rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 3. Grow**|**th in the Value of**<br>**Panel C: F**|**Cross-border**<br>**oreign Direct Inv**|**Investment**<br>**estment**|**Positions (con**|**t.)**||
|---|---|---|---|---|---|---|
|**Level of Data Aggregation**|**Region-Region**|**Country-R**|**egion**|**C**|**ountry-Country**||
|**Estimation Method**|**OLS**|**OLS**|**PPML**|**OLS**|**PPM**|**L**|
|**Dependent Variable**|**Log(Value)**|**Log(Value)**|**Value**|**Log(Value)**|**Value**|**Value**|
|<br>North-to-North Trend|0.059 ***<br>|0.053 ***<br>|0.055 ***|0.066 ***<br>|0.054 ***|0.055 ***|
||(0.005)|(0.006)|(0.006)|(0.005)|(0.006)|(0.006)|
|North-to-South Trend|0.072 ***|0.113 ***|0.068 ***|0.094 ***|0.068 ***|0.064 ***|
||(0.005)|(0.008)|(0.006)|(0.005)|(0.010)|(0.009)|
|South-to-North Trend|0.083 ***|0.143 ***|0.074 ***|0.103 ***|0.036 ***|0.065 ***|
||(0.008)|(0.013)|(0.006)|(0.006)|(0.009)|(0.009)|
|South-to-South Trend|0.126 ***|0.251 ***|0.112 ***|0.138 ***|0.120 ***|0.099 ***|
||(0.008)|(0.012)|(0.005)|(0.004)|(0.008)|(0.006)|
|**Wald Tests on Trend Coefficients:**|||||||
|1. South-to-South = North-to-North|0.068 ***|0.198 ***|0.058 ***|0.073 ***|0.065 ***|0.044 ***|
|2. South Destination = North Destination|0.028 ***|0.084 ***|0.025 ***|0.031 ***|0.049 ***|0.022 ***|
|3. South Source = North Source|0.039 ***|0.114 ***|0.032 ***|0.041 ***|0.017 **|0.023 ***|
|**Fixed Effects**|R-R|C-R|C-R|C-C|R-R|C-C|
|**Number of Observations**|72|9,934|11,637|86,410|288,579|125,180|

||**Panel D:**|**International Re**|**serves**|
|---|---|---|---|
|**Level of Data Aggregation**|**Region-Region**|**Country-**|**Region**|
|**Estimation Method**|**OLS**|**OLS**|**PPML**|
|**Dependent Variable**|**Log(Value)**|**Log(Value)**|**Value**|
|North-to-North Trend|0.050 ***<br>(0.004)|0.036 ***<br>(0.012)|0.047 **<br>(0.020)|
|North-to-South Trend|0.121 ***|0.108 ***|0.104 ***|
||(0.011)|(0.012)|(0.019)|
|South-to-North Trend|0.092 ***<br>(0.014)|0.073 ***<br>(0.006)|0.073 ***<br>(0.008)|
|South-to-South Trend|0.198 ***<br>(0.021)|0.179 ***<br>(0.006)|0.135 ***<br>(0.004)|
|**Wald Tests on Trend Coefficients:**||||
|1. South-to-South = North-to-North|0.148 ***|0.143 ***|0.087 ***|
|2. South Destination = North Destination|0.089 ***|0.089 ***|0.059 ***|
|3. South Source = North Source|0.059 ***|0.054 ***|0.028 *|
|**Fixed Effects**|R-R|C-R|C-R|
|**Number of Observations**|72|5,952|5,952|

Note: This table shows the trends in the value of international investment positions for 2001-18. The estimation methods are ordinary least squares (OLS) and Poisson pseudo maximum likelihood (PPML). There are three levels of data aggregation: region-to-region (R-R), country-to-region (C-R), and country-to-country (C-C). Regressions include R-R, C-R, or C-C fixed effects. For the C-R and C-C regressions, standard errors are clustered by C-R and C-C pairs, respectively. *, **, and *** represent statistical significance at 10%, 5%, and 1% level, respectively.
# **_6.1 Soil Salinity and Water Logging_**

Soil salinity and water logging reduce the productivity of agricultural lands and, if a threshold salinity level is exceeded the land becomes unfit for cultivation. According to the conventional welfare economics, if agricultural markets are competitive, the economic costs of salinity would be measured as the losses in consumer surplus (consumer willingness to pay above market price) and producer surplus (profit) associated with the loss in productivity. These losses include direct losses through reduced yields as the land becomes saline or degraded. In practice, the calculations can be more complex as account needs to be taken of crop substitution to more saline-tolerant but less profitable crops and other indirect losses. Because of a lack of data, the losses here are approximated by the value of “lost” output related to the salinity, with some simple adjustment for changes in cropping patterns.

The estimated losses from saline soils were calculated under the assumption that such land is only used for wheat production (if it is used at all). This reflects the assumption that when soils are saline farmers will tend to plant crops that are more tolerant of this factor and wheat is such a crop, as opposed to pulses and rice. FAO estimates indicate a loss of yield of 5% for wheat per unit salinity (dS/m) for levels of salinity over 6 dS/m. Taking these values and applying them to lands under wheat is the basis of the estimated loss of output.

## **Table** **<u>6.1. Land degradation in India, million hectares (2002)</u>**

|Degradation type|Degree|of Degradation||||
|---|---|---|---|---|---|
||Slight|Moderate|Strong|Extreme|Total|
|Water Erosion|27.3|111.6|5.4|4.6|148.9|
|a. Loss of topsoil|27.3|99.8|5.4|-|132.5|
|b. Terrain Deterioration|-|11.8|-|4.6|16.4|
|Wind Erosion|0.3|10.1|3.1|-|13.5|
|a. Loss of topsoil|0.3|5.5|0.4|-|6.2|
|b. Loss of topsoil/terrain deterioration|-|4.6|-|-|4.6|
|c. Terrain deformation/over blowing|-|-|2.7|-|2.7|
|Chemical Deterioration|6.5|7.3|-|-|13.8|
|a. Loss of nutrient|3.7|-|-|-|3.7|
|b. Salinization|2.8|7.3|-|-|10.1|
|Physical Deterioration|-|-|-|-|116.6|
|Waterlogging|6.4|5.2|-|-|11.6|
|Total (affected area)|36.8|137.9|8.5|4.6|187.8|

_Source:_ indiastat.com The estimates indicate a net income from a hectare of land under wheat in 2009 as being in the range Rs 8,000-18,000 and total losses from salinity based on the above assumptions come out at between Rs. 0-10 billion in scenario 1 and between Rs. 3-13 billion in scenario 2<sup>15</sup> .

> 15 Information of the salinity level (slight, moderate, strong) was not available at the time of the study. 2 scenarios were considered to address this issue.

23
workers in the household for data from the British Household Panel Study. Neumark and Postlewaite (1998) introduce relative income concerns into women’s labor supply function and find that a woman whose husband earned less than her sister’s husband was 16% to 25% more likely to look for paid employment.

While a certain degree of heterogeneity of reference groups is likely to be unavoidable especially in more broadly representative samples, some social arrangements are more stable than others. In addition to informational attributes, a specific advantage of reference groups composed by siblings and relatives is that they are also fairly stable.

In the model proposed, utility derived from income is a function of income relative to the income of a reference group, _yr_ , and of absolute income _yi_ .

Utility is increasing in _yi_ , and after a certain income threshold increasing in one’s income relative to a reference group, _yr_ .

For income at subsistence poverty levels, utility is decreasing in _yr_ .

It is further proposed that reference relevance is a declining function of resemblance factors and proximity to the comparison group. Accordingly, comparison points such as siblings, parents, oneself in the past, and friends promise to be reference relevant, which will be tested in Section 4. The data available will be indicative of direction and significance of effects, but does not provide the detail for more precisely estimating the shape of the sample’s income utility curve. While individuals are likely to care about comparisons in other dimensions, the focus of this paper is on income.

9
We rely on the SCM initially proposed by Abadie and Gardeazabal (2003) (8), then developed and proposed by Abadie et al. (2010, 2015) (18,19). The method uses a weighted combination of unit of observations (i.e., countries) to create a `synthetic’ control CAR, which provides an estimate of expected socio-economic outcomes of interest (i.e., GDP _per capita_ , satellite NTL, industrial and manufacturing valued added, human asset index) if the civil war (our `treatment’) had not occurred. The countries that comprise CAR’s synthetic control are selected by using all pre-2012 outcomes (36) among a donor pool of comprising all LICs according to the World Bank classification to avoid pre-selection bias in the composition of the donor pool, with the exception of Afghanistan, the Syrian Arab Republic and the Republic of Yemen which faced a mix of interstate and civil armed conflicts, including in the perimeter of their capital cities during the same period (see Table A1, for more details). Those that are best able to predict the pre-2012 outcome trends in CAR are chosen for the synthetic control group. The expected socio-economic outcomes for CAR from 2012-17 in the absence of civil war are then compared against the observed outcomes. A difference between the observed and expected values can therefore be interpreted as the impact of the civil war on our outcomes of interest.

Formally, the synthetic outcomes are computed as follows: Formally, the synthetic outcomes are computed as follows: ∗ S S RR , h;SS rr rr,SS YY<sup>�</sup> = �ww YY where and S S h stand for respectively for the outcome of interest in country rr=1 and synthetic ∗ CAR, at year , stands for the vector of countries in the donor pool, and stands to the optimal rr YY YY rr weight attached to country . The impact of civil war on CAR’s development outcomes can rr therefore be calculated as follows: tt RR ww rr C S S for years ≥ 2013, where stands for the post-treatment impact and ααSS = YY RR;SS − YY<sup>�</sup> h;SS, C stands for the outcome of interest in CAR. SS RR αα YY While traditional statistical inference techniques do not work with this approach, given the small sample size, a permutation test (also called placebo test) can be used to assess how unusual such an effect would be if it were due to chance and thus control for the effect size. This permutation test involves implementing the synthetic control technique for each country in our donor pool, as though it were the one that had experienced civil war during 2012-17. The estimated effect for CAR can then be compared to the size of these other effect estimates. Typically, the permutation test results are compared for states in which pre-war outcome trends are well predicted by the synthetic control. Results of the test for all countries, in addition to the results for those states with up to 5 times the mean squared prediction error (MSPE) observed for CAR are presented. Countries with a poorly matched synthetic control might indeed appear to have more extreme differences as an artifact of poor prediction.

6
between housing and health. In a critical survey of this literature, Thomson et al. (2001) report no randomized or quasi-experimental studies and assert that most of the work in this area consists of cross-sectional associations. The economic literature has focused on the identification of the market value of various housing characteristics (see, among others, Chay and Greenstone 2005; Crane et al., 1997; and Jimenez, 1983) or the gain in welfare obtained from housing upgrades as reflected by the compensating variation measured via estimated utility functions (Kaufman and Quigley, 1987; and Takeuchi et al., 2005). An exception is Katz et al. (2001), who examine the impacts of changes in residential neighborhoods on the well-being of families residing in high-poverty areas that received housing vouchers through a random lottery in the U.S. They find that households that were offered vouchers experienced improvements in multiple measures of well-being, including improved health among heads of household and a reduction in the likelihood of asthma attacks and injuries among children.

The rest of the paper is organized as follows. In Section 2, we present a discussion of the Mexican _Piso Firme_ program and the conceptual framework. In Section 3, we outline our identification and sampling strategies. In Section 4, we describe the data, and in Section 5, we present our estimation methods. In Section 6, we present reduced-form estimates of the program’s average impact, while in Section 7, we present estimates of the impact of cement floors on child health and maternal happiness. Finally, in Section 8 we outline our conclusions.

# **2. Conceptual Framework**

_Piso Firme_ –which translates into English as “firm floor”– is a Mexican government program designed to replace dirt floors with cement floors in the homes of disadvantaged urban population groups. It began initially as a local effort in the State of Coahuila and was later adopted by the federal government. Today, the program is a cooperative effort in which the federal government provides the funding and the state governments implement the program. Since 2000, _Piso Firme_ has installed cement floors in about 300,000 of the estimated 3 million houses that reported dirt floors in the 2000 census.

Eligibility for the program is based on two main factors. First, the household must have dirt floors, as this is a key indicator of poverty in Mexico. Second, the household 5
primary sector—that they persist in the main trading partners (U.S. and Europe), despite the intensification of trade relations.

De facto, developing countries have less leeway to discriminate against trading partners and to flout the rules of trade agreements. One example is that because litigation of trade rules requires significant resources, wealthy countries are more able to undertake litigation compared to developing countries. For instance, a common trade dispute involves dumping, which describes a situation in which company is alleged to have exported a product at a price lower than the price it normally charges on its domestic market. Trade rules allow countries to charge tariffs to offset dumping, which can be large, on average 10–20 times higher than tariffs imposed under most-favored-nation status. Nunn (2019) shows these are much more frequently initiated by wealthier countries against developing countries, rather than the other way around.

In other cases, high-income countries enact policies that openly conflict with international trade rules. Global Trade Alert is a nonprofit organization which compiles perhaps the most comprehensive lists of trade-limiting policies. Juhász et al. (2022) use these data to catalog “the who, what, when, and how of industrial policy,” with a focus on policies that “discriminate against foreign commercial interests” and finds these are most implemented by high-income countries. An example is the provision in the US Inflation Reduction Act of 2022 that, if a consumer is to receive a tax credit after purchasing an electric vehicle, a percentage of the value of minerals in the battery must be extracted or processed in the United States, or a country with which the United States has a free trade agreement. The intention of this requirement is to stimulate battery manufacturing in the United States and the 20 countries with which it has free trade agreements. Such a requirement appears to conflict with the World Trade Organization Agreement on Subsidies and Countervailing Measures, which prohibits any subsidies that are available contingent on the use of domestic inputs or “local content.” WTO rules also prohibit industrial subsidies contingent on export performance, which were used historically as an industrial policy tool—for example, in Korea.

An underlying principle behind international trade agreements is to avoid subsidy races, in which dumping or domestic content rules spread from country to country. This fear has some foundation. Using data from the Global Trade Alert and other sources, Evenett et al. (2024) show that implemented industrial policy “measures are correlated with the past use of measures 18
The transformed economy<sup>7</sup> possesses a well-defined steady state as long as nonconverging paths for the public finances are ruled out. To guarantee debt sustainability, the following reaction function of the fiscal authority describing the behavior of taxes is a sufficient condition for averting explosive paths: where B<sup>ˆcg</sup> Y<sup>ˆ</sup> is the steady state central government debt to GDP ratio.

Finally, the economy faces an upward-sloping supply schedule for debt expressed as a two-component aggregate borrowing rate composed of a constant world interest rate (r •<sup>•</sup> ) and a variable premium (zt ) . The borrowing premium depends on the central government debt to GDP ratio and is assumed to reflect the capital markets’ assessment of the economy’s ability to service its debt. This is why the same rate is charged to both government and private sector borrowing. The borrowing rate is of the form: where ε is the debt/GDP semi-elasticity of the borrowing premium.

# **3. CALIBRATION AND SOLUTION METHOD**

# **3.1 Parameter Values and the Structure of the Economy**

The model economy is parameterized in such a way that its long-run features mimic the observed structure of the Colombian economy in 2003. In other words, if the 2003 fiscal policy stance is assumed to remain unchanged, the long-run structure and growth performance of the model economy will be like that observed in 2003.<sup>8</sup> In particular, in the steady state of the model economy the expenditure side of the national income accounts coincides with the 2003 observed structure: household consumption represents 62% of GDP and total private investment 11.5% of GDP, of which 2.74% of GDP is investment in infrastructure, according to Departamento Nacional de Planeación (DNP) data. The public sector represents 26.8% of GDP, made up of central government purchases (16% of GDP), the rest of the public sector’s expenditures (8% of GDP) and

> 7 With logarithmic preferences there is no need to transform the subjective discount factor.

> 8 The correction of existing macroeconomic imbalances requires the use of lump-sum adjustments. Specifically, the 2003 fiscal policy stance is made sustainable through lump-sum taxation.

11
investment companies. (The importance of these various institutions is summarized in table 2.) In the 1820s and 1830s Brtish investors purchased American state securities and canal bonds recommended by Samuel Gurney, the Quaker bill broker, and American Joshua Bates, managing partner of Baring Brothers from 1825 to 1864. Ihe head of a British investnent banking firm like Barings would only occaionally visit the region in which the investment took place but retained an Ameican agent to monitor developments. Here, too, peronal contacts mattered: Barings replaced its furst American agent, T.W. Ward, with his son, Samuel Gray Ward.

Table 2. roportion of Overseas New lhsues lItrodued by the Main Type. of lisuing H}ues, 1W70-194

||Official<br>ad wemi-<br>official<br>agenciets|Private<br>banke|Joit-stock<br>bank|ov0a<br>ban and|Companis<br>vi thir<br>bankss|Oher<br>**nmi**|Totll<br>anwunt<br>insed<br>**(finiin)**|
|---|---|---|---|---|---|---|---|
|1870-74|1.8|53.0|4.4|9.6|18.2|13.0|390.6|
|1875-79|14.5|36.5|0.8|24.7|13.0|10.5|149.2|
|1820-84|6.7|38.5|3.3|14.1|26.7|10.7|355.3|
|i8S-89|9.9|43.7|5.3|7.5|26.1|7.5<br>|479.2|
|1890-94|10.4|46.4|9.0|8.8|19.6|5.8|349.6|
|1895-99|8.7|25.1|11.2|20.3|25.2|_9.5_|359.6|
|1900-04|27.4|19.2|17.8|14.4|16.7|4.5|258.2|
|1905.09|10.3|32.7|12.2|22.4|18.7|3.7|509.9|
|1910-14|8.3|35.2|17.4|18.8|17.5|2.8|783.8|
|1870-1914<br>Tota|9.8|37.2|10.3|15.4|20.5|6.8|-|
|amount||||||||
||_(u)355_|1,354|371|562|746|248|3,636|

# a. For exaalplc, merchin bank=r

- b. Conqpising: invsuent trut (£23n uUion); finance, lund, aLnd prapediy comies (£18 nil ion); specid purpose syndicat (Mi mllIion); issuc hou with stDck exchane connections (222 miDlion); companics a ueir own iBsuers (13nmiion); and mnellaaeous isuers _(fL31_ nmilo).

- Source: Bsed oc a table prepard by W.A. Brown publshed in 7h _Econovi_ (November 20, 1937) and rprinted in Balough, 1947: 233.

The leading **invesment** houses **spedcalid** in **recommending only the hihst** quality foreign bonds. To **signal** their **cotment** to this advice, they bought **the** same bonds for 14
scenario includes the implementation of trade facilitation measures that would reduce the trade transaction costs by 2% annually.

## **_US-Vietnam FTA_**

This scenario assumes that the United States signs a bilateral FTA with Vietnam in 2018. The tariff and NTM reductions in goods and services are consistent with the bilateral concessions under the TPP. Tariff commitments originate from the ITC database (MacMaps, 2016). NTMs in goods estimates are based on the ongoing work of DECTI of Kee et. al. (2016). These estimates are based on the surveys of NTMs conducted in 2015/2016. Estimates of NTMs in services are based on Jafari and Tarr (2015). We run a central scenario with the percentage NTM reductions similar to those as in Petri and Plummer (2016).

## **_Suspension of AGOA_**

This scenario studies the impact of the loss of duty-free access to the US market under AGOA in 2018. As a result, Lesotho starts paying the GSP rates on its exports to the United States and its trade weighted import duty on wearing apparel sector increases to 24 percent.

## **_Suspension of AGOA with trade facilitation_**

This scenario studies the impact of the loss of duty-free access to the US market under AGOA in 2018 as in the previous scenario, but it includes a reduction of bilateral trade costs between Lesotho and all its trading partners at 2% per year. Moïsé and Sorescu (2017) estimate that a full implementation of the WTO Trade Facilitation Agreement could reduce trade costs in Sub-Saharan Africa by 18.3%.

# 4.2. Results

Our results indicate that the negative impacts due to a sudden suspension of AGOA privileges in 2018 would have been much higher than losses resulting from the potential US-Vietnam FTA. The decline of income would reach 1% relative to the baseline in 2020, while exports of textiles and apparel would drop by 16% leading to a drop of output of textiles and apparel by 9%. Given that employment in textiles and apparel accounted for 4.4% of total employment and 10.5% of female employment in 2014 with several other family members believed to be supported by these jobs, these impacts are significant. The decline of average real consumption of 0.5% would have significant negative consequences for the poor.

While the impact of AGOA suspension is immediate, the implementation of a US-Vietnam trade agreement is assumed to be gradual over 10 years. The declining preferential margin of Lesotho vis-a-vis Vietnam is a legitimate reason for concern (the unit costs of Lesotho are much higher than those of its competitors), but other major exporters such as China, Indonesia, Bangladesh or Jordan would continue to pay MFN rates. The impact of Lesotho’s exports of apparel and textiles is a 3.4% decline relative to the baseline in 2020, but the impacts on average income or real consumption are negligible.

15