rafmacalaba/datause-encoder-data huggingface dataset:
Table F.9. Heterogeneous Effects on Altruism Towards Migrants by PEP Benefits Knowledge

||Colombian<br>government<br>has to help<br>Venezuelans|In favor to<br>a law that<br>helps<br>Venezuelans|Venezuelans<br>compete with<br>Colombians<br>jobs|Venezuelans<br>increase<br>crime|Venezuelans<br>improve<br>Colombian<br>culture|Positive<br>effects of<br>Venezuelans in<br>Colombia|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_β_1=I(Treat)_×_I(PEP Benefts Knowledge)|-0.047|0.062|0.007|-0.031|-0.003|0.055|
||(0.138)|(0.140)|(0.140)|(0.144)|(0.143)|(0.070)|
|_β_2=I(Treatment)|0.043|-0.044|0.031|-0.056|-0.058|-0.077|
||(0.119)|(0.120)|(0.120)|(0.124)|(0.123)|(0.060)|
|Knowledge PEP Diff. Effect =_β_1+_β_2|-0.003|0.018|0.038|-0.088|-0.061|-0.022|
||(0.072)|(0.072)|(0.073)|(0.075)|(0.074)|(0.036)|
|R-squared|0.043|0.043|0.019|0.031|0.033|0.042|
|Observations|1,002|1,002|1,002|1,002|1,002|1,002|

_Notes:_ This table reports an OLS estimate interacting the treatment variable with a variable =1 if the respondent know about the PEP Benefits. Dependent variables are the logarithm of the answer of the respondent about the distribution of 5,000 Colombian pesos between him (Column (i)), a vulnerable Venezuelan migrant (Column (ii)), and a vulnerable Colombian (Column (iii)). All the columns control for sex (female and male), two age groups (21– 28 and 29+) and three economic strata (high, medium, and low). *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.
19-related labor market shocks by pushing them into occupations that are more amenable to working from home.

The literature on the labor market impact of the COVID-19 pandemic has grown exponentially over a very short period. As these papers all show, the impact of COVID-19 on labor markets has been very profound, with asymmetric effects across occupations and population groups (see for example, Adam-Prassl et al. 2020; Beland, Brodeur and Wright, 2020; or Coibilion, Gorodnichenko, and Weber, 2020). Several studies indicate that the employment risks associated with COVID-19 disproportionately affect workers who were already exposed to wage and job uncertainties prior to emergence of the pandemic (Casarico and Lattanzio 2020; Rio Chanona et al., 2020; Mongey, Pilossoph, and Weinberg, 2020; Papanikolaou and Schmidt, 2020; Yasenov, 2020). Similarly, Garrote-Sanchez et al. (2020) show that COVID-19 is likely to exacerbate inequality between workers – especially in richer countries, where better paid and better educated workers with secure jobs are more insulated from the shock. Using monthly data in the US for 2020, Montenovo et al. (2020), Liu and Mai (2020) and Cajner et al. (2020) show occupations that require greater physical proximity and less suitability for teleworking exhibited larger employment losses.

By focusing on another category of vulnerable workers – immigrants – our paper contributes to the recent literature on the occupational exposure to COVID-19 related risks. The handful of papers specifically comparing the COVID-19 exposure of immigrant and native workers finds that immigrant workers are typically more exposed to both economic and health shocks associated with the pandemic (Basso et al., 2020; Fasani and Mazza, 2020a and 2020b; Yasenov, 2020). Among the reasons proposed by Borjas and Cassidy (2020) is the higher concentration of migrants in jobs with lower potential to be performed remotely. Yasenov (2020) finds that only 30 percent of the migrants in the United States have jobs suitable for telework, compared to 45 percent of natives. While these papers compare migrants and natives, the impact of immigrants on native workers’ economic and health exposure to the COVID-19-related shocks – the causal linkages – have not yet been fully explored to the best of our knowledge.

Although our paper focuses on health and economic vulnerability in the context of the COVID-19 shock, it also contributes to the broad and older literature on the general relationships between 5
product-destination dataset. The authors find a positive relationship between export unit values and both distance and GDP per capita (GDPpc) but find a weak negative relationship between unit values and market size – as previously found in Baldwin and Harrigan (2011). Görg et al. explain that this might be evidence for quality differentiation across markets and they find that tariffs do not affect Hungarian firms’ fob unit values significantly.

The work of Kugler and Verhoogen (2009) focuses on product-level heterogeneity and investigates the hypothesis that input quality and plant productivity are complementary in generating output quality. They use product-level data for Colombian manufacturing plants to test the cross-sectional predictions of the model. Their findings support the quality-complementarity hypothesis that output prices are positively correlated with plant size within industries, with a stronger effect in industries with more scope for quality differentiation.

In conclusion, this short review of the literature shows that there is weak consensus on how firms adjust their prices according to the characteristics of their trade partners. However, there is evidence that some adjustment happens and that more investigation is needed to explain the different patterns observed in different countries. This work contributes to the literature on pricing behavior in an effort to analyze how exporters in small Least Developed Countries (LDCs) – normally considered price takers - behave with respect to pricing decisions and how firms adjust their prices according to their trade partner’s characteristics. This paper’s analysis benefits from a novel, firm-level dataset from Tanzanian exporters<sup>7</sup> . By accessing this disaggregated information, this paper is able to examine diverse aspects of pricing behavior across both firms and destinations.

# **3. Tanzania Trade Performance**

Tanzania is considered one of the best performers in Sub-Saharan Africa (SSA) for its economic performance over the last decade<sup>8</sup> . Over the 2003-2009 period the economy grew at an average rate of 6.7 percent, nevertheless GDP per capita performed less brilliantly (on average 3.7 percent over the 20032007) due to fast population growth. This indicates that poverty reduction keeps being a concern. The composition of merchandise exports has evolved overtime, characterized by a decrease in the role played by agriculture and food exports versus an increase in the percentage of manufacturing and ores products (as per Figure 1)<sup>9</sup> . The impressive growth of the mining sector has led to a doubling in the contribution of

> 7 The dataset was previously used by Freund and Pierola (2011) and by Cadot et al (2011). 8 Economist Intelligence Unit (2008). 9 Economist Intelligence Unit (2004).

7
of rice, they benefit from a decline in its price. Benefits are especially high for net buyers of rice, non-farmers and for the rural poor in the bottom 30 percent of total per capita household expenditures. Net sellers of rice and farmer households lose from the price decline, especially large farmers.

|**Table 5. Welfare**|**impact of a 27 pe**<br>|**rcent decline of ri**<br>|**ce prices, 1995-9 **<br>|**6 HIES.**<br>|
|---|---|---|---|---|
||Net benefit ratio<br>(NBR=CR-PR)|Simulated<br>short-run<br>welfare impact<br>(% of total<br>expenditures)|Simulated<br>long-run<br>welfare impact<br>(% of total<br>expenditures)|Actual<br>income<br>growth from<br>1995 to 2000<br>(% of total<br>expenditures)|
|Urban|13.2|3.6|4.1|-5.7|
|Rural|-1.8|-0.5|1.3|4.0|
|All households|0.7|0.2|1.8||
|_Rural only:_|||||
|Bottom 30%|16.0|4.3|5.8|9.1|
|Middle 40%|-6.9|-1.9|0.1|0.8|
|Top 30%|-18.8|-5.1|-3.2|3.8|
|Net sellers|-60.2|-16.3|-12.7|-7.7|
|Net buyers|21.0|5.7|6.9|8.7|
|Non-farmers|19.1|5.1|6.2|2.2|
|All farmers|-25.2|-6.8|-4.2|10.5|
|Small farmers|-5.0|-1.3|0.6||
|Medium||-11.1|||
|farmers|-40.9||-7.9||
|Largefarmers|-68.8|-18.6|-14.3||

Note: consumption and production includes the value of own consumption and production. Long-run scenario assumes elasticity of demand equal to -0.5 and elasticity of supply equal to 1.5. Source: 1995 HIES.

An approximation of the long-term welfare effect that accounts for adjustments in household behavior yields higher gains and lower losses. The estimated welfare impact ranges from a loss of 14 percent for large farmers to a gain of nearly 7 percent of total expenditures for net buyers when rice price declines by 27 percent, as it did during the period from 1995-96 to 2000. The losses are especially high for medium and large farmers.

16
productivity growth (Lucas, 1988). For example, Heckman et al (1998) estimate that individuals invest in human capital over the whole life-cycle, but more than one-half of lifetime human capital is accumulated through post-schooling investments taking place on the job. Moreover, differences in total factor productivity account for approximately half of the differences in income across countries and are generally associated with differences in technological progress (e.g. Hall and Jones, 1999). These differences are also large between firms within a single country (Hsieh and Klenow, 2007) and technology adoption and investment in human capital are shown to be core factors in explaining how firms catch up to the technology frontier, and for designing policies to enhance growth and development. Surprisingly, very little research has been done on the differences across firms in the investment in job training around the developing world and the reason also relates to lack of data.

This paper explores a unique firm level data set across 99 countries in the developing world to (1) document how different are the propensities to invest in job training across firm sizes; and to (2) investigate the reasons behind this differential. In particular, we conjecture that SMEs could be less likely to invest in job training because of three broad sets of reasons. First, the _expected_ return on the investment in job training might be smaller for SMEs. This could happen if either SMEs have lower expected benefits or higher marginal costs of the investment in job training. Second, SMEs could be more likely to lack the financial resources to invest, in spite of the possibly large expected returns. Third, in spite of possibly large average returns, SMEs could have worse access to information, face greater uncertainty regarding the returns of this investment or have larger coordination problems with their workers than larger firms.

We explore a large firm level data set, Enterprise Surveys, collected by the World Bank with unique information to study these questions. First, the surveys explore an almost standardized questionnaire across countries, and thus collect information that is comparable across and within countries. Second, the surveys are available for 99 developing countries covering all the geographical regions of the world and income levels. This wide range of countries covered allows us to test the extent to which the existing differentials are explained by differences across countries in their institutions and policies. Third, the surveys collect detailed firm characteristics including variables that 3
- 25 -

fixed by decree or government example. Thus they may not reflect labor opportunity costs in areas of high under-employement or unemployment. World Bank or United Nations reports can be consulted to obtain data on shadow wage rates for rural labor in some countries.

2.18 A more serious criticism of the labor opportunity cost method is the same one leveled against the residual stumpage approach. That is, there is no compelling evidence that the time devoted by rural wood producers internalizes the value of a standing tree. What we usually see are surrogate market transactions for the cutting, splitting and local haulage of firewood, much as these same costs may be directly observed as cash transactions in market methods. A labor ,pportunity cost value for fuelwood should thus be considered a lower bound figure for fuelwood stumpage.

2.19 The above problems can be illustrated with a fictitious example. A village study was undertaken in two periods, five years apart, namely in July 1983 and July 1988. The results in Table 2.2 were obtained from observation of the village fuelwood collectors.

Table 2.2: RESULYS OF A VILLAGE FUELWOOD COLLECTION SURVEY

|Species<br>c-llected|Year<br>1983<br>Acacia<br>sp|Year<br>1988<br>Acacia<br>sp|
|---|---|---|
|Average<br>weight<br>of a headload<br>(kg)|22.3|21.9|
|Moisture<br>content<br>(dry<br>basis)<br>**%**|12|16|
|Headload<br>weight<br>without<br>water (0% mc) (kg|)<br>19.9|18.9|
|Energy<br>value<br>of headload<br>(MJ)|366|347|
|Solidvolume<br>(liters)|26.8|**26.3**|
|Percentage stemwood on the trees|56|62|
|Percentage branchwood on the trees|44|38|
|Percentage<br>of wood not collected|||
|(small<br>twigs,<br>chips<br>etc.)|8|5|
|Distance<br>to the collecting<br>area (km)|3.9|6.3|
|Time walking<br>to collection<br>ground<br>(min)|52|84|
|Time walking<br>back from<br>collecting<br>ground|<br>66|105|
|Total<br>walking<br>time (mins)|118|iSV|
|Time to collect<br>and prepare<br>headload<br>(min|)<br>26|33|
|Hourly<br>rate unskilled<br>labor<br>July 88 terms|||
|rural<br>area (USS)|0.18|0.18|
|Shadow<br>price<br>of labor<br>in July<br>88 terms (S|) 0.12|0.12|
form of rainwater is its higher pressure (for uses such as house cleaning, individual showers and so forth) as well as convenience; i.e., not having to collect, filter, and store rainwater or maintain a water pump, in the case of groundwater. The amenity value of high pressure and convenience would not carry over to water stored or shared between households.<sup>11</sup> That said, our data allow a test for the potential effects of arbitrage, both inter and intrahousehold. With respect to the former, data on water sharing obtained in the quarterly monitoring surveys rules out significant inter-household trade. Just under 2 percent of the experimental sample (about 28 households per quarter on average) shared in piped water from their neighbors over the course of the price experiment, whereas less than 2 percent (about 25 households per quarter) shared out their piped water to a neighboring household that itself had piped water. Moreover, we find no evidence that household water consumption or its response to price discounts were affected by water sharing (in or out) during a quarter in which experimental price differentials prevailed (see Appendix C).<sup>12</sup> With respect to intra-household arbitrage, we use information on the volume of the concrete rain water tank, to which households could potentially divert piped water to store for future use. A larger tank implies greater scope for storage; median tank volume is 5 cubic meters (interquartile range 2-9; 4 percent of households have no tank) against median monthly piped water consumption of 6 cubic meters. If such arbitrage were important, we would expect households with larger tanks to have a greater response to discounts. However, as shown in Appendix Table D.1, row (6), we fail to reject equality of price responses across households with varying water tank sizes ( _p_ -value=0.920).

# **2.3 Results of price experiment**

**Overview** The upper panel of Figure 2 summarizes the experimental prices faced by each group of households. Recall that during the six months prior to January 2016 all households regardless of experimental group faced the same linear price, which we normalize to 1 in the figure. As also noted, once the experiment was underway, price discounts were rolled out successively to the different groups and then withdrawn, with each discount prevailing for six months. A break in this pattern, which we shall return to shortly, was a six-month pause in all

> 11While the option value of not running out of rainwater was also cited as a benefit of piped water, and would be amenable to water sharing, this is an unlikely occurrence for most households.

> 12Since we collected identifiers of all households involved in piped water “trade,” we also checked, using dyadic regressions, whether the likelihood of a transaction is associated with the water price differential across trading households. In results available upon request, we find no significant relationship.

10
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26

# **Situation on the day before the gas day**

- SRMCO System short-run marginal cost of gas.

- Ps. Spot market price of gas with delivery on the gas day (day-ahead gas). **_QiQ,QWO_** The quantity of gas nominated for injection into and withdrawal from BGT's pipeline system according to the approved schedule for the gas day. The system must be in balance: _Qi,=QWu._

- I-V, 1-4 Flexibility bids for a system sell and a system buy.

# **Situation on the gas day**

-

- _Case 1: Positive system imbalance system sell_

- SRMC The SRMC shifts outward because of a positive gas supp'y shock.

- _Qil>Qio_ The quantity of gas injected into the system is above the nominated quantity because of an unexpected increase in the supply of gas. Consumers are unable to change consumption in real time, so the quantity of gas withdrawn from the system is equal to the nominated quantity _(QWl=QW.)._

- _Qil_ **_Qw1_** The difference between the quantities of injected and withdrawn gas is equal to the size of the system imbalance, which is possible because _Qil>QW,._ BGT must sell the excess gas in the flexibility market to restore system balance. BGT accepts flexibility bids I-IV.

- SMPSeII The system marginal price of gas sold by BGT to winning bidders. It is equal to the price of the last accepted flexibility bid.

-

- _Case 2: Negative system imbalance system buy_

- SRMC2 The system SRMC shifts upward because of a negative gas supply shock. _QV2<Qi0_ The quantity of gas injected into the system is below the nominated quantity because of an unexpected decrease in the supply of gas. Consumers are unable to change consumption in real time, so the quantity of gas withdrawn from the system is equal to the nominated quantity _(QW 2_<sup>_=QW.)._</sup>

- _Qi 2_ **_QW2_** The difference between the quantities of injected and withdrawn gas is equal to the size of the system imbalance, which is negative because
The changes we have observed in group differentials and in rank ordering have significance for past studies reporting poverty patterns in CMte d'Ivoire based on CILSS data. For example, Kanbur (1990) decomposes the Pa index for 1985 by socio-economic group in making his assessment of the effects of structural adjustment.6/ The evidence we have presented on group poverty estimates suggests that Kanbur's findings may not be robust to the corrections for sampling bias we have discussed. For example, Kanbur places export crop farmers second in a rank ordering of extreme poverty (behind food crop farmers). However, the H-S weights were found to significantly affect the poverty estimate for this group, reducing the head-count from 11.6 percent to 8.6 percent (Table 14). It is clear that Kanbur's results based on data subject to an uncorrected sampling bias must be interpreted with care. Similarly, Kakwani (1990) analyzes the statistical significance of differences between group poverty levels. His groupings are based on region and on various characteristics of the household head. The adjustments made by the H-S weights to group poverty differentials suggest that Kakwani's results may well have been different had he accounted for the underlying sampling bias. Similar comments would apply to other studies which rely on money-metric measures of welfare and poverty, such as Glewwe (1987).

# VI. **Basic** Needs **Indicators**

Taking per capita expenditure as measure of economic welfare, the CILSS data for 1985 and 1986 have been found to under-estimate living standards and to over-estimate poverty. To what extent does this result also apply to other welfare indicators? In this section an assessment is made of how selected basic needs indicators are changed through the application of the H-S weights.

Our estimates of household expenditure per capita differ from those reported in Kanbur (1990). This is due to variations in data cleaning and editing procedures.

30
PSU (village, town, or neighborhood), such that the estimated differences between Hindus and Muslims are restricted to households in close geographic proximity. Column 5 reports a difference of 5.3 percentage points when jointly including controls for household characteristics, location characteristics, and PSU fixed effects (95% confidence interval: difference – of 1.5pp 9.1pp). The standard errors on the estimates are similar, or decreasing, as additional controls are added because the decrease in degrees of freedom is counterbalanced by the predictive power of the controls. Columns 6 and 7 report this difference separately for urban areas (3.2pp) and rural areas (5.6pp).

For other sanitation practices, panels B and C report little substantive difference between Hindus and Muslims after controlling for the same characteristics. There are some unconditional differences between Hindus and Muslims, with less handwashing and less observed fecal material around Hindu households, but these differences are attenuated when comparing households within similar locations. For handwashing, column 5 reports that Hindus are 1.1 percentage points more likely to report washing their hands after defecating (95% confi– dence interval: 1.7pp less likely 3.9pp more likely). For observed fecal material, column 5 reports that the surveyor is 1.7 percentage points more likely to not observe fecal material near Hindu households (95% confidence interval: 2.2pp less likely – 5.7pp more likely).<sup>17</sup> Table 3 reports estimated differences in latrine ownership for Hindus and Muslims, using data from the DLHS-3. There is an unconditional 22.9 percentage point difference in latrine – ownership between Hindus and Muslims (95% confidence interval: difference of 21.7pp 24.0pp), which is substantively similar to the unconditional difference in latrine ownership in the IHDS data. This unconditional difference is in large part attenuated in columns 3, 4, and 5 when controlling for household location characteristics and/or fixed effects for PSU. Comparing Hindus and Muslims within the same location, with similar access to sanitation infrastructure, Hindu households are 5.7 percentage points less likely to report owning a – latrine (95% confidence interval: 5.2pp less likely 6.3pp less likely). This estimate is

> 17Note that surveyor-observed fecal material near homes reflects that household’s sanitation practices and potentially those of nearby homes.

11