rafmacalaba/datause-encoder-data huggingface dataset:
period as Oaxaca, Chiapas, Veracruz and Guerrero all move closer to the vertical line at 1 in the X axis. This causes an extension of the kernel toward the vertical that suggests convergence in income. Nevertheless, we still observe the presence of clear outliers and the alignment along the 45 degree line of the majority of the central states indicating a high degree of spatial independence overall.

The kernel using distance to the US (not shown) is similar to that using the bands but the results are less clearly delineated. This arises mainly because the south is split in two leaving the poor states of Chiapas and Oaxaca associated with the far richer states of Baja California Sur, Quintana Roo and Yucatan.

In sum, the convergence clubs suggested in figures 3a and 3b partially map into border and southern regions of Mexico. However, there are no other obvious other patterns of spatial association or clustering in the rest of the country. For any of our definitions of space, northern and central states mostly align along the 45 degree line showing little resemblance with their neighborhoods.

_Parametric measures of spatial dependence_ To establish whether the patterns suggested by the spatially conditioned kernels are statistically significant we turn to the Moran statistics. Figure 5 plots Moran’s I normal standardized values for the period 1970-2002 for the three different definitions of space considered, as well as the standard deviation of the GDP per capita for the same period as a measure of sigma convergence. What is immediately clear is that, viewing Mexico as a whole, spatial dependence in income levels has increased along with the sigma divergence after a period when both had fallen. The contiguity based neighborhood most closely track the standard deviation (with a correlation coefficient of 0.85), but the other neighborhoods arguably also follow a similar pattern, albeit, less clearly. The relatively subtle indications of spatial dependence suggested in the kernels emerge as statistically significant in the Moran test, most notably, consistent with the kernels above, in the bands measure that most clearly captures the border and southern concentrations of wealth and poverty.

The local Moran statistics identify whether particular groupings of states co-vary in a statistically significant way and largely confirm the observations from the kernels. We present them in several different formats. First, the maps in figures 6a and 6b show the geographical distribution of significant local Morans based on contiguity for both the 10% 13
TS=((input* OR fertilizer* OR fertiliser*) AND (subsidies OR subsidy OR subsidizing OR intervention OR voucher* OR discount*) AND (productivity OR income OR yield) AND (agriculture OR agricultural OR farm*)) Refined by: WEB OF SCIENCE CATEGORIES: ( ECONOMICS ) Timespan: 2000-2021. Indexes: SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH, ESCI, CCR-EXPANDED, IC.

This search yields 213 articles. Date of search: June 3, 2021 Database 2: Scopus ( TITLE-ABS-KEY ( input OR fertilizer OR fertiliser ) AND TITLE-ABS-KEY ( subsidy OR subsidies OR subsidizing OR voucher OR discount ) AND TITLE-ABS-KEY ( productivity OR yield OR income ) AND TITLE-ABS-KEY ( agricultur* OR farm* ) ) AND ( LIMIT-TO ( SUBJAREA , "ECON" ) ) AND ( LIMIT-TO ( PUBYEAR , 2021 ) OR LIMIT-TO ( PUBYEAR , 2020 ) OR LIMIT-TO ( PUBYEAR , 2019 ) OR LIMIT-TO ( PUBYEAR , 2018 ) OR LIMIT-TO ( PUBYEAR , 2017 ) OR LIMIT-TO ( PUBYEAR , 2016 ) OR LIMIT-TO ( PUBYEAR , 2015 ) OR LIMIT-TO ( PUBYEAR , 2014 ) OR LIMIT-TO ( PUBYEAR , 2013 ) OR LIMIT-TO ( PUBYEAR , 2012 ) OR LIMIT-TO ( PUBYEAR , 2011 ) OR LIMIT-TO ( PUBYEAR , 2010 ) OR LIMIT-TO ( PUBYEAR , 2009 ) OR LIMIT-TO ( PUBYEAR , 2008 ) OR LIMIT-TO ( PUBYEAR , 2007 ) OR LIMIT-TO ( PUBYEAR , 2006 ) OR LIMIT-TO ( PUBYEAR , 2005 ) OR LIMIT-TO ( PUBYEAR , 2004 ) OR LIMIT-TO ( PUBYEAR , 2003 ) OR LIMIT-TO ( PUBYEAR , 2002 ) OR LIMIT-TO ( PUBYEAR , 2001 ) OR LIMIT-TO ( PUBYEAR , 2000 ) ) This search yields 149 articles. Date of search: June 3, 2021.

Database 3: JSTOR (((((ab:(input OR fertilizer OR fertiliser) AND (subsidies OR subsidy OR subsidizing OR voucher OR discount)) AND (productivity OR yield OR income) AND (agriculture OR agricultural OR farming))))) AND disc:(economics-discipline) Restricted to publications between 2000 and 2021. We are unable to include additional search terms due to character restrictions for the search.

This search yields 293 articles. Date of search: June 3, 2021

# **A2. Abstract screening criteria for exclusion**

- Non-original research

- Not related to agriculture

- Published earlier than 2000

- Not developing countries

- Simulation/calibration study

19
[Insert Table 6 about here]

[Insert Table 7 about here]

## [Insert Table 8 about here]

# 4.4 Regression-based estimates of the impact on other outcomes

The available census data allows us to examine the impact of the land reform on two other outcomes: female labor market participation and a woman’s decision as to at what age to get married. For each outcome variable we ran one plus eight different regressions for a particular outcome variable for all women in their childbearing years as well as by age group of women in their childbearing years (in 5-year intervals). The regressions follow the same identification strategy as before. We focus on the rural sample as we have demonstrated that it is the main driver of our findings.

The impact on female labor market participation could be an indirect one. If women have fewer children, they may have more time to engage in labor market activities outside the household. The results are reported in Tables 9, 10 and 11 for self-employment, wage employment, and housework, respectively. We find some evidence for an increase in the proportion of self-employed women in the age groups between 20—25 and 45—49. However, the point estimates for all other age groups as well as for the full sample of women are not statistically significant even if they do have a positive sign. We do not find any evidence for an increase in the proportion of women participating in formal wage employment in response to the land reform. In line with these findings, our results do not show a decrease in the proportion of women reporting housework as their main occupation in the reform relative to the non-reform areas. Nonetheless, it is worth mentioning that the time trend is consistently positive for self-employment while it is consistently negative for housework, implying, for the whole of Amhara, a continual increase in female self-employment outside the house but a decrease in housework as the main activity over the two census periods.

## [Insert Table 9 about here]

[Insert Table 10 about here]

## [Insert Table 11 about here]

Furthermore, the land reform may have had a direct effect on the decision to get married. Table 12 has the results for the proportion of unmarried women.

16
_Mexico: The 1997 Pension Reform_ _28_ _Inflation_ is especially important for the old PAYG system, since those pensions were computed taking the average of a worker's nominal wages in the previous _5_ years. In a high inflation environment we can expect the fiscal costs arising from the recognition of acquired rights to fall (Table 14).

<!-- Start of picture text --> : 1 1 1 11 |52.0°/ 0 52.0/o<br>Offical forect on te 22.0% -j38.0%<br>l | | | | 1 16.0% 30.0%l<br>| | | 0 g |12.0% I 24.0%l<br>30.0% 45~~1.0%<br>al 1!1 | i | |1 1.0% 20.0%<br>2 11g | 2 29.0% 19.0%l<br>1 |.| 11 | |r 6 .0% 17.0%<br>g | ' X 0 ~6.0% 110.0%l<br>ig 111 l E leel...... .......<br><!-- End of picture text -->

Note: These assumptions are for modeling purposes only and can not be taken as O)fficial forecasts on the subject.

IMSS data shows _contribution density_ for its affiliates is on average 0.83 (i.e., for each period of time the effective contribution period is only 83%). Years of service and contributions are affected in the model by this density factor.

Finally, regarding _operational costs,_ the AFORES will be private firms whose only source of income will be commissions charged to their affiliates. Experience in Chile and Peru indicates that commissions (excluding disability insurance premiums) can reach up to 2% of workers' wages. Therefore, we have assumed that commissions charged by the AFORES to the workers will be equivalent to 2% of covered wages. This arnount will not accumulate in the individual account.

**Government contributions to the pension scheme.** The contributions that the Government is required by law to make to the new pensions scheme are as follows:

<!-- Start of picture text -->
||Social quota per worker: 5.5% of I minimum salary as of December 31, 1996,<br>||indexed to the CPI.<br>Proportional quota: 0.425% of the worker's salary.<br>11Proportional quota: 0. 125%/ of the worker's salary.l<br><!-- End of picture text -->

The social quota is the largest cost item among government contributions to the new pensions system. Its cost will be close to 0.3% of GDP in 1997 and will remain steady at that level during the first
14

# **_Social health insurance status_**

We define our SHI dummy _SHIit_ as taking a value of one if in country _i_ at time _t_ earmarked payroll taxes for health care were collected from formal-sector workers and there was a SHI agency in place. The required information was obtained mainly from the _Health Systems in Transition_ ( _HiT)_ document series published by the European Observatory on Health Systems and Policies, a partnership between the European Office of the World Health Organization (WHO) and governmental, national and international agencies. World Bank reports and consultations with its staff working in the ECA region were also used in order to obtain data on countries for which HiTs have not yet been published, and to double-check the information assembled through the HiTs.<sup>19</sup> Our derivation of the SHI status indicator is shown in Table 1. Our strict definition means that we end up classifying as non-SHI some country-year combinations that are often—we believe, erroneously—classified as SHI (such as Latvia and Poland). Furthermore, we classify Romania as SHI only after 1998; despite the fact that payroll taxes were used somewhat before then, it was not until 1998 that SHI was fully set up with a SHI agency and with payroll contributions making up the majority of health care revenues. We explore the sensitivity of our results to not classifying these as SHI countries, by re-running our models with the three of them classified as SHI for the years indicated in Table 1.

> 19 Our classification is consistent with the evidence presented in Langenbrunner et al. (in press) which came to our attention after the empirical work for the paper hade been completed.
Policy Research Working Paper 8558

# **Abstract**

Despite the well-known positive impact of tobacco taxes on health outcomes, policy makers hesitate to use them because of their possible regressive effect, that is, poorer deciles are proportionally more negatively affected than richer ones. Using an extended cost-benefit analysis to estimate the distributional effect of white and clove cigarettes in Indonesia, this study finds that the long-run impact may be progressive. The final aggregate effect incorporates the negative price effect, but also changes in medical expenditures and additional working years. The analysis includes estimates of the distributional impacts of price rises on cigarettes under various scenarios using 2015–16 Indonesia National Socioeconomic Surveys. One contribution is to quantify the impacts by allowing price elasticities to vary across consumption deciles. Overall, clove cigarette taxes exert an effect that depends on the assumptions of conditional price elasticity. If the population is more responsive to tobacco price changes, then people would experience even more gains from the health and work benefits. More research is needed to clarify the distributional effects of tobacco taxation in Indonesia..

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at afuchs@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1. The context: Foreign direct investment in Turkey**

Under the auspices of international financial institutions, Turkey initiated in the 1980s a successful case of economic liberalization through export oriented policies (see Botarav and Yeldan, 2001). Turkey opened its capital account to international flows in 1989 following a slowdown in economic activity in the late 1980s. Contrary to expectations, foreign direct investment (FDI) remained subdued, averaging just about 0.4 percent of GDP during the 1990s. On the other hand, portfolio inflows increased significantly, and their volatility contributed to heightened macroeconomic volatility and to the crises of 1994 and, especially, 1998-99 and 2001, when the country suffered net portfolio capital outflows above 2 percent of GDP (Ertuğrul and Selçuk, 2001). This is also a typical outcome for emerging markets with fixed exchange rates, high inflation and unrestricted capital inflows.

In the 1990s and early 2000s, and except for India, Turkey lagged significantly behind the rest of the now called BRICs (large emerging economies) in terms of foreign direct investment attraction, as shown in Table 1. According to some scholars, domestic inefficiencies, persisting controls, and redtape were the reasons behind the struggle to attract foreign direct investment (Celasun et al., 1999), whereas others blame deficiencies in the design and timing of the economic liberalization approach (Önis, 1998; Demir, 2004). In addition, macroeconomic instability and inflation have been often highlighted as significant deterrents of FDI (Erdilek, 2003). Furthermore, the decade of the 1990s was one of frequently changing political coalitions, which might have also hindered capital flows.

**Table 1. Average net foreign direct investment to GDP inflows in emerging economies**

||**1980-89**|**1990-99**|**2000-04**|**2005-08**|**2009-13**|
|---|---|---|---|---|---|
|Turkey|0.2|0.4|0.8|3.0|1.6|
|Eastern Europe and<br>Central Asia median|0.6|1.7|3.6|6.2|3.0|
|Brazil|0.7|1.5|3.4|2.5|2.9|
|China|0.6|3.9|3.2|4.7|4.0|
|Malaysia|3.2|5.8|2.7|3.9|3.3|
|Mexico|1.2|1.9|3.1|2.4|2.3|
|India|0.0|0.4|0.9|2.1|1.8|
|Russian Federation|-|0.7|1.5|3.7|2.9|
|South Africa|0.0|0.6|1.7|2.1|1.6|

Source: World Development Indicators using World Bank MacroStats to Go 2
<u>Table 8: Determinants of the Within-...rm Increase in the DVAR</u>

||(1)|(2)|(3)|
|---|---|---|---|
|Dep. Var<br><br>|4t;00DV ARjt|4t;00 ln(P <sup>I</sup>=P <sup>D</sup>)jt|4t;00 ln<br>~~�~~<br>V <sup>D</sup><br>jt<br>~~�~~|
|4t;00ln<br>~~�~~<br>P <sup>I</sup>=P <sup>D</sup><sup>~~�~~</sup><br>jt|0.269***<br>(0.026)|||
|4t;00ln(Ejt) (RMB appreciation)||1.479*<br>(0.891)|-0.089***<br>(0.031)|
|4t;00ln<br>�<br>V <sup>D</sup><br>jt<br>�||17.108***<br>(3.177)||
|4t;00ln<br>�<br>e�<sup>U</sup><br>jt<br>�|||-0.012*<br>(0.007)|
|4t;00ln (FDIjt)|||0.017***<br>(0.002)|
|Industry Fixed E¤ects||||
|N|105|105|105|
|R-sq|0.030|0.106|0.006|

4t;00 is the operator that subtracts the variable of interest from its corresponding value in 2000. Bootstrapped standard errors (with 500 repetitions) are reported in parentheses. Coe¢ cients are estimated using 3SLS. Columns (1), (2), and (3) are third, second, and ...rst stages, respectively. * p<0.10; ** p<0.05; *** p<0.01.

52
<mark>Atkinson, A. Messy, F. 2012. “Measuring Financial Literacy: Results of the OECD / International Network on Financial Education (INFE) Pilot Study” OECD Working Papers on Insurance and Private Pensions 15, OECD, Paris.</mark> Ausubel LM. 1999. “Adverse selection in the credit card market.” mimeo, University of Maryland, College Park.

Banerjee A, Chattopadhyay R, Duflo E, Keniston D, Singh N. 2014. “Improving Police Performance in Rajasthan, India: Experimental Evidence on Incentives, Mangerial Autonomy and Training”, mimeo MIT.

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<mark>Bertrand M, Mullainathan S. 2004. “Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination”</mark> _<mark>American Economic Review</mark>_ <mark>94 (4): 991-1013</mark> Campbell JY, Jackson H, Madrian BC, Tufano P. 2011. “Consumer Financial Protection.” _Journal of Economic Perspectives_ 25(1): 91-114 Chetty R, Looney A, Kroft K. 2009. “Salience and Taxation: Theory and Evidence.” _American Economic Review_ 99(4):1145-1177.

Choi J, Laibson D, Madrian BC. 2011. “$100 bills on the sidewalk: suboptimal investment in 401(k) plans.” _Review of Economics and Statistics_ 93(3): 748-763 <mark>DellaVigna, S. 2009. "Psychology and Economics: Evidence from the Field."</mark> _<mark>Journal of Economic Literature</mark>_ <mark>47(2): 315-72.</mark> Duarte F, Hastings JS. 2012. “Fettered consumers and sophisticated firms: evidence from Mexico's privatized social security market.” NBER working paper 18482.

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23
variance, the correlation is 0.715 (refer to Table 7).

# **4. Conclusions**

Knowledge of soil quality indicators and overall health is becoming increasingly important as food security issues become more pressing and climate change threatens to change the face of agriculture. Soil health, both perceived and actual, can have impacts on the targeting and uptake of improved agricultural practices, which can improve both the quality and quantity of food produced. For instance, Marenya and Barrett (2009) show that fertilizer effectiveness, and in turn, demand, is dependent on soil carbon content. However, results of the LASER study suggest that subjective soil quality indicators fail to effectively reflect true levels of organic carbon, thereby limiting the value of subjective assessments of soil quality in policy making.

Certainly, asking a farmer to categorically rate overall soil quality has limited benefit. This particular subjective soil quality question does not successfully distinguish between soil carbon levels or predict soil quality index scores, at least within this sample in Ethiopia. Subjective questions on soil color and texture were more effective in predicting soil quality index scores and organic carbon content, although the low explanatory power of these variables leaves much to be desired. The value of subjective soil quality indicators is further questioned by the severe lack of intra-household variation observed. Further research validating different subjective questions, potentially formulated with soil scientists, may yield more optimistic results. However, the questions included in the LASER study are those that have been historically included in LSMS-ISA surveys in multiple countries.

From a fieldwork implementation standpoint, the experience of the LASER study gives promise that the integration of soil spectroscopy into socioeconomic household panel surveys is feasible. The methodology is a relatively rapid and cost-effective soil measurement technique that could unlock further understanding of the effects of farm management practices and changes in soil health over time. Detailed guidance on implementation strategies and protocols implemented in the LASER study can be found in Aynekulu et al. (2016).

Despite the weak correlation observed here between laboratory analysis and subjective assessment, several studies find subjective assessments of soil quality to be a significant determinant of plot-level productivity (for example, Carletto et al., 2013). This suggests that if subjective soil quality assessments are not capturing true soil properties, they must be capturing something else relevant to agricultural production. As a potential explanation for this, we echo the sentiments of Tittonell et al. (2008) and others, who suggest that farmers have a ‘holistic’ view of soils, and that rather than assessing the soil properties explicitly, they often incorporate other components such as overall agricultural productivity 24
Figure 6: Expenditure Shares of Flour versus Subsidies per Capita <!-- Start of picture text --> 8 a. Expenditure share 50 b. Benefits per capita<br> Djibouti Djibouti<br> Iran, Islamic Rep. Iran, Islamic Rep.<br> Libya 40 Libya<br>6<br>30<br>4<br>20<br>2<br>10<br>0 0<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 0 .1 .2 .3 .4 .5 .6 .7 .8 .9<br>Percentiles of population Percentiles of population<br>US$-PPP<br>Flour expenditure share (in %)<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

# Simulations of Subsidy Reforms

In order to simulate comparable reforms across countries, we consider a flat reduction of unit subsidies by 30 percent across all products and all countries. We measure the impact of these reforms on household welfare, inequality, and the government budget in this order. We also consider the cost for the government of compensating the population to reach the pre-reform level of welfare. The implied changes in prices of the proposed simulations are large for most countries and products, which makes the standard linear approach to subsidies simulations inappropriate. We therefore model the demand function using Cobb-Douglas preferences.<sup>2</sup>

## Welfare

Figures 7 and 8 show the impact on household welfare (measured in terms of household expenditure per capita). For each product in the figures we have two panels. The top panel illustrates the welfare impact in annual per capita US$-PPP terms. The bottom panel illustrates the welfare impact in terms of share of total household expenditure. Therefore, the top part of the figures is the absolute welfare effect, and the bottom part is the relative welfare effect.

For LPG, the greatest impact of this reform would be in Morocco, with a per capita impact per year of about $20-PPP on average. The smallest impact is in Djibouti, the poorest of the countries considered. It is also instructive to see that the distributions of these impacts can be 16