rafmacalaba/datause-encoder-data huggingface dataset:
among the rest; domestic remittances are more evenly distributed among the poor and the nonpoor. There are stark geographic disparities in the incidence of foreign remittances. Twenty-four percent of households in Chittagong and 16 percent of those in Sylhet received remittances from abroad in 2005, compared to less than 5 percent of households in Rajshahi, Khulna, and Barisal (Table 4). This disparity roughly mirrors the geographic distribution of poverty in Bangladesh in 2005.

[Insert Table 4 here] Access to microfinance increased significantly in recent years, with membership increasing by 62 percent between 2003 and 2005. Unfortunately, the lack of adequate information on savings/credit in the HIES does not allow for incorporating microfinance access at the household level into the analysis. Instead, data obtained from Bangladesh’s micro-finance apex body (PKSF) on changes in micro-finance coverage at the sub-district ( _thana_ ) level is merged with HIES data. On the average, microfinance membership expanded faster in areas that were poor in 2000 (Table 5). Moreover, sub-districts with higher growth in microfinance coverage experienced a higher rate of poverty reduction.

Two important caveats must be noted. First, since the membership figures are _thana_ /sub-district level aggregates, the association is between the geographic patterns of microfinance access and poverty reduction, rather than at the household level. Second, in the absence of membership figures from 2000, the change in microfinance membership during 2003-2005 is used.<sup>2</sup> Although the lack of data limits the scope for national level analysis, some of the earlier studies using smaller data sets have found a significant

> 2 Table 5 thus assumes that the annual rate of client growth in each sub-district during 2003-2005 is similar to what would have occurred between 2000 and 2005.

9
# **_APPENDICES_**

# **_APPENDIX 1. Sampling Procedure_**

<< insert table A.1 here.>> When we compare the sample sizes in the column “1997 data available” with the sizes in table 1 in the main text, we see that 12 (three) observations dropped out in the final control (treatment) sample because of missing observations on circumstances.

# **_APPENDIX 2. Results of the logistic regression_**

Our specification for the logistic regression is close to the specification used for propensity score matching by Behrman et al. (2009b) and Behrman and Parker (2010). The dependent variable equals one if the observation comes from the control sample and zero otherwise. Explanatory variables are based on preprogram characteristics of the treatment sample and the 1997 recall characteristics of the control sample. We have five types of explanatory variables: (1) Household characteristics, which include the ages of the head of the household and spouse (in years); the sex of the head of the household; whether the head of the household and spouse speak an indigenous language; whether the parents completed primary education; whether the parents work; and the composition of the household (number of children and women and men of different ages) (2) Dwelling conditions of the household, which include the number of rooms in the house and a list of dummy variables indicating the presence of electric light, running 28
extent to which public health care providers perform according to expected standards. By contrast, the governance determinants address one particular policy instrument.

For example, while adopting an essential medicines list may be a good health sector policy – because it is useful for guiding clinical practice, public procurement, or distribution – the existence of such a list does not, in itself, demonstrate much about authority, information, and motivation in the health sector. A more general governance indicator would document whether or not the public system has an effective policy regarding which medications are purchased, distributed and prescribed. This can be done by specifying who has authority to make such decisions (e.g. a single national procurement agency, district officers, health facilities), the capacities and discretionary powers of those in authority (e.g. through regulations, professional codes), who provides the information that is used to make decisions (e.g. pharmaceutical companies, public drug agencies), and what factors motivate decisions (e.g. profit, cost-effectiveness). Operationalizing this may be more useful but also requires greater effort. For example, WHO‟s Governance and Pharmaceutical Assessment Tool uses 66 separate questions to judge whether the system of purchasing, distributing and dispensing medications is procedurally effective.<sup>9</sup> All but six of the indicators presented in WHO 2008 are efforts to measure governance determinants. They include:

- Existence of a national health strategy

- Existence of an essential medicines list

- Existence of policies on drug procurement

- Existence of a national strategic plan for TB

- Existence of a national malaria strategy/policy

- Completion of the UNGASS questionnaire for HIV/AIDS

- Existence of a comprehensive reproductive health policy

- Existence of a comprehensive childhood immunization plan

- Existence and dissemination of key health sector documents

These proposed indicators for governance determinants are defensible: they are relatively easy to verify and measure with expert interviews, they represent an important international initiative to improve health sector governance, and they do describe a coherent set of procedures for changing authority and information. On the other hand, they achieve this at the expense of missing alternative procedures aimed at the same ends, e.g., the essential medicines list is only one way of establishing procedures for drug selection, and have not been tested empirically. I would contend that expert opinions like this should be treated as hypotheses to be tested rather than evidence to be followed.

Lewis and Pettersson (2009) include relatively few governance determinants among their proposed list of governance indicators. These include the procedural measures tracked by Public Expenditure Financing Assessments (PEFA) and the existence of licensing

> 9 See WHO‟s Good Governance in Medicines initiative at: <u>http://www.who.int/medicines/ggm/en/index.html</u>

______________________________________________________________________________________ -21-
# Fi ure 1: Shares of female ownershi b state <u>g p y</u>

<!-- Start of picture text --> A. Female ownership of unorganized manufacturing in<br>2000<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>State GDP per capita in 2000<br>Female-owned share of firms<br><!-- End of picture text --> <!-- Start of picture text --> B. Female ownership of services in 2000<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>State GDP per capita in 2000<br>Female-owned share of firms<br><!-- End of picture text --> <!-- Start of picture text --> C. Growth in female ownership share of unorganized<br>mfg., 2001-2010<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>-10%<br>-20%<br>State GDP per capita in 2000<br>ownership<br>Growth in share of female<br><!-- End of picture text --> <!-- Start of picture text --> D. Growth of female ownership share of services,<br>2001-2010<br>8%<br>6%<br>4%<br>2%<br>0%<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>-2%<br>-4%<br>State GDP per capita in 2000<br>ownership<br>Growth in share of female<br><!-- End of picture text -->
through the firms' extensive rather than the intensive margin. The fact that early studies suggest that the programs tend to encourage exports through the intensive margin could raise questions regarding the social desirability of these programs.

Our econometric evidence from more than 2,000 Latin American firms between 2006 and 2010 suggests that fixed costs are at play, because we found that firms that were subjected to export promotion services prior to 2010 were more likely to enter into exporting or survive as exporters than firms that did not receive export promotion services. We found no robust evidence that exporting firms that received export promotion services experienced an increase in export intensity.

The difference between our results and those for Ireland reported by Gorg, Henry and Strobl (2008) might be due to the differences in priorities and objectives pursued by the Latin American export promotion agencies. But they also suggest that the channels through which Latin American agencies operate tend to be more consistent with the type of market failures that economists think export promotion agencies should correct.

Future work should further explore the potential heterogeneity that may exist across the geographic and sectoral dimensions. Indeed, the impact of export promotion services on export growth and the channels through which the services affect growth are also likely to vary across destination markets and products.

14
the cost-of-living changing only slowly, since they reflect climate, infrastructure, population density, topography, and other factors that are unlikely to vary much in the short-run.

The time-space price indexes derived from the WCPD and Engel curve methods are reported in table 2. Also reported is a test of the hypothesis that the price index for a particular region, sector, and year from the Engel curve method differs statistically significantly from WCPD-vw (using<sup>*</sup> to denote significance) or from WCPD-fw (using<sup>#</sup> for significance). There are 13 (out of 23) such occurrences of significant differences when the full consumption aggregate is used, regardless of which WCPD benchmark is used. If housing and durables are excluded there are 19 (18) significant differences between the Engel curve deflators and those from WCPD-vw (WCPD-fw). It appears that an abbreviated consumption aggregate (dropping two major items with hard to survey prices) does not improve the performance of the Engel curve method in terms of matching benchmark price indexes that are typical of what statistics offices would report if they had a survey of spatially disaggregated prices. Therefore, in the rest of the paper, the comparisons use the “all consumption” results, which also lets us match to the published poverty and inequality estimates for 2010 that are based on this same comprehensive consumption aggregate.

The different spatial patterns for the WCPD and Engel curve deflators are illustrated in figures 2a and 2b, using the results for 2010 (based on the first eleven rows and first three columns of table 2). The Engel curve deflator implies that several rural areas have higher costs of living than in the capital city—markedly so in the case of the Mid-Northern Mountains region. According to the Engel curve, the price level in this region in 2010 is up to 39% above the price level of the urban Red River region.

24
GDP, or roughly US$160 million. An oil fund was established at the beginning of activities (2006) and it has slowly grown in spite of the subdued oil production, due to rising oil prices. Currently (early 2014) it is estimated to be equivalent to US$ 100 million.

**The start of oil production came at an important juncture in the country’s political history.** A few months earlier, in August 2005, a coup d’état ended the 21-year rule of President (and former Prime Minister) Maaouya Ould Sid’Ahmed Taya; this was the beginning of what local observers described as a “transition” period. The initial regulations, exploitation agreements and general management of the oil sector occurred in a climate of severe political instability and very weak governance, which was characterized by rapid changes in leadership. As a result there are serious limitations in the availability, reliability and accuracy of oil-sector data. In recent years many large companies have operated in the country, such as Agip, Mobil, Shell, an Exxon, and a number of major international firms are still operating in the Mauritanian oil sector, including TOTAL, TullowOil, Repsol, and China National Petroleum Corporation. Annual reports describing the government’s revenue collection from oil and mining operators are regularly prepared and published on a government website.<sup>13</sup>

**Significant natural-gas reserves have recently been discovered** . Natural-gas deposits are estimated to be particularly large in the southern part of the country and offshore. Both public and private interests in the sector have increased rapidly, and plans are well advanced for a gas-to-power project in Banda, which could more than double Mauritania’s power-generation capacity. This project includes energy exports to Senegal and Mali, while domestically it is expected to substantially reduce electricity costs and improve electricity connectivity for more than 50 percent of the urban population living in informal settlements. The project will be developed through a public-private partnership with the support of IBRD and MIGA; it is expected to be completed by end-2015<sup>14</sup> . The considerable potential of the natural-gas sector highlights the significant role and growing importance of extractive industries in the Mauritanian economy. However, the extent to which natural capital can be used to address the country’s substantial development and poverty reduction challenges will depend on how the sector is governed and how the fiscal resources it generates are managed and invested.

**The government has registered some progress in improving the extractive industry governance and transparency** , and Mauritania is fully compliant with the Extractive Industries Transparency Initiative (EITI) standard and its specific program for resource-rich developing countries known as EITI++.

> 13 See: www.cnitie.mr.

> 14 Cf. The World Bank Group’s Country Partnership Strategy (CPS) 2014-2016 for the Islamic Republic of Mauritania, World Bank, 2013.

5
Industrial production refers to the output of industrial establishments and covers sectors such as mining, manufacturing, electricity, gas and steam and air-conditioning. We use IPI data in other exporting countries ( _ipiot_ ) which are weighted using sectoral export shares obtained from 2018 UN Comtrade data to compute the average competition shock in third countries ( _competition shockijkt_ ). We also rely on IPI <mark>data to capture</mark> supply shocks in source countries ( _ipist_ ). Both allow for the inclusion of China for which mobility measures are unavailable. In robustness checks, we use IPI as alternative supply shocks in exporting countries ( _ipiit_ ) and alternative demand shocks in partner countries ( _ipijt_ ).

To capture the potential heterogeneous impact of reduced mobility across sectors, we construct a variable that measures the percent of occupations within an ISIC Rev. 3 4-digit sector that can be performed remotely based on U.S. 2017 O*NET data.<sup>17</sup> In order to obtain a remote labor measure that varies across exporting countries ( _remoteik_ ), we multiply this percentage with a country’s internet density defined as individuals using the Internet (as % of population) from the World Development Indicators for 2017. These measures are then indexed to range from 0 to 1. It seems counterintuitive that production-related activities like assembly can be performed remotely. However, the trade data and remote labor index are classified by sectors and not tasks. That is, services tasks that are embodied in goods such as research and development, design or marketing are also classified under goods sectors. Appendix 1 ranks ISIC Rev.3-2 digit sectors by their average remote labor index and shows that remoteness is highest in publishing, printing and reproduction of recorded media, followed by electronics and machinery sectors, while it is lowest in labor-intensive forestry, fishing, agriculture and food production.

To assess the durability of products we calculate the sector’s share of durable consumer products, semidurable consumer products and cars and transport equipment ( _durablek_ ). Durable, semi-durable and transport products at the HS6-digit level are identified based on the UN BEC classification.<sup>18</sup> Durability measures the percentage of HS-6 products that are classified in each of these three non-overlapping categories within an ISIC Rev. 3 4-digit sector. If an ISIC Rev. 3 4-digit does not contain any products classified as durable, semi-durable or transport products, the share takes the value of 0. In an extension, we examine the role of durable consumer products ( _cons_durk_ ), semi-durable consumer products ( _cons_semik_ ) and transport products ( _transpk_ ) separately and also include a measure that combines the share of both durable and semi-durable consumer products only ( _consk_ ).

> 17 Following del Rio-Chanona et al. (2020).

> 18 Durable consumer products correspond to BEC sector 61, semi-durable consumer products correspond to BEC sector 62, and transport products correspond to BEC sectors 51 and 522.

13
Duranton, Gilles, Peter M. Morrow, and Matthew A. Turner. "Roads and Trade: Evidence from the US." The Review of Economic Studies 81.2 (2014): 681-724.

Eck, Kristine. "In data we trust? A comparison of UCDP GED and ACLED conflict events datasets." Cooperation and Conflict 47.1 (2012): 124-141.

Emran, Shahe, Fenohasina Maret, and Stephen Smith (2009) “Education and Freedom of Choice: Evidence from Arranged Marriages in Vietnam,” Working Paper Esteban, Joan, Mayoral, Laura, Debraj Ray. 2012. Ethnicity and Conflict: An Empirical Study. American Economic Review 102:4, 1310-1342.

Esteban, Joan, and Debraj Ray. 1999. “Conflict and Distribution.” Journal of Economic Theory, 87(2): 379–415.

Esteban J and Schneider G (2008) Polarization and conflict: Theoretical and empirical issues. Journal of Peace Research 45(2): 131–141.

Faber, Benjamin, (2014), “Trade Integration, Market Size, and Industrialization: Evidence from China's National Trunk Highway System,” The Review of Economic Studies, 0, 1–25 Fan, S., P. Hazell, and S. Thorat (2000). “Government Spending, Growth and Poverty in Rural India,” American Journal of Agricultural Economics 82 (4), 1038{1051.

Fearon J 2007 Economic Development Insurgency and Civil War. In Helpmann Elhanan (eds) _Institutions and Economic Performance_ Harvard University Press Felix, M. L., & Meur, C. (2001). Peoples of Africa: Enthnolinguistic map. Brussels: Congo Basin Art History Research Center.

Foster, Vivien, and Daniel Benitez. "Congo, Democratic Republic of-The Democratic Republic of Congo's infrastructure: a continental perspective." World Bank Policy Research Working Paper 5602 (2011).

GAO Report, (2008), “Afghanistan reconstruction: Progress Made in Constructing Roads but Assessments for Determining Impact and a Sustainable Maintenance Program are Needed”, United States Government Accountability Office.

Gachassin, Marie and Najman, Boris and Raballand, Gaël, The Impact of Roads on Poverty Reduction: A Case Study of Cameroon (February 1, 2010). World Bank Policy Research Working Paper Series, Vol. , pp. -, 2010. Available at SSRN: http://ssrn.com/abstract=1559726 Garcia-López, Miquel-Àngel, Adelheid Holl, and Elisabet Viladecans-Marsal. Suburbanization and highways: when the romans, the bourbons and the first cars still shape Spanish cities. No. 2013/5. 2013.

Gibson, John and Scott Rozelle, (October 2003), “Poverty and Access to Roads in Papua New Guinea,” Economic Development and Cultural Change, Vol. 52, No. 1, pp. 159-185 26
# **Table 5**

# **Estimated Range of Transfers to Households with Elderly Residents**

Results for Neighborhoods of Different Multiples of the Poverty Line

||||1995<br>|-1998<br>|2000<br>|-2003<br>|
|---|---|---|---|---|---|---|
|Multiple of the<br>Poverty Line|Pre-Transfer<br>Income/Capita<br>Range|Portion of<br>Estimated<br>Range|HHs<br>w/Out<br>Migrants|HHs<br>w/Migrant<br>Children|HHs<br>w/Out<br>Migrants|HHs<br>w/Migrant<br>Children|
|One Quarter|119 to 319|Upper Bound|1356|1643|1489|1655|
|Poverty Line||Mean|948|1216|1081|1071|
|||Lower Bound|531|718|664|479|
|One Half|338 to 538|Upper Bound|1155|1276|1293|1484|
|Poverty Line||Mean|811|910|942|953|
|||Lower Bound|469|610|601|461|
|Poverty Line|775 to 975|Upper Bound|785|908|1019|1381|
|||Mean|517|648|709|821|
|||Lower Bound|307|420|473|290|
|Twice Poverty|1650 to 1850|Upper Bound|421|605|701|1192|
|Line||Mean|212|369|433|615|
|||Lower Bound|87|120|216|55|
|Four Times|3400 to 3600|Upper Bound|125|216|259|862|
|Poverty Line||Mean|-48|-137|-7|274|
|||Lower Bound|-185|-568|-249|-345|

Note: All values are in 2000 RMB Yuan per capita. The nutrition-based poverty line is equal to 875 RMB Yuan per capita. We use the RCRE Rural Household Surveys for 1995 to 2003 from Anhui, Henan, Jiangsu and Shanxi and the matched RCRE 2004 Supplemental Rural Household Social Network, Labor Allocation and Land Use Survey.

37
**Figure 3: Income elasticity estimates at various income levels**

<!-- Start of picture text --> Elasticity<br>3.0<br>Metals Energy<br>China China U.S. U.S.<br>(1997) (2017) (1997) (2017)<br>2.0<br>1.0<br>0.0<br>0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000 55,000<br>US$ per capita<br>Source : Authors’ calculations based on model’s parameter estimates.Authors’ calculations based on model’s parameter estimates.<br>Notes : The vertical lines denote the level of per capita income of the respective countries and years. Diamonds mark<br>elasticities at $ 9,900 per capita income (sample median). $ 9,900 per capita income (sample median). .<br>Figure 4: Income elasticity estimates for various countries for 1997 and 2017<br>Elasticity<br> 2.50<br> 2.00<br> 1.50<br> 1.00<br> 0.50<br> ‐<br>India China Turkey Korea U.S. Japan India China Turkey Korea U.S. Japan<br>Energy Metals<br><!-- End of picture text -->

**Source** : Authors’ calculations based on model’s parameter estimates.Authors’ calculations based on model’s parameter estimates. **Notes** : The vertical lines denote the level of per capita income of the respective countries and years. Diamonds mark elasticities at $ 9,900 per capita income (sample median). $ 9,900 per capita income (sample median). .

**Source** : Authors’ calculations based on model’s parameter estimates. **Notes** : The upper and lower ends of the bars denote elasticity estimates for 1997 and 2017, respectively.

_— 22 —_