rafmacalaba/datause-encoder-data huggingface dataset:
compared to 80%, 42%, 85%, 74% and 60% in these crops for the largest commercial farms. As fertilizer will be most effective if applied together with seed, this could pose a serious constraint; in fact smallholders may even apply more fertilizer than can productively be absorbed by their traditional seed material.

# **4.2 Regression results**

While lack of prices and labor input in our data makes it difficult to estimate a production function, we use pooled smallholder and commercial farm data to run naïve regressions to estimate the relationship between farm size and yields as well as the likelihood of applying fertilizer or improved seeds. Assuming a quadratic relationship and allowing for structural differences in technology between smallholders and commercial farms, our estimating equation takes the form: where �� is the log of physical yield of farm _i_ from a given crop (maize, sorghum, teff, wheat, haricot beans, sesame or coffee) in quintals per ha; �� is the log of cropped area allocated to this crop; �� is a commercial farm dummy; �� denotes district (woreda) fixed effects to capture differences in agro-ecological conditions or infrastructure; � and � are vectors of parameters to be estimated, and �� is a random error term. We use a similar specification to estimate a linear probability model for the incidence of chemical fertilizer and improved seed use. As output is in physical units and no information on prices was collected, computing monetary output values is impossible.

Results from regressions for yield and the incidence of fertilizer and improved seed use are given in table 6, 7, and 8, respectively. Figures 2-6 graphically depict the relationship between farm size and yield as well as incidence of fertilizer use by plotting the marginal effects together with the associated 95% confidence interval for different farm sizes (in logs), separately for smallholders and commercial farms in maize, sorghum, teff, wheat, and sesame. We focus on three parameters, namely (i) if marginal effects are different from zero at 5% statistical level (as indicated by the 95% confidence band in the graph); (ii) if the marginal effect, at a given farm size, is greater or smaller than zero; and (iii) the slope of the marginal effect.

In line with what was obtained earlier, our analysis illustrates that underlying relationships may be crop specific. For maize, we obtain a significant negative and slightly downward-sloping relationship between farm size and yield for smallholders, implying that the marginal effect is negative and decreases with farm size. For commercial farms, the marginal effect of farm size is marginally positive at the smallest farm size (1.4 ha) but turns negative above a farm size of some 30 ha. By contrast, the impact of farm size on fertilizer application is positive but downward-sloping, i.e., larger farm size makes fertilizer application more likely but at a decreasing rate. For wheat, marginal effects are also negative throughout but increase and become indistinguishable from zero for the largest smallholder and commercial farms. The likelihood of fertilizer 13
some very interesting trends in Ethiopia. The total instances of rapid growth increases dramatically in the second time period (2004-2007), and then falls, equally dramatically, in 2008-2011 – the last two years of this period are particularly depressing. However, what is interesting is that size distribution of gazelles across different periods. Between 2000-2003, micro and small firms accounted for the lion’s share (81%) of gazelling. In later years, their proportion almost halved, while the proportion of large and very large firms tripled in 2004-2007, and then more than quadrupled in 2008-2011. The table and figure show the change in the size distribution of gazelles.

**Table 5: Gazelling by firm-size**

|**_Years_**|**_2000-2_**|**_003_**|**_2004-_**|**_2007_**<br>**_2008-_**|**_2011_**|
|---|---|---|---|---|---|
|**Size**|**#**|**%**|**#**|**%**<br>**#**|**%**|
|Micro|265|60.36%|246|29.89%<br>28|22.05%|
|Small|95|21.64%|229|27.83%<br>30|23.62%|
|Medium|41|9.34%|124|15.07%<br>22|17.32%|
|Large|19|4.33%|106|12.88%<br>31|24.41%|
|Very large|19|4.33%|118|14.34%<br>16|12.60%|
|Total|439|100%|823|100%<br>127|100%|
|*Micro=20<br>employees;<br>100%|or fewer employees; Sm<br>Large=101-1000 emplo|all=21-50 e<br>yees; Very L|mployees<br>arge=10|; Medium=51-100<br>01+ employees.||
|90%||||||
|80%||||||
|70%||||||
|60%||||||
|50%||||||
|40%||||||
|30%||||||
|0%<br>10%<br>20%||||||
||Micro<br>Small|Medium|Large|Very large||
||2000‐2003<br>|2004‐2007|2008‐|2011||

# Source: LMMIS, CSA

<mark>93% of gazelles are private or co-operative firms, and yet state-owned enterprises, or publicly owned enterprises account for a much</mark> larger proportions of gazelles in many size categories, compared to their overall distribution – see Table 6. Almost 35% of state-owned firms are large or very large, compared to only 12% of private firms.

stellar performance over a second 4-year period, but it is not easy to ascertain if the remainder include high-volatility firms.

26
Climate Centre) (Boer et al. 2000) and PCM (Parallel Climate Model) (Washington et al. 2000).

Table 7 presents the mean temperature and rainfall predicted by the two models for the years 2020 and 2100. In Africa in 2100, PCM predicts a 2°C increase and CCC a 6°C increase in temperature. Rainfall predictions vary. PCM predicts a 10% increase in rainfall in Africa and CCC a 10% decrease by 2100. Even though the mean rainfall in Africa is predicted to increase/decrease depending on the scenario, there is also substantial variation in rainfall across countries. Examining the path of climate change over time reveals that temperatures are predicted to increase over time for all models. Precipitation predictions, however, vary across time for Africa: CCC predicts declining precipitation whereas PCM predicts a slight increase. However, it should be noted that predicted changes vary slightly for individual countries and regions.

# **_5.1 Analysis for Africa_**

We simulate climate change impacts on the choice of livestock ownership and livestock species based on the parameter estimates in the previous section conditional on each of the above two climate scenarios. Table 8a indicates that more farmers in Africa will own livestock by 2020 under the hot CCC scenario and that much fewer farms will own livestock under the wet PCM scenario. Table 8b indicates that by 2100, the effects of each climate scenario will intensify. There will be a small reduction in livestock ownership with the PCM scenario and an increase under the CCC scenario. These results are due to a combination of seasonal changes in precipitation and temperature<sup>13</sup> .

The results for each livestock species reveal that by 2020, beef cattle, dairy cattle, and chickens will begin to decline, but goats and sheep will increase. The results, however, depend on the climate scenarios. For example, although sheep selection will increase under the CCC scenario, it will decrease with the PCM scenario. Beef cattle will be chosen more often with the PCM. These trends intensify through 2100.

> 13 These results may not hold if there will be a great deal of changes in the demand for livestock in the future (Delgado 1999). Population changes and urbanization in the coming decades will affect the overall results described in this paper. Although it is desirable to model these additional changes, it is difficult without a good general equilibrium model of the world economy which does not exist at the moment.

13
# **TABLE 3.1: DEFINITION OF DEPENDENT VARIABLES**

|**Variable**|**Name**|**Source**|**Level of Analysis**|**Definition**|
|---|---|---|---|---|
|DIS1|Discouragement|Households'<br>Survey|Household level|Dummy variable equal to 1 if household<br>reported to be discouraged in at least one public<br>institution|
|DIS2|Discouragement|Households'<br>Survey|Household level|Dummy variable equal to 1 if household<br>reported to be discouraged in at least two public<br>institutions|
|ACCESS|Accessibility of<br>Public Services|Public Official's<br>Survey|Public Agency level|Index of accessibility to poor citizens of the<br>public service provided by the public agency.<br>High numbersimplyhigh levels ofaccess.|

# **SURVEY QUESTIONS USED TO CONSTRUCT DEPENDENT VARIABLES**

|**To measure:**|**Source**|**Questions available in the survey**|
|---|---|---|
|DISCOURAGED|Households'<br>Survey|Have you ever needed the services of any of the following public<br>institutions and chose not to use them?<br>(Enumeration of 13 distinct institutions)|
|ACCESSIBILITY|Public Official's<br>Survey|Our agency's services are accessible by the poor.|

24
# **Figure 5.3. Financial System: Exposure to Public Sector**

<!-- Start of picture text --> Total Assets 164,100<br>(Millions of Dollars) 146,417 153,252<br>1576 130,929 ;1 ||l ;1 125,069<br>91,534<br>82,5289'3 l 1.,<br>1994 1995 1996 1997 1998 1999 2000 2001<br>| * Claims on the public sector LUquid assets and claims on the private sector<br><!-- End of picture text -->

Source: Central Bank of Argentina.

Moreover, Argentina illustrates that, as confidence collapses, a _general_ liquidity requirement (available to all deposits) fails in its most basic intended function-it does not protect the payments system. The lesson is sobering. In the absence of a credible lender of last resort, the payments system is vulnerable and can collapse under a run even where liquidity is high, but still a fraction of deposits, and equally available to finance _any_ deposit withdrawal. It thus would appear that, under a currency board and formal dollarization, the protection of the payments system from bank runs may actually require a structure where there is either full liquidity backing for transactions balances - a sort of narrow banking for demand deposits -- or at least liquidity is earmarked for those balances under specified conditions, to allow the payments system to continue functioning (without deposit freezes and payments interruptions) even in the extreme scenario where banks are unable to honor withdrawals of time deposits 4 49 De la Torre et al (2003) 48
the upper bounds and lower bounds based on the synthetic panels.<sup>4</sup> In particular, the interval between the bound estimates can be narrowed if an appropriate range for the correlation between the error terms can be postulated. Dang et al. (2014) suggest that panel data, where available from other sources such as other countries with similar characteristics, might be scrutinized to identify such a narrower range. Despite its infancy, applications of Dang et al.’s method in various settings have been yielding encouraging results.<sup>5</sup> In this paper we generalize the method introduced by Dang et al. in several important aspects. First, by introducing a method to approximate the appropriate correlation term and its theoretical upper bound using each country’s _own_ cross sectional surveys, we overcome the limitations of having to draw on external estimates from actual panels in similar settings. Notably, such “similar” actual panels might neither be available, nor offer very convincing estimates (see further discussion in section III below). Thus with the more general framework offered here, we can apply our method to study poverty dynamics using _just only_ two rounds of cross section data that satisfy our (rather standard) assumptions. The approximation of point values for the correlation term allows us to move beyond the _bound_ estimates proposed by Dang et al. (2014) to actual _point_ estimates of poverty mobility. This represents a major advance in terms of interpretation, and potential application in practice. In particular, we can investigate different measures of poverty dynamics, such as the population shares in different poverty status categories in both survey periods considered together (i.e., unconditional or joint probabilities) or the population shares in different poverty status categories in one period

> 4 For presentational convenience, we generally refer to household movements between poverty or consumption categories as mobility.

> 5 For example, recent applications/ validations of Dang et al.’s method against true panel data include Bierbaum and Gassmann (2012) for the Kyrgyz Republic, Cruces et al., (forthcoming) for Chile, Nicaragua, and Peru, and Martinez et al. (2013) for the Philippines.

4
# **Table 3: Analysis of Attendance**

||(1)|(2)|(3|)|
|---|---|---|---|---|
||Autonomous<br>Trainer Selection<br>(T1)|Fixed Trainer<br>Selection (T2)|P-val<br>T1 =|ue<br>T2|
|Sought Financial Advice in Past Year|0.27|0.27|0.81||
|Attended Financial Education Training at SACCO|0.21|0.23|0.13||
|_If Attended:_|||||
|Attended More than Two Training Sessions|0.63|0.54|0.04|**|
|Number of TrainingSessions Attended(out of 6)|3.59|3.28|0.10|*|

This table presents analysis of attendance records for the financial education program by household recipients. Columns (1) and (2) present weighted averages for T1 and T2; and column (3) presents the p-value for the equality of means test between T1 and T2.
- **Rottenschweiler, Sergio** , “Un mismo comienzo y dos caminos dispares: la Reparación Histórica y la Pensión Universal para el Adulto Mayor (2016-2019),” _Revista Latinoamericana de Desarrollo Económico_ , 2020, (34), 67–92.

- **Slemrod, Joel** , “Tax Compliance and Enforcement,” _Journal of Economic Literature_ , December 2019, _57_ (4), 904–54.

- **Sturzenegger, Federico** , “Macri’s Macro: The Elusive Road to Stability and Growth,” 2019, _2019_ (2), 339–436. Project MUSE.

**Telam** , “El sinceramiento fiscal ya alcanzo los U$S 21.863 millones,” https://www.telam.com.ar/notas/201611/

171267-sinceramiento-fiscal-blanqueo-capiales-afip-alberto-abad. html November 2016.

**Tørsløv, Thomas, Ludvig Wier, and Gabriel Zucman** , “The Missing Profits of Nations,” _The Review of Economic Studies_ , 07 2022. rdac049.

- **World Bank** , _World Bank Support for Domestic Revenue Mobilization_ , Washington, DC: World Bank, 2023.

- **Zucman, Gabriel** , “The missing wealth of nations: Are Europe and the U.S. net debtors or net creditors?,” _Quarterly Journal of Economics_ , 2013, _128_ (3), 1321—-1364.

- , _The Hidden Wealth of Nations: The Scourge of Tax Havens_ , University of Chicago, 2015.

23
Our main findings show substantial overall economic benefits of the construction of the NEN and of abolishing the Hukou restrictions. The spatial impact of these two policies is however very different. We find that the construction of the NEN has only reinforced existing urbanization patterns. The initially largest and most urbanized prefectures have gained most from its construction. Although not necessarily the initially richest places, these cities saw the largest improvements in their connectivity due to the construction of the NEN. The initially lagging regions not connected to the NEN have not benefitted much from its construction. Hukou reform would instead benefit the initially richest prefectures along China’s South-Eastern coastline. These cities are initially not necessarily the most urbanized nor those with the largest urban populations, but their economic success will attract migrants from across the country when the Hukou restrictions are relaxed. However, many places in China’s interior also benefit more from Hukou reform than from the construction of the NEN, be it mostly because of rural outmigration. By allowing everyone to gain by moving to where he/she is most productive, the benefits of easing the Hukou restriction are more widespread.

The structure of the remainder of the paper is as follows. Section 2 briefly reviews China’s two main spatial development policies— transport investments and migration restrictions. We present our modeling strategy in Section 3. Section 4 presents the data, estimation and modelling strategy, and Section 5 discusses the estimated impacts of highway construction and migration restrictions on both overall and spatial economic outcomes. Section 6 concludes.

# **2. A brief history of the NEN and the Hukou system**<sup>**12**</sup>

China’s national expressway network, also known as the National Trunk Highway System, was conceived in 1988 with the goal of establishing seven highways radiating from Beijing, nine NorthSouth connections, and 18 East-West connections, giving it the unofficial name “7918 network”. The first phase of the network—connecting all cities with population above 200,000 people—was completed by 2007 with a length of about 40,000 km (see Roberts _et al._ , 2012; World Bank, 2007). Since then, the network has further expanded, reaching more than 96,000 km in 2012 (China Statistical Yearbook, 2013)—larger than the U.S. Interstate Highway System. In this paper, we restrict analysis to the impact of the first phase of construction, which was largely concentrated between 1997 and 2007. The main reason for this is that in 2008 China introduced the first high speed railway lines, and today China has the largest high speed rail network in the world. Although these two networks have different user profiles (more goods transported on highways, more business travel on high speed rail), separating the impact

> 12 We keep the discussion in this section brief given that both of these policies have been described in much greater detail elsewhere in the literature, see in particular the references cited in this section.

6
Suppose that there is no measurement error in governance, that is, c,. = 0, so that the right-hand side of equations 8 is zero. Then the only way there can be positive feedback from incomes to governance, that is, y > 0, is if there are omitted variables in equations 4 and 5 that move income and governance in opposite directions, that is, p < 0.

In the absence of such variables, the only other way that there can be positive feedback from incomes to governance is if there is substantial measurement error in governance, a,, > 0. In this case, the IV estimates are larger than the OLS estimates simply because they are eliminating the usual attenuation bias caused by measurement error in the dependent variable.<sup>2</sup> ' The key question is whether there is enough measurement error in the data to allow for the possibility of positive feedback from per capita income to governance, that is, y > 0. We investigate this question in the remainder of this section.

# **Results**

To implement this exercise, we begin by estimating equation 4. We measure per capita income using real per capita gross domestic product (GDP) from the Summers and Heston Penn World Tables, version 5.6, which we extend into the 1990s using constant local currency growth rates. We measure governance using the rule of law index discussed in the previous section. We focus on this particular dimension of governance for two reasons. First, it corresponds most closely to the concept of protection of property rights that has been widely used in this literature. Second, as discussed in the introduction, this is one of the dimensions of governance for which simple OLS regressions suggest that countries in Latin America appear to do relatively poorly given their per capita incomes. Since rule of law is highly correlated with both corruption and government effectiveness, we can view this one dimension of governance as representative of the broader areas in which the quality of governance appears relatively poor in Latin America and the Caribbean.

Our preferred instrument for governance is the settler mortality measure introduced by Acemoglu, Johnson, and Robinson.<sup>22</sup> While it is only available for sixty-eight countries, empirically it is strongly correlated with both tropical location (as measured by distance from the equator) and colonial origins (as measured by the fractions of the population speaking English or a major European language). To expand the size of the sample, we use these last three variables to impute the missing values of settler mortality for all of the countries for which we have data on per capita income and governance. This results in a sample of 153 countries for which we have data on y, g, and x. We do, however, obtain similar

> 21. Throughout, we assume that the stability condition, y* -3 < 1, holds.

> 22. Acemoglu, Johnson, and Robinson (2001).

17
data is sizeable. A majority of those who think they have poor health do not rate themselves as poor. Amongst the 17% of adults who rated their health as “very bad” or “bad,” only 33% placed themselves on the lowest two rungs of the economic welfare ladder. And of the 27% of adults who rated their health as “good” or “very good,” 60% put themselves on the lower four rungs of the welfare ladder.

Table 2 exploits the panel structure of the RLMS to compare the changes over time in _SAH_ with changes in _SAW_ . This reduces the impact of any time-invariant individual effects (such as due to latent ill-health or personality traits) that jointly influence subjective health and welfare. (Although it can also reduce the signal-to-noise ratio in the presence of time-varying measurement errors or extraneous transient effects.) We find that 18% of adults in the panel thought that their health had improved, while 20% thought it had worsened. There is little difference by gender, though a slightly higher proportion of men reported that their health had worsened than did women. The panel data suggest an even lower statistical association between the two categorical variables, with an overall Cramer’s V of 0.05. Of the 32% of the panel sample who felt that they had moved down the welfare ladder, 21% felt that their health had declined (by one or more categories in the _SAH_ question). By contrast, this was true of 17% of the 38% who felt that they had risen on the _SAW_ ladder. Changes in perceived health are almost orthogonal to changes in perceived economic welfare. This holds for both men and women.

However, we find stronger signs of the gradient for a number of the more specific health indicators in the data, as can be seen in Figure 3. Most of the physical activity variables tend to show a deterioration at low incomes (though the causal interpretation is of course unclear). A number of the reported ailments (gastro-intestinal disorders, kidney problems, hypertension) tend to have higher incidence at middle expenditure levels. Adjusting for the other covariates has 15