rafmacalaba/data-use-mentions-extended huggingface dataset:
93 suggest an elasticity of approximately .20 for particulate density: An increase of 1% in air emissions induces an increase of approximately .2 % in citizen complaints to the environmental authorities. _However, neither SO2 density nor COD density has any measured impact._ Estimated income and education effects are both positive and highly significant. After the opportunity cost of time is accounted for, the income results suggest an elasticity of demand for environmental quality somewhat greater than 1.4 (p 3 > 1 **>** 1). This is in line with previous work on China (Wang and Wheeler, 1996) and willingness-to-pay surveys in the OECD countries.7 The education results are particularly striking. Controlling for income and pollution density, they suggest a literacy-elasticity (P2) somewhat above the range 1.7-1.8 (P2> X3 > 0). Remarkably, a 1% increase in the literacy rate seems to induce a 2% increase in environmental complaints.

# **5.2 Simulation Results**

We explore the implications of our econometric results with simulations over the existing range of provincial income, pollution density and education. Dividing the twenty-nine Chinese provinces into two income groups, we use group medians (750 and 1330 yuan/year) to define low- and high-income prototypes. Within each income group, we use minimum and maximum levels of particulate pollution density and literacy to establish low and high classifications for these variables. The low-income group has literacy rates ranging from 60-84% and particulate densities from 0.1 - 2.0 tons/sq. km.

**7We** are indebted to our colleague Maureen Cropper for the latter point.

11
household size has a negative impact on per capita consumption or income (Christiaensen & Subbarao, 2005; Jha, Dang, & Tashrifov, 2010; Bronfman, 2014; Klasen, Lechtenfeld, & Povel, 2015; Imai, Gaiha, & Thapa, 2015). Studies that include measures of health shocks also generally find that these do increase vulnerability, as expected (Abimbola, Yusuf, Omonona, & Okunmadewa, 2011). Various measures of wealth and education, on the other hand, often reduce vulnerability, though this is generally through higher expected consumption or income per capita rather than reduced variance (Ligon & Schechter, 2003; Christiaensen & Subbarao, 2005; Makoka & Kaplan, 2005; Jadotte, 2010; Jha, Dang, & Tashrifov, 2010; Klasen, Lechtenfeld, & Povel, 2015). Interestingly, education increased vulnerability in a small‐ sample study undertaken in Ethiopia by Bogale (2012), and post‐secondary education also led to higher consumption variance in Vietnam (Imai, Gaiha, & Kang, 2011). Another interesting finding is that most studies that have included a dummy for gender of household head do not find that female‐headed households are more vulnerable (Christiaensen & Boisvert, 2000; Ribas & Machado, 2007), and in some cases, are less vulnerable (Bronfman, 2014; Imai, Gaiha, & Thapa, 2015). An exception is Klasen, Lechtenberg & Povel (2015), who exploit a two‐year panel data set from Thailand and Vietnam to show that _de facto_ female‐headed households are more vulnerable to consumption poverty than male‐headed households.

# **3. Data and Descriptive Statistics**

This paper exploits the rural sample of Malawi’s nationally representative panel data set, which became available in 2013 via the Integrated Household Panel Survey (IHPS). The Malawi National Statistical Office conducted the surveys on which our data set is based, with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture
Tartu.

- Elsby, Michael, Bart Hobijn, and Aysegul Sahin, 2010, “The Labor Market in the Great Recession”, NBER Working Paper 15979.

- Fallon, Peter and Robert Lucas, 2002. The Impact of financial Crisis on Labor Markets, Household Incomes, and Poverty: Review of Evidence. _The World Bank Research Observer_ , 17(1). pp 21-45.

- Freeman, Richard. 2005 “Labour Market Institutions without Blinders: The Debate over Flexibility and Labour Market Performance”, NBER working paper 11286

- Freeman, Richard. 2009, “Labor Regulations, Unions, and Social Protection in Developing Countries: Market Distortions or Efficient Institutions”, NBER working paper 14789

Ghosh, Jayati, 2010. “Financial Crises and the Impact on Women” _Development,_ v. 53(3) p. 381-385.

- Glewwe, Paul and Gillette Hall, 1998. Are Some Groups More Vulnerable to Macro Shocks Than Others Hypothesis Tests Based on Panel Data from Peru. _Journal of Development Economics, 56._ pp. 181-206

23
-|---|---|---|---|---|---|
||Concession|BOT/BOO|_Marchand_|_Location _|Partialprivatization|_Affermage_|
|Energy|2($88)|2($146)||1($6)|||
|ICT|||2($408)||1($1,536)||
|Transport|4($720*)||||||
|WSS||||||2($20)|

_Source:_ World Bank 2010a. _Note:_ (*) 2009 preliminary data reported. BOT = build operate transfer; BOO = build operate own; ICT = information and communication technology; WSS = water supply and sanitation.

But while Senegal is among the countries in the region to have already attracted the private sector’s investment in infrastructure, a comparative assessment of Sub-Saharan Africa shows that these private flows remain modest in comparison to countries in other regions (figure 29a, b). Increasing private sector participation in infrastructure, however, is not an easy task, especially in an environment where investment resources are scarce and private investors highly selective. In the case of Senegal, it will be important for the government, based on sector needs over the next 5 to 10 years, to carefully prioritize projects. The government could then identify a pipeline of projects in which the private sector could play a major role in either bringing in investment resources or, equally important, bringing in technical and 43
N=10849|
|Father's Sch.|3.31|2.08|3.18<br>2.06|3.50<br>2.10|
|Educ. Exp.|386.44|477.52|398.38<br>484.29|369.03<br>466.95|
|No. of Children|2.47|1.06|2.37<br>1.04|2.61<br>1.07|
|NSS1995 (6-18)|N=3|7155|N=23172|N=13983|
|Father's Sch.|3.36|2.10|3.21<br>2.08|3.59<br>2.12|
|Educ. Exp.|543.89|655.29|568.55<br>677.05|503.02<br>615.41|
|No. ofChildren|2.45|1.10|2.35<br>1.08|2.63<br>1.11|

Notes: CFPS stands for China Family Panel Survey, IHDS stands for Indian Human Development Survey, and NSS stands for National Sample Survey.
builds pseudo-panels in Jamaica identified by sampling cluster and finds that remittances reduced labor force participation (but not hours worked). Funkhouser (2006) uses a 3-year panel from Nicaragua and finds no effect of migration or remittances on the labor supply of adults who did not migrate, but finds that young adults in remittancereceiving households are less likely to work. Rodriguez and Tiongson (2001) mitigate the bias from endogeneity by considering only households with temporary migrant members, arguing that in these cases household labor supply decisions and the decision to migrate are inseparable. They find a negative correlation between remittances and the number of hours worked and labor participation of recipients. CoxEdwards and Rodriguez-Oreggia (2009) use propensity score matching to limit the sample to recipient and non-recipient individuals who are similar across observable characteristics, mitigating but not fully addressing the endogeneity. They conclude that remittances have a neutral effect on participation rates, in essence replacing the income of the missing household member.

To better understand the causal impact of remittances on labor supply, suppose that a measure of labor supply of individual _i_ in household _h_ in region _r_ , Y�,�,�, is a function of a set of individual, household, and regional characteristics. This is written out as equation (I) where the key variable of interest is R�, a variable identifying households in which any member received remittances.<sup>17</sup> Beyond receipt of remittances, other characteristics that affect labor supply decisions include: a vector X� of individual-level characteristics; a vector Z� of household characteristics; and a vector R� of local and regional characteristics (such as urban status).

As discussed above, a significant challenge to address in this analysis is the selection on unobservables associated with both labor force participation and the receipt of remittances. Suppose the migration of one

> 16  In particular, the presence of young children and adult men in the household, and the educational level of adults in the household. 17 The variable R�could also be
Bodenhorn, Howard (2003). “Short-term loans and long-term relationships: relationship lending in Early America,” _Journal of Money, Credit and Banking_ 35: 485–505.

- Budina, Nina, Harry Garretsen, and Eelke de Jong (2000). “Liquidity Constraints and Investment in Transition Economies: The Case of Bulgaria,” _Economics of Transition_ 8: 453–475.

- Cressy, Robert and Otto Toivanen (2001). “Is there adverse selection in the credit market?” _Venture Capital_ 3: 215–238.

- CGAP (Consultative Group to Assist the Poor) and WBG (The World Bank Group) (2010). _Financial Access 2010: The State of Financial Inclusion Through the Crisis_ , Washington, DC: CGAP and the World Bank Group.

- de la Torre, Augusto, Maria Soledad Martinez Peria, and Sergio Schmukler (2010). “Bank Involvement with SMEs: Beyond Relationship Lending,” _Journal of Banking and Finance_ 34: 2280-2293.

- Degryse, Hans and Patrick Van Cayseele (2000). “Relationship lending within a bank-based system: evidence from European small business data,” _Journal of Financial Intermediation_ 9: 90–109.

- Dietrich, Andreas (2010). “Explaining Loan Rate Differentials Between Small and Large Companies: Evidence from Switzerland,” _Small Business Economics_ , Forthcoming.

- Duan, Naihua (1983). “Smearing estimate: A non-parametric retransformation method,” _Journal of the American Statistical Association_ 78(383): 605-610.

- EC (European Commission) (2009). _European SMEs Under Pressure: Annual Report on EU Small and Medium-Sized Enterprises 2009_ , Brussels, Belgium: European Commission.

- Galindo, Arturo and Fabio Schiantarelli (2003). “Credit Constraints and Investment in Latin America,” RES Working Papers 4305, Inter-American Development Bank, Washington D.C.

- Gros, Daniel and Marc Suhrcke (2000). ‘Ten Years After: What is Special About Transition Countries?’ European Bank Working Paper N
migration outflows.

I use the percentile ranks to identify a subset of years for each region that can be considered to be a period with large anomalous outflows (or at least larger than usual) and run the baseline parametric regression on them. Figure 5 displays the estimates of regional exposure effects for the three age segments used in the parametric specification (see equation 5).<sup>11</sup> I find estimates that are very stable until the percentile 50 and then become more volatile. However, despite the volatility, the estimates remain qualitatively similar with slopes that are non-statistically significant in early ages, a significant childhood exposure effect in the segment of primary school age, and smaller significant effect post primary school age. This suggests that the regional childhood exposure effects found in the baseline results are not driven by families choosing the timing of the move.

# **V.1.b Expected destination of moving households**

To alleviate concerns that time-varying factors may jointly drive household choice of destination and children’s educational investments in proportion to exposure to the region with higher mobility, I use past migration destinations from each origin to predict where moving household will settle with a “shift-share” design.

For each year of move _y_ and origin _o_ , I compute the share that moves to destination- _d_ as _σody_ = _D_ <u>�</u> _xy−_ =10 _Ty_ <u>0</u> _−_<sup>_movers_</sup> 10<sup>_odxD_isthetotalnumberofregionsinagivencountryand</sup> <u>�</u> _d_ =1 <u>�</u> _x_ = _T_ 0<sup>_moversodx_where</sup> _T_ 0 is the first year in which I observe a mover from this origin.

For individuals who move in year _y_ from region _o_ to region _d_ , I compute the predicted difference in mobility for permanent residents ∆<sup>ˆ</sup> _odby_ as the historic share-weighted analog,
basis. This ensures not only good record keeping at the MFI level, but also provides the opportunity to access information on loans taken by sampled clients from all micro-finance providers in the study areas. We received the credit bureau reports for sample clients from a leading provider, used by the MFI partner when making decisions on loan applications. We obtained this information for around 88 percent of clients in our sample.<sup>14</sup> For more information on the source of the survey, MFI and credit bureau data we refer to ACGMS 2019.

Figure 2 provides an overview of the timeline of events, including the dates when the SBM data snapshots and survey data were collected, and how these relate to, the launch of the SBM program and the micro-credit intervention.

# **3.3 Matching result and sample description**

Linking our survey data to the SBM data was done by matching full names of household heads, their spouses and their fathers provided in our survey data to full names of household heads and their fathers available in the SBM data.<sup>15</sup> After using a computer algorithm based on a combination of exact and fuzzy matching - where for the latter we used a conservative threshold such that the probability of including erroneous matches was higher than the probability of excluding true matches - we carefully checked each match, dropped obviously wrong matches and flagged matches for which there was some uncertainty. Uncertain matches concern cases for example where the first and last name of the household head uniquely matched between datasets but where the first name of the father did not match. In our regression analysis we control for whether or not there was some uncertainty about the matching accuracy, which was the case for 25% of all matched observations. Appendix A explains the matching process in more detail.

We were able to uniquely match 1,312 client households to the SBM data, 47% of the 2,772 clients interviewed for our endline survey. These live in 75 of the 81
difference-in-differences approach, we opt for estimating a structural gravity model, as explained below, in which bilateral patent applications are related to joint membership in an IPA versus cases where the patent-destination country is in an IPA and the patent-source country is not. A particularly attractive feature of our structural gravity approach, in which we account for unilateral origin- and destination-specific time-varying factors through the use of highdimensional fixed effects, is that we are able to implicitly control for a wide assortment of confounding factors, such as countries’ adoption of and compliance with the TRIPS Agreement. Our approach seeks to distinguish between the impacts on patent flows of mutual IPA membership (within-agreement effects) and those on patent flows from outside IPAs and on patenting from IPA partners to non-member nations (extra-agreement effects).

# **2. Background**

In recent decades, PTAs have evolved greatly in subject matter and intent. Earlier agreements sought primarily to reduce tariffs and other formal barriers to trade, thereby increasing market access between member countries. Beginning in the 1990s they expanded in ambition and depth. The earliest major agreements with enhanced coverage were the North American Free Trade Agreement (NAFTA) and several bilateral accords between the European Free Trade Association (EFTA) and individual countries, such as Estonia, Latvia, and Mexico. This round of trade agreements included rules covering certain regulatory regimes, including IPRs. Not long after, the EU announced its “new trade policy,” increasingly emphasizing regulatory systems, including enhanced standards for patents, copyrights, GIs, and other IPRs in its PTAs with countries in Eastern Europe, the Middle East, Latin America, Canada, and Japan.

NAFTA was among the first multi-country PTAs to set minimum standards for nearly every aspect of IPRs as they existed then. Regarding patents, the agreement established certain TRIPS-Plus rules, including confidentiality for pharmaceutical clinical trials data and extensions in patent length to compensate for administrative delays in granting protection. These provisions, and others, were the foundation on which other TRIPS-Plus patent standards were erected in subsequent
Finally, the HFPS were uniform across countries (same questionnaire, same time window, etc.), so the comparability of the results and the possibility to construct aggregate regional measures are enhanced compared to the much more heterogeneous national household surveys.

# **3.2 Occupations**

The HFPS dataset includes seven questions on the occupation of a worker: one for the pre-pandemic situation and two in each of its three rounds, one for people who worked the previous week and another for those who did not work but who still have a job. These questions on occupation capture important information on the activities performed by workers, and hence are very useful for the analysis of the labor market, as stressed by the task-based approach (Acemoglu and Autor 2011). Although the HFPS have been used as the main input for several recent papers on the impact of the COVID-19 crisis, to the best of our knowledge, the occupation variables were not considered in those studies. The most likely reason for this neglect is that, in contrast to the rest of the variables in

> 3The COVID-19 monitoring global dashboard provides harmonized indicators across countries in Latin America and the world. For more information on the HFPS, see Mejía-Mantilla et al. (2021).

> 4The sample weights take into account the attrition in the second and third rounds. The weights were calibrated to incorporate population projections of the United Nations Economic Commission for Latin America and the Caribbean (ECLAC). See Mejia-Mantilla et al. (2021).

> 5The sample is based on a dual frame of cellphone and landline numbers generated through a Random Digit Dialing (RDD) process. For a detailed description of sampling and weighting see Flores Cruz (2020).

6
5 smaller than those between urban and rural areas, but they may still matter, and we do report a robustness test with respect to cost-of-living differences in the only country in our sample for which data permits it, namely Brazil.

Fourth, we do not test the robustness of our findings to variation in equivalence scales – which would matter if family sizes and composition varied systematically across city sizes. Fifth, we assess poverty in a fairly restrictive way: focusing on the share of the population with incomes or consumption levels below the poverty line. It is widely acknowledged that poverty can be viewed more broadly, reflecting multiple dimensions of wellbeing. We seek to mitigate this concern by reporting the association between city size and access to various publicly-provided services and, in one instance, by looking separately at a health outcome (child malnutrition). Nevertheless, it should be acknowledged that the patterns observed in these spaces need not be repeated when other dimensions of poverty are considered.

Each of the limitations of the analysis presented in this paper points to the need for additional research. This paper simply documents the existence of systematic differences in the intensity of poverty (and access to services) across city sizes in eight geographically diverse developing countries, and raises questions about the possibility that policies designed to reduce urban poverty may suffer from a metropolitan bias.

The paper is structured as follows. Section 2 provides an overview of the poverty mapping methodology which was used in each of the six countries, in order to generate reliable poverty estimates for every urban area captured in the population census. Section 3 describes the data sources to which this method was applied, in each country. Section 4 presents the consumption poverty profiles by city size in each country. Section 5 turns to the evidence on access to publicly provided services across city sizes. Section 6 looks at differences in poverty (and inequality) within specific cities in Morocco, focusing in particular on poverty and inequality differences between slums and non-slum areas in larger towns. Section 7 subjects our findings to some
Poorly performing firms either did not exit or exited but accounted for only a small percentage of aggregate output. Or the productivity of some firms that exited was average or better. Similarly, there were no gains from reallocating resources (labor, capital, and intermediate inputs) to more productive firms, which would have increased aggregate productivity, all else equal. In fact, the contribution was slightly negative. In advanced countries, this is the most important source of productivity growth,<sup>12</sup> and thus stands out as a possible major source of future productivity growth in China.

After 2007, average manufacturing productivity growth in China decreased almost by half. The most important reason for the decline was that the contribution of better entrants disappeared. In some sectors, the contribution of new entrants was actually negative, implying that these firms entered the productivity distribution lower than the sector average. One

> 11 New firms are those that were established in either the current year or previous year (the date is given in the NBS survey).

> 12 See, for example, Bartelsman and Dhrymes (1998).

10
>thorities|0.0%|
|Workers absences|_<_1.0%|Inadequate premises/land|0.0%|
|Too much interference by foreign|_<_1.0%|Lack of transparency in ofcial or|0.0%|
|ofcials||government procedures||
|Lack of energy (electricity, fuel,|0.0%|Lack of energy (electricity, fuel,|0.0%|
|etc)||etc)||
|Difculty to engage with foreign|0.0%|Difculty to engage with foreign|0.0%|
|frms||frms||
|Use of intermediaries in order|0.0%|Use of intermediaries in order|0.0%|
|to get access to local markets<br>(within Palestine)||to get access to local markets<br>(within Palestine)||
|Other factors (specify)|2.0%|Other factors (specify)|3.3%|

_Notes:_ ICT<sup>_<u>∗</u>_</sup> excludes retailers and repairers. All options available are included in the table. _<_ 1.0% indicates a non-zero fraction. _Sources:_ Authors’ survey.

When asked directly whether they think that mobility restrictions affect the growth of their businesses, ICT answers are also similar to other firms (table 8). Differences in answers between industry groupings are not statistically significant with only a few exceptions. More than half of ICT firms answered that obstacles have an impact on the mobility of people, compared to virtually 40% of other firms, consistent with the fact that ICT workers are commuting for a longer distance, but the difference is not statistically significant. Firms were less likely to see obstacles to the mobility of goods as an obstacle, and, interestingly, ICT firms were significantly more likely to mention
- Start of picture text --> Yunan National<br>70000<br>59660<br>60000 53980<br>50251<br>46531<br>50000 43214<br>40000<br>30000<br>34546<br>31265<br>28806<br>20000 27185<br>25009<br>10000<br>0<br>2 0 1 3 2014 2015 2016 2017<br>IN RMB<br><!-- End of picture text --> _Source_ : National Bureau of Statistics of China 2017.

The lack of human capital is a major constraint for Yunnan’s economic development, as in many less developed regions in China. Yunnan people, on average, have fewer years of schooling. As mentioned in Section 1 of this report, in 2015, the employed labor force in Yunnan has on average 8.4 years of schooling, lower than the national average of 10 years. This is consistent with the statistics from the population survey carried out by the National Bureau of Statistics in 2016 (see Figure 4.2). It shows that for the population of age 6 and above, Yunnan has smaller shares of its population having attained education levels of secondary and above while much larger shares of education levels of primary and below. Perhaps not surprisingly, Yunnan has one of the lowest 46