rafmacalaba/data-use-mentions-extended huggingface dataset:
the quality of the data system. Under the assumption that international guidelines provide the benchmark for ideal data systems, adherence to such standards implies that the quality of data systems meets wellestablished standards. Countries are evaluated against a set of criteria such as use of an updated national accounts base year, use of the latest balance of payments manual, the external debt reporting status, an updated consumer price index, an updated industrial production index, updated import/export prices, an accounting basis for reporting government financial data, vaccine reporting to WHO (discrepancy between WHO and government estimates), subscription to the IMF’s Special Data Dissemination Standard, and enrollment data reporting to UNESCO). Each criterion has equal weight.

The periodicity and timeliness dimension measures the availability and frequency of key socioeconomic indicators, of which nine are indicators of Millennium Development Goals (MDG). This dimension attempts to measure the extent to which data are made accessible to users through transformation of source data into timely statistical outputs. The frequency of the main indicators considered, each receiving equal weight, includes: an income poverty indicator; a child malnutrition indicator; a child mortality indicator; an immunization indicator; an HIV/AIDS indicator; a maternal health indicator; a gender equality in education indicator; a primary-school completion indicator; an access to water indicator; and a GDP growth indicator.

**Table 1. Subcomponents of the Statistical Capacity Indicator**

|**Dimension**|**Definition**|
|---|---|
|**Statistical**<br>**Methodology**<br>**(scale: 0-100)**|Measures a country’s ability to adhere to_internationally recommended standards_<br>_and methods_. This aspect is captured by assessing guidelines and procedures used<br>to compile macroeconomic statistics and social data reporting and estimation<br>practices.|
|**Source Data**<br>**(scale: 0-100)**
both formal and informal.<sup>16</sup> This analysis could further be enhanced by including infrastructure data to identify patterns of firm locations, typologies, and synergies (such as sectors, size, status, and agglomeration effect).

Such analysis could also improve understanding of both job creation and self-employment. Regarding the latter, a separate draft (Cuevas and Rodella, 2014) that the authors prepared for the Haiti Systematic Country Diagnostic (SCD) looks into the different types of self-employment (one-person firm, family firm, and micro-enterprise). The draft’s goal is to provide a finer lens on the differences between survival and growth entrepreneurs.

With the caveat that correlations rather than causal relations are being found, the analysis of correlates of successful self-employment uses three measures: revenues per worker (in log), revenues per worker that are in the top 30 of the distribution, and profits per worker that are in the top 30 of the distribution. These help identify the characteristics and inputs that are significantly more relevant for entrepreneurial success. This type of study, combined with evidence-based analysis like the one conducted for the present paper, can help improve understanding of what it will take to elevate productivity and generate jobs in the self-employed and small business sector. The five main findings of the draft’s analysis are:

> 16 Although the coordinates have not been shared at this point and remain with the respective national agencies (IHSI, ONPES, MCI and MEF).

21
thus further from cities, coasts, and rivers, less suitable for agriculture, but it is only slightly more exposed to drought than the sample of 2003 cropland pixels used for the cropland loss regression.

For the second stage regression for both cropland losses and cropland gains, we use variables from a set of seven national-level explanatory variables presented in Appendix Table A3.2. These institutional and economic variables include the quality of land administration, an index of Rural land tenure insecurity, the indicator Enabling the business of agriculture, GDP per capita, the Gini index, and two measures of Cropland per capita, one for 2003 and one for 2018. Cropland per capita was calculated by dividing the country’s aggregate amount of cropland for these years in the MODIS product by the corresponding national population for the same years. Note that some of these variables, however, are not available for all countries: Whereas cropland per capita can be calculated for more than 200 economies, the indicator from Enabling the business of agriculture is only available for 101 countries, potentially limiting the size of the sample in the second stage when introducing this indicator as an explanatory variable. Table A3.2 shows that these variable vary much across countries.

# **Section 4.  Results**

We ran the first stage regression of movements out of cropland on pixel-level measures capturing local contexts. Table 2 below reports the OLS estimates for regression (1) focusing on the conversion out of cropland. Given the large dataset (over 48 million observations), all coefficients are significant at the 1 percent level. Estimated coefficients are consistent with theoretical intuitions: Cropland far from cities is likely to disappear at a more intense pace than cropland close to cities. With cropland pixels distant by 60 km from the nearest city on average, an additional one standard deviation in the distance to a city (i.e., considering locations that are an additional 55 km away from a city) increases the likelihood of cropland loss by .8 percent over the 15-year period
technical aspect only.

## **Licensing requirements on management Score 0**

No restrictions.

**Movement of people - Temporary Score 0**

No restrictions.

**Other restrictions (Addition categories) Score 0.33**

Restrictions on hiring professionals: Investment Promotion Act 2004 (cap 172) section 13.1. The employment of foreign natural persons for the implementation of foreign investment shall be agreed upon by the contracting parties and approved by Government.

# **Sources:**

Dee, P. (2005), ―A compendium of barriers to services trade‖, prepared for the World Bank, <u>http://www.crawford.anu.edu.au/pdf/staff/phillippa_dee/Combined_report.pdf</u> Nguyen-Hong, D. (2000), ―Restrictions on Trade in Professional Services‖, Productivity Commission Staff Research Paper, Ausinfo, Canberra. Available at: http://www.pc.gov.au/research/staffresearch/rotips World Bank Regulatory Survey in East Africa conducted in the context of the Project ―Trade in Professional Services in East Africa‖ in 2009.

World Bank Survey on Applied Policies in Services conducted by Development Research Group, in 2008-2009.

111
the direct jurisdiction of Xinjiang Province, also enters our dataset.<sup>34</sup> Our sample size of 331 regions exceeds that of previous studies in the empirical NEG literature which have made use of prefectural level data (Au and Henderson, 2006; Hering and Poncet, 2010a; and Bosker _et al_ ., 2010). This is because these studies have, by and large, restricted themselves to prefectural level cities.

_Variables used in the estimation of the urban and rural wage equations_

**Urban wage rate (** **_w_**<sup>**_U_**</sup> **):** proxied by average disposable income per capita for urban households.  Urban household disposable income is the actual income at the disposal of members of a household which can be used for final consumption, other non-compulsory expenditure and savings.  It is equal to the total income of an urban household minus payments to cover income tax and personal contributions to social security, and the income subsidy received by a sample household for keeping a diary. **Source:** _Regional Economic Statistical Yearbook 2008_ .

Data is missing for 14 prefectures on this variable. These missing values have been filled-in using the predicted values generated from OLS estimation of the regression equation:

> 33 Prior to 1997, Chongqing was a sub-provincial city in Sichuan province.   The area that was added to Chongqing as part of its upgrading to a municipality in 1997 mainly consists of the two wings, i.e. the Northeast and Southeast wings.

> 34 There are 4 county-level cities in Xinjiang that are under the direct jurisdiction of Xinjiang Province. These cities are also called "sub-prefecture-level cities" or "vice-prefecture-level cities"  (http://en.wikipedia.org/wiki/ <u>Sub-prefecture-level_city). Shihezi City is included in our sample because, after Urumqi, it is the second largest</u> city in terms of population in Xinjiang (http://en.wikipedia
positive impact of the TUP intervention on treatment women’s psychological well-being, compared to the control group. This, together with the fact that treatment women report large levels of under-employment and idle time, and the positive impacts across multiple socioeconomic indicators, support the argument that treatment women’s well-being is not decreased by the intervention, even when taking into consideration the increased market participation. Taking all together, our results suggest that women’s empowerment was increased by the program, even in the most conservative scenario, that is, one when we exclude the direct effect of women’s market participation from our women’s empowerment indicator, and when we do not consider other indicators of socioeconomic well-being.

# **5.9 Shocks, Coping Mechanisms and Resilience**

To secure long-term, sustained growth out of extreme poverty in fragile settings, households need to be resilient to shocks. How does the TUP program help households cope with or limit the effects of shocks in ways that might help protect their potential future consumption and investments? The period between midline and endline presents an important test of the TUP program’s ability to increase household resilience in this way due to the level and extent of the exogenous shocks that occurred in Afghanistan, covered in detail in Section 2.

Table 9 shows that treatment and control households report a similarly high incidence of shocks affecting their income or livelihoods. We present two indicators: (i) the incidence of any shock affecting households’ livelihoods in the past year (collected in January–February 2021), and (ii) the incidence of the 16 types of shocks in the past month (collected in May–June 2021). Both indicators confirm that shocks are equally likely to occur in treatment and control households irrespective of the period analyzed. Our preferred measure highlighting the full extent of exposure to shocks comes from our 1-month incidence measure since it prompts for each one of 16 different types of shocks. Here we find that 60% of households in the treatment vs.
be expected from application of the means-testing rule to household survey data, and are a good deal lower for most workers than the social security contribution.

> 4 The Thai scheme excludes a few high-cost interventions (e.g. renal transplants) but for the most part the schemes are fairly comparable in terms of their generosity.

> 5 One feature of the Mexican SP program that may limit the disincentive to formalize is that it provides access to a different network of health facilities – sometimes these may be more accessible for a household than the facilities operated by the formal-sector health scheme.
not have the incentive to return to the workforce until the youngest child enters preschool.

Similar to Brodeur and Connolly (2013) and Herbst (2017), our non-eligible “comparison” group is the group of women without any preschool-aged children, which includes non-mothers and mothers with all children younger than or older than preschool ages. While employment trends of non-mothers may not be similar to those of preschooler-mothers, the identifying assumption in DDD is less stringent than that in DD. It requires that absent differences in preschool access (be it across regions or over time), the employment gap between mothers with preschool-aged children and mothers without preschool-aged children observe similar trends. Therefore, the common

> 14 See for example: Gelbach (2002), Baker et al (2008), Cascio (2009), Berlinski et al. (2011), Brodeur and Connolly (2013), Nollenberger and Rodriguez-Planas (2015), Herbst (2017), and Contreras and Sepulveda (2017). 15 In most cross-sectional data without detailed fertility history, e.g. Labor Force Survey or Census, we have to define treatment pertaining to the youngest child because the first child might have moved out of the household and, thus, is no longer observed. The youngest child, on the other hand, has a very small likelihood of living apart from her mother. With detailed fertility history, we know all children eligible for preschools and we define our treatment status accordingly.

11
7 the approach to regulating this group of behind-the-border NTMs differs and has evolved over time both in the GATT/WTO system as well as in a number of Preferential Trade Agreements (PTAs).

In Figure 1, we rely on available data from the Trade Analysis Information System (TRAINS) developed by UNCTAD to present a simple correspondence between the theory-based classification and the one used by data collecting agencies. We have limited this exercise to the chapters for which data are publicly available (i.e. Chapters A to F, excluding Chapter E which is not covered by this survey). The mapping between the classifications is based on our own reading of the detailed taxonomy provided by UNCTAD (2013) and the full table of conversion used is presented in the Appendix Table 1. Figure 1 shows that product and process regulations are mostly TBT and SPS measures, while consumer regulations take the form of price control measures such as excise taxes or charges on the consumption of certain goods (e.g. cigarette taxes, CO2 emission charges). The figure also reveals that most available data on NTMs cover customs regulations that are evenly distributed across different policy measures.
LIBERIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE equivalent to an impossible 13 percent of the monthly budget, well beyond the affordability threshold. In fact these calculations suggest that power will not become widely affordable to any significant share of the population until costs (and hence prices) can be reduced to below $0.30 per kilowatt-hour.

In the case of water, subsistence water consumption of 6 cubic meters per month (equivalent to 40 liters per capita per day) would cost $6 per month at cost recovery tariffs. That total would absorb 4 percent of the monthly budget of the illustrative household, which may be affordable. At existing tariffs that largely cover operating costs, subsistence consumption would only represent 2 percent of household budgets, which would be quite affordable for the population.

**Figure 17. Affordability of subsistence utility bills for representative family living under poverty line**

<!-- Start of picture text --> a. Power<br><!-- End of picture text --> <!-- Start of picture text --> subsistence consumption of 25 kWh/month<br>price of power (US$/kWh)<br>b. Water<br>subsistence consumption of 6 cubic meters/month<br>price of water (US$/cubic meter)<br>% of household budget<br>% of household budget<br><!-- End of picture text --> Source: Illustrative calculations based on Liberia Poverty Assessment.

At first glance, utility operational inefficiencies do not look very significant relative to GDP _._ Distribution losses of power and water utilities total $1.8 million annually, while under-collection amounts to a further $1.1 million annually. This amounts to no more than 0.2 percent of GDP for each sector, which is much better performance than that found in the benchmark group of low-income fragile 31
542|0.535|
||(0.500)|(0.494)|(0.492)|(0.493)|(0.488)|(0.490)|(0.496)|(0.498)|(0.499)|(0.499)|
|Any Practice Implemented (Yes/No)|0.473|0.373|0.387|0.533|0.515|0.635|0.540|0.581|0.694|0.727|
||(0.500)|(0.484)|(0.487)|(0.499)|(0.500)|(0.482)|(0.499)|(0.494)|(0.461)|(0.446)|
|Any Problems Encountered (Yes/No)|0.425|0.288|0.319|0.329|0.294|0.471|0.358|0.356|0.352|0.360|
||(0.495)|(0.453)|(0.467)|(0.470)|(0.456)|(0.500)|(0.480)|(0.479)|(0.478)|(0.480)|

**Notes:** This table presents data collected by the facilitators during the first and second session of the assistance visits. The variables include the interest shown by the retailer in the practices described in each chapter of the handbook, whether they started engaging with the material of the chapters and implementing its practices, as well as whether they encountered problems during the assistance. Data were collected by the facilitators during the first and second session of the assistance visits.
Bank, Washington, DC.

Ferreyra, M. M., C. Avitabile, J. B. Álvarez, F. H. Paz, and S. Urzúa. 2017. At a Crossroads: Higher Education in Latin America and the Caribbean. Washington, DC: World Bank.

Ferreyra, M., and M. Roberts. 2018. Raising the Bar for Productive Cities in Latin America and the Caribbean. Washington, DC. World Bank.

Frattini, T., and I. Solmone. 2022. “The Labour Market Disadvantages for Immigrant Women.” VoxEU.org, CEPR Policy Portal, March 30, 2022. https://voxeu.org/article/labour-market-disadvantages-immigrant-women.

Friedlingstein, P., M. W. Jones, M. O'Sullivan, R. M. Andrew, D. C. Bakker, J. Hauck, C. Le Quéré, et al. 2022. “Global Carbon Budget 2021.” Earth System Science Data 14 (4): 1917-2005.

Fuglie, K., M. Gautam, A. Goyal, and W. F. Maloney. 2020. Harvesting Prosperity: Technology and Productivity Growth in Agriculture. Washington, DC: World Bank.

Garcia-Saltos, R., J. Teodoru, and F. Zhang. 2016. “Potential Output Growth Estimates for Central America and the Dominican Republic.” IMF Working Paper 16/250, International Monetary Fund, Washington, DC.
otherwise, the estimated coefficient of the RTA variable combines the NTB effect and the tariff effect. However, as shown by Teti (2020), tariffs from standard sources, such as the World Bank’s World Integrated Trade Solutions (WITS), suffer from substantial measurement error. As deep RTAs also feature the most comprehensive tariff cuts, it is impossible to say if the RTA effect identified in a gravity equation measures the NTB reduction caused by the new RTA or instead only captures mismeasured tariff reductions.

This paper uses a new database containing carefully cleaned tariff data to apply the indirect approach. Do RTAs really lower NTBs, and if yes, by how much and through which provisions? Or is all the talk about NTBs only distracting from the lowering or elimination of tariffs? We proceed in two steps. First, we estimate the average effect of RTAs on NTBs across many agreements using the newly available Global Tariff Data (new GTD) provided by Teti (2020) that deals with measurement error. In this exercise, we highlight the important role of general globalization trends. Omitting these overstates the role of NTBs. The second part of the paper sheds light on the heterogeneity between RTAs and on the role of specific provisions implemented in those deals. Following Baier, Yotov, et al. (2019), we estimate a separate coefficient 2
economic effect is small is—as we show—FDI comes to the more productive firms in the first place and hence once this effect is controlled for (which is only done by a few papers so far in the literature) further improvements in productivity are small.

**Table 4: Foreign ownership and firm productivity—Fully Owned Subsidiaries**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||∆ln(TFP)|∆<sup>2</sup>ln(TFP)|∆<sup>3</sup>ln(TFP)|∆<sup>4</sup>ln(TFP)|∆<sup>4</sup>ln(TFP)|
|∆ln(FO)|0.000<br>(0.003)|||||
|∆<sup>2</sup>ln(FO)||0.001<br>(0.003)||||
|∆<sup>3</sup>ln(FO)<br>∆<sup>4</sup>ln(FO)|||0.001<br>(0.003)|0.023***|0.022***|
|||||(0.003)|(0.003)|
|Observations|3008|2889|2765|1823|1055|
|Year Fixed Effects|Yes|yes|yes|yes|yes|
|Sector Fixed effect|Yes|yes|yes|yes|yes|
|Sector-Year Fixed effect|No|no|no|no|yes|

_Source:_ authors’ calculations based in the Orbis dataset for Turkey _Notes:_ The regressions
, India and Indonesia, for which we do an urban-rural split. (Given that Method 2 allows different lines for urban and rural areas we plan to do urban-rural splits for more countries in future applications.) The measures of consumption (or income, when consumption is unavailable) in our survey data set are reasonably comprehensive, including both cash spending and imputed values for consumption from own production. But we acknowledge that even the best consumption data need not adequately reflect certain ―non-market‖ dimensions of welfare, such as access to certain public services, or intra-household inequalities. For these reasons, our poverty measures need to

> 19 For further discussion see Ravallion (1994) and Deaton and Zaidi (2002).

> 20 For a few cases we do not have consumption distributions but we still have survey-based estimates of mean consumption. Then we replace the income mean by the consumption mean leaving the Lorenz curve the same (i.e., all incomes are scaled up by the ratio of the consumption mean to the income mean).  There is, however, no obvious basis for adjusting the Lorenz curve.

11
<br>10%<br>0%<br>PHL IDN VNM MNG LAO Non-EAP<br>Strongly agrees Agrees Does not know Disagrees Strongly disagrees<br>money will not improve learning<br>Share of officials believeing that more<br><!-- End of picture text --> Sources: CGD–World Bank survey, 2022. Per capita GDP is not statistically linked with the displayed variable.

Most officials in all the other EAP countries believe that additional resources would improve learning, in line with responses of surveyed officials from countries outside the region. In Indonesia however, only about half of officials believe more resources would be beneficial, while in Mongolia, the figure is about a third.

18 This is likely explained by the constitution, according to which public spending for education should match 20 percent of the total budget.

21
|0.384|0.354|0.307|0.300|
|_Between-group i_|_nequality_||||||
||0.041|0.036|0.025|0.029|0.024|0.026|

<mark>Source: Authors, based on DOSM data. Notes: Inequality indices are Theil L index, also known as the mean log deviation (MLD), based on household income per capita.</mark> <mark>However, while relative inequality was decreasing, absolute inequality was increasing rapidly. That is, poorer Malaysians were gaining a larger share of total income, but the currency-denominated income gap between poor and rich was</mark> 17