rafmacalaba/datause-encoder-data huggingface dataset:
economy, such as through rural labor-market tightening and remittances back to rural residents stemming from migration to urban areas.

Claim 4 is interpretable as the outcome of what can be termed a “mixed Kuznets process.” Intuitively, urbanization entails gains to the poor (both directly as migrants and indirectly via remittances), but the gains are not large enough for all previously poor new urban residents to escape poverty. Thus the migration process puts a brake on the decline in urban poverty incidence, even when rural poverty and total poverty are falling. To give a sharp characterization of this effect, suppose that a proportion δ of the population shifts from rural to urban areas, of which a proportion α attains the pre-existing urban distribution of income (the successful migrants) while 1 − α keeps the rural distribution. (Only when α = 1 do we have the pure Kuznets process.) The initial difference in poverty rates between rural and urban areas is _H r_ − _H u_ > 0 where _H k_ is the headcount index in sector _k_ = _u,r._<sup>6</sup> It is plain that this urbanization process will reduce aggregate poverty — the national headcount index falls by αδ ( _H r_ − _H u_ ) — but it will increase the poverty rate in urban areas, which rises by 1( − α ) δ ( _H r_ − _H u_ ) /( _S u_ + δ ) , where _S u_ is the initial urban population share.

The following section describes our methods and data. Section 3 assesses whether our estimates of urban and rural poverty measures for the developing world are consistent with Claims 2-4 while section 4 does the same for Claim 5. Section 5 looks at implications for the future urbanization of poverty and section 6 concludes.

# **2. Measuring urban and rural poverty in the developing world**

We focus on poverty defined in terms of household consumption per capita. Following standard practices, the measures of household consumption (or income, when consumption is unavailable) in the survey data we use are reasonably comprehensive, including both cash spending and imputed values for consumption from own production. But we acknowledge that

> 6 The headcount index is the proportion of the population living in households with consumption per person below the poverty line.

6
majority in 87 out of 116 countries (Table 2). This finding holds even if we focus just on SMEs as opposed to firms of all sizes. We use three definitions of SMEs: SME100 – firms with up to 100 employees, SME250 – firms with up to 250 employees and SME500 – firms with up to 500 employees. The distribution of credit constrained status does not change across the three different definitions as shown in Figure 3. Therefore, for the remainder of the analysis we will use the SME100 definition since this is the one traditionally used in Enterprise Surveys.

Aggregating the data at the regional level, we find that in Sub-Saharan Africa (AFR), East Asia and Pacific (EAP), and in South Asia (SAR), firms are more likely to be fully credit constrained than in other regions (Figure 2). In these three regions, 23-25 percent of firms are fully credit constrained, meaning that these firms sought credit and were unable of getting any form of external credit. . For the two countries included in the Middle East and North Africa region, this percentage was even higher, 28%. In the EAP region firms tend to be at the two extremes of the credit constrained status. Firms are either not credit constrained at all (43%) or they are fully credit constrained (25%) with fewer firms in the two middle categories.

The distributions of credit constrained status in Latin America and the Caribbean (LAC) and Eastern Europe and Central Asia (ECA) are very similar. In those two regions around 42 percent of firms are not credit constrained and 9 to 10 percent are fully credit constrained. The total proportion of firms who are maybe and partially credit constrained is also similar in these two regions, at 48 percent.

Analyzing the size composition within credit constrained categories indicates that SMEs are more likely to be fully credit constrained than large firms (Figure 4 ). The proportion of SMEs that are fully credit constrained is always larger than the proportion of large firms except the Middle East and North Africa (MNA) (only the Republic of Yemen and Iraq are included in 13
A third potential confounding event was the government purchase of stocks to stabilize the market following the 2015 crash in equity prices. The Securities Finance Corporation and other government institutions targeted selected firms, possibly benefiting connected firms relatively more. We exclude firms with stocks purchased by the government during 2015-19 (following Ling et al., 2022), which constitute about 37 (41) percent of the connected (unconnected) firms in our PSM sample. The estimates of the impact of internationalization become slightly larger when we exclude these firms.

Overall, the results show a robust and significant difference in issuance activity between connected and unconnected firms during the post-2012 internationalization period. Because margin trading and intervened firms were, on average, 130 and 75 percent larger than the other domestic listed firms, the larger effect we find when excluding them is consistent with the sizerelated reaction to the internationalization events discussed earlier.

# **4.2 Event Studies**

This section presents event-specific results for the implementation of the Stock Connect in Shanghai (2014) and Shenzhen (2016). In contrast to the baseline analysis, here we restrict the sample in each event study to firms listed in that specific stock market. We separately analyze only the Shanghai listed firms connected in 2014 and the Shenzhen listed firms connected in 2016. Hence, the treatment group dummy variable T T in Equation (1) becomes event specific. iiT ii DD This exercise not only allows us to understand and compare the impact of different events, but also helps us better identify the impact of these events. If our baseline estimates were confounded by other concurrent shocks or policy changes in the domestic financial markets, we would not see a significantly positive for both events, unless the confounding factors also ii occurred in two different periods and in each equity market. ββ̂ We run PSM regressions for each event to ensure that firms in the treatment and control groups had similar characteristics before the event in each case. As there is significant variation in 19
areas, while the proportion of producers in the population is lower, the revenues from maize are not negligible. On average, maize sales among producers are similar to rice sales among rice producers, at about 1.0 million cedis per household. But because the proportion of maize producers is much larger, average income from maize in the population (including auto-consumption) as a whole is seven times larger than average incomes from rice.

# 3 **. Combining consumption and production data to assess poverty impacts**

The impact on poverty of the change in food prices is the result of the combined impacts on the consumption and production sides. We first provide in this section estimates of the impact of changes in the price of rice, bread and maize, as well as cereals as a whole. For rice, we consider together imported and locally produced varieties assuming that prices move in tandem, which is apparently indeed the case when one looks at time series data on rice prices. We measure likely impacts of food price increases on three poverty measures. The headcount index of poverty is the share of the population with a level of consumption per equivalent adult below the poverty line. The poverty gap takes in addition into account the distance separating the poor from the poverty line (while giving a zero distance to the non-poor). The squared poverty gap takes in addition into account the square of that distance (and thus inequality among the poor; for an introduction to the concepts of poverty measurement, see Coudouel et al., 2002).

We carry the simulations in a very simple way. First, for rice and maize producers, we measure the additional income or the loss in income obtained from the sale 11
Figure 1: Estimated HAZ score trajectories, by country <!-- Start of picture text --> Ethiopia India<br>Peru Vietnam<br>1 3 5 7 9 11 1 3 5 7 9 11<br>2 4 6 8 10 12 2 4 6 8 10 12<br>Age (years)<br>+2 SD YL mean -2 SD<br>1<br>0<br>-1<br>-2<br>-3<br>1<br>0<br>-1<br>-2<br>-3<br><!-- End of picture text --> Note: trends are derived from the FEIS estimates reported in Table 4; the blue line (YL mean) is the estimate of the mean HAZ score for the sample; the two red lines are estimated at _±_ two standard deviations (SDs) of the estimated individual fixed-effects distributions; dashed horizontal line at _y_ = 0 is the reference median. Source: own estimates.

(HAD) between the sample and a healthy reference population median. Based on this metric, the authors find evidence for growth faltering (deterioration in relative heights) across many low income countries, including those surveyed by the Young Lives project.

In light of these concerns, we investigate the extent to which the conclusions of Section 4.2 are sustained when other anthropometric outcomes are employed. To do so, we re-run the same analysis but now use a range of height-for-age transformations. The results are reported in Table 5, which replicates the format of Table 4 in summary form (dropping the probability values) and focussing uniquely on estimates from the FEIS model.<sup>20</sup> In line with the discussion around Table 3(b), as well as Figures A1–A3, different height transformations do yield substantively different conclusions. A key finding is that the HAD metric yields a much less positive view of catch-up. Based on this metric, there is no evidence of any form of catch-up in any country over time. Moreover, there appears to be material within-group _divergence_ – estimates for _ραβ_ in Table 5 are at least 39%

> 20 The first set of results shown in Table 5 repeats those of Table 4. A complete set of estimates, based on other estimators, is available on request.

20
Table 7: Random effect probit panel Dependent variable: Dummy variable indicating the occurrence of renegotiation initiated by **the** firm

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Existenceof<br> <br>|0.41|0.03|-1.20|0.84***|1.91*|
|relato<br>body|(1.00)|(0.05)|(-1.40)|(1.87)|(2.94)|
|Pncecap(IV)|8.42*|8.09*|6.57*|18.15*|13.37*|
||(3.97)|(3.65)|(2.90)|(4.49)|(4.77)|
|Durationsince|0.23*|0.23*|0.20*|0.17*|0.20*|
|award|(4.12)|(4.19)|(3.45)|(2.93)|(3.41)|
|Investment|0.86**|0.79***|0.93**|0.82**|0.77**|
|requirements|(2.19)|(1.96)|(2.36)|(2.06)|(1.97)|
|Privatefinancing|4.56*|1.48|2.89**|3.87*|4.28*|
|(IV)|(3.96)|(0.66)|(2.07)|(2.99)|(3.59)|
|Bureaucraticquality|-0.75*|-0.85*|-0.21|-0.23|-0.23|
||(-3.77)|(-4.05)|(-0.65)|(-0.83)|(-0.87)|
|Arbitrationprocess<br>(IV)|_|3.74<br>(1.61)||_||
|Minimum income<br>uarantee(IV)|||7.98**<br>(2.10)|||
|Biddingprocess||||-3.48*||
|(IV)||||(-3.25)||
|Durationofcontract<br>|||||-0.13*|
|(IV)|||||(-3.15)|
|Election-1|0.21|0.23|0.30|0.23|0.21|
||(1.02)|(1.16)|(1.41)|(1.09)|(1.02)|
|GDP growth-I|-0.07*|-0.07*|-0.08*|-0.07*|-0.07*|
||(-3.18)|(-3.33)|(-3.52)|(-3.02)|(-3.15)|
|GDPgrowth-2|-0.16*|-0.16*|-0.18*|-0.20*|-0.17*|
||(-6.41)|(-6.50)|(-6.10)|(-6.11)|(-5.98)|
|Transportsector|-1.85**|-2.79*|-2.45*|-2.20*|-2.87*|
||(-2.43)|(-2.86)|(-2.93)|(-2.65)|(-3.35)|
|Numberofobs.|1132|1132|1132|1132|1132|
|Log Likelihood|-126.43|-125.08|-124.07|-119.60|-121.05|

Note: IV in parenthesis denotes an instrumented variable.

Coefficients significant at the 1% (*), 5% (**) and 10% (***) level.

The price cap variable remains positive and significant once instrumented. Thus, despite the potential self-selection effect, the higher riskiness of price caps still leads to more renegotiation of the concessions under this regulatory scheme.<sup>3 0</sup> Concessions financed exclusively by the private sector are renegotiated more often and the effect remains after instrumenting it. Again, on top of the self-selection effect, the prevalence of private finance appears to be linked to more renegotiations.

> 30 It must be noted however that since the instrumented variables are dummy variables varying in the range [0, 1) (except for the duration of contracts), their predicted values cover a reduced range. This induces a scale effect that explains the observed increases in the coefficients' sizes and does not allow for clear inferences on the magnitude of the effects. For example, the predicted value corresponding to the price cap variable has a mean very close to the actual variable (0.92 compared to 0.94), but varies only in the range [0.52, 1].

28
- 38 -

H the probability of hypertension PbA, = initial air lead level (ugIdl) PbA. = new air lead level (ugldl) The initial air lead level is the caurent average concentmration for the population, while the new air lead level is the presumed ambient standard. The calculated change in risk of hypertension is then multiplied by the adult male population to obtin the total number of cases. We can also estmate the effect of a change in air lead on diastolic blood pressure and subsequent circulatory disease. Based on Schwarte the change in diastolic blood pressure can be expressed as: change in DBP = 2.74 (In PbA, - In PbA2 ) As a baseline level of DBP, the U.S. average of 76 mm Hg-is apprprate. The change in diastolic blood pressure (due to a change in air lead) can be calculated and substituted into the following equations to estimate the subsequent changes in mortality and a coronary heart disease.

change in Pr(CHD) = (1 + exp (5.0 - 0.030(DBPj))-) - (1 + exp (5.0 - 0.030 (DBP2))-' change in Pr(MORT) = (I + _exp (v5.32_ - 0035(DBP,)))-' - (1 + exp _(5.32_ - 0.035(DBP 2<sup>)-'</sup> where PR(CED) = the 10-year probability of a coronary heart disease event; DP, = initial and new diastolic blood pressure levels; and PR(MORT) = the 12 year probability of death from all causes.

Effects of Blood Lead on Neurodevelopment in Children. Research indicates that lead's neurodevelopmental effects, at low to moderate exposure levels appear to include: (1) deceased intelligence, (2) short-term memory loss, (3) reading and spelling underachievement, (4) impairment of visual motor fimctioning, _(5)_ poor perception integration, (6) disruptive classroom behavior, and (7)
<mark>Malhotra, N., and J. A. Krosnick (2007), “The Effect of Survey Mode and Sampling on Inferences about Political Attitudes and Behavior: Comparing the 2000 and 2004 ANES to Internet Surveys with Nonprobability Samples,” Political Analysis, 15, 286–323.</mark> <mark>Malhotra, N., & Kuo, A. G. (2008). Attributing blame: The public's response to Hurricane Katrina.</mark> _<mark>The Journal of Politics</mark>_ <mark>, 70(1), 120-135.</mark> <mark>Mercer, A., Lau, A., & Kennedy, C. (2018). For weighting online opt-in samples, what matters most? Pew research center. January 2018.</mark> <mark>Moskalenko, S., & McCauley, C. (2009). Measuring political mobilization: The distinction between activism and radicalism.</mark> _<mark>Terrorism and political violence</mark>_ <mark>,</mark> _<mark>21</mark>_ <mark>(2), 239-260.</mark> Pasek, J. (2016), “When Will Nonprobability Surveys Mirror Probability Surveys? Considering Types of Inference and Weighting Strategies as Criteria for Correspondence,” International Journal of Public Opinion Research, 28, 269–291 Rizo C, Deshpande A, Ing A, Seeman N. A rapid, Web-based method for obtaining patient views on effects and side-effects of antidepressants. Journal of Affective Disorders. 2011;130(1-2):290-3. doi: 10.1016/j.jad.2010.07.027.

Roder-DeWan, S., Gage, A. D., Hirschhorn, L. R., Twum-Danso, N. A., Liljestrand, J., AsanteShongwe, K., Rodríguez, V., Yahya, T., & Kruk, M. E. (2019). Expectations of healthcare quality: A cross-sectional study of internet users in 12 low-and middle-income countries. PLoS Medicine, 16(8), e1002879. https://doi.org/10.1371/journal.pmed.1002879 Rosenbaum, Paul R. 1987. ‘‘Model-Based Direct Adjustment.’’ Journal of the American Statistical Association 82:387-94.

Sargent RH, Laurie S, Weakland LF, Lavery JV, Salmon DA, Orenstein WA, Breiman RF (2022). Use of Random Domain Intercept Technology to Track COVID-19 Vaccination Rates in Real Time Across the United States: Survey Study. J Med Internet Res 2022;24(7):e37920. https://www.jmir.org/2022/7/e37920/ Scherpenzeel, A. C., and J. G. Bethlehem (2011), “How Representative Are Online Panels? Problems of Coverage and Selection and Possible Solutions,” in Social and Behavioral Research and the Internet: Advances in Applied Methods and Research Strategies, eds.M. Das, P. Ester, and L. Kaczmirek, pp. 105–132, New York: Routledge.

Seeman, N., Tang, S., Brown, A. D., & Ing, A. (2016). World survey of mental illness stigma. Journal of affective disorders, 190, 115-121.

Schonlau M, Couper MP. Options for Conducting Web Surveys. Statist Sci. 2017;32(2):27992. doi: 10.1214/16-STS597.

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## **WHOM WE ARE TALKING ABOUT**

As to the developing countries we have in mind, like the WTO Ministers in their 1996 Declaration, our concern focuses on the countries that are being marginalized. What we will argue will apply particularly to these countries - generally, the least developed countries - rather than to those developing countries that have already established themselves as major trading economies and active members of the WTO diplomatic community. Throughout the paper we will use the term, "least developed countries," to refer to all countries that are struggling to use the international trading system effectively - a list of countries approximated by the United Nations official list of least developed countries, but not necessarily identical to that list.<sup>2</sup>

## **WHAT WE ARE TALKING ABOUT**

Our analysis is based on World Bank project experience in customs reforrn, SPS, and intellectual property regulation. In each of these areas we reviewed Bank experience with three questions in mind:

1. What are the development problems in this area?

2. Does the WTO agreement correctly diagnose the development problems?

3. Does the WTO agreement prescribe an appropriate remedy?

"Appropriate" in the third question refers both to correct identification of the problem and to recognition of the capacities (resource constraints) of the least developed countries.<sup>3</sup>

## **STRUCTURE OF THE PAPER**

In the next section we will explain how implementation of such obligations differs from implementation of more traditional market access conmuitments such as tariff reduction or elimination of quantitative restrictions. In sections 3 and 4 we look briefly at least developed countries' participation in the Uruguay Round negotiations. In section 3 we point out that this participation, or lack thereof, has left these countries with no sense of ownership of the commitments they have undertaken. In section 4 we point out that in the Uruguay Round market access negotiations, the developing countries received no more than they gave - this in response to the possible criticism that implementation of the reforms we review here is what the developing countries owe for what they received in the market access negotiations. Sections 5, 6 and 7 present our examination of the challenges developing countries will face in implementing their obligations on customs valuation, sanitary and phytosanitary standards and intellectual property regulation. In each area we outline basic WTO obligations and we examine how implementation might be managed so that it best helps the least developed countries to use trade as a vehicle for development. Our conclusions are in the final section.

# 2. **THE SCOPE OF "IMPLEMENTATION"**

2 Like the WTO Ministers, we offer no precise list of the countries who share the problems we address.

3To take a brisk 30-minute walk every day would not be a good prescription for a paraplegic.

2
Table 28. Indonesian Exports, ICT use, and Log of Skills Measures Excluding Industries Involved in GVCs. All workers. 2011-2015

||||||N|on-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|ytical|||Interp|ersonal|||M|nual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|0.00439||0.00456|0.00546|0.00358||0.00375|0.00452|-0.00298||-0.00315|-0.00366|
||(0.00236)*||(0.00238)*|(0.00268)**|(0.00205)*||(0.00206)*|(0.00225)**|(0.00185)||(0.00185)*|(0.00197)*|
|Share of hhlds with computer||0.0315|0.0237|0.0113||0.024|0.0169|0.00568||-0.0202|-0.0136|-0.00463|
|||(0.0244)|(0.0264)|(0.0243)||(0.0223)|(0.0252)|(0.0217)||(0.0215)|(0.0256)|(0.0208)|
|IDN exports per worker *||||0.00144||||0.00126||||-0.000902|
|Share of hhlds with computer||||(0.00147)||||(0.00123)||||(0.00111)|
|Observations|2,588|2,588|2,588|2,588|2,588|2,588|2,588|2,588|2,588|2,588|2,588|2,588|
|R-squared|0.847|0.852|0.845|0.845|0.484|0.497|0.477|0.476|0.876|0.879|0.874|0.875|
|||||Routine ski|ll measures||||||||
|||Ma|nual|||Cog|nitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|-0.00331||-0.00349|-0.00237|-0.0024||-0.00252|-0.0032|||||
||(0.00264)||(0.00264)|(0.00291)|(0.00155)||(0.00153)|(0.00173)*|||||
|Share of hhlds with computer||-0.0317|-0.0257|-0.0341||-0.01|-0.00512|0.00498|||||
|||(0.0331)|(0.0372)|(0.0337)||(0.0234)|(0.0261)|(0.0234)|||||
|IDN exports per worker *||||0.0014||||-0.0011|||||
|Share of hhlds with computer||||(0.00176)||||(0.00112)|||||
|Observations|2,588|2,588|2,588|2,588|2,588|2,588|2,588|2,588|||||
|R-squared|0.976|0.976|0.976|0.976|0.875|0.877|0.874|0.874|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

45