6) and (7) report the percentage changes in inward and outward FDI, respectively, when DTAs change FDI frictions, while in columns (8) and (9) in addition to the change in FDI frictions DTAs also change trade frictions.

Given the main purpose of our analysis, and to keep the presentation of our results manageable, we focus the discussion of our counterfactual results on the percentage changes (between the baseline and the counterfactual scenarios) in the stocks of FDI per country. Specically, we construct and report percentage changes in inward and outward FDI stocks, i.e., the percentage changes in technology capital used in total at home and technology 37
In sum, our simple model of schooling decisions for a multi-child household predicts that a childspecific conditional cash transfer will lead to increased enrollment for eligible children. This increase reflects the combination of effects. First, there is a displacement effect among those households that only enroll one child: they tend to replace their ineligible children with their eligible siblings in school. Second, some households that would not send any children to school in the absence of the program are now compelled to send an (eligible) child to school, due both to a substitution effect (the opportunity cost of that decision has fallen from _w_ to _w – τ_ ) and to an income effect (an increase in unearned period 1 income reduces the utility loss from forgoing that opportunity cost).

The effect on the enrollment of ineligible children is ambiguous. The displacement effect works against them, with families that send a single child to school shifting away from them towards their eligible children. Furthermore, they do not benefit from a substitution effect, since the opportunity cost of their going to school remains equal to _w_ . However, some ineligible children can benefit from an income effect. Those are children in households whose income levels in the absence of the transfer were just insufficient to enroll both children but who, given the extra income from the transfer, are now willing to forgo the child earnings from their second child as well. (These are households with exogenous incomes

> between<sup>_A_</sup> τ ** and A** in Figure 2.) This is a pure income effect, since they receive no additional transfer for this added enrollment.

# **3. Program and data**<sup>6</sup>

Cambodia has had programs that offer “scholarships” to poor children making the transition from primary to lower secondary school for a number of years. These programs have operated in some regions of the country and not others, and have been funded from a variety of sources, including government budgets, loans from multilateral and bilateral donor agencies, and NGOs. One of the programs that predated the CSP, known as the Japan Fund for Poverty Reduction (JFPR) scholarship program, was

> 6 Sections 3 and 4 draw from Filmer and Schady (2009b).

11
real terms (0.41 percent of GDP), which would have to be financed through expenditure reductions or tax increases to avoid increasing the deficit and impinging on fiscal sustainability. Depending on the financing instrument chosen, the distributional impacts could be different than what is presented here.

<u>Figure 13. Croatia: Poverty impact of a VAT rate reduction to 24 percent</u> <!-- Start of picture text --> 60%<br>49.0% 48.4%<br>50%<br>40%<br>27.9% 27.7%<br>30%<br>20%<br>10%<br>0%<br>Poverty headcount Poverty 2017 + change Poverty headcount Poverty 2017 + change<br>2017 in VAT 2017 in VAT<br>US $10 PPP-a-day 60% of median equivalized disposable<br>income<br><!-- End of picture text --> Source: World Bank estimates based on Croatian PIS and HBS 2014. Note: This simulation does not consider the distributional impact of alternative financing <u>sources to compensate for the loss of revenue envisaged due to a reduction in the tax rate.</u>

# **VIII. Conclusions**

The analysis presented in this paper finds that starting from a relatively low level of inequality compared to other emerging high-income countries, such as Poland, the Croatian direct tax and transfer system is redistributive, with progressive and redistributive direct taxes and transfers, in line with other European countries, and more so than other developing countries. However, indirect taxes are regressive and unequalizing, so much so that the redistributive power of direct taxes and transfers is reversed. In cash terms, inequality is similar after all taxes and transfers than before them.

The results on poverty, on the other hand, show that indirect taxes effectively lead some households under the poverty line, and are therefore poverty increasing. With regards to in-kind transfers, when the value of education and health is included in the analysis, there is an important redistributive effect, primarily on account of primary education.

In terms of the recent changes to taxes implemented, the analysis finds that both direct and indirect taxes became more progressive in relative terms in 2017 compared to 2014, as poorer households paid lower taxes as a share of their incomes. However, in absolute amounts, most of the tax relief accrued to the top of the income distribution. As a result, the redistributive impact of personal income taxes and social security contributions was reduced, and inequality after taxes and transfers 26
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26
that the phone-based measures have promising potential to complement and extend traditional sources of data collection on firms in developing and conflict-affected countries, with advantages in terms of cost and fidelity.

We use this new measure of firm activity to develop a more nuanced understanding of how the private sector in Afghanistan responds to violence. To provide intuition for this response, we first present a detailed case study of the response of the private sector to the Taliban’s September 2015 attack on Kunduz, the country’s fifth largest city. An empirical analysis of the phone-based measures of firm activity in Kunduz illustrates the short- and long-term response of firms to a massive security shock, and helps further develop our approach.

We then expand our quantiative analysis to all of Afghanistan by combining terabytes of mobile phone data with four years of geocoded data on confirmed fatalities. We employ an increasingly restrictive series of econometric specifications to identify the effect of unexpected violence on firm activity, and to explore heterogeneous effects by firm type..

We find that in response to local insecurity, firms decrease presence in affected areas. Specifically, we find a significant, negative impact of major violent events on firm presence, which persists for up to six months. The effect in our sample is driven by the response of larger firms - where firm size is measured by the total number of subscribers (phone numbers) assigned to a corporate account. This suggests an under-studied channel through which insecurity may impede the growth of firms in poor economies.<sup>6</sup> We explore the mechanisms underlying the heterogeneity by firm size using original survey data from a subsample of more than 450 firms and show that larger firms are significantly more likely to report business disruptions from anti-government group activity and to adapt their business practices in response to such experiences.

Our work engages two existing literatures that currently have limited intersection: the first on the economic consequences of conflict, and the second on the industrial organization from other economic activity.

> 6Recent work by Hsieh and Olken (2014) using data from India, Indonesia and Mexico indicates both medium and large-firms may be underrepresented relative to small firms in emerging economies compared to the size distributions observed in developed economies.

4