rafmacalaba/datause-encoder-data huggingface dataset:
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**Table A1**

# **List of variables, acronyms and sources of data**

|**Variable**|**Acronym**|**Data source**|
|---|---|---|
|Current account balance, US dollars||IMF, Balance of Payments Statistics|
|GDP, US dollars||World Bank, World Development Indicators|
|Current account balance (% of GDP)|CA||
|Workers’remittances (credit), US dollars||IMF, Balance of Payments Statistics|
|Workers’remittances (% of GDP)|REM||
|Workers’ remittances: dummy equal to 1<br>if the country level of workers’<br>remittances over GDP is above the<br>median or mean or 75<sup>th</sup>percentile value<br>of the yearly distribution|DREM||
|Workers’ remittances: dummy equal to 1<br>if the country mean of workers’<br>remittances over GDP is above the<br>median or mean or 75<sup>th</sup>percentile value<br>of the distribution of country means.|DREMave||
|International Reserves, US dollars||World Bank, Global Development Finance|
|International Reserves (% of GDP)|RES||
|External debt (% of GNI)|EXTD|World Bank, Global Development Finance|
|Short term debt (% of external debt)|SHORTD|World Bank, Global Development Finance|
|Domestic credit (Monetary Survey)||IMF, International Financial Statistics|
|Domestic credit annual growth rate|CREGRO||
|GDP per capita, PPP 1995 US dollars|PPPGDP|World Bank, World Development Indicators|
|Trade in goods (% of GDP)|OPEN|World Bank, World Development Indicators|
|Net official transfers, US dollars|OT|World Bank, Global Development Finance|
|Concessional debt (% of external debt)|CONCD|World Bank, Global Development Finance|
|Dummy equal to 1 if private credit over<br>GDP is above median|FD|World Bank, Global Development Finance|
|Dummy for exchange rate arrangements|ARR|Rogoff and Reinhart (2002)|
|Dummy equal to 1 if population<500.000|SMALL|World Bank, World Development Indicators|
|Dummy equal to 1 if the literacy rate is<br>above the median|LIT|World Bank, World Development Indicators|
|Dummy equal to 1 if country-year with a<br>civil conflict with more than 1000 battle<br>deaths|WAR|Armed Conflict Database, International Peace<br>Research Institute of Oslo (Norway) and the<br>University of Uppsala|
|Dummy equal to 1 if country is located in<br>Africa|AFRICA||
|Dummy equal to 1 if country is located in<br>Asia|ASIA||
A second strategy consists of finding an external instrument that generates fluctuations in spending that are unlikely to be correlated with contemporaneous macroeconomic events. For example, Cohen, Covall and Malloy (2010) use changes in Congressional committee chairmanships to identify changes in federal spending at the state level in the United States that are driven by national‐ level electoral outcomes. They find evidence that these spending changes are negatively correlated with private investment and employment at the state level. Fishback and Kachanovskaya (2010) also study the state‐level effects of federal spending, but focus on the New Deal era. They use a measure of swing voting behaviour as an instrument for public spending and find output multipliers ranging from 0.9 to 1.7 depending on the type of spending, although no appreciable impact on employment.

The third identification strategy consists of isolating a subcomponent of spending or taxes that arguably is uncorrelated with contemporaneous economic shocks. For example Barro (1981), Ramey and Shapiro (1998), Ramey (2009), Hall (2009), Fisher and Peters (2010) and Barro and Redlick (2010) all argue that changes in US military expenditures during major wars can be thought of in this way. This argument relies on the fact that these conflicts occurred outside the US (so that there was no direct effect of conflict on the US economy), and that their timing was determined by geopolitical factors unrelated to US macroeconomic fluctuations. In the same spirit, but on the tax side, Romer and Romer (2010) develop a narrative description of the rationale for individual tax policy changes in the US, and use this to distinguish between those changes that were taken for countercyclical purposes and those that were motivated by other considerations, such as claimed benefits for long‐run growth, or for ideological reasons. They then argue that the latter subset of tax policy changes are unlikely to be correlated with contemporaneous macroeconomic shocks and thus can be used to estimate tax multipliers.

My approach falls in this third category of identification strategies, as it also consists of identifying a subcomponent of public spending whose fluctuations are plausibly uncorrelated with contemporaneous macroeconomic events. In my case, this consists of changes in government spending in a given year that are attributable to changes in World Bank project approval decisions made in previous years. My basic identifying assumption is that project approval decisions made in previous years do not react to shocks to growth in the current year, and so current‐year disbursements on projects approved in previous years are unlikely to be correlated with current‐year shocks to growth. Using these fluctuations in spending associated with project approval decisions from previous years, I consistently find point estimates of the government spending multiplier that are small and sometimes 3
In Figures 4 and 5, NSOs above the 45o line undersupply data, NSOs near the 45o line meet the demand for data, and NSOs below the 45o line oversupply data, given the characteristics of their countries. We discuss different capacity-building strategies for these three groups of countries in the next section.

Our econometric approach could be criticized from multiple perspectives. The coefficients of our regressions could be biased because of reverse causality. One could argue, for example, that poor statistical performance negatively affects a country’s GDP or prevents it from becoming part of global value chains, reducing economic complexity. Although these concerns have merit, we think that statistical performance in many developing countries is sufficiently weak that it reflects rather than causes the factors we examine.

Some unobserved factors could affect both the SPI and our independent variables. Given our data limitation, we see no way to address such a bias. Once the next round of SPI data becomes available, some of these concerns could be addressed.

We replicate our analysis on the earlier version of SPI developed by Cameron et al. (2021). Given the commonalities in design, both the SPI and the earlier version of the index are highly correlated (Figure A in the Appendix) and the analysis generates qualitatively similar results.<sup>9</sup> The similarity of the two sets of results provides a degree of confidence in the robustness of our main conclusions.

# **5. Discussion**

Our findings suggest three questions that could stimulate the discussion about building sustainable statistical capacity in developing countries.

# **_Question 1: Should technical assistance delivery mechanisms change, given the marked difference between the cluster of high-supply/high-demand countries and the rest of the world described in Figure 1?_**

Over the past two decades, high-performing NSSs have provided much of the technical expertise to improve developing countries’ statistics. The organization, technology, and data sources of high-performing NSSs are drastically different from those of the rest of the world, however. Highcapacity countries have long been adapting their statistical production processes to accommodate the ever-expanding set of administrative data that have become the foundation of many statistical

> 9 The results of this analysis are available from the authors on request.

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Table 10 shows the results of the difference regressions for debt portfolio flows panels of Latin America on Asia with a constan **t** erm, the dummy for South Asia (i.e., d =1 for South Asia and 0 otherwise), with and without a trend variable. When the first difference of debt portfolio flows to LAC (a stationary series) was regressed on the 11d of debt portfolio flows to Asia (also a stationary series), a constan **t** erm, South Asia dummy and the trend variable, the negative relationship between portfolio flows to Latin America and Asia continues to be statistically significant at the 10 percent level. However, the F-test for these regressions fail to reject the null hypothesis that the coefficients are together equal to zero.

Table 10. RegressIon Results - Debt Portfolio Flows

|Vadable|D(LAC)|D(LAC)|D(LAC)|D(LAC|
|---|---|---|---|---|
|Constant|26.05<br>(0.82)|-150.25<br>(0.33)|12.63<br>(0.90)|-101.64<br>(0-49)|
|ASIA|-0.21<br>(0.26)|-0.34<br>(0.09)|||
|Dummy|132.38|157.36|110.52|110.73|
||(0.32)|(0.24)|(0.38)|(0.38)|
|Trend||21.39||13.44|
||(0.00)|(0.10)||(0.25)|
|D(ASIA)|||-0.54|-0.55|
||||(0.03)|(0.03)|
|R-squared|0.03|0.07|0.08|0.10|
|Adi R-squared|0.00|0.03|0.05|0.06|
|S.E.|442.42|436.08|429.80|428.62|
|DW statslic|2.51|2.56|2.52|2.58|
|F-<br>|0.91|1.55|2.75|2.31|
|statistic<br>Prob(F-statisfic)|OAl|0.21|0.07|0.09|

Note: _p-values are in paentheses._ 25
12. However, the literature on the effectiveness of HEs in poverty reduction is limited as the focus of most existing studies on small enterprise development has been on MSMEs and the informal sector in general. Yet the number of households with an HE is remarkably large. This signifies the importance of HEs to poor households as a livelihood strategy and as a means to supplement their income, and therefore a practical option for increasing their welfare.

# **2.2 Trends in the Growth of Employment and HEs**

13. By far, agriculture remained the main economic activity of the labor force in Tanzania in 2006, employing 77 percent of women and 72 percent of men. However, within the nonagriculture sector, when family helpers are included, the HE share in the labor force increased to over 50 percent for male and 75 percent for female (Figure 2.1). In urban areas in 2006, HEs employed a larger share of the labor force than wage employment, i.e., 40 percent, the largest category (Figure 2.2).

14. Non-farm sector employment has been growing very rapidly in Tanzania as incomes in agriculture stagnate. However, despite a very rapid growth in non-farm wage employment, especially in urban areas, the supply of labor seeking non-farm employment outpaced the demand in the wage sector, leaving many labor force participants with no choice but to create their own employment. A comparison of data from the two rounds of the ILFS survey, 2000/01 and 2006, shows that employment in the HE sector grew by 13 percent, higher than the overall change in the labor force and faster than the growth of wage employment in both non-agriculture and agriculture sectors (Figure 2.3).

15. Since the labor force perspective considers only one activity per individual, it understates the economic role of HEs. Even more remarkable than the importance and growth of HEs as a primary employment source is their role as secondary employment as Tanzania transitions from an agrarian economy. For individuals who declared a secondary employment, the overwhelming majority cited an HE, including 36 percent of the labor force in rural areas (Table A1.5, Appendix A). Forty-two percent of all females in the labor force cited their HE as their secondary employment (Table A1.6). Therefore, although only 16 percent of labor force participants reported working in an HE as their primary employment, 66 percent of households in Tanzania ran some kind of a household enterprise, either as a primary or secondary activity (Table 2.3). As a livelihood source for households, on a full- or part-time basis, HEs are increasingly viewed as an alternative or complement to agriculture. In contrast, households in peri-urban areas rely much more on their HE as a source of livelihood given the limited wage employment opportunities, and less or no farming activities in these areas.

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results are not comparable to official socioeconomic indicators released by the NSOs or government agencies.<sup>17</sup> _3.3 The most up-to-date Purchasing Power Parity from the 2011 ICP round._ In order to be able to compare the standard of living of households across countries in LAC, household welfare needs to be expressed in common units. Pursuing that purpose, the ICP launched in May 2014 an update of its PPP data based on information of goods and services for almost every country in the world.<sup>18</sup> The updated PPP data allow for the comparison of household welfare across countries by providing a real exchange rate from local currency to US dollar in a particular year (2011 in this case).<sup>19</sup> The 2011 update of PPPs increased its coverage, collecting prices from 199 countries in the world, up from 146 in the previous update, and included several methodological and operational improvements with respect to 2005. See Ferreira et al. (2015) for a detailed description of the 2011 PPP data.

Why not use exchange rates to express welfare measures in common units across countries? The main difference between the PPP data and the nominal exchange rate is that the former is created as an index of prices of the same basket of goods in the same period of time, whereas the latter is the price of a local currency in terms of a foreign one (i.e., the rate by which both currencies are exchanged). The nominal exchange rate reflects prices of only tradable goods and, hence, a significant proportion of goods and services consumed by the population are not taken into account (Ferreira et al. 2015). Thus, the exchange rate is not appropriate to compare levels or changes over time of any economic indicator, as it does not express the current cost of living of an economy based on the prices of a fixed basket of commonly purchased goods and services.

> 17  NSO’s statistics and SEDLAC numbers serve different purposes. The first ones are the best possible representation of individual countries, while the second ones represent regional comparable indicators. Therefore, numbers presented in this paper should not be interpreted as a claim of methodological superiority over official poverty estimates. 18  The ICP includes nearly 200 countries. Some countries did not participate in the program. As stated in the official report, Afghanistan, Argentina, Lebanon, Libya, South Sudan, and the Syrian Arab Republic are the only large economies that did not take part in the 2011 ICP round. 19  One of the new features of the 2011 ICP round is the availability of global PPP values in addition to USD PPP. For this paper, we still consider the USD PPP values.

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growth of the working age shares and population to determine their contribution to future economic growth, it is found that with the exception of Malaysia and the Philippines, the growth rates of all East and Southeast Asian countries become negative as a result of the demographic changes (column III, Table 1).

**Table 1: Contribution of Demographic Change to Average Economic Growth (Percentage Points)**

||**Contributi**<br>|**on to Economic Growth from**<br>|**Growth of**<br>|
|---|---|---|---|
||**Labor Force and**<br>**Population(1965-2005)**|**Working-Age Share and**<br>**Population(1965-2005)**|**Working-Age Share and**<br>**Population(2005-2050)**|
||I|II|III|
|**China**|0.91|16.46|–0.36|
|**Japan**|0.35|9.53|–0.91|
|**South Korea**|2.01|36.4|–0.87|
|**Singapore**|2.21|51.13|–0.78|
|**Southeast Asia**||||
|**Indonesia**|1.19|41.01|–0.05|
|**Malaysia**|1.12|26.71|0.13|
|**Philippines**|0.53|36.18|0.46|
|**Thailand**|0.88|20.81|–0.45|
|**Bangladesh**|0.15|9.53|0.42|
|**India**|0.01|0.27|0.36|
|**Nepal**|–0.63|–50.32|0.66|
|**Pakistan**|–0.10|–3.90|0.73|
|**Sri Lanka**|1.15|32.3|–0.26|

Source: Reproduced from Bloom and Finlay (2009) Policies targeting the changes to the labor force that have been considered include such measures as increasing the retirement age of workers as well as encouraging higher fertility rates (Bloom et al., 2007; Watanabe and Fujimoto, 2008). However, the impact of the latter approach is ambiguous, since policies to increase higher fertility rates may dampen female labor force participation, reducing the labor force in the short run (Bloom and Finlay, 2009). Inoguchi (2009) cites evidence from South Korea, where social policy emphasizing traditional family values - to promote a society with higher fertility - accelerated out-migration of specific groups of women.

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# **1. Introduction**

Tajikistan, a low income transition country which is the poorest of the Commonwealth of Independent States (CIS), will be hit hard by the international economic crisis. The economy will suffer both a large fall in national income and earnings of foreign exchange, mainly because of a drop in transfers of remittances from Tajik migrant workers in Russia. The unavoidable economic adjustment to this external shock will involve lower output growth, lower private consumption and lower government revenues. The incidence of poverty and extreme poverty, estimated at 53 percent and 17 percent respectively of the population in 2007, will inevitably rise.<sup>1</sup> The external shock facing Tajikistan is exogenous and beyond the control of public policy. However, public policy can, arguably, mitigate to some extent the fall in domestic output, and hence incomes, given the macroeconomic constraints, of which the most pertinent is the need to ensure external balance. In addition, given the extent of poverty in the country, a strong normative case can be made that public policy should aim to protect the living standards of the poor and most vulnerable, and in particular those of the poor who will suffer a sharp loss in income as a result of the crisis. The question we address in this paper is whether fiscal policy can play a role in achieving these two objectives, and if so, what specific fiscal policies are best suited to these objectives and what sort of external support is required.

Although the Tajik economy recovered strongly, with real GDP growth averaging nearly 9 percent per annum in the 2000s, the circumstances under which fiscal policy must be framed in the current crisis are far from favorable. A lack of international reserves to support the balance of payments, a very shallow and heavily dollarized domestic financial system, virtually no access to external commercial finance and large quasi fiscal deficits all constrain the room for fiscal policy to play a countercyclical stabilization role.

> 1 Poverty estimates are derived from the Tajikistan Living Standards Survey 2007 (Ivaschenko, 2009).

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recent surveys. Between 70 to more than 90 percent of the population agree or strongly agree that men have precedence over women in obtaining jobs when these are scarce and, at best, less than 30 percent agree or strongly agree that both spouses should contribute to the household income. At the same time, 85 to almost 100 percent view maternity as key to a woman’s fulfillment. The most progressive position is that towards education where the population is about 50-50 divided between agreeing and disagreeing that university education is more important for boys than for girls. More importantly, the table highlights that these large majorities holding conservative views have been either stable for decades or even increased in some cases. Referring again to the same color codes of Table 3 – where red means worsening, dark orange stagnation and green improvements – Table 4 has mainly red numbers, and no green number.

# **Right to a job**

Discrimination by gender in employment contributes to low female labor force participation ( <mark>Özbilgin et al., 2012</mark> ). The attitudes in a society over who gets a job may influence women’s supply of labor. The top left panel of Figure 11 shows that in Bangladesh, India, and Pakistan, the majority of respondents agree that when jobs are scarce, men have more rights to a job than women. In all three countries, more than half of the respondents agree with this attitude. Examining the trend across cohorts, this conservative attitude has increased among more recent cohorts. For instance, in Pakistan, the share of respondents who believe that men have more right to jobs averaged about 70 percent for cohorts born until 1970. This share increased to over 80 percent for cohorts born from the late 1980s. Similar increase can be seen in Bangladesh and, though less obvious, in India.

# **University education**

While the years of education has increased for South Asian women, the conservative attitude towards girls’ education is not reducing as one would expect (Figure 11, top right panel). However, the attitude about university education being more important for boys than for girls, although conservative, is relatively less so among respondents, compared to the other attitudes examined in this paper. About 30 to 60 percent of respondents in the three countries believe that university education is more important for boys than for girls. Comparing the trend across cohorts, the share had reduced slightly for the younger cohorts in Bangladesh, remained constant across cohorts for India, and has a U-shape for Pakistan.

# **Contribution to household income**

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migrants to rural areas the stochastic dominance was already present at baseline and gets exacerbated by the end line (except for consumption per capita values to the far right of the distribution, where one would unlikely want to place a poverty line). There is a clear urban gradient to poverty, just as in Ferre et al. (2012) and Lanjouw and Marra (2018).

# **2.3 Census**

The KHDS survey informs us on the plight of the average Kagera migrant at the different destinations. This section considers the general population characteristics across the whole of Tanzania. In 2002, about halfway between the baseline and endline KHDS rounds, the Tanzania Population and Housing Census was conducted. The Integrated Public Use Microdata Series (IPUMS) provides access to the long-form census data on over 3.7 million individuals in the country, which can be disaggregated by district, and within each district by rural and urban areas, giving a total of 254 geographical areas to potentially consider (Minnesota Population Center, 2014).

The long-form sample contains basic census information, such as the age and sex of all members, from which we can derive household size and a dependency ratio. Information on housing characteristics includes the number of bedrooms in the dwelling and the materials out of which the roof, walls and floors are made. Further details on utilities allow us to calculate who has electricity, piped water and a flush toilet. We further know about asset ownership. For each individual there is information on their employment status and the industry or sector they work in. The location means for each variable are presented in Table 3.

Many of the variables collected reflect the wealth of the household. Lacking weights, such as prices, to meaningfully add up the various wealth components available in our data, we reduce the dimensionality of our wealth data by extracting the first principal component of a number of housing, utility and asset outcomes of sampled households.

We use the following variables for the principal component analysis: number of bedrooms per person, whether the dwelling has an iron sheet roof (or better), walls from baked bricks 7