rafmacalaba/datause-encoder-data huggingface dataset:
For the purpose of this paper, the PPI database is an appropriate source of information because it reports the _commitments_ of the investments for each year by country and by sector once a contract has come to financial closure—that is, a license, sale, concession, lease, BOT or other contractual agreement is signed by both parties and financial arrangement have been secured. Having the commitments instead of the actual investments allows us to establish a clearer relationship between investments and country risk at a given point in time. The decision of investing (commitment) and the willingness of financiers to come to closure on that commitment are made, inter alia, in the context of the political conditions, economic performance, sovereign credit worthiness and fear of expropriation at the time of financial closure. Because there may be exogenous reasons for differentiation between an original commitment to invest and the eventual disbursement levels—including external shocks, canny renegotiations or changes in tariffs or relative prices--the best time to value an investment relative to country risk is the moment that the commitment comes to financial closure.

Taking the data from the PPI database we gather information regarding 130 developing countries from 1990 until 2010. The panel data were complemented with data from World Development Indicators with variables such as GDP, GDP growth, inflation, country openness, and foreign direct investments.

For completeness in matching with the PPI database, we chose Euromoney’s Country Risk ratings. While both Euromoney and the International Country Risk (ICR) ratings move similarly and are highly correlated<sup>7</sup> , Euromoney provides a larger sample, covering countries like Cambodia, Georgia, Rwanda and Tajikistan that are not covered by ICR ratings. The Euromoney country risk index is a weighted average of the following indicators: i) Political risk (25%—nonpayment or non-servicing of payment for goods or services, loans, trade-related finance and dividends; and non-repatriation of capital); ii) Economic performance (25%—GNP per capita, and average from poll of economic projections); iii) Debt indicators (10%); iv) Debt in default or rescheduled (10%); v) Credit ratings (10%—average of Moody's, S&P and Fitch IBCA); vi) Access to bank finance (5%); vii) Access to short-term finance (5%); viii) Access to capital markets (5%); and ix) Discount on forfeiting (5%) (Average maximum tenor for forfeiting and average spread over riskless countries).

The country risk index goes from 0 to 100, where the highest risk is associated with an index of 0. The sample covers 131 countries over 21 years.<sup>8</sup> The list of countries covered by the sample is presented in the Appendix 1.

> 7 With a Pearson’s correlation of 0.66

> 8 The database increases throughout the years, with information of 80 countries in 1990 to 131 countries in 2010.

6
For each child under age seven, in the CCT and UCT programs, the mother or father would receive 4,000 FCFA per year<sup>12</sup> , distributed in four quarterly payments. For each child age 7 to 10 (or in grades 1 to 4 in the CCT villages), the mother or father would receive 8000 FCFA per year, while for each child age 11 to 15 (or in grades 5 or higher and younger than 15 in the CCT villages), the mother or father would receive 16,000 FCFA per year, distributed in four quarterly payments. Given the average number of children per household, each household expected to receive about CFA 27,835 per year or the equivalent of $67 USD using the exchange rate at baseline. This corresponds to about to 7 percent of household total expenditures. For comparison, Fiszbein and Schady (2009) note CCT program generosity levels of 1, 6, 17, 22, and 29 percent of household expenditures in Bangladesh, Brazil, Colombia, Mexico, and Nicaragua, respectively. Hence, the Burkina transfer program is between the least and the most generous of the programs described by Fiszbein and Shady (2009).

The program design assumes that each of the treatment groups would receive equal amounts of resources per capita over the two-year transfer program period, if households randomly allocated to the conditional cash transfers fully satisfied the conditionality. In practice, because there was not full compliance with conditionality, the households under the UCT programs, on average, received more resources per capita.

# **3. Empirical Identification Strategy**

The key question we want to answer is whether cash transfers improve education, health, and other welfare outcomes in the recipient households. For educational outcomes, we focus on the impact of transfers on school enrollment, attendance, and achievement tests in math and French for children between the ages of 7 to 15. For enrollment and attendance, we can rely both on household self-reports and on administrative data collected from the school ledgers. For child health outcomes, we focus on routine preventive health clinic visits, anthropometrics (weight, arm circumference, and height), self-reported illnesses, and biomarker blood tests that measured C-reactive protein, which is a marker of inflammation and infections. For household welfare, we focus on livestock, agricultural production, demographics, assets, self-perceptions of social status, housing, and entrepreneurship.

The randomized experimental design provides a strong identification strategy and allows us to attribute any differences in outcome indicators between the treatment and control groups to

> 12 At baseline in 2008, the exchange rate was CFA 415 = $1 USD.

8
education. Then the odds of dying are 0.05/0.95 = 0.053 to 1, or, 18.86 (1/0.053) to 1 in favor of survival. Assume now that the probability goes down to 4 percent if the mother has gone to school. Now the odds of dying are 0.04/0.96 = 0.042 to 1. The odds ratio in this case would be 0.042/0.053 = 0.79, i.e. mother’s primary education decreases the odds of dying by 21 percent. Note that the relative risk, i.e. the ratio of the probabilities of dying, 0.04/0.05 = 0.8 is very close to the odds ratio. For many people, relative risk is more intuitively to grasp (but not straightforward to compute in our proportional odds model). In our specific datasets however, relative risk and odds ratio will always be of similar size, because of the relatively low probabilities of child death (this is meant in a mathematical sense).

Our choice of explanatory variables in the survival models follows similar studies based on DHS data (e.g. Omariba et al., 2007; Ssesewanya and Younger, 2007). They include child characteristics (the child’s gender and whether it is the first-born), mother’s characteristics (a dummy for mothers younger than 18 years and mothers’ educational achievement), a dummy for short-birth interval (less or equal to 24 months), household size, dummies for private or public access to tap water, and wealth groups by rural and urban areas. Furthermore, we include regional dummies on a similar level of aggregation (approx. 5 regions per country) and controls for the birth year (since the children in the sample are born over a time span of 5 years). The more recent surveys also allow us to include the vaccination status, although we have to rely on an imputation method for dead children.

Some additional remarks on the wealth groups are required. Households are categorized into rural poor (the first two rural quintiles), rural rich (rural quintiles three to five), urban poor (the first two urban quintiles), and urban rich (the remaining three upper urban quintiles) based on a wealth index that we compute separately for rural and urban areas using principal component analysis following Filmer and Pritchett (2001). In our specification, poor rural households will serve as the reference category<sup>7</sup> . This implies that the urban wealth dummies also capture the effect of an urban dummy. The variables used for the wealth index include a range of assets, for which ownership is reported in the DHS (for example housing characteristics, bicycle, car, radio, television, fridge etc.). The correlation of the asset ownership with the underlying wealth concept may however differ considerably between rural and urban areas. While owning a bicycle in rural areas may classify one as a rich person, this is certainly not the case in urban areas. A similar reasoning applies to certain housing characteristics, such as iron sheets on the roof or cement floor. Therefore, we prefer our own

> 7 In the fixed effects estimation, since each cluster is either rural or urban, only the dummy for being rich is used to control for wealth.

27
# **Appendix 3: Full results for effects on welfare (OLS)**

For tables A7 and A8, year and cluster fixed effects are included in regression (1) to (3). In Regression (4), household fixed effects replace the cluster fixed effects. All the other controls are included in regression (4), but the time invariant ones drop off. Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Robust standard errors in parentheses. Significance level are: _*** p<0.01, ** p<0.05, * p<0.1._

**Table A7: Detailed Effects of refugee presence on household welfare with analysis at cluster level and** **<u>the distance weighted refugee index at 50km from the clusters (Panel 2009-2012)</u>**

||Log of|welfare(consumpt|ionaggregate peradu|lt equivalent)|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Log of refugees (50km)|0.0235**|0.0236**|0.0273**|0.0217|
||(0.0108)|(0.0108)|(0.0133)|(0.0144)|
|HH head age||0.0026|0.0119**|0.0000|
|||(0.0051)|(0.0056)|(0.0165)|
|Age squared||-0.0000|-0.0001|-0.0000|
|||(0.0000)|(0.0001)|(0.0001)|
|HH head sex (male)||0.0046|0.0244|-0.0778|
|||(0.0347)|(0.0377)|(0.0634)|
|Household size|||-0.0262***||
||||(0.0054)||
|**_HH head marital status_**|||||
|Married monogamously|||-0.4265**||
||||(0.1660)||
|Married polygamously|||-0.3720**||
||||(0.1736)||
|Separated (divorced/widowed)|||-0.4128**||
||||(0.1736)||
|**_HH head education level_**|||||
|Completed primary sch|||0.2228***||
||||(0.0372)||
|**_HH main source of income_**|||||
|Commercial farming|||0.1069||
||||(0.0896)||
|Wage employment|||-0.0003||
||||(0.0486)||
|Non-agricultural self-employment|||0.0901**||
||||(0.0420)||
|Remittances & Others|||-0.0092||
||||(0.0966)||
|**_Other source of HH income_**|||||
|Property income and investments|||0.2060**||
||||(0.0846)||
|Transfers and other benefits|||0.1148***||
||||(0.0418)||
|**_HH Asset_**|||||
|Own > median size of land (2.5 acres)|||0.1999***||
||||(0.0320)||
|Observations|5,453|5,450|4,105|4,102|
|R-squared|0.3897|0.3899|0.3839|0.7172|

7
Table 1: Ratio of female to male child mortality (4ql)

||N|Med-<br>ian|Std.<br>Dev|Highest|Ra|nge<br>Lowest||
|---|---|---|---|---|---|---|---|
|Non-South<br>Asian<br>countries|58|1.01|.17|Egypt (1992)<br>Paraguay (1990)<br>NE Brazil (1991)|1.46<br>1.23<br>1.22|Kazakstan (1995)<br>Ghana (1993)<br>Colombia (1990)|.47<br>.51<br>_.51_|
|India<br>(States)|19|1.44|.35|Haryana<br>Punjab<br>Uttar Pradesh|2.35<br>1.81<br>1.70|Tamil Nadu<br>Kerala<br>Goa|.80<br>.94<br>1.11|
|Pakistan<br>(Provinces)|4|1.52|.54|Punjab<br>Baluchistan<br>_Sindh|2.06<br>1.79<br>1.24|NWFP|.86|
|South Asia|_5_|1.33<br>_|.25<br>_____|Pakistan (1990)<br>India (1992)<br>Bangladesh (1993)|1.66<br>1.43<br>1.33|Sri Lanka (1987)<br>Nepal (1996)|.99<br>1.24|

Notes: A higher ratio indicates that females are MORE likely to die between ages one and four than are males.

What do the data on child mortality ratios show? First, that gender disparities are worse in South Asian countries than in the rest of the world. Figure la shows a map of the world where the shading given each country reflects the female to male ratio in child mortality<sup>9</sup> . The median in tiLe non-South Asian countries is exactly one, meaning that in a typical DHS country outside of South Asia, male and female children are equally likely to die between ages one and five. In Pakistan, India and Bangladesh, however, this is not the case.

A girl is between 30 and 50 percent more likely to die between her first and fifth birthdays The cutoff points between the various levels are determined by pooling all the (country, Indian state, and Pakistani province) data and selecting the values at the 15th, 50th, and 85th percentiles.

8
will do better than bad firms in sales, and at the same time more likely to innovate. In other words, the correlation between innovation and firms’ performance might be driven by unobserved characteristics of the firms. We address that issue by using panel data: we only consider the change of sales within the same firm, not across firms. By looking for the change within a firm, we effectively control for firms’ time-invariant characteristics.

The second issue is the omitted variable problem. We will not be able to capture any unobserved change in firms’ characteristics between the two waves of the survey. For example, a firm might go through a restructuring and at the same time introduce a new product. The observed change in sales could then be the results of both innovation and the restructuring. In our regression, we try our best to capture unobserved changes by controlling for the change of the top manager. Specifically, we include a dummy which equals 1 if the firm changes the manager between the two waves of the survey. Many changes in a firm’s structure, management style or marketing strategies come from a new manager (see Bloom and Van Reenen, 2010).

Another concern is the issue of reverse causality between the change in sales and innovation. Specifically, one could argue that perhaps changes in sales also affect innovation. For example, when a firm witnesses a decline in sales and market share, it might want to introduce a new product or service to halt the declines In this case, the correlation between innovation and the change in sales would tend to be negative (i.e. a negative change in sales leads to a positive change in innovation). The OLS results would then underestimate the true impacts of innovation on sale and employment. The reverse causality is more severe if the innovation process is quick, for example, when the sales decline, the decision to innovate and the introduction of a new product all take place in the same period. The reverse causality is less severe if the innovation process takes time. If there is a “time-tobuild” period between when the innovation decision is made and a new product is introduced, the introduction of a new or improved product is likely too late for, and hence uncorrelated with the sale declines.

None of the variables available in the survey can serve as a good instrument variable for innovation. An ideal instrument should capture firms’ perception about _intellectual_ right protection. Unfortunately the variable is not available. The best two candidates for the instrument that we can find are (1) firms’ concern about competition practice of the informal sector and (2) the amount of R&D a firm invested in the previous wave of survey. They are still flawed because they still violate the exclusion restriction. Regarding (1), although a large part of the concern about informal sector has to do with intellectual property right infringement, the concern can also be about the labor or tax 10
models that economic shocks are the basis for filing behavior. However, we extend the notion to consider a setting with sectors that contain more than one product (not all of which are filed against) and more than one source country (again not all of which are filed against). The disaggregated nature of the data helps to isolate the likely source of the various shocks (foreign and specific or domestic and common to all sources and products) and also to address a number of issues on which the theoretical models are silent. For example; what happens if more than one shock occurs and how persistent do shocks need to be to induce filing?<sup>9</sup> While our focus is on dissecting the import data, it is also possible that filing is primarily about political factors. For example, it may be that domestic characteristics (industry concentration, location in a marginal electorate, etc.) will be more important than changes in imports.<sup>10</sup> Another set of arguments suggests that target countries are chosen in an international game of tit-for-tat. If such political factors dominate filing behavior then decomposing the import data is likely to yield little information. We focus on economic phenomena observable in the import data to provide insight into why some countries are filed against and not others, and why some products are filed against and not others.

# **3 Data**

To achieve these goals we utilize the information on the nature of the products named in the AD petitions and combine it with detailed U.S. trade data.<sup>11</sup> Information on the individual anti-dumping cases is drawn from Bown (2012). This dataset contains information on the case numbers, countries filed against, HS codes and the date a petition is filed. The presence of the HS codes allows us to link this information to detailed U.S. trade data. Our trade data are the HS product level information on U.S. imports available in the monthly reports of the _U.S. Imports of Merchandise Data_ .<sup>12</sup> These data contain substantial information on the value and quantity of imports at the product level. From this value and quantity information we derive unit values, providing us with a very detailed dataset for analysis. We use _Imports for Consumption_ , the classification of U.S. imports used in

> 9The focus on supply and demand shocks is similar to that in Grossman (1986), Pindyck and Rotemberg (1987) and Kelly (1988). However, their methods and data differ from ours along with the specific questions they address.

> 10We employ product-specific fixed effects, which should sweep out average differences in the underlying propensity to

> 11Unfortunately the relevant trade data contained in the petition is not made available to the public.

> 12Despite the public availability of these data they are relatively underutilized. Exceptions include Hummels and Schaur (2010), Chor and Manova (2012), and within the AD literature, Besedeˇs and Prusa (2013).

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- 36 -

# **Export-Related Barriers**

A3. 19 Some export-related barriers are identical to those affecting imports, although their relative impact may be different. They are handling costs, freight conference agreements, domestic transport regulations, and _transit charges._ Described below are the barriers specifically related to export activity.

A3.20 **Unloading** Costs. Analysis of the citrus transport chain in Tunisia (Maltese oranges) made it possible to identify in detail the costs affecting exported merchandise. Unloading costs are one link in this transport chain. For unloading costs, the exporter can neither choose the handling company nor negotiate the cost of its services, since the cost is usually incorporated in the sea freight charge. The excess cost may be as high as 10% (Categories: TDA1, TDA2. Transport chain: Transport/Maritime, TU.) A3.21 **Dumping Backhaul Rates.** TIR traffic is growing apace in both Morocco and Tunisia. 80% of this traffic is controlled by European international transport companies. Their powerful position in Europe enables them to channel and regroup not only North-South traffic but also return traffic (refer to the study on textile chains in Morocco and Tunisia). The cost of getting TIR trailers back on the road, if not covered by revenue-producing traffic in both directions, is supported by their North-South fees. Here, their offer of dumping rates for the return journey defeats any potential competition from Maghreb operators, who are unable to find competitive North-South return rates because they lack commercial offices or any logistical organization abroad. (Categories: TDA1, TDA2. Chains: TIR Transport, MO; Textiles/TIR, MO.) A3.22 **Lack of International Marketing.** Lack of international marketing largely explains why some Maghreb exporters miss the opportunity to expand their sales abroad. They are content to rely on FOB export arrangements. A good case in point is that of Tunisian olive oil, a product of recognized quality and appreciated internationally for its very low acidity. The entire output is sold FOB to importers in Italy, where it is blended and refined. The difference in profit margins between FOB Tunisia and FOB Italy is 1:5. (Category: TDA2. Chain: Transport Federation, TU.) A3.23 **Brokerage Costs.** Getting certain export products (especially citrus fruits and early crops) to market requires organization at destination that is especially focused on distribution through Europe. Although the broker or commission agent on whom the exporter must rely can be forced to compete with others, it is still very difficult for the exporter, who maintains no permanent supervisory office at the destination point to evaluate the quality of the service he is getting (sale of his goods at the highest price) or the cost at which it is being provided. This common organizational gap causes a significant excess cost charged against the sale proceeds repatriated to them in the country of origin. (Category: TDB2. Chain: Transport/Maritime, TU.)
||Mar-Jun 2021(RRPS R5)|47.9||Fullyopen**|
|---|---|---|---|---|
||Jul-Nov 2021(RRPS R6)|80.1||Fullyopen**|
|Malawi|||||
||Apr-Nov 2019(LSMS W5)|66.9||Pre-COVID|
||Apr-May2021(HFPS R10)|83.7||Fullyopen**|
|Nigeria|||||
||Jan-Feb 2019(LSMS W4)|73.2||Pre-COVID|
||Oct 2020(HFPS R6)|59.0|54.3|Partiallyopen|
||Mar 2021(HFPS R11)|81.6||Fullyopen|
|Uganda|||||
||Mar 2019-Feb 2020(LSMS W8)|85.4||Pre-COVID|
||Oct-Nov 2020(HFPS R4)|53.8||Partiallyopen|
||Mar-Apr 2021(HFPS R6)|31.5||Partiallyopen|
||Sep-Nov 2021(HFPS R7)***|65.2||Partiallyopen|

_Notes:_ * estimates for Kenya refer to children aged 10-17 years. Estimates for Malawi refer to children aged 15-18. ** with academic break. *** Uganda R7 asked about school attendance in May 2021.

Before the onset of the pandemic and according to the last pre-COVID 19 multi-topic household survey available to the research team, adolescent girls were less likely to be in school than adolescent boys at the aggregate level, but this masks important disparities across countries. Particularly, adolescent boys were more likely to attend school than adolescent girls in Kenya (92.5 vs. 91.1 percent), Malawi (73.5 percent vs. 60.5 percent) and Nigeria (74.6 vs. 71.7 percent). Only Uganda showed the opposite pattern (83.6 vs. 87.2 percent), while in Burkina Faso and Ethiopia pre-COVID gender gaps were not significant.

During the pandemic, strikingly, all gender gaps favoring boys disappear. In 2020-2021, adolescent girls are nowhere less likely than adolescent boys to attend school. In fact, girls were significantly more likely to attend school than boys in 5 rounds out of the 13 rounds, compared to zero rounds with significant differences favoring boys (Figure 2). When comparing pre- and post-COVID gender gaps directly, Table A3 in the Appendix shows that gender gaps significantly change to become favorable to girls in three survey rounds compared to pre-pandemic gender gaps. In Kenya, two out of 4 post-pandemic rounds of data reveal a significant increase in the attendance of girls compared to boys, leading to a reversal of the gender gap in favor of boys observed pre-pandemic. This trend is also notable in Nigeria in the round of data collected at the start of the pandemic, although the change in the gender gap becomes close to zero and insignificant in the last round of data collection (see Table A3 in Appendix). In Uganda, where the preCOVID gender gap was already in favor of girls, we see an increase in this gap in favor of girls in the round collected at the start of the pandemic. In the two subsequent rounds, the gender gap becomes insignificant, as in Nigeria. In contrast, in Burkina Faso, while we did not observe a significant gender gap between boys and girls before the pandemic, girls are more likely to attend schools compared to boys post-COVID (Figure 2). Based on these data, none of the countries included in this study saw gender gaps to the disadvantage of girls emerging during the pandemic.

10