rafmacalaba/data-use-mentions-extended huggingface dataset:
/sup> Nonetheless, I note that this value is higher relative to many countries in the OECD database and it is likely that the actual values are lower. Finally, I use a labor income share of 0.65 to adjust the education expenditure share for all countries. There is a wide range of estimates of this parameter with many well-known measurement concerns. The chosen value lies within the range of the estimates and near the median and the average across countries and years (Gollin (2002), Guerriero (2019)).

The Fréchet shape parameter governs the skill dispersion and an increase in its value reduces the variability of innate talent. To estimate this parameter, I fit the residuals obtained after regressing the log of hourly wages on occupation-group-dummies to estimate in an intermediate step. The value of is recovered given the estimate of via the process discussed previously. θθ(1 −ηη) θθ The only time-invariant parameters that need calibration are the experience-related returns to ηη human capital . The parameters 0 1 2 relate to the young, middle and old cohorts and I normalize 0 to 1. To isolate the experience-related human capital gain for the other cohorts, I target the wage cc γγ γγ , γγ , γγ growth of young men over their lifetime after controlling for occupational choices. The average across all γγ occupations serve as the baseline for each country. Table 3 reports the time-invariant parameters for each country. The calibrated values of El Salvador are close to that of Panama. The preferences structure of

> 12 Another strategy is to use a long-term average considering data from the mid-1990s onward only. However, this truncated average is very close to the baseline considered here for each country.

> 13 The private education share consists of early childhood spending which is approximately 0.2 percent of GDP.

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average value of total farm production varies from a low of $168 (Uganda) to a high of $664 (Nigeria), with most countries falling in the $100 to $300 range.<sup>8</sup> The value of total farm production is not particularly informative given the large differences in farm size. On average, farms in Ethiopia, Malawi, and Tanzania tend to be small - less than two hectares (ha). By comparison, the average farm in Uganda is over five ha and the average farm in Niger is 11 ha. Because of these variations, farms in Niger are, on average, the least productive among the LSMS-ISA countries, with a per ha value of only $60. By comparison, farms in Nigeria, on average, produce $680 in value per ha. Labor also varies substantially across countries, with farms in Ethiopia using an average of 434 days per ha, while farms in Niger use less than 100 days of labor per ha. Similarly, fertilizer use rates vary across countries, with less than one kg per ha used, on average, in Uganda compared to nearly 100 kg per ha used, on average, in Nigeria. In terms of other purchased inputs, Nigerian farms tend to apply more than the other countries. About 20% of farmers in Nigeria apply pesticide and herbicide, while less than 10% of farmers in most of the other countries apply these chemicals. Across all

> 8All monetary values are converted to U.S. dollars at current exchange rates and then appreciated/depreciated to 2010 dollars. We use data from the World Bank World Development Indicators, last updated on 9 April 2020, to make the exchange rate and inflation conversions.

16
distributed in terms of their attitudes towards foreigners with respect to the Himalayan Frontal Thrust where the earthquake occurred.

To provide explicit justification for this claim we regress a large number of village and household-level characteristics drawn both from the population census in 1998 and retrospective questions in our household survey on the distance to the fault-line. To the extent that these correlations are small and insignificant, we can confirm that the fault-line was orthogonal to preexisting population conditions. Note that in this particular case, the lack of correlations on observed attributes provides a particularly strong justification for lack of correlations on unobserved attributes as well, since housing choices are unlikely to have been made on the basis of population attitudes towards foreigners that are not related to observed characteristics such as education.

The village characteristics are regressed on the mean distance of the households in the village from the fault-line and district fixed effects. In addition, since the epicenter of the earthquake was quite close to the city of Muzaffarabad, we also control for distance to the epicenter. The census provides us with population variables (total and female) as well as education (village adult literacy rate and fraction of women with a secondary education) and some housing infrastructure variables—fraction of houses with electricity, with indoor water and a variable reflecting the type of construction. Using principal components methods, we create a village infrastructure index that combines the three infrastructure variables.

Table 2 confirms the basic results that the distance to the fault-line is not correlated with pre-quake village-level education and infrastructure. The distance to the fault-line effect is small in magnitude and statistically insignificant. In Appendix Table 1, we then examine further correlations using data from our household survey and on retrospective and current location data 17
of years, there were extremely high rates of real per capita growth (exceeding 10 percent in one year) associated with the boom in the resource sector.

But in addition to the economic growth that occurred over this time period, there was also a substantial amount of foreign assistance provided to the government of PNG that has been increasing over time. As mentioned in the Introduction, the country is becoming more geopolitically important and foreign assistance has potentially been increasing from a wide range of countries as a result (e.g., Hayward-Jones 2017). Figure 1 reports total assistance provided by Australia, as reported by the Australian government’s Department of Foreign Affairs and Trade; the total assistance provided by the United States, as reported by the U.S. government’s State Department; and total assistance provided by all OECD partners for a subset of years. The figure illustrates these patterns.

# **Section 3a. Data**

The analysis focuses on the change in well-being indicators between the 2009 HIES and the 2016-2018 DHS. The 2009 HIES is a nationally and regionally representative survey of 4,104 households, and is also able to report statistics at the rural and urban levels. The survey includes detailed information on consumption that was used to construct estimates of the national poverty rate for 3,658 households from the entire sample. The extensive survey also captured access to several essential services.

The DHS was conducted in four waves between 2016 and 2018. The survey is nationally and provincially representative, and can report estimates at the rural and urban levels. Data collection was difficult and fieldwork could not be completed in 33 of the 800 census units originally selected in the sample design. Reasons for the delays and for not completing work in each census unit include difficulties in handling the terrain in the country, adverse weather, and security issues (e.g., DHS 2019).

The survey consisted of a household survey, and then separate surveys for eligible men and women in the household. In
Source: Authors’ own estimate based on NSSO data.

Drawing on the categorization of activity status, many empirical studies have raised concerns about the credibility of the NSSO database in appropriately capturing women’s work. Hirway (2012) argues that the NSSO surveys are incapable of capturing women’s work. The author draws attention to the ‘activity status’ category ‘93’ that records those who “attended domestic duties and were also engaged in (a) free collection of goods such as vegetables, roots, firewood, cattle feed, etc., and (b) sewing, tailoring, weaving, etc.” The author argues that the NSSO marks category ‘93’as non-workers despite the fact that that they are engaged in economic activities covered under the production boundary of the United Nations-System of National Accounts. Although the Indian System of National Accounts (ISNA) considers the number of workers in category ‘93’ too small to have any serious impact on the labor force, the author argues that extrapolation using _time use survey_ data shows that the number of workers who are excluded from the labor force due to this categorization is in fact substantial. She further argues that the expansion of this category of workers as home based workers is probably the reason for the decline in FLFP rates as measured by the NSSO. Similarly, Sudarshan and Bhattacharya (2009) expressed doubts on the credibility of NSSO data, suggesting that the survey underestimates women’s actual work participation. In support of their argument, they provided evidence from a separate survey done in urban Delhi, which showed higher labor force participation rates for women than the national survey. While recognizing the basis for such arguments, nonetheless NSSO data represent the strongest empirical basis for assessing female labor participation. For the rest of the paper, we adhere to the NSSO definition, since this data source is the main national database for this purpose, and it follows international protocols that are well accepted in the literature (NSSO, 2011).

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using the approach recommended in this paper. To see this, we revisited the May 2015 DSA (IMF 2015), which rated Zambia at moderate risk of external debt distress but judged public debt dynamics sustainable. However, using the data from the May 2015 DSA, ADF Lab (2016) concluded in March 2016 that public debt dynamics were “definitely unsustainable” with a primary deficit of 5% of GDP and r close to 5 percentage points above g. Public debt had been projected at 40% of GDP for end-2015, but full-year data indicated that d was above 50%.

What about the high level of private external debt? Would its impact have been ignored if the focus had been on public debt dynamics combined with market signals and a thorough analysis of international liquidity? No, because this vulnerability would have been picked up in the assessment of foreign exchange reserve adequacy and measures like the CAD plus short-term external debt divided by foreign exchange reserves. Indeed, the column for 2015 in the external DSA in IMF (2017a) has a large residual of 57 percentage points of GDP, which probably indicates massive under-reporting of the CAD. The fact that the private sector was borrowing large sums abroad means that private investment greatly exceeded private saving.

Zambia’s experience underlines that LIC DSF world has changed dramatically and that LIC DSF countries can no longer ignore the lessons from EM crises. If we combine the results from the three generations of crisis models, LIC DSF countries need to go beyond the narrow confines of PPG external debt.<sup>14</sup> They need to broaden their macroeconomic monitoring to include:

- Fiscal and growth fundamentals as reflected in the sustainability of public debt dynamics

- International liquidity

- Market signals on devaluation and default risks

- Vulnerabilities posed by private external borrowing and currency mismatches on private sector balance sheets.

It makes obvious sense to have a DSF that reflects the above monitoring list instead of the present overwhelming emphasis on PPG external debt. Box 2 summarizes the lessons from Zambia.
Despite the heterogeneity in preferences, there is not much evidence of leader preferences reflecting those of one specific group more than another i.e. they do not seem to side with the wealthy or the poor, with those who lost more or less. We ran a multinomial regression (see annex) to further assess whether there exist systematic differences between the preferences of leaders and citizens. Data from the authorities and household respondents were pooled to explain the choice between various governance, security and socioeconomic initiatives which had been regrouped in seven categories: a development strategy that puts the north at par with the rest of the country, social services, rural development and employment, infrastructure, security, governance and jobs. We find that explanatory power is low: i.e. it is hard to predict preferences based on observed characteristics. Authorities generally represent the preferences of the population (with some slight preference for social projects in urban areas), rural households in Kidal are less interested in rural development and employment and those in rural Gao are particularly interested in infrastructure and jobs. Based on this, the general conclusion is that local leaders’ opinions are appropriate proxies for the citizens they represent.

# **V. Operationalizing the Priorities in the Peace Accord**

As mentioned above, Mali's Peace and Reconciliation Accord called on the World Bank, the African Development Bank and the Islamic Development Bank to carry out a Joint Assessment Mission (Mission d’évaluation conjointe, MIEC) to operationalize the Peace Accord. Using the various measures mentioned in the Accord, the MIEC was expected to prioritize and cost needs with an eye to ensuring a rapid recovery, to addressing poverty and promoting development, and to allowing the northern regions to reach the same level of development as the rest of the country within the next 15 years.

A prioritization framework was thus developed over the course of 60 days, along three pillars: 1) Strengthen peace, social resiliency and decentralized governance; 2) Restore and expand basic social services and social
a Chinese province as a potential location for FDI. Taken together, these variables represent a province's level of economic development and its overall foreign investment policy environment.

_Gross National Product._ GNP generally reflects the economic development of a country -- or in our case a province. It reflects a province's potential demand, and thus gives a good estimate of the province's market size. In this regard, GNP usually is an especially important factor for foreign investors seeking to sell as well as produce in a local market. The variable used here is provincial GNP for 1992. Our expectation is that it is positively related to a province's stock of FDI.

_Labor costs._ Foreign investors generally aim to take advantage of host countries' cheaper factor inputs (relative to their home countries), and the cost of labor is often considered important in this regard. In other words, foreign investors' display sensitivity to inter-country variations in labor costs in making their location decisions. However, the sensitivity of FDI location decisions to _intra-country_ labor cost differentials is unlikely to be as pronounced. Indeed, even though the decision to invest in China is no doubt heavily influenced by the country's prevailing low wage rate, once the choice is made to locate an investment in China, finding the cheapest possible labor _within China_ may not be an important consideration as wage differentials may not be significant. In fact, it is likely to be the case that observed wage rates (including bonuses and in-kind benefits) do not vary as much between regions within China as within other countries because of China's legacy of central planning, which has tended to homogenize 8 Although data on cumulative stocks of FDI are available for more recent years, data constraints on the other variables force us to focus on end-year 1992.

9 See, for example, Caves (1982).

7
The Moroccan export sector grew after trade liberalization. Total exports increased 307 percent from 1993, with a decrease in 2020 due to the COVID-19 pandemic shocks (Figure 2.3). At the same time, the country’s trade balance (exports minus imports) has remained negative and decreasing (Figure 2.4), in part reflecting high dependence on fossil fuel imports.

# **Figure 2.3. Exports benefited from Figure 2.4. Trade balance remained openness negative**

Exports of goods and services, billions 2015 US$ <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Development Indicators.

Trade balance, billions 2015 US$ <!-- Start of picture text --> 0<br>-4<br>-8<br>-12<br>-16<br>Source: Authors’ elaboration using data from<br>the World Development Indicators.<br>1993 1996 1999 2002 2005 2008 2011 2014 2017 2020<br><!-- End of picture text --> One reason why the EU agreement was so important is that Morocco’s trade, in terms of partners, concentrates on Europe. In 2018, nearly 70 percent of Moroccan exports went to high-income countries. Its two main partners are France and Spain, accounting for 22 and 24 percent of total exports, respectively.

Morocco also exports a limited, but increasing, range of products. Between 2001 and 2019, Morocco increased the number of products it exported from 2,704 to 3,129 (WITS, 2022), which is both higher than neighbors the Arab Republic of Egypt and Tunisia and about 2/
(figure 7A). Regional studies on Latin American economies demonstrate that progressive taxation, through increased direct taxes, leads to better equality outcomes (Martorano, 2018; Tsounta and Osueke, 2014; Cornia et al., 2011) while noting that the effectiveness in promoting equity is limited due to lower tax capacity (Martorano, 2018).

A progressive tax system can also promote growth through reduced inequality. The impact of tax progressivity on economic growth depends on how tax revenues are used and the economic context. Potential downsides, such as reduced work and investment incentives, can be mitigated by funding productive public investments and reducing inequality. Theory suggests that reducing inequality fosters economic growth by mitigating under-investment in human capital because of credit market imperfections (Galor and Zeira, 1993) and by lowering high fertility rates among the poor, which hinders human capital accumulation (de la Croix and Doepke, 2003). Conversely, some argue that inequality can spur growth by fostering innovation and entrepreneurship (Lazear and Rosen, 1981), increasing savings and investments (Kaldor, 1957), and creating business and education opportunities (Barro, 2000). While theoretical perspectives vary, empirical research supports the notion that lower inequality is associated with faster and more durable growth (Berg et al., 2018). Banerjee and Duflo (2003), while inconclusive about the negative impact of inequality on growth based on macro data, advocate for micro studies; for instance, Banerjee et al. (2001) show evidence from Indian cooperatives that those with the highest inequality in land ownership are the least productive.

total taxes correlates with reduced inequality. The choice of the progressivity measure results from data availability and ability to measure progressivity of the tax system that requires details on a tax incidence by tax type and micro data at household and firm level. Thus, the measure should be 12
_β3_ ) provides the part of the assimilation effect that is common to all migrants regardless of country of origin.<sup>18</sup> That effect is negative and significant in each specification, indicating that, other things being constant, the occupational placement of an individual that has migrated during the second part of a decade is about 3-4% lower than that of one who has arrived earlier, i.e, in the first part of the decade.<sup>19</sup> Thus, in accordance with the previous literature focusing on earnings, experience acquired in the US labor market is an important determinant of performance.

> 17 Jasso et al. (2000) claim that recent immigrants sampled in the decennial censuses “may be neither recent nor immigrant.”

> 18 Whatever is country-specific in the assimilation effect is captured by the coefficients of the country dummies.

> 19 The effect is 6% for the 1975 arrival, evaluated in the first decade after migration.

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1.2%|5|0.0%|0|0.01|4|409(100%)|
|Group||||||||||
|Four|97.8%|400|2.0%|8|2.0%|1|0|0|409(100%)|
|Control||||||||||
|Group|100.0%|400|0.0%|0|0.0%|0|0|0|400(100%)|
|Total|97.9%|2000|1.5%|31|0.1%|3|0.4%|8|2042(100%)|

# B. **<u>Data collection</u>**

Facility administrative records from the e-health database collected at the end of the study period serve as the primary data source on whether the study participants came for hypertension and diabetes screening. In addition, a baseline survey for the impact evaluation was administered face-to-face to study participants during the visits by the study fieldworkers. Its main objective was to collect socio-demographic variables not available in the e-health database to assess the balance across study groups at baseline and to serve as control variables in the estimation models. For intervention groups 1-4, the baseline survey took place between July 22, 2019, and September 2, 2019. It consisted of administering a short socio-demographic survey to the sample of 1,600 study participants. Fieldworkers administrated the baseline survey in the same visit that they distributed screening invitations (and vouchers for group 3).

To avoid contamination of the control group (n=400) from asking specific questions about diabetes and hypertension screening, the baseline survey was not administered to the control group between July and September 2019 but instead was administered in January 2020, at the end of the intervention period. This short time difference in administering a
We have used official information on the listing of all rural localities receiving the program (broken down by each program component) at the closing of each fiscal year in 1998 and 1999 in order to verify which localities were receiving the program in late 1998 and 1999. A fraction (about 20 percent) of control localities started receiving the program’s food stipends by November 1999, but none of those villages had received any scholarship by that date. We thus continue to treat those observations as belonging to the control group in November 1999.

9
. Source: own elaboration on the ILO School-to-work transition survey of Azerbaijan._ Table A2. Descriptive statistics by <u>gender</u>

||||**All**<br>|||||**Women**<br>|||||**Men**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variable**|Obs|Mean|Std.<br>Dev.|Min|Max|Obs|Mean|Std.<br>Dev.|Min|Max|Obs|Mean|Std.<br>Dev.|Min|Max|
|**Log of total monthly labor income (in th.**<br>**of old Manats)**|1896|5.9355|0.5171|2.3026|8.8537|658|5.8693|0.5647|2.3026|7.8240|1238|5.9706|0.4865|3.9120|8.8537|
|**Women**|4972|0.4670|0.4990|0|1|2322|1|0|1|1|2650|0|0|0|0|
|**Log of weekly hours of work**|1896|3.6412|0.3126|0.6931|4.4308|658|3.6070|0.3325|0.6931|4.4308|1238|3.6594|0.3001|0.6931|4.4308|

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