rafmacalaba/datause-encoder-data huggingface dataset:
This section describes the timeline of the PEP rollout with a detailed illustration of the 1. exact dates and sequence of events in Figure

# **II. A Registry of Irregular Migrants —- January–April 2018**

In 2018, the Colombian government conducted a survey to estimate the number of irregular Venezuelan migrants living in Colombia. The survey, known as the _Registro Administrativo de Migrantes Venezolanos_ or RAMV, was collected between January and April of 2018 in 441 municipalities with the largest populations of Venezuelan migrants.<sup>6</sup> The registry was voluntary and largely advertised through local migrant organizations and the media. Roughly half a million migrants had registered by the time it ended.

# **II. B The PEP program —- August–December 2018**

In July 2018, just prior to leaving office, then-President Juan Manuel Santos unexpectedly announced that all migrants who had registered in the RAMV would be eligible for regularization through a program called the Permiso Especial de Permanencia (PEP). PEP offered a generous agenda of a two-year residency permit, a work permit, and access to SISBEN (a scoring program to award public resources) and financial services. By granting migrants access to SISBEN, PEP arguably enabled them to apply to all Colombian social programs for vulnerable populations, including full health care services through the subsidized regime. PEP boosted the consumption and labor income of treated migrants (Ibanez et al., 2022) and had negligible effects on the labor prospects of Colombian native workers in the short term (Bahar et al., 2021). We hypothesize that by giving Venezuelan migrants access to social programs and the formal labor market, PEP might have also impacted other household decisions, including fertility choices.

# **III THEORETICAL FRAMEWORK**

In the standard Beckerian framework, where demand for children depends on a family’s budget constraint (Becker, 1960), PEP should have effectively reduced the cost of having

> 6There are 1,122 Colombian municipalities.

6
33 As a result, the identification exercise proposed also permits the testing of a hypothesis about Fairtrade intervention. In the discussion on the data used in the following section, it is indicated that the Costa Rican price data used in the analysis _does not include_ the famous Fairtrade premium floor price of US$1.26/lb. Furthermore, all Fairtrade mill/exporters are also cooperatives. If there is any Fairtrade ‘effect’ separate from the fact of simply being a cooperative,<sup>33</sup> then a different margin would be observable for Fairtrade mills. That is, a Fairtrade ‘effect’ can be seen only if Fairtrade mills are operating on a relatively more efficient demand curve, somewhere to the right of D1. So, for example, in terms of Figure 7: iii) p(D) > p(B) which implies that the margin at point D is lower than the margin at point B This last hypothesis permits the empirical analysis of what are commonly treated as the ‘intangible’ effects of capacity building and non-price support of Fairtrade. In sum the stylized testable propositions (i) and (ii) can inform on the presence of market power in Costa Rica and (iii) informs on any Fairtrade ‘effect’ on the (cooperative) producers with which it intervenes.

# **VII. Empirical Analysis**

In the theoretical discussion above, it was seen that the variable (p* - p*R ) measures the mark-down of producer prices from the perfectly competitive MES level. While a vast quantity of detailed data was mobilized to construct annual mill-specific (p* - * p R ), it is almost certainly simplistic to assume that it is measured entirely without error. For example, world price p* for Costa Rican will differ based on the destination market (e.g. US or EU). Since destinations of each contract for each mill cannot be known, average proportions are applied to each mill based on the national average proportions of coffee sent to the EU or the US. Any specific mill may, of course, export its coffee to the EU or the US in proportions that differ from the national average. In a fixed effects regression context, for example, the stable nature of these proportions (see Ronchi (2005) ) means that this potential source of error can be controlled for in the estimated fixed effect term; something that is not possible in deterministic, value-chain type measurements.<sup>34</sup> Furthermore, given that market power in particular is to be detected by analyzing variations in the data relative to a particular class of mills (cooperatives), it is of interest to move beyond a simple deterministic measure of ‘mark-down’ and into a regression context. The econometric analysis in this section estimates a reduced form equation to test for the presence of market power (hypotheses (i) and (ii) above) and for the presence of any ‘Fairtrade effect’ (hypothesis (iii) ). It also analyzes the possible determinants of any general mark down behavior detected in the Costa Rican market.

> 33 The non-price aspects of Fairtrade intervention were covered in Section II.

> 34 Ronchi (2005) undertakes a detailed sensitivity analysis of the dependent and independent variables used in the data set for this exercise.
lender-time level (see Table A3 in the appendix for variable descriptions and data sources). The sample includes only non-financial borrowing companies. Throughout the analysis we use a monthly frequency in the time series dimension. If the information about a bank’s share in a loan is missing, the loan is discarded in constructing the volume variable. In addition, we exclude the years before 1995, because Dealscan contains significantly fewer observations in these years. Our sample spans the period from January 1995 to March 2015. Since we focus on cross-border lending, we also exclude observations if the borrower’s and lender’s country of location coincide. Following the literature, e.g. de Haas and van Horen (2012), we define the nationality of a bank based on the location of the ultimate parent. Table 1 shows that the average borrower-lender loan volume is US$55.6 million and ranges between US$1 million and US$410 million. As seen in Figure 1, the total volume of cross-border syndicated loans rose rapidly before the crisis, fell back substantially during 2008-2009, and subsequently recovered to pre-crisis levels towards the end of the sample period.

We also examine various non-volume terms of the loan contracts. First, Maturity is the maturity of a facility in months. In Table 1 we can see that the average maturity of loans in our sample is 60.6 months, or about 5 years. Next, Spread is the loan spread over the reference rate in basis points for drawn credit of a facility with a sample average of 245.7 basis points. The next loan characteristic variable is Collateral, which is a dummy variable indicating that a loan is collateralized.<sup>2</sup> Table 1 reports that about 81.7% of loans are collateralized in our sample. Finally, Covenant is a dummy variable indicating that there is a net worth or financial ratio covenant in the loan contract. About 29% of loans to nonfinancial companies have at least one of these covenants. Unlike the volume regressions,

> 2 <mark>The observation is dropped in collateral regressions if the “secured” field in Dealscan is empty.</mark>

7
# **2 Related literature**

## **2.1 Globalization and taxation**

Our paper first relates to the literature on globalization and taxation. Since Adam Smith (1776), economists have conjectured that increased openness pushes governments to reduce taxes on the most mobile factors of production (e.g., high-wage workers, capital) and recover the revenue shortfalls by increasing the taxation of less mobile factors (Bates, Da-Hsiang, and Lien, 1985; Rodrik, 1997). This effect is thought to be particularly strong for countries competing for capital (Wilson, 1999; Kanbur and Keen, 1993).

In an important contribution, Egger, Nigai, and Strecker (2019) show that globalization led to a decline in the progressivity of labor taxation in OECD countries since 1994 (namely, an increase in labor taxation for the middle class and a decline for the top 1 percent). Our approach, which focuses on the changing balance of labor vs. capital taxation in both OECD and non-OECD countries, is complementary. For OECD countries, our findings reinforce Egger, Nigai, and Strecker (2019): we show that in addition to reducing labor tax progressivity, globalization has been associated with a sharp decline in capital taxation. This decline reduces overall tax progressivity (above and beyond the decline due to falling labor tax progressivity) given the concentration of capital income at the top.

Our paper also adds to the macroeconomic literature on the link between trade and taxes. Due to the lack of systematic statistics on the evolution of tax structures in developing countries, this literature has focused on high-income countries or a single tax (e.g., the corporate income tax).2 Our contribution to this literature is to build and analyze a new global dataset of effective tax rate, extending prior work which focused on developed countries (Mendoza, Razin, and Tesar, 1994; Carey and Rabesona, 2004; McDaniel, 2007). This allows us to uncover new trends (most importantly, the rise in capital taxation in

> 2Rodrik (1997) finds that trade openness is associated with a decline in effective capital tax rates and an increase in effective labor tax for 14 OECD countries with high levels of capital mobility, between 1965 and 1991. In a sample of 14 OECD countries between 1981 and 1995, Swank and Steinmo (2002) find that trade is not associated with changes to effective tax rates, neither on capital nor on labor. Over the same period Slemrod (2004) finds that trade is not associated with changes to the statutory corporate income tax rate.

5
**Table 7. Sector Exports as a Percent of Total Exports of the Regional Market**

|||||||||St.||North-|
|---|---|---|---|---|---|---|---|---|---|---|
|Good|Central Fa|r East|North|Siberia|South|Urals|Moscow|Petersb.|Tumen|west|
|MAR|3|2|2|1|2|2|2|2|1|2|
|AGR|2|1|0|0|2|1|0|0|0|1|
|AIR|3|3|2|2|3|2|8|3|0|2|
|CHM|14|1|2|7|8|12|6|5|0|22|
|CNM|1|0|0|0|1|0|1|1|0|0|
|COL|0|4|3|5|1|0|||||
|CON|2|0|0|1|2|1|3|1|0|1|
|CRU||8|26|3|27|20|||50|2|
|ELE|1|0|0|0|0|0|1|0|0|0|
|FIN|0|0|0|0|0|0|1|0|0|0|
|FME|26|1|4|5|5|11|3|4||41|
|FOO|5|10|2|3|10|1|11|8|0|5|
|GAS||2|3|1|5|3|||45|0|
|HEA|0|0|0|0|0|0|0|0|0|0|
|HOU|0|0|0|0|0|0|0|0|0|0|
|MWO|12|15|2|9|10|13|40|28|0|7|
|NFM|2|16|18|42|5|13|7|10||1|
|OIL|19|15|4|10|13|15|5|19|1||
|OTH|1|0|0|0|0|1|1|1|0|1|
|OTI|4|0|0|0|1|1|2|1|0|1|
|PST|0|0|0|0|0|0|0|0|0|0|
|RLW|0|0|0|0|0|0|1|0|0|0|
|SCI|0|0|0|0|0|0|0|0|0|0|
|TMS|0|0|0|0|0|0|1|0|0|0|
|TPP|2|18|28|7|1|2|1|12|0|12|
|TRD|1|1|1|1|1|1|2|1|0|1|
|TRK|0|0|0|0|0|0|0|0|0|0|
|TRO|0|0|0|0|0|0|1|0|0|0|
|CLI|1|1|1|1|1|1|4|1|0|1|
|Total|100|100|100|100|100|100|100|100|100|100|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

34
of knowledge of the West, arrived in Nanjing, and became the _de facto_ prime minister of TPHK.<sup>13</sup> He advocated industrialization by adoption of Western technology, and supported private businesses and foreign trade. He tried to win support from the West using his connections established in Hong Kong. Perhaps more importantly, the military leader in the new territory of Zhejiang and Jiangsu areas was Li Xiucheng, an outstanding general who was pragmatic and considered the most “pro-citizen” among all Taiping Army generals. He made a policy of reducing land taxes and offered famine relief, for which local residents felt so grateful that they erected his statue before his area fell to the Qing (Tao, 2016, p. 69-70).<sup>14</sup> Thus, around the time that the Taiping Army established its Jiangsu and Zhejiang bases in 1860, the expectation for its future was brighter, the leadership was more stable and “pro-citizen,” and the TPHK, in setting up its land policy, behaved more like stationary bandits. Indeed, Li Xiucheng built one of China’s most beautiful palaces in Suzhou, signaling his intention of ruling for the long run. In contrast, the TPHK had weaker control and governance over the regions that it had occupied before 1860 (perhaps excluding the areas near Nanjing). The army relied to a larger extent on looting and contributions for war financing. Control over previously conquered regions was also fragile: Wuhu prefecture in Anhui, for instance, changed hands between the Taiping Army and the Hunan Army eight times between 1853 and 1855 alone (Spence 1996, p. 218). Thus, TPHK should have behaved more like “roving bandits” for these areas where it had established early rule.

# **Stationary Bandits and Land Property Rights**

We now consider how stationary banditry affects land property rights. The revenue of the Qing Empire came mainly from land taxes, which were collected from landowners. They in turn collected rents from tenant farmers where applicable. The empire protected the ownership rights of land owners to ensure that tenant farmers paid their rents (Guo 1991, p. 238). Indeed, the Qing government maintained careful written records of land transactions (Brandt et al. 2014).

The rebellion dramatically changed _de facto_ (and sometimes _de jure_ ) land ownership. TPHK proposed the egalitarian redistribution of land based on its Land System of Heavenly Dynasty, its initial land policy announcement. But this policy was never implemented due to

> 13 He had a solid classical education and was assistant to top missionaries in Hong Kong SAR, China, after he fled there from organizing the rebellion in Guangxi. Fluent in English, he helped translate Chinese classics with a missionary who later became the first professor on China in Oxford.

> 14 After the famous Anhui militia general Li Hongzhang, future _de facto_ prime minister of the Qing, reconquered Suzhou, he asked local residents why they had erected the status of Li Xiucheng. Local residents responded by praising Li Xiucheng’s benign economic policies (Tao, 2016).

10
# **Program description**

Unemployment for the young in Sweden has been high both in absolute terms and relative to older workers since the severe recession of 1991-93, although much of youth unemployment is attributable to students looking to enter the labor market. In the mid-2000s, high youth unemployment became a central political issue, prompting the first Reinfeldt government to introduce a payroll tax cut for youths. Payroll taxes in Sweden were hence reduced substantially for young people on July 1, 2007.

Employers in Sweden must pay payroll taxes (“arbetsgivaravgifter”) for every employee. These taxes are proportional to the wage, with the standard rate being 31.42%. The standard rate was cut by 1 percentage point on January 1, 2009, hence the standard rate at the time of the payroll tax cut for youths was 32.42%. Payroll taxes can further be decomposed into a number of subcomponents (see table 3).

On July 1, 2007, the payroll tax rate was reduced from 32.42 percent to 21.32 percent for all individuals who at the start of the year were at least 18 years old but had not yet turned 25. The reform was intended to lower the cost to employers of hiring young workers.

On January 1, 2009, the payroll tax for young workers was cut further, from 21.32 to 15.49 percent (taking into account the general payroll tax cut that was enacted at the same time). Furthermore, the maximum age of eligibility was increased from 25 to 26 years and the lower eligibility limit was removed completely.

From May 2015 the payroll tax cut for workers born in 1989 was abolished. From August 2015 payroll taxes for all other youths (born in 1990 onwards) were increased to 25.46 percent, i.e. close to their pre-reform level.

Payroll taxes are a substantial source of revenue for the Swedish central government as well as for the public sector overall. In 2013, payroll taxes are estimated to have provided the Swedish public sector with 409 billion SEK, equivalent to 10.6 percent of GDP or a quarter of public sector tax revenues (Swedish Tax Authority, 2014).

3
# **6.3 Poverty Impact of the VAT**

**Table 9: Impact of the Value-Added Tax on Poverty Measures (Proportion)**

|**Foster-Greer-Thorbecke**<br> <br>**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|---|---|---|---|---|---|---|---|---|
|**Poverty Measures**|**2012**||**8% In**|**crease in**|**Prices**|**12% In**|**crease in**|**Prices**|
|**All**|**Rural**|**Urban**|**All**|**Rural**|**Urban**|**All**|**Rural**|**Urban**|
|Poverty Headcount (p0)<br>0.465<br>[0.005]|0.336<br>[0.006]|0.67<br>[0.008]|0.453<br>[0.005]|0.334<br>[0.006]|0.644<br>[0.008]|0.451<br>[0.005]|0.334<br>[0.006]|0.637<br>[0.008]|
|Poverty-Gap (p1)<br>0.182<br>[0.003]|0.121<br>[0.003]|0.279<br>[0.005]|0.172<br>[0.003]|0.12<br>[0.003]|0.257<br>[0.005]|0.171<br>[0.003]|0.119<br>[0.003]|0.252<br>[0.005]|
|Squared Poverty-Gap (p2)<br>0.095|0.061|0.148|0.088|0.06|0.133|0.087|0.06|0.13|
|[0.002]|[0.002]|[0.004]|[0.002]|[0.002]|[0.004]|[0.002]|[0.002]|[0.004]|
|Observations<br>21,239|11,551|9,688|21,239|11,551|9,688|21,239|11,551|9,688|
|Standard errors in brackets<br>*** p<0.01, ** p<0.05, * p<0.1<br>Source: 2012 1-2-3 Survey, DRC|||||||||

The adoption of the VAT eroded the purchasing power of all households. Household aggregate food consumption decreased, with the highest losses, measured by the compensating variation, occurring among rural poor households. The relative drop in aggregate food consumption varies between 10 (for 8 percent price increase) and 12 (for 12 percent price increase) percent (results not shown).

In this sub-section, we investigated the poverty impact of the VAT reform. We first calculated counterfactual welfare aggregates by adding the welfare loss to the 2012 adjusted food consumption aggregate. Then, we generated new food poverty lines that accounted for the changes in relative prices. To do so, we calculated the welfare loss among those whose food consumptions were in a 15 percent bandwidth above and below the food poverty line, and we added the welfare loss corresponding to each scenario to the food poverty line prevailing in 2012. Finally, we derived the three poverty measures of interest (poverty headcount, poverty gap, and squared poverty gap) in each scenario using the appropriate food consumption aggregate and food poverty line.

Not surprisingly, the adoption of the VAT increased the food poverty headcount by 1.2 and 1.4 percentage points in the scenarios of 8 and 12 percent increase in prices, respectively. In both urban and rural areas of residence, the VAT reform led to worsening of the living conditions of the households based on the poverty headcount. The poverty gap and the squared poverty gap further supported this deterioration in the food poverty status due to the adoption of the VAT. The short-term effect of the adoption of the VAT, hence, contributed to the high level of poverty headcount recorded in 2012.

22
41 <u>Table 3. Nutritionally Adequate Diet Feasibility, Cost, and Affordability in Month of Survey</u>

||2013–|2015|2016–|2017|Ove|rall|
|---|---|---|---|---|---|---|
||Median|(SE)|Median|(SE)|Median|(SE)|
|**_Lower Bound: Individualized Diets_**|||||||
|Households with a feasible diet_in month of survey_<br>(%)|83.71|(3.02)|88.45|(2.39)|86.73|(2.12)|
|Cost per day (household)|7.51|(0.26)|8.31|(0.37)|7.96|(0.29)|
|Per capita|1.85|(0.07)|1.82|(0.08)|1.83|(0.05)|
|Per 1,000 kcal|0.95|(0.04)|0.94|(0.04)|0.94|(0.03)|
|Cost/Food Expenditure|1.01|(0.05)|1.19|(0.07)|1.11|(0.06)|
|Cost/Total Expenditure|0.72|(0.04)|0.81|(0.05)|0.78|(0.04)|
|N households with a solution in month of survey|1,125||1,451||2,576||
|N households with no solution any month in year|37||52||89||
|of survey|||||||
|**_Upper Bound: Shared Diet_**|||||||
|Households with feasible diet in month of survey<br>(%)|58.98|(3.71)|54.50|(3.25)|56.13|(2.58)|
|Cost per day (household)|9.49|(0.47)|9.13|(0.43)|9.24|(0.36)|
|Per capita|2.39|(0.10)|2.25|(0.08)|2.31|(0.06)|
|Per 1,000 kcal|1.26|(0.05)|1.20|(0.04)|1.21|(0.03)|
|Cost/Food Expenditure|1.24|(0.09)|1.40|(0.09)|1.35|(0.06)|
|Cost/Total Expenditure|0.88|(0.06)|0.95|(0.07)|0.92|(0.04)|
|N households with a solution in month of survey|792||956||1,748||
|N households with no solution any month in year|187||223||410||
|of survey|||||||
|**_Scenario Comparison_**(annualized)|||||||
|Shared Cost/Individualized Diets Cost|1.34|(0.02)|1.31|(0.02)|1.33|(0.02)|
|N Households with solution under both scenarios<br>in month of survey|727||897||1,624||
|Households (total N)|1,424||1,693||3,117||

Population statistics corrected using sampling weights, standard errors clustered at the enumeration area level.

If we consider that households for whom the diet is infeasible as also not being able to afford the diet, then we can estimate the share of the population for whom the adequate diet (in the market) is out of reach. Since the lower bound cost is the least costly way for a household to meet all members’ nutrient needs, those for whom it is infeasible, or costs more than their total expenditure, do not have access to an adequate diet at all. Our results show 44% of rural Malawian households face this situation. For an additional 18%, the lower bound diet is feasible but unaffordable without increasing current food expenditure (though technically affordable within total expenditure). In total, 62% of rural Malawians cannot access any adequate diet in the
7 each farming activity (land preparation, sowing, weeding, fertilizer application, pesticides application, and harvesting), disaggregated by gender (male, female, and children), both for both household and hired labor. This approach to measuring labor input in agriculture follows the latest recommendations (Sagesaka et al., 2021). Information on the type of equipment used in land preparation (e.g., manual, draught animal, tractor, etc.) was also collected.

To have a broader picture of the household living standards, detailed information regarding household assets and food insecurity was asked. Food insecurity is measured using the food insecurity experience scale survey module (FIES-SM) (Cafiero et al., 2018). The household head and/or his/her spouse were asked to answer eight questions about the experience of food insecurity in the past twelve months. These detailed questions about the food experience allow to calculate the food insecurity experience scale score. To proxy for the level of village development and access to household labor for agricultural development, we also include geospatial population density values in the analysis, extracted from NASA Socioeconomic Data and Applications Center (SEDAC). The 2019 available data were merged, with the resolution of one square kilometer per pixel, to each household in our survey data, based on the GPS coordinates. Overall, the household interviews were conducted with the household head or the person responsible for farming activities. Table 1 lists the key outcome variables used in the analysis.

**<u><mark>Table 1: Definition of outcome variables</mark></u>**

|**Variable**|**Definition**|
|---|---|
|Yield|Rice output per hectare of rice land (kg/ha)|
|Variable cost|Cash expenses of seeds, fertilizer, herbicides, land preparation and<br>hired labour per hectare of rice land (FCFA/ha)|

the respondent is asked how many people worked on the plot during the implementation of each of the farming activities and for each person we then asked how many days the person worked on this plot during the production season. The gender disaggregated amount of person-days for each activity is calculated as the sum of the person-days of all individuals of the same gender working on that activity.