rafmacalaba/datause-encoder-data huggingface dataset:
0.9 percent of GDP (cash basis) was achieved for 2002. With domestic demand depressed, the external current account surplus soared to nearly 10 percent of GDP (including interest on nonperforming debt, but not interest on arrears).<sup>36</sup> In broad terms, macroeconomic policy was “conservative” in terms of regaining control of the money supply, bringing down inflation and increasing primary surpluses.<sup>37</sup> This process continued through 2003. External conditions also began to improve, in 2003, as the terms of trade (See Figure 2 and Table 2) began to improve sharply. In addition, the removal of the burden of external debt service payments also assisted the recovery. Economic growth reached nearly 9 percent in 2003. In May of that year, President Nestor Kirchner entered office under these favorable conditions. Consumer confidence also soared, as displayed in Figure 3.

**Table 2:  Basic Macroeconomic Indicators, 1980-2014, %** (period averages)

||1980-1991|1992-2001|2002-2014|
|---|---|---|---|
|Inflation|744.3|3.6|12.5*|
|GDP growth|0.0|2.8|4.4**|
|Terms of Trade|83.9|96.4|120.0|

*Disputed data in recent years. **5.7 percent, excluding 2002.

Source: WDI and WEO databases.

<!-- Start of picture text --> Figure 2: GDP per capita and Net Barter Terms of Trade, 1990 to 2013<br>210 140<br>200<br>130<br>190<br>180 Real Per Capita GDP (1990=100; LHS)<br>120<br>170<br>Net Barter Terms of Trade (2000=100; RHS)<br>160 Global Financial Crisis<br>110<br>150<br>140 100<br>130<br>120 90<br>110<br>100 Convertibility Crisis 80<br>90<br>70<br>80<br>70<br>60<br>60<br>50 50<br>Source: World Development Indicators, author’s calculations<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> 36 IMF December 2003 Staff Report on Argentina.

37 Buera et al, 2011, highlight this period as one of two brief periods where fiscal management and growth performance rose above the slower trend post-1974.

17
Thaler, R. (1994). Psychology and savings policies. _American Economic Review_ , 84, (2), 186-92 Mullainathan, S. and Thaler, R.H. (2000). Behavioral Economics. _Working Paper 7948_ , National Bureau of Economic Research. Cambridge, USA.

Tsur, S. (2016). Liquidity constraints and human capital: The impact of child allowances on Arab families in Israel. Bank of Israel, Israel.

United Nations Children’s Fund. (2015). Unicef Annual Report Madagascar. New York, United States.

<mark>Vargas-Barón, E. (2009). Going to scale: Early childhood development in Latin America.</mark> _<mark>The RISE Institute, Washington</mark>_ <mark>.</mark> Wampfler, B., Bouquet, B., & Ralison, E. (2014). Does juggling mean struggling? Insights into the financial practices of rural households in Madagascar. In Microfinance, Debt, and Over-indebtedness – Juggling with Money. Edited Guérin, I., Morvant-Roux, S., & Villarreal, M. Routledge Studies in Development Economics.

Wegner, D. M. (1994). Ironic processes of mental control. _Psychological Review, 101_ (1), 34-52. <u>http://dx.doi.org/10.1037/0033-295X.101.1.34</u> World Bank. (2018a). “World Development Report 2018: Learning to Realize Education’s Promise”. World Bank, Washington, DC.

World Bank. (2018b). The Human Capital Project. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/30498 License: CC BY 3.0 IGO World Bank. (2018c). Economic Outlook for Madagascar. World Bank, Washington DC. <u>https://www.worldbank.org/en/country/madagascar/publication/madagascar-economic-update-fosteringfinancial-inclusion</u> World Bank. (2017). “Early Childhood Development Topic Page” World Bank, Washington, DC. Retrieved from: https://www.worldbank.org/en/topic/earlychildhooddevelopment <mark>World Bank Group. (2015). World Development Report: Mind, Society, and Behavior. Washington, DC: World Bank.</mark> World Data Lab. Retrieved from: https://worldpoverty.io/ World Food Programme. (2018). WFP Madagascar Country Brief, May 2018. Rome, Italy. - <u>https://reliefweb.int/sites/reliefweb.int/files/resources/Madagascar%20Country%20Brief%20 %20May%202018.pdf</u> World Health Organization. (2018). Nurturing care for early childhood development: A framework for helping children survive and thrive to transform health and human potential. Geneva, Switzerland. https://apps.who.int/iris/bitstream/handle/10665/272603/9789241514064-eng.pdf 20
Policy Research Working Paper 10078

# **Abstract**

The paper studies the dynamics of agricultural land use at the global scale as measured from space using satellite imagery between 2003 and 2018. It shows large global movements in and out of cropland and correlates these movements with biophysical, economic, and institutional variables. The empirical identification of these effects relies on a two-stage approach that disentangles the effect of local geography from national-level characteristics. The paper finds that weak land governance, inequality, and pressure on land resources contribute to land degradation but are less able to explain movements into cropland which could more likely reflect national policies.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa; the Development Research Group, Development Economics; and the Urban, Disaster Risk Management, Resilience and Land 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/prwp. The authors may be contacted at hpark@worldbank.org and hselod@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
# p(Ỹ|Y) = ∫p(Ỹ|θ) p(θ|Y)dθ.

# Estimating the impact of the pandemic on activity

The evolution of the pandemic can dramatically influence the economy by triggering voluntary social distancing on the part of risk-averse individuals and the imposition of pandemic-control policies by governments. Maloney and Taskin (2020) find that the dynamics of high-frequency mobility indicators are more driven by “voluntary” individual response to increased COVID-19 case incidence, proxying for awareness or fear of the virus, rather than lockdowns. In contrast, Coibion (2020) and IMF (2020) show that consumer expenditures are more affected by strict pandemic-control policies such as lockdowns than voluntary shifts in behavior.<sup>7</sup> Following Coibion (2020) and IMF (2020), the impact of infection and lockdown measures on consumption is estimated based on pooled panel regression as follows: where a country i = 1,. . .21, at a time t = 2020Q1-Q3. FEi,t is the forecast revision of real consumption using the difference between pre-pandemic forecasts and the latest forecast by Oxford Economics; stringencyi,t is <mark>Oxford Covid-19 government response stringency index</mark> ; and covid19i,t is covid-19 cases per capita estimated by Imperial College London and is assumed to proxy voluntary social distancing measures.<sup>8</sup> Estimated daily new case counts are used instead of confirmed case counts to overcome severe distortions caused by limited testing early on in the pandemic. In the United States, for example, large-scale seroprevalence studies as of July 2020 were consistent with a much higher rate of community transmission than suggested by cumulative confirmed case counts (Anand et al. 2020; Demonbruen et al. 2020).

# Table 1. Panel regressions

> 7 The impacts of voluntary shifts in behavior and government-imposed lockdowns on economic activity are likely to vary significantly over time, depending on the severity and duration of the pandemic, as well as household adherence to pandemic-control policies.

> 8 The estimated number of new daily cases from the Imperial College of London model is accessed via the Our World in Data (OWID) COVID-19 database.

8
Table 4.1 – Regression Results: Pass-through Price Elasticities. Depentent variable – Log of domestic price.

|**_Agricultural Produc_**|**_ts_**||||||||
|---|---|---|---|---|---|---|---|---|
|Variable|Cereals|Dairy|Fruit|Pulses|Meat|Oils and Fats|Sugar|Vegetables|
|Constant|-4.761***|6.999***|-1.776*|22.709***|11.326***|5.565***|1.417***|-0.764|
||(1.570)<br>|(1.902)<br>|(0.914)<br>|(2.319)<br>|(1.187)<br>|(0.479)<br>|(0.516)<br>|(1.663)<br>|
|Distance|-0.005|-0.046***|-0.027**|0.015|0.028***|-0.004|-0.021***|-0.012|
||(0.017)|(0.017)|(0.012)|(0.010)|(0.009)|(0.006)|(0.008)|(0.012)|
|International Price|0.168***|2.123***|0.047|4.200***|1.733***|0.770***|-0.061*|0.026|
||(0.059)|(0.346)|(0.131)|(0.461)|(0.103)|(0.052)|(0.036)|(0.257)|
|Local supply|0.010|-0.012|-0.013|-0.045***|-0.015|0.006|0.021***|0.042**|
||(0.015)|(0.024)|(0.011)|(0.010)|(0.009)|(0.004)|(0.005)|(0.017)|
|Regional CPI|3.695***|2.163***|1.919***|-0.084|1.039***|0.391**|0.150|1.737***|
||(0.671)|(0.633)|(0.332)|(0.344)|(0.301)|(0.196)|(0.215)|(0.430)|
|Tariff|0.326*|0.317|0.227***|-0.979***|-3.175***|0.223***|0.266***|0.144***|
||(0.179)|(1.020)|(0.074)|(0.142)|(0.580)|(0.056)|(0.064)|(0.046)|
|Tariff*Distance|-0.171***|0.161|0.035*|0.034|-0.027*|0.063***|0.001|0.137|
||(0.045)|(0.132)|(0.019)|(0.033)|(0.016)|(0.015)|(0.016)|(0.134)|
|Observation|192|192|192|192|192|192|192|192|
|R squared|0.452|0.318|0.439|0.360|0.811|0.757|0.241|0.434|
|**_Manufacturing Prod_**|**_ucts_**||||||||
||Food|Household|Other|Textiles and|||||
|Variable|Products|Appliances|Manufact.|Apparel|||||
|Constant|2.103*|-1.772|-4.296***|-4.451***|||||
||(1.203)|(1.081)|(0.848)|(0.583)|||||
|Distance|0.035**|0.021|-0.004|0.026***|||||
||(0.015)|(0.016)|(0.008)|(0.010)|||||
||-0151|0011|-0015|0020***|||||
|International Price|.|.|.|.|||||
||(0.157)|(0.033)|(0.197)|(0.005)|||||
|Local sul|-0.007|-0.009|-0.022|-0.074***|||||
|ppy|(0.013)|(0.008)|(0.016)|(0.013)|||||
|Regional CPI|0.270|3.075***|3.577***|3.824***|||||
||(0.487)|(0.494)|(0.247)|(0.267)|||||
|Tariff|1.019***|0.769***|0.671***|0.546***|||||
||(0.104)|(0.133)|(0.065)|(0.058)|||||
|*|-0.123***|-0.109**|-0.116***|-0.097***|||||
|TariffDistance|(0.030)|(0.044)|(0.020)|(0.014)|||||
|Observation|192|192|192|192|||||
|R squared|0.404|0.774|0.769|0.418|||||

Note: All variables, except distance, are in log. White corrected standard errors are shown in brackets. Significance level of 1%, 5% and 10% are indicated by ***, ** and * respectively.

49
participation, all principals reported positive changes: 40 percent reported increased parental concern about their children’s academic performance; another 30 percent reported increased parental interest in the school overall; and a final 30 percent reported increased interaction between parents and teachers. Indeed, 95 percent of principals reported that AGE increased parental interest in the work of teachers. They reported that parents follow the work of the teachers closely and complain if they do not like what is happening. More than 80 percent of the principals reported that parents complained if teachers were absent. Principals also reported that AGE changed parental attitude towards their children’s performance in school, with 53 percent reporting increased parental help for kids studying and monitoring that the homework was done. Another 42 percent of the principals reported that parents increased going to school to talk to teachers and followed up on their kids learning.

Both parents and principal reported that AGE increased parental participation in school, made parents more demanding in terms of attention to their children’s learning needs and teacher effort, and increased parental involvement with homework. In the following section we test whether AGE improved intermediate schooling outcomes and provide an estimate of the size of the impact.

# **4. Did AGE Reduce Grade Repetition, Grade Failure, and Drop out?**

We estimate the effects of AGE on three educational outcomes: the probability that the student fails an exam, repeats a grade or drops out of school.<sup>9</sup> We use data from a variety of sources including administrative data from the Compensatory Program

> 9 There is very little overlap between the AGE schools and the nation-wide representative sample of schools with standardized test score data. As a consequence, there are too few schools with which to estimate robust effects of AGE on test scores.

9
6 The idea central to Ball and Mankiw's 1995 model is the firms' response to supply (cost) shocks. Authors assume that responding to shocks by changing prices is costly because of various menu costs that have to be incurred. In such a setting firms do not react if shocks are relatively small and respond only to shocks large enough to rnake paying menu costs worthwhile. With the assumption that the average relative shock is zero, Ball and Mankiw consider different distributions of shocks and look at the consequences that these differences have after the imposition of menu costs. In a symmetric distribution, the range of inaction arising from menu costs covers the same number of positive and negative shocks and the net effect on the price level is zero. However, when a distribution is skewed 5 to the right the upper tail is larger than the lower tail and menu costs imply that firms react to more positive shocks than they do to negative. Finally, if the distribution is skewed to the left the situation is reversed: the mass in the lower tail of the distribution is bigger and more firms experience downward shocks to their prices which results in the fall of the aggregate price level.

The basic implication of this reasoning is that in periods during which the distribution of shocks is skewed to the right, aggregate price level may rise as menu costs imply more price increases than price decreases. The authors formalize these ideas in a one-period theoretical model. They also observe that the analysis refers to the relationship of the distribution of **_unobserved_** real sectoral shocks and the rate of inflation. Recognizing that fact, they carry out a numerical analysis that proves that the relationship between the first, second and third moment of the distribution of unobserved shocks carries over to actual price changes under reasonable assumptions. In other words, one can use relative price data as a proxy for unobserved shocks since the two phenomena move monotonically closely together.

While Ball and Mankiw's 1995 paper offers sound justification for including skewness of the distribution of relative price changes as an explanatory variable, their earlier paper "Asymmetric Price Adjustment and Economic Fluctuations' (1994) explains the mechanism 5Positive skewness arises when there are few unusually large positive shocks and many small negative ones
of wages. However, the impact of wages on land allocated to forests is indeterminate. Thus, the discussion here serves to highlight the fact that there are various channels through which migration and remittances may affect forests but some of their individual effects as well as their joint impact on forests are not clear theoretically.

# **4 Data**

We combine satellite data for land use and land cover change together with two rounds of nationally representative household surveys to create a unique village-level panel dataset spanning 1995-2004. In this section we describe these data in detail.

## **4.1 Satellite Data**

We use satellite imagery from the Multispectral Scanner (MSS), the Thematic Mapper (TM), and the Enhanced Thematic Mapper (ETM+) for various years to derive a measure of land use and land cover change. Ideally, we would have liked the land use data to coincide perfectly with our base years of interest, particularly 1995 and 2003. However, this was not possible due to unavailability of the satellite data for particular years and due to excessive cloud cover. Additionally, given the particular vegetation phonology in Nepal, most of the vegetation contains a minimum amount of leaf moisture during the months of October and February rendering scenes captured during these months best suited for measurement and analysis of land cover. This put a further restriction on the images we could use for our analysis. In the end, we were left with MSS images for 1975 and 1976, TM for 1991, 1992, and 1996 and ETM+ for 2000, and 2001. In this paper, we obtain land use data for the years 1975, 1995 and 2003 using TM and ETM+ imageries respectively.

Shadows in the mountainous areas especially above 2,400m elevation, and mostly those with aspects facing east posed a significant challenge. We tried correcting for these shadows using ATCOR 3 in Erdas Imagine 9.3, but did not have much success. Additionally, there were cloud covers in MSS (1975-1976) images and TM (1991, 1992, 1996) images. All shaded and shadow areas were manually digitized from the panchromatic eighth bands of 2000 and 2001 ETM+ images and the resultant polygons were used to represent the shaded and cloud cover areal extents. Generally, these shaded areas occurred in the higher elevation ranges in extremely hilly and mountainous terrain. Further, we overlaid the polygons digitized from the panchromatic image of 2000 and 2001 on all other images under the assumption that if mature forest was present in 2000-2001 panchromatic images, forests were present in all the earlier images because Nepal lies between 26-30 N latitudes and vegetation can maintain maturity between 20-25 years.

All images are classified individually because images of different dates could have specific spectral properties that complicate analytical comparisons. Theoretically, calibration should standardize all 17
85 fuel, preparing family food, and caring for children. Innovations developed to relieve these female tasks can also allow women to transfer some of their production from the home to the market, as in the case of rice milling in Indonesia (Timmer, 1973) or the production of boer in Africa (Haggblade, 1987). Reductions in consuuer prices should distribute widely the benefits from such new technology in the long run. If this process is monitored more frequently, policymakers will be alerted to **the** home and price mnvironm&nt in which technical change will permit women to capture more of the resulting economic opportunities. Technical change will undoubtedly continue to transfer production from the family to market-oriented firms. Instigation and guidance of this complex process will require empirical evaluation studies that distinguish between the repercussions for men, women, and children.

Agricultural **Exteusion** and Research Studies of the effectiveness of agricultural extension programs do not provide a stron3g foundation for policy although $5 billion a year is now spent on it, according to recent world estimates (Birkhaeuser et al. , -1988). Most studies are from India and the United States, and do not necessarily agree on why some programs are more effective than others. To determine the environment in which extension is either more or less cost effective requires more comparable studies in a broader range of countries, with different management structures, staff training, technological infrastructure, price policies and, most of all, the extent of the knowledge to be taught that is profitable for farmers to use.

A number of studies suggest that extension, by increasing farmer productivity, narrows the difference between **more** and less educated farmers. This parallels family plannlng, where contraceptive adoption and fertility
Comparing demographic and labour market characteristics of Colombians and Venezuelans during the first quarter of 2021 (when our survey fieldwork occurred) reveals patterns that largely accord with prior studies. As shown in Table 2, while both labour market populations have similar sex ratios, Venezuelans are younger and have slightly higher levels of education (Bahar et al. 2021; Santamaria 2020) compared to Colombians. Venezuelan migrants also work longer weekly hours and report lower wages. They also present a much higher level of informality.

**Table 2** : Demographic and labour market characteristics of Colombian and Venezuelans – 1st quarter of 2021.

||**Colom**<br>|**bians**<br>|**Venez**<br>|**uelans**<br>|
|---|---|---|---|---|
||Average|Std. Dev|Average|Std. Dev|
|**Gender**[1 = Male]|0.48|0.49|0.48|0.49|
|**Age**|35|22.0|25.7|16.5|
|**Married**|0.15|0.36|0.08|0.26|
|**Years of education**|5.6|3.9|6.1|4.4|
|**Education**|||||
|None|0.05|0.22|0.06|0.24|
|Primary|0.25|0.43|0.24|0.43|
|Secondary|0.40|0.48|0.52|0.50|
|Tertiary|0.23|0.42|0.14|0.35|
|**Labour market**|||||
|Labour force|0.84|0.36|0.76|0.42|
|Employment|0.46|0.49|0.56|0.49|
|Informal|0.34|0.47|0.76|0.42|
|Weekly hours worked|44.4|14.3|47.8|15.3|
|Monthly wages|1,392.3|1,522.8|814.3|571.7|

_Note_ : Data comes from the Colombian Labour Market Survey (GEIH), Q1 2021. Monthly wages expressed in thousands of Colombian pesos.

In February 2021, immediately prior to our survey fieldwork, the Colombian government announced a mass regularisation for Venezuelan migrants currently living in the country, called _Estatuto Temporal de Protección para Migrantes Venezolanos_ (ETPV). This granted Venezuelans a temporary regular migratory status for a non-renewable 10-year period.<sup>6</sup> It also granted permit holders access to formal labour markets and public services

> 6 https://www.unhcr.org/news/press/2021/2/60214cf74/unhcr-iom-welcome-colombias-decision-regularizevenezuelan-refugees-migrants.html

12