rafmacalaba/data-use-mentions-extended huggingface dataset:
riches, no refinement, no comfort, and no benefit to those who may be possessed of wealth.” (Colquhoun, 1806, p.7).

The word “poverty” often appeared in both English and French literary works of the time. Measured by the incidence of references to “poverty” in English-language _Google Books_ , the peak of the First Poverty Enlightenment was 1787. There are 204 books listed as containing a reference to poverty in the database for 1787 used by the _Viewer_ —representing 0.005% of all words that year, which was the highest incidence in a single year over 1700-2000. The 204 books containing wide-ranging references to poverty, and no single volume dominates, though the largest count is found in Samuel Johnson collected works (published after his death in 1784). Dr Johnson wrote (often amusingly) about the foibles and hypocrisies found in how people (often rich people) think and talk about poverty. While an awareness of poverty is certainly evident in these writings, it is by-and-large an accepting and unchallenging awareness. While the first Poverty Enlightenment saw far greater attention to poverty in the literature, it was not associated with widespread empathy for poor people as fellow citizens on the part of those writing books, and it was not yet widely accepted that there was any public responsibility for poverty.

Nor did the economics of the 18th and 19th centuries offer any serious challenge to the view that poverty was to be largely accepted. Workers were often equated with “the poor.” Some economists saw poverty as an essential condition for economic development. No doubt it was agreed that rising real wage rates of the proletariat would reduce poverty, but it was argued that this would undermine wealth accumulation by reducing labor supply and (in the Mercantilist schema) making exports uncompetitive, and even corrupting the values of workers as they aspired to luxury goods.<sup>13</sup> Thomas Malthus famously saw ecological disaster ahead, with poverty and famine as the only check against rising population.
0.090)|(0.088)|
|**Santa Cruz**|-0.2919***|-0.2965***|-0.2893***|-0.2816***|
||(0.062)|(0.061)|(0.063)|(0.064)|
|**Beni**|-0.3139***|-0.3130***|-0.3063***|-0.3046***|
||(0.068)|(0.063)|(0.072)|(0.072)|
|**Pando**|-0.3353***|-0.3765***|-0.3251***|-0.3303***|
||(0.064)|(0.034)|(0.074)|(0.071)|
|<br>**Observations**|327|327|327|327|
|**Pseudo R2**|0.223|0.218|0.227|0.235|
|***** p<0.01, ** p<0.05, * p<0.1**|||||

Note: Marginal effects reported; standard errors reported in parenthesis. Source: Authors from World Bank (2010) APER database.

# _<u>Differential estimation</u>_

We further explore the inter-temporal effects of agricultural spending on VAM by controlling for an initial level of spending, A0 (2003 level), and estimating the effect that the inter-temporal change in spending, ΔA (the increase between 2006 and 2007), has on the observed risk of 2007 food insecurity. We call that change ̳incremental spending‖.
econometric research affects their interpretation and validity. The second is instrumental variables estimation. We note that there are many bad or unconvincing instruments used in migration research, and that Bartik or shift-share instruments have been thought of as more plausible, but have also been the subject of several recent econometric advances. We outline in detail issues to consider in using shift-share instruments for migration. Finally, we also consider the possibilities for using regression-discontinuity methods for migration research, discussing how language tests, points systems, and discontinuities in time can and cannot be used.

An underlying theme in much of the recent econometric research is that treatment effects are likely to be heterogeneous, and this heterogeneity has implications both for the validity and interpretation of many estimates. We conclude by noting this means we should aim to move away from talking about “the” impact of migration, and that likely areas for future work will involve approaches that work to better identify who is most likely to be affected by migration policies, use machine-learning methods to better estimate treatment effect heterogeneity, and consider external validity issues more fully.

# **2 What is the problem experimental methods help to solve?**

Many research and policy questions surrounding migration are causal questions.<sup>2</sup> In the immigration literature this typically involves estimating the impact of immigration on wages or employment rates of natives. A large part of the development literature in migration attempts to answer questions like what is the impact of migration on the incomes, health, and life opportunities of migrants; and what is the impact of having a household member migrate or of receiving remittances on outcomes of household members remaining in the sending community, such as the education and health of children, their levels of entrepreneurship and labor supply, and on community poverty and inequality levels. A common approach to answering such questions is to use data on migrants and non-migrants, and attempt to control for differences between them in a linear regression framework. For example, researchers may attempt to estimate an equation of the
6 still be zero. What needs to happen for _E_ 1 to be positive is that there needs to be sufficiently large amount of reranking and sufficiently large income changes for those involved that _entire groups of equals_ change order in the move from the period-0 distribution to the period-1 distribution.<sup>5</sup> Eqn (5) indicates that a small or near-zero change in the Gini coefficient could arise for a variety of reasons. It could be that _V_ , _H_ and _R_ are all zero or close to zero, so that everyone— whatever their initial income—experiences a similar proportionate income change. But it could also be that inequality-reducing vertical redistribution ( _V_ <0) is offset by inequality-increasing redistribution associated with differential income changes _within_ groups of period-0 equals and reranking ( _H_ + _R_ equals – _V_ ). Policymakers and society at large may well feel quite differently about these two scenarios, and knowing the relative magnitudes of _V, H_ and _R_ could be useful for both normative purposes (e.g. has economic growth been equitable?) and positive purposes (e.g. why has economic growth generated less controversy in some countries than others?).

# III. **DATA**

Operationalizing eqn (5) requires panel data. The data for China are from the 1989 and 1997 waves of the China Health and Nutrition Survey (CHNS), a survey run jointly by economists and nutritionists, with fairly complete income data.<sup>6</sup> The panel consists of 2,592 households (11,422 individuals) from seven of China’s 22 provinces, namely Guangxi, Guizhou,

> 5  An example may help to clarify.  Consider 9 individuals, 1 through 9, ranked in ascending order of period-0 income. Individuals 1-3 are in the poorest group of period-0 equals, group A.  Individuals 4-6 are in the middle group of period-0 equals, group B.  And individuals 7-7 are in the richest group of period-0 equals, group C.  In
-3|-1|-1|-1|0|0|0|
|**All measures**|**31**|**25**|**16**|**13**|**22**|**33**|**34**|**37**|**37**|**54**|**55**|**48**|**33**|**15**|**12**|**9**|**0**|**1**|**1**|

Source: Updated from Anderson and Nelgen 2012a, based on NRA estimates from Anderson and Nelgen 2012b.

Note: International agricultural prices spiked upwards in 1974 and 2008 and downwards in 1986. “All measures” includes farm domestic support/tax measures. Trade tax equivalents of quantitative measures are included in the trade tax rows.
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# References and bibliography

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic (AICD). All of these can be downloaded from the project Web site: www.infrastructureafrica.org. For papers go to the document page (www.infrastructureafrica.org/aicd/documents), for databases to the data page (www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (www.infrastructureafrica.org/aicd/tools/models) and for maps to the map page (www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

- AICD (Africa Infrastructure Country Diagnostic). _Africa’s Infrastructure: A Time for Transformation_ . <u>www.infrastructureafrica.org.</u>

———. 2010. ECOWAS’s Infrastructure: A Regional Perspective. http://www.infrastructureafrica.org.

EIU (Economist Intelligence Unit). 2010. _Senegal Country Report_ . London, UK: EIU.

Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for_

- _Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

- World Bank. 2007a. _Country Assistance Strategy for the Republic of Senegal for the period FY08–FY11_ . Washington, DC: World Bank.

- ———. 2010a. ―PPIAF-PPI Project Database.‖ www.ppi.worldbank.org/.

- ———. 2011a. _Doing Business 2011: Making a Difference for Entrepreneurs_ . Washington, DC: World Bank.

- ———. 2011b. ―Enterprise Surveys’ Database.‖ www.enterprisesurveys.org/.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits,
assess the equilibrium exchange rate and measure the extent of misalignment.<sup>1</sup> In this paper, I measure misalignment in the CFA zone since the euro introduction using a traditional relative PPP methodology with a base year representing the equilibrium real effective exchange rate. I constructed a panel dataset of two separate real effective exchange rate series for both CFA zones (UEMOA and CEMAC) from January, 1999 to December, 2004. Nineteen countries were selected in UEMOA and thirteen were selected in CEMAC based on their trade shares with the zone, and sixty monthly calculations were made for each country to compute the bilateral effective exchange rate.

Using methodology adapted from Hinkle and Nsengiyumva (1999), I calculated a monthly real effective exchange rate index for the two zones in which a trade-weighted nominal effective exchange rate is adjusted for relative inflation in the home country and its trading partners. The multilateral real exchange rate was defined as a geometric product of the bilateral real effective exchange rates. Trade weights using both imports and exports are averaged for two years and are calculated for each regional grouping, while monthly CPI indices are calculated for the two zones and for the trading partners based on a weighted average of individual countries’ monthly CPIs. Monthly exchange rates are used in the model to ensure the minor fluctuations in exchange rates are captured in the analysis. A 1999 base year is chosen in which it is assumed that the balance of payments was in equilibrium, and the percentage difference between the actual and equilibrium exchange rate measures the extent of misalignment. This approach also assumes a constant equilibrium exchange rate and stable long-run parameters within the period. While the approach has certain shortcomings, it has a number of practical advantages in estimating the equilibrium RER in low-income developing countries: The simplest methods of estimating the long-run equilibrium RER are based on relative PPP. Although more sophisticated methodologies that take into account variations in the fundamentals determining the LRER have been developed, the PPP-based approaches are still widely used in both graphical analyses of individual countries and in
minimum, the authors recommend using the broadest possible instrument list, including indicators of world demand, competitors' prices in export markets, exogenous capital flows, and international reserves.

Option 3: The direct incorporation of quantitative restrictions is the main method of recovering structural (notdonal) demand parameters and assessing, for example, the impact of removing import restrictions. Unfortunately, a good indicator of quantitative restrictions is not usually available and, even if it exists, interpreting its behavior may be difficult.

This paper is a product of theTrade Policy Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Karla Cabana, room N8-065, extension 61539.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these fmdings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
Another observation is that at the median and lower tail of the private educational expenditure distribution, variables such as income, household head schooling and housing facilities are important to determine the _MM•FWPv._ At the upper tail of the distribution, household per capita income is the only relevant variable that determines _MA'IWPv_ (table 9 in appendix 2).

**Table 10. Effect" of public schools supply on household education spending through out the conditional'**<sup>**5**</sup>

|||**ex**|**penditure distr**|**ibution**||
|---|---|---|---|---|---|
||**Quantile**|**Quantile**|**Quantile**|**Quantile**|**Quantile**|
||**0.1**|**0.25**|**0.5**|**0.75**|**0.9**|
|**MMWPv**|1101.48|2843.56|5781|16291.04|39533.6|
|MMWPu|16.52|252.12|1636.2|6241.2|12897.04|
|Effect|1084.96|2591.44|4144.84|10049.88|26636.56|

_Source:_ Own calculation based on ENIGH 96 1/ Annual 1996 pesos It is seen from the above analysis and from table 10 that those households with a high level of educational expenditures receive the largest subsidy from public educational services. Given that there is a strong positive relationship between educational expenditures and per capita household income, a reasonable conclusion might be that the government should charge a fee to those in the upper tail of the income distribution especially considering that the wealthy individuals
In this case management is measured as a dummy variable that takes the value of one if IT use has led to better communication within the firm, increased logistics efficiency, better activities management or to the implementation of automatized systems. These factors were selected as a proxy because ENTIC does not include specific information regarding management practices. The results of these regressions indicate that the interaction term is not statistically significant. However, these results could be driven by the limitations of our proxy.

Additionally, considering that business environment variables such as the ones included in the World Bank’s Doing Business reports could be factors explaining higher returns to IT use over productivity, we included an interaction of IT use with business environment variables such as the state Doing Business rankings from 2007<sup>12</sup> and its components (ease of starting a business, ease of registering property, ease of enforcing a contract, ease of obtaining credit) as well as distance to the U.S. border and financial inclusion (measured as the number of ATMs for each 10,000 adults in 2009).<sup>13</sup> The results indicate that only the interaction with the ease of enforcing contracts yields statistically significant results and only for the computers-per-worker and share of labor with computer variables. This is a research line that should be analyzed further using data sources.

As a robustness test of the interaction between IT use and Chinese competition, we considered competition in the U.S. market instead of the domestic measure of competition. The rationale behind this robustness test is that firms may not be facing pressure from Chinese competition in their domestic market but in the U.S. market and that should lead to the same kind of heterogeneity. As Table 11 shows, the effects are smaller but in the same direction as the ones observed in Table 10. Additionally the coefficients associated to Chinese competition solely are no longer significant, indicating that Chinese competition in the U.S. market only has effects over productivity when
# **3. Empirical Strategy**

To control for observed worker characteristics that would affect wages, we estimate a standard Mincerian wage regression with a dummy variable indicating whether the worker is employed in the public sector or private sector.<sup>5</sup> The basic specification is:

# logw� �α�β∙PUBLIC� �X� ∙γ�ε�

Where β is the adjusted public‐private earnings difference; logw� is log(weekly wages in local currency) of employee i; PUBLIC� is a dummy=1 if wage employee works in public sector; and X� is a vector of standard controls including age, age squared, level of education (four levels in the I2D2 and three in the LIS), location (urban/rural), and sex. We estimate the equation separately for each country using OLS. The estimated standard errors are robust to heteroscedasticity but are not adjusted for clustered survey design.<sup>6</sup> The data used include only salaried employees with a positive wage. We estimate additional specifications that restrict the private sector to formal wage employees only, other specifications that interact PUBLIC� with gender, education, and occupation, and finally a specification that uses quantile regression to estimate the premium at different points in the conditional earnings distribution.

We chose this simple specification in part because there is a limited variety of variables available in the I2D2 and LIS databases that are consistently defined across countries. For a similar reason there is no variable in the I2D2 database that can reasonably be used as an instrumental variable, which limits the ability to properly control for selection or endogeneity. As noted above, the earnings variable is also limited to self‐reported wages, and does not include bonuses, allowances, and in‐kind payments, which can be significant in the public sector. The data set has information on the presence of some work benefits, such as health insurance and social security, but these are not monetized
|||||_Average_|_Total_||
|---|---|---|---|---|---|---|
|||||_annual_|_percentage_||
|||_Increase in_|_Increase_|_Import_|_bias in_|_Gains from_|
|||_number of_|_in variety_|_(% of_|_price_|_Variety_|
||_Country_|_products_|_(%)_|_GDP)_|_indices_|_(% of GDP)_|
|26|Zambia|21%|131%|29%|37.3|7.35%|
|27|Zimbabwe|11%|43%|35%|31.3|7.94%|
||**Average (18-27)**|**36%**|**144%**|**29%**|**29.7**|**5.47%**|
|28|Fiji|23%|176%|43%|8.8|3.39%|
||**Average (1-28)**|**39%**|**148%**|**41%**|**20.0**|**5.49%**|

_Note:_ Authors’ calculation based on six-digit disaggregated data. See text for detailed explanation.

Now let us move on to the welfare effect of the fall in the exact import prices. It should be noted that the welfare gain from this price fall is based on the functional forms assuming the Dixit-Stiglitz structure and cannot be general. Although our estimate of the impact of imported varieties on import prices is correct for any domestic production structure (Broda and Weinstein, 2006), it is not possible to translate this into a welfare gain without making explicit assumptions about the structure
**Table 3** Impact of Treatments on Male IPV Attitudes and Perpetration <u>(Male</u> Data)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Boys**<br>**Treatment**|**Goal**<br>**Treatment**|**Endline**<br>**Control Mean**|**Observations**|
|Violence attitudes index|||||
|treatment x post|-0.290**|-0.066|-0.000|2,314|
||(0.118)|(0.086)|||
|treatment x post x Goal|0.179<br>(0.125)||||
|Tolerate violence from<br>husband/partner|||||
|treatment x post|-0.174***<br>(0.050)|-0.014<br>(0.038)|0.099|2,314|
|treatment x post x Goal|0.069<br>(0.062)||||
|Men can beat women under<br>certain circumstances|||||
|treatment x post|-0.039<br>(0.072)|-0.025<br>(0.053)|0.169|2,314|
|treatment x post x Goal|0.056<br>(0.070)||||
|IPV often index|||||
|treatment x post|0.019<br>(0.094)|-0.049<br>(0.087)|
interviewers (i.e., the ICC-I estimates). The comparable ranges are 7 to 27 percent for the household roster module and 5 to 17 percent for the non-farm enterprises module. Given the importance of the household roster and food consumption modules for consumption-based monetary poverty and inequality measurement in these contexts, minimizing the extent of interviewer effects in the administration of these modules can have non-negligible implications for the quality of survey data on demographics, poverty and inequality. Country-specific findings indicate additional household questionnaire modules that rank among the top 5 in terms of sum of variance and that are also associated with ICC-I estimates of at least 10 percent. These include housing, non-food consumption, consumer durables and livestock, with interviewer effects explaining 14 to 40 percent of total variance in module duration, depending on the module.

Similarly, in the majority of the countries, labor, health and land rank among the top 5 individual questionnaire modules in terms of sum of variance. The ICC-I estimates range from 10 to 25 percent for the labor module, 11 to 15 percent for the health module and 6 to 50 percent for the land module, depending on the country. Additional modules emerge in specific countries with high total variance and ICC-I estimates (of at least 10 percent), including time use and subjective wellbeing, and modules that aim to capture the ownership of and rights to livestock and financial assets. In these cases, depending on the module, interviewer effects explain 11 to 29 percent of total variance in module duration. The individual questionnaire modules that we single out in this discussion all collect requisite information for an extensive range of individual-level indicators that are required for the monitoring of the Sustainable Development Goals.

22
– are driven by differences in parental resources, a frequent concern has been that tracking might reinforce the effects of family background on educational achievements. I.e. that it might reduce intergenerational mobility, and exacerbate inequality of educational opportunity.

We briefly examine the correlation between our measure of IOp and these two policies, using data on the policy indicators from the UNESCO Institute for Statistics (UIS).<sup>26</sup> Our indicator of the distribution of educational expenditures is the share of spending in primary schools - defined as the first ISCED level, corresponding to grades 1 to 6 - in total public educational expenditure. The indicator of tracking is the share of technical or vocational enrollment at the secondary level (including lower and upper secondary or the second and third ISCED levels, usually corresponding to grades 7 to 12) in total enrollment at that level. The information on the distribution of education expenditure across levels is missing for six countries (Canada, Montenegro, Qatar, Russia, Serbia and Taiwan, China) and the information on the share of technical and vocational enrollment at the secondary level is missing in five countries (Latvia;

> 25 Early tracking may also be costly in terms of the misallocation of students to tracks, and in terms of forgone versatility in the production of skills (Brunello and Checchi, 2007).

> 26 The data for 2006 correspond to the school year 2005-06 for countries where the school year laps over two calendar years.

22