rafmacalaba/datause-encoder-data huggingface dataset:
farms were surveyed.

Climate data come from two sources: temperature observations came from US Defense Department Satellites and the rainfall observations came from ground station data of the World Meteorological Organization. The satellite temperature measures proved superior to the weather station observations at least for rural areas of the world (Mendelsohn et al 2005). The satellites can observe the entire surface of the earth whereas many rural areas do not have a weather station nearby and so require interpolation. Unfortunately, the satellites cannot directly measure precipitation and so the weather station data is the best that can be done at the moment.

Soil data were obtained from the FAO digital soil map of the world CD ROM. The data was extrapolated to the district level using Geographical Information System. The data set reports 116 dominant soil types organized into 26 major groups. We extract texture and slope of the soils at the district level.

The analysis relies upon land values and farm characteristics as reported by the interviewed farmer. In many parts of Latin America, land has been reallocated by the government. Land use is also restricted in many cases. For example, farmers in Brazil face official limitations on land clearing. The analysis was not able to control for all of these imperfections in the land market. However, separate analyses comparing Ricardian regressions that use land values and net revenues for the dependent variable lead to very similar results, suggesting the land value data is consistent and unbiased.

# **4. Empirical Results**

The study identified three types of farms in the region: crop only, livestock only, and crops/livestock together. We further break down farms that grow crops by whether or 10
|**Appendix table 3: Results for numb**|**er of transfers**<br>|**& mortgages w**<br>**Mortgages**<br>|**ith CBD distance**<br>|**(CBDD) intera**<br>|**ction, 2000-20**<br>**Transfers**<br>|**19**<br>|
|---|---|---|---|---|---|---|
||**Dist. to CBD**|**Plot size**|**Dist*plot size**|**Dist. to CBD**|**Plot size**|**Dist*plot size**|
|**Panel A: Registered mortgages**|||||||
|Spor. reg. plots pre-2010 (_θ1_)|0.0167***|0.0178***|0.0167***|0.0096*|0.0272***|0.0233***|
||(0.0033)|(0.0017)|(0.0018)|(0.0055)|(0.0024)|(0.0025)|
|Spor. reg. plots post-2010 (_θ2_)|0.0145***|0.0299***|0.0283***|-0.0007|0.0419***|0.0376***|
||(0.0025)|(0.0011)|(0.0011)|(0.0019)|(0.0015)|(0.0015)|
|CBDD*Spor. reg. plots pre-2010|0.0001|-0.0328|-0.0019|-0.0010|-0.0798***|-0.0007|
||(0.0005)|(0.0209)|(0.0037)|(0.0008)|(0.0281)|(0.0050)|
|CBDD*Spor. reg. plots post-2010|0.0017***|-0.0320***|-0.0012|0.0027***|-0.0193|0.0054***|
||(0.0003)|(0.0108)|(0.0013)|(0.0009)|(0.0146)|(0.0017)|
|Syst. reg. plots post 2010 (_θ3_)|0.0004|-0.0000|0.0003|0.0017***|0.0059***|0.0044***|
||(0.0006)|(0.0006)|(0.0004)|(0.0004)|(0.0008)|(0.0005)|
|CBDD*Syst. reg. plots post 2010|0.0002**|0.0182***|0.0029***|0.0015***|-0.0278***|-0.0009|
||(0.0001)|(0.0069)|(0.0007)|(0.0001)|(0.0094)|(0.0009)|
|No. of obs(250*250 m blocks)|38,600|38,600|38,600|38,640|38,640|38,640|

_Note:_ Block fixed effects and constant included throughout. Standard errors in parentheses; * p<0.10, ** p<0.05, *** p<0.010 40
<!-- Start of picture text --> Pollcy Reearch<br><!-- End of picture text -->

**Trade and Finance** WPS 840

This papera product of the Trade and Financc Division, Technical Department, Furop. NMiddle E'ast, and North Africa Regional Office (EMENA) -- is part of EMENA's regional study on extemal debt and inflation. Copies of the paper are available frce from the World Bank, 1818 lI Street N'W, Washington 1)(' 20433. Please contact Lanha Ly. room H9-071, extension 37352 (68 pages). January 19S2.

Thome and Dastgheib analyzed the experience of six countries in the EMENA region (Algeria, Morocco, Pakistan, Portugal, Turkey, and Yugoslavia), and compared it with the experiences ol Latin American countries.

They conclude that some countries successfully absorbed the extemal shock of the 1980s by: Minimizing the ef'fects of the external shock by cormbining extemal and domestic debt strategies.

# • Adjusting their fiscal deficits,

* Fostering growth by stimulating export growth and developing domestic financial markets.

No single Country fully impiclmented ihis strategy; those most successful in doing so wkerc Morocco, Porutual. and Turkev.

Their experience contraslN with thalt of >onme Latin American countries that experienced a similar cxtemrnl shock but lailcd to uneicrake fiscal adjustment anid financed i most of their deficit through money finance --- thus c\perjenc ing high inflation levels and ovcrburdenine their private sector. In some respeect', Yuicsf0avia had the same experience.

* Experiencing a positive external shock.

ThcPolicyResearch Working Paper Sericsdi,eniinaacs tee findings,ft **otk** under xi) 0% **Bank** A;1 .... '. is to gct thcse findings oul quickly. c%en if **pTesentatains** are Ics" tdiak fu,l pol' heid Th.e **mI.** Turns rj':. .. conclusions in these papers do not neee"u is repr.scnt offl i.al Bank poilm Produced by Lhe l'o1lcN Rcsearcih DiLs. emindation Cent. r
transport or logistics infrastructure which facilitate exports as well as for the presence of special economic zones and distance to ports. These fixed effects address a concern that could be raised by Figures 3-5, that �� is picking up the effect of not internet penetration but of other provincelevel time-varying developments. The interaction of ���_�������� with industry internet intensity in Eq. (1) further ensures that we identify the impact of provincial internet penetration only through firms whose businesses rely more intensively on the internet as opposed to other infrastructure services.

A potential reverse causality concern could still arise with Eq. (1): industries with higher internet intensity could be seeing faster export growth in provinces where internet penetration is growing faster for reasons unrelated to improvements in internet availability. It is difficult to construct a compelling alternative economic argument, other than the one suggested in this paper, for this specific correlation. It would require, for example, the existence of unobserved export promotion industrial policies, exclusively targeting industries intensive in the use of the internet in the provinces where internet penetration is also growing faster. One way of addressing this concern, to estimate a variant of Eq. (1) including industry-province time trends, proved to be computationally impossible for the Chinese firm-level data set. As an alternative, we estimated a variant of Eq. (1) that accounts for time variation at the province-industry level by controlling for the following additional variables: province-industry growth in output and province-industry growth in exports.<sup>21</sup> We address any remaining concerns about the endogeneity of internet penetration by employing an instrumental variable strategy. We use as an instrument for internet penetration in a province, an indicator of the state of telecommunications services in neighboring provinces: the average price of mobile telecommunication services.<sup>22</sup> The dependent variables in our specifications vary at the firm-year level but our regressor of interest varies at a more aggregated province-industry-year level, therefore, inference will be based on Huber-White standard errors robust to heteroskedasticity clustered at the provinceindustry-year level, following Moulton (1990).<sup>23</sup> To sum up, the main coefficient of interest, ��, is estimated based on the within-firm variation in performance over time due to increases in internet penetration in the firm’s province for firms that operate in industries using the internet

> 21 Additionally, a selection problem could arise with Eq. (1) if a disproportionally large number of exporting firms in industries that use the internet more intensively relocated (for incumbent firms) or entered (for new firms) into provinces with higher internet penetration during our sample period, which our data show was not the case.

> 22 We acknowledge that mobile prices reflect also the level of competition in the telecommunication market. 23 Results are robust to the clustering of standard errors at less stringent levels of disaggregation.

14
# Table 5: Factors of production

||Proportion of<br>plots with<br>phyto-sanitary<br>|Proportion of<br>plots with org<br>fert.<br>|Proportion of<br>plots with<br>chem. fert.|<br>Proportion of<br>rubber plots<br>with phyto-<br>sanitary|<br>Proportion of<br>rubber plots<br>with org fert.|Proportion of<br>rubber plots<br>with chem.<br>fert.|Proportion of<br>cocoa plots<br>with phyto-<br>sanitary|<br>Proportion of<br>cocoa plots<br>with org fert.|<br> <br>Proportion of<br>cocoa plots<br>with chem.<br>fert.|
|---|---|---|---|---|---|---|---|---|---|
|Seedling Subsidy|-0.08***|-0.02|-0.01|-0.03|-0.02|-0.01|-0.02|-0.02|0.02|
||(0.03)|(0.01)|(0.02)|(0.03)|(0.01)|(0.02)|(0.03)|(0.01)|(0.02)|
|Couples' Training|0.14***|0.03|0.05**|0.12***|0.02|0.04**|0.05|0.02|0.02|
||(0.04)|(0.02)|(0.02)|(0.04)|(0.02)|(0.02)|(0.03)|(0.01)|(0.02)|
|Seedling Subsidy + Couples'<br>Training (beta)|0.06*|0.01|0.04*|0.09**|0.01|0.03|0.02|-0.00|0.04*|
|H0: Seedling Subsidy +<br>Couples' Training = 0 (p-val)|0.07|0.52|0.07|0.02|0.73|0.20|0.47|1.00|0.07|
|Observations|1435|1435|1435|1435|1435|1435|1435|1435|1435|
|Mean for control group|0.37|0.08|0.11|0.31|0.07|0.10|0.26|0.06|0.06|
|Mean for individual training|0.30|0.07|0.10|0.28|0.06|0.10|0.24|0.05|0.08|

Robust and clustered standard errors in parentheses.

* p<0.1 ** p<0.05 *** p<0.01

Note: Baseline control variables in all regressions: Age of household head, Household size, Number of adults, Number of children, Dependency ratio, Married planter, Polygamous relationship, Matrilineal ethnicity, Planter has IGA, Asset index, Credit constrained, Share of plots owned, Average distance to plots (minutes), Number of plots, Average plot size.

1
Table 1: Severity of censorship by event characteristics (Ordered probit, country and year fixed effects, all censors)

||(1)<br>Severity of<br>censorship|(2)<br>Severity of<br>censorship|(3)<br>Severity of<br>censorship|(4)<br>Severity of<br>censorship|(5)<br>Severity of<br>censorship|(6)<br>Severity of<br>censorship|
|---|---|---|---|---|---|---|
|Foreign media|-0.299***<br>(0.0593)|||||-0.379***<br>(0.0764)|
|Media reach||-0.180***<br>(0.0240)||||-0.162***<br>(0.0283)|
|Publication frequency<br>(for print media only)|||-0.102<br>(0.0694)||||
|Media location<br>(Abroad)||||-0.442***<br>(0.114)|||
|Gender|||||0.380***<br>(0.0902)||
|Women representation|||||0.00102<br>(0.00991)||
|Economic constraints|0.00904<br>(0.0147)|0.00182<br>(0.0151)|-0.00513<br>(0.0249)|0.0414<br>(0.0294)|0.0377<br>(0.0242)|0.00235<br>(0.0169)|
|Legal constraints|-0.00469|0.0121|-0.0217|0.0121|-0.00564|0.00784|
||(0.0129)|(0.0134)|(0.0263)|(0.0236)|(0.0205)|(0.0150)|
|GDP pc (log)|0.113|0.294*|0.724**|0.124|0.236|0.220|
||(0.121)|(0.134)|(0.270)|(0.261)|(0.183)|(0.151)|
|Population (log)|-0.209|-0.711|0.301|-0.0774|1.069|-1.091|
||(0.665)|(0.685)|(1.363)|(1.550)|(0.990)|(0.804)|
|Political competition|-0.00102|-0.00121|-0.00324|-0.00301|-0.00184|-0.000702|
||(0.00123)|(0.00128)|(0.00274)|(0.00235)|(0.00183)|(0.00147)|
|Gov. fractionalization|0.289~<br>(0.159)|0.105<br>(0.164)|-0.104<br>(0.322)|0.0926<br>(0.287)|0.0519<br>(0.244)|0.250<br>(0.186)|
|Incumbent tenure|0.00688|0.00593|0.0140|-0.00297|0.00390|0.0107~|
||(0.00533)|(0.00517)|(0.0101)|(0.0119)|(0.00600)|(0.00591)|
|Corruption|-0.639|-0.300|-1.464|0.319|-0.384|-0.192|
||(0.486)|(0.469)|(1.211)|(0.993)|(0.818)|(0.549)|
|Democracy|-0.0845*|-0.0446|0.0177|-0.0136|-0.00969|-0.0409|
||(0.0398)|(0.0410)|(0.0760)|(0.0865)|(0.0648)|(0.0456)|
|Ethnic fractionalization|-31.60|-72.62|69.07|-16.25|98.39|-112.9|
||(68.56)|(70.57)|(140.1)|(159.8)|(104.2)|(82.65)|
|Country FE|√|√|√|√|√|√|
|<br>Year FE|√|√|√|√|√|√|
|_N_|2988|2772|877|993|1705|2206|
|_Countries_|134|134|116|113|130|132|
|_Years_|12|12|12|12|12|12|

Standard errors in parentheses ~ p<0.10, * p<0.05, ** p<0.01, *** p<0.001 32
import value sourced in the EU, and 1.3 percent from other sources. IHC firms cleared approximately half of their import value under the program for both source regions.

**<u>Table 2.  Shares of Total 2013 Import Value and Import Shares</u>**

|**Panel A. By Sector **|||||
|---|---|---|---|---|
||Share of total<br>import value|IHC firms’<br>share of sector<br>import value|Share of total import<br>value cleared under<br>IHC|IHC share of<br>IHC firm<br>imports|
|Animal and vegetable products<br>and foodstuffs|0.101|0.039|0.032|0.812|
|Chemicals, plastics and rubbers|0.210|0.089|0.033|0.367|
|Raw hides, skins, leathers, furs,<br>|0.058|0.054|0.032|0.601|
|textiles and footwear|||||
|Machines and electrical|0.164|0.034|0.012|0.340|
|Metals|0.093|0.091|0.057|0.626|
|Minerals|0.208|0.002|0.000|0.182|
|Misc. and services|0.039|0.027|0.010|0.384|
|Stone and glass|0.017|0.053|0.018|0.334|
|Transportation|0.063|0.000|0.000|0.142|
|Wood andpaperproducts|0.047|0.194|0.136|0.702|
|Total|1|0.051|0.026|0.512|
|**Panel B. By Broad Source**|||||
||Share of total<br>import value|IHC firms’ share<br>of source region<br>import value|Share of total<br>import value<br>cleared under IHC|IHC share of<br>IHC firm<br>imports|
|European Union|0.518|0.076|0.039|0.514|
|Other|0.482|0.025|0.013|0.506|
|Total|1|0.051|0.026|0.512|

Source: Authors’ calculations based on Serbian customs data.

We now turn to an analysis of 2010 data in order to show that even before the IHC program was undertaken, IHC firms and non-IHC firms differed in the distribution of customs clearance times, in their inspection rates, and in the scale of their imports. Table 3 shows summary statistics on customs clearance times in 2010 for firms that do and do not adopt the IHC program by the end of 2013. The striking pattern that emerges is that the distribution of clearance times for firms that adopt the IHC program by 2013 is well below that of firms that do not adopt, at all locations in the distribution. In particular the firms that will later adopt the program exhibit substantially faster clearance times at the 90<sup>th</sup> percentile - 3.6 hours versus almost 3 days (70.4 hours) - which indicates that they have far fewer outlier import declarations that take an abnormally long time to clear customs than do other firms.

26
|MSCE|6.51|24.67|1.60|12.56|0.000|
|---|---|---|---|---|---|
|Non-Univ Diploma and above|1.90|13.65|0.15|3.82|0.000|
|Highest level of formal education acquired<br>in the household||||||
|None|57.11|49.50|74.05|43.85|0.000|
|PSLC|16.41|37.04|13.17|33.82|0.001|
|JCE|14.16|34.87|8.94|28.54|0.000|
|MSCE|10.02|30.03|3.69|18.86|0.000|
|Non-Univ Diploma and above|2.30|15.00|0.15|3.82|0.000|
|Size of the household|4.68|2.35|6.12|2.27|0.000|
|Number of infant (<5yo) in HH|0.73|0.80|1.03|0.89|0.000|
|Number of children (5-14yo) in the|1.32|1.33|2.12|1.40|0.000|
|household||||||
|Number of prime adults (15-60yo) in HH|2.34|1.37|2.65|1.41|0.000|
|Number of elderly (60yo+) in HH|0.28|0.56|0.31|0.59|0.141|
|Household access to loan (0/1)|18.42|38.77|13.61|34.29|0.003|
|Normalized wealth index|0.07|0.10|0.02|0.03|0.000|
|Normalized TLU index|0.01|0.04|0.00|0.01|0.000|
|Total land area owned by HH in Acres|3.88|59.96|2.46|20.45|0.448|
|Household was affected negatively by|86.41|34.27|86.30|34.40|0.640|
|some income Shock During the last 12<br>months (0/1)||||||
|Household has a migrant network (0/1)|36.30|48.09|34.55|47.56|0.737|
|Rain - EA level CoV of Dec-Jan rainfall|0.25|0.04|0.26|0.04|0.706|
|from 1983/84 - 2012/13||||||
|Average 12-month total rainfall (mm)|846.63|83.60|856.55|97.75|0.158|
|Annual Mean Temperature (∞C * 10)|215.16|18.38|218.25|20.17|0.030|
|HH Distance in (KMs) to Nearest Road|9.05|9.26|11.19|10.65|0.000|
|<br>HH Distance in (KMs) to Nearest|37.05|17.71|38.70|18.31|0.121|
|Population Center with +20,000||||||
|<br>Value of daily_ganyu_wage salary in the EA|596.44|628.35|515.51|545.18|0.042|
|(MKW)||||||
|<br>Price of maize grains in the EA (MKW)|204.00|113.01|189.44|111.50|0.006|
|<br>Price of fertilizer in the EA (MKW)|64.59|39.75|58.36|38.34|0.000|
|<br>EA neighbor’s wage employment|16.49|15.74|12.79|13.04|0.000|
|participation||||||
|<br>EA neighbor’s self-employment|22.60|15.62|19.87|14.49|0.000|

45
# **6.2 On the role of uncertainty and credit constraints**

There are at least two prominent factors that could hinder the propagation of job gains during the Recovery. The first is uncertainty. Uncertainty can be rooted in the overall economy (Bloom, 2009) or in economic policies (Baker, Bloom and Davis, 2016). Uncertainty has been touted as one of the main drivers for the sharp downturn of the Great Recession (see Shoag and Veuger, 2016). Uncertainty could affect both consumers and producers’ behavior. Uncertainty about the economy could cause newly-employed tradable workers to refrain from spending, effectively shutting down the demand-driven channel of job gain spillovers. Even if the newly employed workers do spend, uncertainty about future demand could make worried non-tradable firm owners hesitate to make new hires. If this is the case, job gains in tradable sectors do not necessarily lead to job gains in other sectors. The second reason is a credit crunch. If credit is tight, non-tradable firm owners would face difficulty expanding, even if they are willing to hire and make investment.

We test for the effects of uncertainty using the Global Economic Policy Uncertainty (GEPU) index constructed by Davis (2016), building upon the work in Baker, Bloom and Davis (2016). The GEPU index is a GDP-weighted average of national Economic Policy Uncertainty (EPU) indices for 18 countries<sup>6</sup> . The 18 countries that entered the Global EPU index accounts for two-thirds of global output on a PPP-adjusted basis. Each national EPU index reflects the count of articles that contains a triple of terms about the economy (E), policy (P), and uncertainty (U). In other words, each country EPU index is proportional to the share of own-country newspaper articles that discuss economic policy uncertainty

> 6 <mark>Australia, Brazil, Canada, Chile, China, France, Germany, India, Ireland, Italy, Japan, the Netherlands, Russia, South Korea, Spain, Sweden, the United Kingdom, and the United States.</mark>

26
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# **1. Introduction**

A social accounting matrix (SAM) is an economywide consistent representation of the payments in an economy, linking production, primary factors, and institutions (typically, households, government, and the rest of the world). Economywide databases are indispensable for the analysis of policies and shocks that matter enough to generate repercussions throughout an economy. Consistency has the virtue of assuring that an analysis is not based on contradictory pieces of information. Most of the time, a SAM refers to the economy of a country during one year. SAMs may be used to describe the structure of an economy and as data inputs to economic models, most importantly computable general equilibrium (CGE) models.

However, despite the value of SAMs for description and analysis, the methods for constructing them are not well‐known among development practitioners. Via a detailed documentation of a method for constructing macro SAMs (and thus exposing it to criticism and paving the way for improvements), this paper tries to mitigate some of the obstacles that stand in the way of making better and wider use of SAMs in development policy analysis. More specifically, its main purposes are

- to provide a brief introduction to the basic features of SAMs;

- to provide a method and related computer program for constructing macro SAMs from cross‐country data sets; and

- to demonstrate the information about a country’s economy that may be extracted from a macro SAM.

For illustration, throughout the presentation we use data for a representative (or archetype) low‐income country. We also apply the method to cross‐country databases available in the summer of 2017, constructing macro SAMs of three types (with different degrees of complexity) for the most recent year possible (for most countries 2015). For the most complex SAM type, we construct SAMs for 133 countries, distributed across all income levels: 17 low‐income; 70 middle‐income; and 46 high‐income; 11 of these countries are currently in a fragile situation according to the World Bank classification in the spring of 2017.

‐2‐
**Table** **<u>9:</u>** Investment limits <u>per instrument for selected emerging markets, August 2007 (percentage of fund assets)</u>

|**Instrument**|Argentina<sup>a</sup>|Bolivia<sup>a</sup>|Colombia|CostaRica<sup>a</sup>|ElSalvador<sup>a</sup>|Uruguay<sup>a</sup>|Hungary|Poland<sup>d</sup>|
|---|---|---|---|---|---|---|---|---|
|**Fixed-Income**|||||||||
|_Public Sector_|||||80||||
|State issued|50|No limit|No limit|75||60|No limit|No limit|
|Central Bank||||50|30||||
|Central Government|||||50||||
|Decentralized institutions|30|10|20||20|30||40|
|_Financial Institutions_||60|72||||||
|Deposits and bonds|30|50|32|Bonds: 10|40|30|No limit||
|Mortgage bonds|40|50|40||60|30|25|40|
|_Corporate Sector_|||||||||
|Bonds|50|45|30|70|40|45|10|40|
|**Variable-Income**|||||||||
|Stocks|50|40|30|10|20|45<sup>b</sup>|Listed: no limit<br>Non-listed: 10|47.5|
|Investment funds|20|15|10|10|20||50|25|
|**Foreign Investment**||12|20|||15|c|5|
|Fixed-income|||||||||
|State issued/Decentralized institutions|10||||||10||
|Corporate sector|10||||||10||
|Variable-income||||10|||||
|**Hedging**|10||||||5||
|**Others**|30|5|||10||||

Notes:<sup>a</sup> Minimum investment limits are also imposed.<sup>b</sup> Refers to (a) securities issued by public and private companies and shares in domestic investment funds (25 percent) and a maximum in fixed and variable income securities issued by private companies (20 percent).<sup>c</sup> Within investments made abroad, the ratio of investments made in non-OECD countries shall not exceed 20 percent.<sup>d</sup> Refers to open pension funds (OPF). Sources: SAFJP, SPVS, _Ministerio de Hacienda y Crédito Público de Colombia_ , SUPEN, Consar, _Banco Central del Uruguay_ and OECD (2006a).

- 26 -