rafmacalaba/datause-encoder-data huggingface dataset:
25

- We use the same WHO 2006 standards for childhood stunting and underweight in all surveys, whereas the DHS and MICS reports use whatever standard was in force at the time the survey was done. The change of standards apparently makes a difference (Ergo et al. 2009).

# <u>Indicator computation</u>

With the exception of the infant and under-5 mortality rates, which we calculate using the same life-table synthetic-cohort probability method employed in DHS reports and programmed in the Stata module SYNCMRATES (Masset 2016), and the childhood anthropometric z-score indicators from the MICS, which are computed throughout using 2006 WHO growth standards and programmed in WHO’s package IGROWUP,<sup>25</sup> all indicators are based on simple population-weighted means of variables constructed from the questions in the survey.<sup>26</sup> Where an indicator is available in more than one survey for a given year, we average over all data points.<sup>27</sup> Sometimes the way the data were collected in the surveys prevented us from achieving 100% consistency of definition across surveys. For a number of indicators such as pap smear and mammogram rates, and blood pressure and cholesterol levels, the sampled age-ranges differ (see notes in Tables 1-3). For other indicators, we could not fully eliminate _conceptual_ inconsistencies. For example, the MICS only asks about care-seeking for children with coughing _and_ rapid breathing if the caretaker reports that the respiratory problems originate from the chest. For MICS data, our

> 25 Program and documentation can be downloaded from http://www.who.int/childgrowth/software/en/. For the DHS, we use the 2006 WHO growth standard z-scores provided in the datasets to compute rates of childhood stunting and underweight. 26 Generally, construction of both the health equity and financial protection sides of the data set follows the methods summarized in O’Donnell et al. (2008).

> 27 We apply a different aggregation rule for our cancer screening variables – pap smear and mammography – when we have multiple data points for a given year that differ in terms of the age-range of the women for whom the surveys obtained screening rates. The rule we apply here is as follows: Our preferred age-ranges are 30-49 for pap smears and 50-69 for mammography. If for a given year, data are only available for another than the preferred age-range, we use the data point which minimizes the absolute difference between the number of years of the data point’s age-range which are outside the preferred age-range (inclusion errors) and the number of years of the preferred age-range which are not included in the data point’s age-range (exclusion errors). If, for instance, pap smear data for a given year are only available for women aged 25-44 and 35-59, we would choose the 25-44 data point (both age-ranges have an exclusion error of five years, but the inclusion error for the 35-59 age-range is ten years compared to five years for the 25-44 age-range).
Table 4: Deep trade agreements and foreign value‐added exports ‐intermediates vs final, PPML

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||FVA|of final expo|rts|FVA of|intermediate|exports|
|No. of provisions|0.000645|||0.00385***|||
||(0.00168)|||(0.00124)|||
|Core provisions||0.000806|||0.00765***||
|||(0.00340)|||(0.00265)||
|PCA depth (1st)|||0.00553|||0.0290***|
||||(0.0131)|||(0.0101)|
|BIT|0.105**|0.105**|0.105**|0.0736*|0.0710*|0.0720*|
||(0.0473)|(0.0474)|(0.0473)|(0.0413)|(0.0412)|(0.0413)|
|PTA (in force or|||||||
|inactive)|0.143***|0.142**|0.139**|‐0.106**|‐0.134***|‐0.117**|
||(0.0503)|(0.0592)|(0.0542)|(0.0451)|(0.0507)|(0.0474)|
|Observations|26,520|26,520|26,520|26,520|26,520|26,520|
|R‐squared|0.996|0.996|0.996|0.995|0.995|0.995|
|Country‐pair FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|

Clustered standard errors at the country pair level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 24
- [30] Snyder J., and Str ̈ _o_ mberg, D., ”Press Coverage and Political Accountability,” 2007, Working Paper.

- [31] Str ̈ _o_ mberg, D., ”Mass Media Competition, Political Competipion, and Public Policy”, Review of Economic Studies, 2004, 71(1).

- [32] Str ̈ _o_ mberg, D., ”Radio’s Impact on Public Spending”, Quarterly Journal of Economics, 2004, 119(1).

- [33] Triesman, D., ”The Causes of Corruption: A Cross National Study”, Journal of Public Economics, 2000, 76, 399-457.

- [34] Volpin, P.F., ”Governance with Poor Investor Protection: Evidence from Top Executive Turnover in Italy”, Journal of Financial Economics, 2002, 64(1), 61-90.

19
labor force. This data is derived from a series of different questions, and is deemed comparable over time for adults aged 16 or older. For our analysis, we constructed employment variables for individuals between the age of 25 and 65.

The analysis is conducted at the level of US states.<sup>14</sup> We define as poor all individuals who belong to the bottom 40% of state population ranked by disposable household per capita income and as rich all those belonging to the top 40%. As discussed above, these are sufficiently wide partitions to include a large chunk of total inequality and also to have arguably a meaningful impact on growth. The analysis is conducted in anonymous terms because the surveys are not longitudinal and we do not have information about household per capita income of the same persons over several periods. Empirically, we ask in effect, two related questions. First, in a _one-on-n_ formulation, how does the overall Gini at time=0 affect the rate growth at different percentiles of income distribution between times 0 and 1? (The times 0 and 1 are, as indicated, always ten years apart.) And second, in a more flexible formulation, _two-on-n_ , how do inequalities among the poor and the rich at t=0 affect the growth rate at different percentiles between times 0 and 1?<sup>15</sup> The regression for a given income distribution partition _i_ (where _i_ refers here to the poor π or the rich _ρ_ but can obviously be made more general) is written as in (1).

is the growth rate of a given percentile _p_ in state _s_ between time periods 0 or 1 ( _t_ is for simplicity omitted), that is:

> 14 One advantage of working with a single country, with data from a single source, is that the data is perfectly comparable between units of observation. On the other hand, a single country may not offer the same degree of heterogeneity as a cross-country database does, which means that by lowering the “noise” we may also be lowering the “signal”. This does not appear to be an issue in the United States. We observe a large degree of variation in both inequality levels and growth pattern between the different states.

> 15 Note that the assumption of anonymity exists in both the older one-on-one literature as well in Voichovsky’s approach. A further improvement in the study of inequality-growth relationship may come from greater availability of longitudinal surveys.

9
**Table 2: Key variables for land reform planning, monitoring, and impact assessment**

||_Municipal land reform plan_|_Monitoring_|_Impact assessment_|
|---|---|---|---|
|Beneficiaries|Beneficiary identification<br>Beneficiary<br>profile<br>(capacity;<br>welfare)<br>Training requirements<br>Specific projects|Grant per beneficiary/employmen<br>Group formation.<br>Additional employment generated.<br>Targeting efficiency.<br>Improvements in access to land.|t. Increase in income<br>Consumption smoothing (assets).<br>Credit market access<br>Social services.<br>Reduction of violence|
|Projects|Demand and supply of land<br>Char's of productive projects<br>Complementary investments needed<br>Cost by component|Characteristics of farns transfefred.<br>Implementation of projects.<br>Repayment performnance<br>(planned and<br>actual).<br>Targeting of under utilized lands|Agricultural productivity<br>Environmental sustainability<br>Reconcentration of land?|
|Institutions|Institutional capacity:<br>Local and central government (tech.<br>assist. legal framework)<br>Private<br>Sector<br>(banks, input<br>suppliers, marketing)<br>NGOs (training, evaluation))|Effectiveness in dissemination and<br>capacity building<br>Efficiency of land transfer process.<br>Private sector/NGO participation.|Strengthened local govemment.<br>Fiscal sustainability<br>Degree of decentralization|

_Monitoring_ aims to ascertain whether the program actually achieves its objectives and whether it is doing so at low cost. To this end, it describes what is happening on the ground, compares it to original objectives, and uses the information to update original parameters. The information provided can also be used to (i) assess whether the mechanisms utilized do reach target populations and areas (i.e. poor rural dwellers and underutilized lands); (ii) provide an _ex-ante_ estimate of the expected benefits of land reform, both in terms of productivity and poverty reduction; and (iii) provide information on whether implementation is progressing as expected. _Impact assessment,_ in turn, is concerned with the ultimate impact, both direct and indirect, of the program on household well-being, agricultural productivity, environmental sustainability, and institutional strengthening. To this end a nation-wide LSMS-type household survey, and a farm survey, will be used as a control group, to be compared to a panel of beneficiaries and lands that will be established under the program.

# 4 COMPARISON **WITH** OTHER COUNTRIES

In addition to Colombia, Brazil and South Africa have, under different conditions, recently initiated programs of negotiated land reform. With an institutional background very similar to that of Colombia (presence of land reform legislation and a central land reform institute dating from the early 1960s), negotiated land reform in Brazil has been driven by individual states' initiative. The purpose of the Brazilian interventions is to establish cheaper, more agile policy alternatives to centralized land reform in an environment where the issue of land reform is high up on the political agenda and potential beneficiaries have at least some idea of what to do with the land.

21
<!-- Start of picture text --> 40<br>35<br>30<br>25<br>20 - -<br>15<br>10<br>5<br>0 -<br>8O_<br>° E' E = 2 ° °<br>-E -5 ( Eo<br>0 o< E ' ED ' "Union,<br>01994 31998<br><!-- End of picture text --> Figure 10: Reform status in Sub-Saharan Africa in 1994 and 1998.

Notes: Data is for 42 Sub-Saharan Africa Countries Source: ITU (2001).

_Sector Reform Elsewhere in the Region and Technological Change._ When democratization took place in 1994, few countries in SubSaharan Africa had significantly reformed their telecommunication sectors. Although 26 of the 42 Sub~~~~~~~~~~Saharan countries for which data were available had separated postal and telecommunications services (International Telecommunication 1998a), Malawi was not significantly different from other countries in most other ways. At this time, **the** regulator was separate from both the Ministry and the incumbent in only 8 countries in Sub-Saharan Africa, only 4 countries had privatized their incumbent operators, only 7 countries had a cellular operator that was not at least partially owned by the incumbent and only 2 had introduced competition in cellular (see Figure 1O).

> However, by 1998, Malawi had started to lag behind the leading reformers. By this time,

> 34 countries had separated postal services from telecommnunications, 20 had separated the regulator from both the ministry and the incumbent, 10 had privatized the incumbent operator, 16 had a cellular company not even partially owned by the incumbent, and 14 had introduced competition in cellular telephony. Further, many other countries that had not yet implemented reform were in the process of doing so. In addition, it was becoming clear that MPTC lacked the resources and human capital to keep up with the rapid pace of technological change. Combined with its poor performance in other areas (i.e., the slow system expansion), this made policymakers feel like they were being left behind in a rapidly evolving field. In turn, they were concerned that Malawi would be put at a competitive disadvantage vis-a-vis its neighbors.

28
# **Figure 8. Growth Decompositions for LatAm Commodity Exporters**

<!-- Start of picture text --> (a) EP + EL (b) DR<br>0.80<br>0.50<br>0.40 0.60<br>0.30<br>0.40<br>0.20<br>0.10 0.20<br>0.00<br>0.00<br>‐0.10<br>‐0.20 ‐0.20<br>‐0.30<br>‐0.40<br>‐0.40<br>‐0.60<br>‐0.50<br>‐0.60 ‐0.80<br>Diversified Undiversified Diversified Undiversified<br>1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999<br>1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999<br><!-- End of picture text --> Note: The variables EP, EL, and DR are calculated according to the growth decomposition method explained in Section 2a. “Diversified” include Brazil, Colombia and Mexico. “Undiversified” include Argentina, Chile, Peru and Uruguay. _Source: WDI._
affect inclusion errors. Survey errors in PMT and FCS scores significantly affect inclusion errors by 6 percentage points based on consumption and food security, respectively. This magnitude is small, and again should be considered an upper bound attributing all measurement error to the registry survey.

_Figure 6: Percentage change in FCS and PMT scores between registry and baseline survey_ <!-- Start of picture text -->
-2 -1 0 1 2<br>FCS  -2 -1 0 1 2<br>PMT<br>Note: Figures presents the distribution of percentage changes in<br>FCS and PMT scores between the registry and baseline surveys.<br>Figure 7: Differences in FCS and PMT scores along the consumption distribution<br>20 40 60 80 100 0 100000 200000 300000<br>Diff FCS scores 95% CI Diff PMT scores 95% CI<br>semiparametric regression semiparametric regression<br>2 2<br>1.5 1.5<br>1 1<br>Density Density<br>.5<br>.5<br>0<br>0<br>100 2<br>50 1<br>0 0<br>Diff. FCS scores Diff. PMT scores<br>-50 -1<br>-100 -2<br><!-- End of picture text -->

_Figure 7: Differences in FCS and PMT scores along the consumption distribution_ Note: Dots represent data points corresponding to differences in FCS and PMT scores between the registry and baseline surveys. Red lines represent a semiparametric regression of the differences in scores on consumption. Grey areas represent the 95% confidence interval of such regressions.

21
size of the carbon

# **1.1 Our approach**

To estimate the impact of adoption of no-till agriculture on local air pollution levels we leverage two recent advances in data collection and modeling. One obstacle to estimating the benefits of no-till agricultural has been poor data on the uptake of no-till over the entire U.S. Corn Belt growing region. Historically, analysis of the uptake of no-till practices has depended on five year surveys conducted by the Conservation Technology information Center and the National Resources Inventory database (e.g. ref.<sup>19,20</sup> ). These sources do not provide data that is spatially or temporally granular. Instead here we rely on advances in remote sensing and machine learning to measure uptake of no-till at approximately the field level and at an annual frequency.<sup>21</sup> This enables us to conduct an examination of how year-to-year changes in no-till usage in a given county impact the air quality of that county.

A second obstacle has been the lack of data on PM2 _._ 5 levels with sufficient spatial and temporal resolution. Data based on ground monitors generally offers daily, or even hourly, information on pollution levels but over a very limited geographic region. These monitors are rarely located in the rural regions that are most likely to be impacted by the changes in tillage practices that we examine here. The problem of limited geographic scope can be solved by using remotely sensed data on pollution levels that takes advantage of satellite data to provide estimates of pollutant levels across a large geographic area. However, the increase in geographic scope comes at the cost of low temporal frequency. Because we expect changes to local air pollutants due to the uptake of no-till to be concentrated at the times when no-till practices are most likely to occur, the lack of temporal frequency makes it difficult to detect impacts with existing remotely sensed pollution data.

In the current study, we utilize a recently released dataset of highly localized pollution estimates for the United States at a daily scale.<sup>22</sup> These estimates combine data from multiple sources including satellite measures of aerosol optical depth (AOD), emissions inventories, and ground-based pollutant monitors. These data are combined in a multi-model ensemble approach to estimate a pollution surface that provides daily estimates of PM2 _._ 5 on a 1km _×_ 1km grid for the entire U.S.

We estimate the relationship between changes in no-till adoption and pollution levels econometrically with a two-way fixed effects model. Our model examines how daily pollution levels in a county change during the period that farmers are most likely to be engaged in tillage in that county. We define the period when tillage is most likely to occur based on data on crop specific harvest times by state reported to the USDA (for full details see SI1.1).<sup>23</sup> We examine how pollution levels in this “tillage period” change from year to year within a county as the level 3
<!-- Start of picture text --> 25.0% $90.0<br>@ $343 $80.0<br>20.0% @ 3706 @ $762 $70.0<br>16.8%<br>$60.0<br>15.0% @ $553<br>$50.0<br>* $417 soe<br>10.0% 9.1%<br>71% $30.0<br>° 44% $20.0<br>3.0% 29%<br>$10.0<br>0.0% $0.0<br>Total Education Food Pensions Other<br>TM%ofvulnerable population Average amount<br><!-- End of picture text --> <!-- Start of picture text --> 30.0% Incidence and average amount of monetary transfers $200 30.0% Incidence ofnon-monetary transfers<br>27.3% 27.6%<br>25.0% $173.1* $180$160 25.0%<br>20.0% $140 20.0%<br>16.8% 5120 17.4%<br>15.0% * $100 15.0%<br>$93.5 $80<br>10.0% 10.0%<br>7.0% $60 7.2%<br>5.0% 26% $40 5.0%<br>$20 0.9%<br>0.0% $0 0.0%<br>Poor Vulnerable Middle Class Upper Class Poor Vulnerable Middle Class Upper Class<br>Incidence Average amount ofMonetary transfers p/capita<br><!-- End of picture text -->
_COTE D’IVOIRE: Growth, Specialization and the Terms of Trade_ These two sets of estimates (in this paper versus those of Mahajan) were arrived at independently, and differ in assumptions and details of their calculations, and perhaps some differences in input data. Nevertheless, they present very similar profiles of the evolution of TFP and its components for Cote d’Ivoire.

**Table A-2:  TFP, Physical & Human Capital Growth (Mahajan, 2003)**

|**Growth Rate**||||||
|---|---|---|---|---|---|
|**Summary**|**1961-1970**|**1971-1980**|**1981-1990**|**1991-2000**|**1961-2000**|
|GDP|8.71|7.61|-0.24|2.84|4.46|
|Capital Stock|10.54|10.22|2.55|0.43|5.76|
|Human Capital|3.70|4.39|4.20|4.58|4.22|
|TFP|2.28|0.89|-3.77|-0.09|-0.37|

For Cote d’Ivoire. Source: Pradeep Mahajan, PRMEP, 2003, World Bank

# **Stochastic Regression Model of Factor Productivity**

We now turn from a deterministic “accounting” model of factor productivity (which asserts parameter values) to a stochastic regression model of factor productivity (which estimates parameter values). By fitting such a model, we can verify to some extent the validity of the analysis in the last section. We will use the capital and labor-quality variables developed for the deterministic calculations, and test them in an econometric model.<sup>26</sup> We performed standard Augmented Dickey-Fuller (ADF) Tests (assuming no constant or trend in the ADF test equation) on the natural logarithms (logs) of the variables we used: real output per worker and estimated capital stock per worker (in constant local currency, CFA franc), schooling per worker, and the index of terms of trade (normalized to unity). As expected, there existed unit roots in the levels, but all variables were integrated of order one (stationary in the first differences). The ADF Z(t) statistic was less than 5% critical value in all cases, usually less than 1%. So, our point of departure is to estimate the regressions in the first differences (an error-correction model exists for the levels). Since these are logged variables (except for average schooling), the first differences are the estimates of the growth rates.

The results were satisfactory, especially when on considers that the sample is small, the main explanatory variables were estimates we constructed, and that many analysts have experienced major difficulties with data from Sub-Saharan Africa.<sup>27</sup> Furthermore, there is presumably a whole host of other omitted factors (variables) “explaining” productivity, such as policy, warfare, institutional factors, politics, the environment, and so forth.

> 26 See Bosworth & Collins (2003), Cohen & Sots (2001), and Ghosh & Kraay (2000)

> 27 For example, Bosworth and Collins did not extend their global TFP estimates to Africa in their paper “The Empirics of Growth: An Update”, September 22, 2003.

28
-24-

through monetary expansion.1 I This could happen if enterprises fail to service their debts with SCBs and CCBs and banks decide to finance this short-fall by running arrears on NBB deposits. Therefore, NBB will have to finance the interest shortfall by expanding reserve money.

Table 12: Estimates of sank Non-Performing Loans in End-1990

||.......<br>............<br>. ......<br>Totel<br>Specialtized<br>Caswmercial<br>Comuercial<br>Banks1/<br>Banks<br>....................................|.<br>b....<br>Common<br>Conmercial<br>Banks|
|---|---|---|
|In billion<br>Levs<br>As Share of GOP<br>|21.3<br>13.3<br>34.4X<br>21.4X<br>|8.0<br> <br>12.9X|
|As Share of Banks' Extended<br>...................................|<br>53.9X<br>64.6<br>........................................................|2<br>42.3X|

.. ... **.** ....

Source: National Bank of Bulgaria, Monetary Survey Notes: see text for assumptions used.

The second source of nonperforming assets is bank assumed foreign exchange risk (BFTB's foreign exchange losses). Bank foreign exchange risk is defined as the sum of total net foreign exchange liabilities and total foreign exchange deposits less enterprises' foreign exchange credits."' Defined in this way, bank foreign exchange risk indicates the cost to banks of having assumed the enterprises' foreign exchange risk. Estimates indicate that most of this risk is accounted for by BFTB and thus, its main effect has been on BFTB's net worth and cash-flow. In general, the greater the foreign exchange risk, the lower the net worth and the greater the cashflow shortfall. Foreign exchange risk was equivalent to about 8 percent of GDP in 1986 and increased to 39 percent in 1990 (see Table 13); and measured as the ratio of total BFTB assets, it was 23 percent in 1986 and increased to 54 percent in 1990.

|Table 13: Estimate of Banks' Assumed Foreigin Exchange Risk<br>(PercentageofGDP)|
|---|
|........<br>...<br>.<br>.......<br>1986<br>1990|
|........................<br> <br>|
|Total Banks<br>8.0<br>38.9<br> <br> <br>|
|Of ihich: BFTB<br>8.0<br>38.5<br>.........................<br>.................................................................<br>....|

Source: National Bank of Bulgaria, Monetary Survey.

Notes: Ratios to GDP have been corrected as explained in Table 7. The 1990 estimate is for November. See text for the definition of foreign exchange risk.

The recent inflation rate hike starting in 1989 and the devaluation of the lev against international currencies in 1990, however, has introduced additional effects on lev-denominated nonperforming assets and foreign exchange risk. The fall in real interest rates as a result of the 11/ This is because NBB has no other sources to finance its carh-flow deficit. NBB cannot increase further reserve requirements on SSB (it is committed to reduce them) and foreign finance is limited.

1.I Net foreign exchange liabilities are the difference of foreign excharge liabilities ond foreign exchange assets, including General Govert mont foreign exchange credits as part of the foreign exchange **assets.**