rafmacalaba/datause-encoder-data huggingface dataset:
Supplementary Table 5. Directory of variables in the individual-level dataset

|**Variable name**|**Label**|**Data processing notes**|**Additional notes**|
|---|---|---|---|
|_indiv_id_obs_|Individual ID<br>(panel<br>identificator)|Variable created by assigning<br>a numeric code to each unique<br>individual. The first number<br>of the code determines the<br>country.||
|_indiv_id_merge_|Individual ID<br>(to merge<br>with raw data)|<br>ID to merge with raw LSMS<br>data|Can be merged with the following<br>variables in the raw data<br>(concatenated variables are separated<br>by hyphens):<br>ETH:_individual_id_(wave 1)_,_<br>_individual_id2_(wave 2)_,_<br>_individual_id2_(wave 3)_,_<br>concatenation of_household_id_and<br>_individual_id_, (wave 4)_,_<br>concatenation of_household_id_and<br>_individual_id_(wave 5)<br>MWI:_PID_(wave 1),_PID_(wave 2)_,_<br>_PID_(wave 3)_, PID_(wave 4)<br>MLI: concatenation of_grappe_<br>_menage,_and_numero d’ordre_(wave<br>1), concatenation of_grappe_<br>_exploitation_and_numero d’ordre_<br>(wave 2)<br>NER: concatenation of_hhid_and<br>_numero d’ordre_(wave 1)_,_<br>concatenation of_GRAPPE,_<br>_MENAGE, EXTENSION_and_numero_<br>_d’ordre_(wave 2)<br>NGA: concatenation of_hhid_and_id_<br>(wave 1)_,_concatenation of_hhid_and<br>_id_(wave 2)_,_concatenation of_hhid_<br>and_id_(wave 3)_,_concatenation of<br>_hhid_and_id_(wave 4)<br>TZA: concatenation of_hhid_and<br>_member number_(wave 1)_,_<br>concatenation of_y2_hhid_and_indidy2_<br>(wave 2)_,_concatenation of_y3_hhid_<br>and_indidy3_(wave 3)_,_concatenation<br>of_y4_hhid_and_indidy4_(wave 4)_,_<br>concatenation of_sdd_hhid_and<br>_sdd_indid_(wave 5)<br>UGA: concatenation of_Hhid,_and<br>_PID_(wave 1)_, HHID_and_PID_(wave<br>2)_, HHID_and_PID_(wave 3)_, HHID_<br>and_PID_(wave 4)_, HHID_and_PID_<br>(wave 5)_, hhid_and_PID_(wave 7)_,_<br>_hhid_and_PID_(wave 8)|
|_relationship_head_<br>_age_|HH Member<br>Relationship<br>to HH Head<br>Age (inyears)|Absent of unified codes, this<br>variable is coded as a string<br>Capped at100||

38
compensated income at step _t_ is evaluated as follows: where _C_ 0 = _m_ , the Slutsky matrix element _φt_ [ _i, j_ ] =<sup>_∂x_</sup> _∂p_<sup>_i_</sup> _j_<sup>_<u>,t</u>−xi,t_</sup> _∂x∂Cj,t_<sup>and ∆</sup><sup>_pt_+1=</sup><sup>_pt_+1</sup><sup>_−pt_.</sup> As indicated in Breslaw and Smith (1995), the estimation of the _EV_ is based on an algorithm which is quite similar to that of the _CV_ such as: _EV_ ( _p_<sup>_a_</sup> _→ p_<sup>_b_</sup> ) = _−CV_ ( _p_<sup>_b_</sup> _→ p_<sup>_a_</sup> ).<sup>14</sup>

# **4.7 The ordinary differential equations methods**

Welfare change measurements can be expressed as an integral of Ordinary Differential Equations (ODE). It is assumed that the welfare measurement function, like _CV_ or _EV_ , is continue and first order differentiable with respect to the price (i.e. _w_<sup>_′_</sup> = _f_ ( _p, m_ )). It follows that the integral of the function _f_ ( _p, m_ ) between _p_<sup>_a_</sup> and _p_<sup>_b_</sup> is equal to the welfare change. Mathematics offers numerous numerical methods mostly based on an adaptive process of estimation with successive steps of evaluation. Economists are generally less familiar with these approaches while they use extensively the Taylor approximation approach, even with large changes in independent variables (price in our case). However, the Taylor approach can fail to evaluate accurately the function when the order of approximation is low or when the price change is large.

Numerical approximations can be helpful for several reasons. First, they enable to overcome the integrability problem, since they use an adaptive process and evaluate the impact for successive small price changes. This also makes the substitution effect practically nil given the small price change in each step. Second, they enable to overcome the problem of higher order derivatives, especially in the case of complex functional forms. This simplification derives from using the first derivative to approximate higher orders derivatives. Third, compared to the Taylor approximation approach, this method is the most relevant in the case of large price changes (see Lim (2012) for an extensive review of these methods). As an example, we introduce two popular numerical approximation methods: The Euler method and the Fourth Runge and Kutta method.

The **Euler method** can be viewed as a solution of a 1<sup>_st_</sup> order Ordinary Differential Equations (ODE). Formally, it is based on the following main function: where _h_ = ( _p_<sup>_b_</sup> _− p_<sup>_a_</sup> ) _/n_ ; _pi_ = _p_<sup>_a_</sup> + _ih_ and _pn_ = _p_<sup>_b_</sup> .

The **Fourth Runge Kutta Method** (RK4 in short) is based implicitly on the fourth Taylor approximation: where 14Breslaw and Smith (1995) claim that their algorithm converges faster than the Vartia’s algorithm while the work done by Sun and Xie (2013) shows the superiority of the latter. We tested the performance of the two algorithms both programmed in Stata and we found the Breslaw and Smith algorithm to be faster.

27
sold and the sales value of each of 2,018 products at the 7-digit ISIC level (revision 2). Appendix Table 1 provides some examples of 7-digit ISIC level categories to illustrate the level of detail of the products. The ENIA census described in detail in Fernandes and Paunov (2008) is an unbalanced panel of plants capturing entry and exit that includes information on basic plant characteristics such as employment or ownership, and on accounting variables such as sales.

For each product _p7_ of plant _i_ in year _t_ we construct a unit value as _UVitp_ 7  _Sitp_ 7 _Qitp_ 7 , where _S_ is the value of sales and _Q_ is the physical quantity sold. A unit value measures the average price charged by a plant for each product in a year. We assume that an increase in unit values proxies for plant product quality upgrading. Our dataset reports the physical quantities of the 2,018 products in 20 different measurement units, some of which are shown in Appendix Table 1. The unit values for products measured in different units (e.g., price per kilogram, price per liter) are not comparable. To obtain our final estimating sample, we address two issues on the measurement units of the products’ physical quantities: (i) some plants do not report the measurement unit of their products’ quantity, and (ii) some plants report their products’ quantity in a different unit than the unit in which the majority of plants report product quantities. The unit values of both types of plants cannot be compared to those of other plants producing the same 7- digit product and are thus excluded from the final sample. Further, to eliminate potential outliers we exclude the top and bottom 5% of the distribution of unit values for any 7-digit product. Appendix 2 describes further the cleaning procedures used for the products dataset and some tests performed to assess the goodness of the data. Our final sample combining the products dataset with the ENIA census includes 55,294 plant-year-product observations with the average number of products manufactured per plant being 2.3. Navarro (2008) shows that many stylized facts 7
person marketing campaign. The personal marketing visit may have swamped the treatment effects of the messaging such that without the personal marketing visits, the messaging would have changed behavior. In other words, people who do not respond to a personalized visit may be more committed nonrecyclers and may be unresponsive to messaging treatments, but those who do respond to a personalized visit may have responded to a messaging treatment even if they had received one without a personalized visit. This issue is not one of internal validity, but it does make the results less widely applicable. Because the messaging treatments were tested in an environment in which everyone received a personalized visit to promote recycling, we can only conclude that messaging is ineffective under those specific constraints. Burn and Oskamp (1986) also used a mix of in-person visits and written messaging, but their control group received neither. Their results showed a large, significant increase in recycling behavior for all treatments relative to their control but no significant differences between the three treatment arms. Although not conclusive, this finding provides further evidence that inperson visits may be the most important factor in incentivizing good recycling behavior.

We propose several other explanations for the failure of the treatment to generate an increase in participation or participation intensity. One possible explanation is that the messages may have been inappropriate for this context because they were motivated by studies in the United States, where there are different norms, attitudes, and knowledge about recycling. A related explanation is that the strength of opinions may differ. It is difficult to find information on attitudes specific to recycling, but we were able to look more closely at the environmental questions from the World Values Survey (2005) in Peru and the United States. As noted previously, Peruvians were just as likely as Americans to report that they felt that protecting the environment was important and were 18
87 where _pik_ and _Qik_ are baseline producer prices and quantities of crop _k_ in district _i_ , _Nk_ is the number of Sri Lankan districts with nonzero production of crop _k_ , _K_ is the set of crops for which data on price increases is available, and _p_<sup>_cf_</sup> _ik_<sup>(</sup><sup>_sK_)isthecounterfactualproducerpriceofcrop</sup><sup>_k_in</sup> district _i_ assuming _σK_ = _sK_ . The crops for which price increase data is available are: rice, maize, potatoes, cloves, onions and groundnuts.

We test several possible candidate values _sK_ , ultimately choosing the value _s_<sup>_∗_</sup> _K_<sup>=2</sup><sup>_._377that</sup> generates a counterfactual average price increase of _p_ ̄<sup>ˆ</sup><sup>_cf_</sup> ( _s_<sup>_∗_</sup> _K_<sup>) = 1</sup><sup>_._2265, which matches the 22.65%</sup> increase observed in the data between January 2021 and January 2022 (shown in Appendix Figure A11). Specifically, our average crop price increase calculation is done in three steps: (i) computing the nominal price increase of each crop in _K_ between January 2021 and January 2022; (ii) deflating these nominal increases using the period’s cumulative inflation (17.61%) to obtain real price increases; and (iii) computing the average crop price increase as a weighted average of crop-level real price increases, with each crop’s weight given by its revenue share at baseline (as in equation 46). We set _σ_ ˆ _K_ = 2 _._ 377 as our estimate for the elasticity of substitution across origins of the same crop.

# **D.B Trade Costs**

In Section VI.A.2, we estimate the parameters ( _α_ , _{βk}k_ ) that govern trade costs according to equation (34). To estimate semi-elasticity _α_ , we use two key pieces of information: first, the tea transportation cost _pT,_ 100 is 1,496 LKR/MT for a distance of 100 km (according to the Ceylon Petroleum Corporation); second, the price of tea _ptea_ is 455,473 LKR/MT (according to the DCS). The tea transportation cost is calculated based on the average distance from farms to tea brokers’ warehouses in the city of Wattala, assuming that the cargo is transported by a lorry with a capacity of 7,500 kg charging typical prices.<sup>114</sup> To compute domestic “factory-gate” tea prices ( _ptea_ ), the price of green leaf needed to produce one MT of made tea is added to processing costs (labour, fuel, electricity, machinery upkeep, packing materials, and factory sundries). The corresponding iceberg trade cost can be written as _τni,tea_<sup>_A_=1 +</sup><sup>_<u>p</u>_</sup> _p_<sup>_T_</sup> _tea_<sup>_<u>,</u>_100, which we substitute in equation (34) to obtain the</sup> following relationship: where _α_ ˆ is our estimate for the semi-elasticity _α_ . We solve this equation to obtain: _α_ ˆ = 3 _._ 3 _×_ 10<sup>_−_5</sup> . To estimate crop-specific international trade costs ( _{βk}k_ ), we use FAO data on each crop’s producer price in the RoW ( _pRoW,k_ ), in USD/kg, and data on each crop’s “benchmark price” at the

> 114 The data is available at: https://ceypetco.gov.lk/historical-prices/.
14

# Box 3: Methodology Used to Construct the Modified IEF Index.

We modify twenty percent of the index constructed by Johnson and Sheehy (I 996) by replacing the two components that relate to the degree of openness of the economy (i.e., trade and capital flows). The original IEF index emphasizes only the consequences of tariffs to evaluate the openness to international trade. We will replace this by considering not only the average level of tariffs, but also the share of industrial output affected by quantitative restrictions. Also, the original IEF evaluates a country's openness to capital flows by examining the degree to which each country interferes or not with foreign direct investment. This evaluation is mostly based on conclusions derived from an analysis of the legal and regulatory framework of the country. We assume a more market oriented, albeit still limited, methodology to measure capital movements by centering only on the existence of premiums in exchange rates as represented by the difference between the market and official exchange rate. Thus, our degree of openness of the economy has three components (i.e., tariffs, QRs, and exchange rate premiums), each with a weight of one third in the final index representing the degree of openness of the economy, and the period under study corresponds to the 1970s and 1980s. Several sources are used, among them Sachs and Warner (1995) and the IMF's International Financial Statistics. The main advantage of this modified IEF index is that it contains more "time series information" than the original series compiled by Johnson and Sheehy.

To construct the index, a value in the range I to 5 is assigned to each of the three indicators of openness previously mentioned and a weighted average of the mean of these three indexes and the remaining eighty percent of the IEF index is calculated. The table in this box represents how the index values were assigned for an economy's degree of openness. To summarize, the countries with a value between the mean and the median for each of the three indicators is assigned an index value of 3. Those countries with indicator values between zero and half the distance to the median (which happens to be the smallest between the latter and the mean) are assigned a value of 5. From there to the median a value of 4 is given to the complying countries. The value of 2 is assigned to countries that are in between the mean value and the upper bound reported in the table below. The index value of one is reserved to those countries which register the worst openness indicators.

## **Intervals Used to Assign Index Values to Openness Indicators.**

|Value<br>Assigned|Average Share<br>of Tariffs|Average Share of Industrial<br>Output Subject to QRs|Premiums in the<br>Exchange Rate Market|
|---|---|---|---|
|5|x < 0.065|x < 0.04|x S 0.05|
|4|0.065•x<0.13|0.04•x<0.1|0.05sx<0.1|
|3|0.135x<0.17|0.1Sx<0.21|0.1Sx<0.25|
|2|0.17Sx<0.7|0.21 sx<0.5|0.25sx<0.5|
|1|0.7Sx|0.5Sx|0.5S•x|
|Minimum Value|0.000|0.000|0.000|
|Median|0.133|0.103|0.115|
|Mean|0.174|0.214|0.426|
|Maximum Value|1.319|0.888|8.015|
all received less than US$ 3 per school-age refugee in ODA.<sup>26</sup> Most notable among this group is Chad, which in 2021 received just over US$ 2 per school-age refugee<sup>27</sup> despite being the 10<sup>th</sup> poorest country in the world and hosting an estimated 200,000 school-age refugees (REFD, 2024).

_Figure L: Financing gap for inclusive refugee education in the 25 largest refugee host LICs and MICs, 2021_<sup>_28_</sup>

|Country of<br>Asylum|Education Financing per school-<br>age refugee (5-17) (US$)|Cost of education per school<br>age (5-17) refugee (US$)|Financing gap (US$)|Proportion of cost met by ODA|
|---|---|---|---|---|
|Jordan|1,901|1,114|787|171%|
|Lebanon|613|1,861|1,249<br>-|33%|
|Turkiye|532|1,711|1,180<br>-|31%|
|Iraq|240|737<br>|497<br>-|33%|
|South Sudan|166|147|19|113%|
|Cameroon|159|237|78<br>-|67%|
|Tanzania|154|199<br>|44<br>-|78%|
|Ethiopia|129|125|5|104%|
|Bangladesh|118|190|72<br>-|62%|
|Kenya|116|407<br>|290<br>-|29%|
|Sudan|62|207|144<br>-|30%|
|DRC|52|56|4<br>-|93%|
|Uganda|42|89|47<br>-|47%|
|Niger|32|106|74<br>-|31%|
|Ecuador|26|789|763<br>-|3%|
|Colombia|21|2,083<br>|2,062<br>-|1%|
|South Africa|17|2,094|2,077<br>-|1%|
|Pakistan|9|363|355<br>-|2%|
|Egypt|7|401<br>|394<br>-|2%|
|India|3|406<br>|402<br>-|1%|
|Peru|3|1,578|1,576<br>-|0%|
|Chad|2|110|107<br>-|2%|
|Iran|2|691|689<br>-|0%|
|Mexico|0|1,923|1,923<br>-|0%|
|Brazil|0|2,548<br>|2,548<br>-|0%|

# _Source: REFD (2024)_

Key Fact 10: ODA for refugee education in LICs and MICs is primarily allocated to Syrian refugees Further disaggregation of ODA for refugee education in LICs and MICs reveals that not only is financing skewed towards specific countries, but towards one specific crisis.

Figure M uses country of origin data for each of the 25 largest refugee hosting countries to create an estimate of financing for refugee education by crisis.<sup>29</sup> This analysis shows that ODA for refugee education in LICs and MICs is heavily skewed towards countries hosting Syrian

> 27 Ibid.

> 28 Cost data on refugee education financing is taken from Silva (2023) and refers to the costs of education for refugee children via the host country’s national education system.

> 29 This calculation assumes that all the refugees in the country receive the same proportion of ODA for education.

26 Ibid.

20
# **Conclusions**

We have provided new estimates of the aggregate poverty measures and their regional and urban-rural breakdown for the developing world based on household survey data. The longest time series we have estimated here follows past practice in the World Bank’s global poverty measures of not incorporating an allowance for the higher cost of living in urban areas. We have provided an update of these measures to 2004. We find a clear trend decline in the percentages of people below each of the international poverty lines, though naturally with less progress in reducing the numbers of poor. Indeed, using our higher line, the count of the poor has been rising over most of the period, and there has not been much progress in reducing the number of people living below our lower line (at roughly $1 a day) when one looks at the developing world outside China. However, it is encouraging that we do find evidence of progress in reducing poverty after about the year 2000.

The overall picture is fairly similar when we allow for the higher cost of living in urban areas, although this change in methodology adds about 100 million to the count of the number of people living under $1 a day at rural prices. We find that three-quarters of the developing world’s poor live in rural areas, when assessed by international poverty lines that aim to have a constant real value (between countries and between urban and rural areas within countries). The poor are urbanizing faster than the population as a whole, reflecting a lower-than-average pace of urban poverty reduction. Over 1993-2002, while 50 million people were added to the count of $1 a day poor in urban areas, the aggregate count of the poor fell by about 100 million, thanks to a decline of 150 million in the number of rural poor.

16
# **Addressing endogeneity**

An obvious concern when analyzing the relationship between suffering and nonviolent conflict is that suffering is endogenous. Nonviolent conflict cannot just be the consequence of human suffering but also the cause of it, albeit to a lesser extent than violent conflict. Therefore, we instrument suffering with the number of people dying from infectious and parasitic diseases per 1,000 inhabitants, obtained from the WHO statistics office. These diseases tend to cause considerable human suffering, as they lead to fear of contracting the disease, suffering due to the disease itself and grief caused by losing loved ones. As such, it reflects the strategy taken by Liberini et al. (2017), who instrument subjective well-being with the deaths of loved ones. In addition, these diseases tend to be difficult to manage and when controlling for GDP, poverty and time-invariant factors, they are likely to be uncorrelated with the error term.

_Table 3 Robustness analyses: regression models with instrumental variables_

||(1)<br>2SLS|(2)<br>2SLS|(3)<br>1-step<br>System<br>GMM|(4)<br>1-step<br>System<br>GMM|(5)<br>2-step<br>System<br>GMM|(6)<br>2-step<br>System<br>GMM|
|---|---|---|---|---|---|---|
|Suffering|0.069<sup>+</sup>|0.118<sup>+</sup>|0.019*|0.019*|0.021*|0.021*|
||(0.041)|(0.070)|(0.009)|(0.009)|(0.010)|(0.010)|
|Struggling||0.041*<br>(0.002)||0.007<br>(0.008)||0.008<br>(0.009)|
|Nonviolent Conflictt-1|||0.269***<br>(0.059)|0.263***<br>(0.058)|0.238***<br>(0.055)|0.228***<br>(0.056)|
|Constant|||-3.347**|-3.821***|-3.296**|-4.015**|
||||(1.066)|(1.140)|(1.170)|(1.223)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Control Var|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|859|859|862|862|862|862|
|Countries|115|115|118|118|118|118|
|Instruments|17|17|99|107|99|107|
|Kleibergen-Paap LM statistic|4.505*|3.444<sup>ꝉ</sup>|||||
|Weak identification F-statistic|7.187|4.601|||||
|Sargan test|||90.49|96.65|90.49|96.65|
|Hansen J statistic|||74.94|77.16|74.94|77.16|
|AR1 test|||-6.430***|-6.393***|-4.869***|-4.853***|
|AR2 test|||0.192|0.211|0.065|0.048|

The instrument is the number of deaths due to infectious diseases (per 1.000 inhabitants), complemented with the lag of difference of the independent variables for the GMM models. The dependent variable is the number of antigovernment demonstrations and strikes, transformed using the inverse hyperbolic sine transformation. Robust standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05,<sup>+</sup> p<0.10 21
firms in six African countries invested more in their workers’ human capital; Mouelhi (2009) also documents an additional human capital investment among internet-connected firms in Tunisia. Internet connectivity can affect human capital development both at home and in school. Bianchi et al. (2020) show that connecting high-quality teachers in urban areas of China with millions of students in rural primary and middle schools improved students’ long-run academic achievement, labor-market outcomes, and internet use. However, the overall evidence on the impacts of connected schools on test scores is mixed thus far. Positive impacts have been documented in Peru (Kho et al., 2018) and Malawi (Derksen et al., 2019); by contrast, work in Peru (Malamud et al., 2019) and Brazil (Bessone and Dahis, 2020) has found no impact on test scores.

Internet access can also affect labor productivity by improving firm-worker matching, especially in developing economies where frictions in the labor market are large. Empirical evidence is largely from high-income countries, such as the US (Kuhn and Mansour, 2011) and Norway (Bhuller et al., 2020). Lederman and Zouaidi (2020) find a robust, negative relationship between internet usage and long-term, frictional unemployment across countries. Internet access can also increase firm-level productivity by facilitating the adoption tangible inputs such as machines, new technologies, and intermediate materials, and the adoption of intangible inputs such as management, organizational practices, and services. Evidence on the take-up of tangible inputs in response to internet connectivity is limited, but such high-speed connectivity has been shown to affect the organization of production and trade in developing countries. Houngbonon et al. (2022) find that individual firms in Africa are 20 percentage points more likely to undertake process innovation and 12 percentage points more likely to undertake product innovation when fast internet becomes available. Tian (2021) shows that internet access allows firms in urban areas to reorganize production to enhance collaboration and facilitate the division of labor.

Using enterprise-level panel data, Gibbons et al. (2016) examine the effects of investments in road construction on employment and labor productivity in the 8