rafmacalaba/datause-encoder-data huggingface dataset:
decline in the share of children combined with a gradual rise in the share of elderly. All of these are important factors in explaining income growth, which we will include as controls in our growth regressions.

Table 2. Summary statistics of independent variables by year

||**1960**|**1970**|**1980**|**1990**|**2000**|**2010**|
|---|---|---|---|---|---|---|
|Gini|0.408|0.404|0.397|0.425|0.461|0.464|
||[0.038]|[0.025]|[0.020]|[0.024]|[0.028]|[0.023]|
|gini_b40|0.242|0.239|0.251|0.260|0.262|0.279|
||[0.036]|[0.023]|[0.017]|[0.020]|[0.015]|[0.010]|
|gini_t40|0.250|0.252|0.235|0.265|0.308|0.288|
||[0.019]|[0.013]|[0.012]|[0.014]|[0.019]|[0.019]|
|age015|0.329|0.308|0.247|0.237|0.231|0.213|
||[0.024]|[0.016]|[0.017]|[0.019]|[0.015]|[0.017]|
|age65|0.089|0.100|0.112|0.124|0.126|0.133|
||[0.013]|[0.016]|[0.019]|[0.021]|[0.020]|[0.019]|
|Hhsize|4.246|4.081|3.566|3.477|3.398|3.328|
||[0.282]|[0.172]|[0.133]|[0.195]|[0.254]|[0.235]|
|inschool5|0.457|0.549|0.764|0.681|0.849|0.866|
||[0.194]|[0.146]|[0.079]|[0.063]|[0.046]|[0.048]|
|edushort1518|0.805|0.697|0.705|0.560|0.593|0.431|
||[0.206]|[0.129]|[0.089]|[0.065]|[0.065]|[0.076]|
|edu_hs|0.157|0.194|0.229|0.206|0.237|0.216|
||[0.024]|[0.024]|[0.024]|[0.027]|[0.025]|[0.024]|
|edu_ba|0.057|0.077|0.114|0.173|0.140|0.160|
||[0.013]|[0.017]|[0.021]|[0.023]|[0.013]|[0.013]|
|edu_ms|0.043|0.059|0.094|0.125|0.145|0.170|
||[0.009]|[0.011]|[0.018]|[0.025]|[0.026]|[0.030]|
|edu_ms_age2139|0.098|0.138|0.198|0.220|0.244|0.289|
||[0.019]|[0.024]|[0.032]|[0.041]|[0.046]|[0.057]|
|olf_female|0.600|0.525|0.414|0.304|0.303|0.271|
||[0.035]|[0.031]|[0.035]|[0.033]|[0.037]|[0.032]|
|olf_male|0.076|0.090|0.116|0.111|0.176|0.163|
||[0.017]|[0.021]|[0.022]|[0.019]|[0.027]|[0.026]|
|race_white|0.854|0.835|0.796|0.758|0.702|0.654|
||[0.104]|[0.098]|[0.109]|[0.126]|[0.141]|[0.150]|
|race_hisp|0.031|0.042|0.062|0.085|0.119|0.153|
||[0.049]|[0.055]|[0.073]|[0.093]|[0.112]|[0.125]|
|race_black|0.105|0.111|0.118|0.118|0.128|0.131|
||[0.092]|[0.083]|[0.082]|[0.080]|[0.082]|[0.080]|
|race_asian|0.005|0.008|0.017|0.029|0.041|0.052|
||[0.030]|[0.036]|[0.043]|[0.049]|[0.056]|[0.058]|
|ethnic frac|0.236|0.268|0.323|0.370|0.433|0.482|
||[0.133]|[0.127]|[0.140]|[0.153]|[0.153]|[0.144]|

16
We also investigate the variability of seasonal rainfall and how this differs by province. We then compare this with poverty incidence at the province level in order to get a sense of the extent of exposure. In line with results in section 5.1, growing season rainfall from November to March is highly variable both at the national and provincial levels (Figures 4).

Figure 4: Trends in total growing season rainfall (in mm) from 1982 to 2019 in Zambia <!-- Start of picture text --> 1980 1990 2000 2010 2020<br>year<br>Growing season spans Nov year t to March year t+1<br>1100<br>1000<br>900<br>Seasonal rainfall, mm<br>800<br>700<br><!-- End of picture text --> Source: Own calculations based on CHIRPS data.

Rainfall variability is more pronounced at province level, with the Western Province showing high rainfall variability on the lower end of the seasonal rainfall distribution. This is the trend with provinces in agro-ecological zones I and II experiencing much lower average seasonal rainfall than those in the high rainfall zones such as the North-Western and Northern Provinces. Droughts and floods are therefore more likely to affect households in zones I and II. This corroborates our earlier findings in section 5.1 and earlier studies suggesting that droughts are the most common climate shock in Zambia (Braimoh et al., 2018; Table 2). While it is difficult to say whether these changes in rainfall patterns lead to poverty or not, what is clear from the data is that chronic poverty—that is, households that are poor across all the three survey rounds—concentrates in parts of the Southern, Eastern, Muchinga, and NorthWestern Provinces (Figure 3). Except for the Southern Province, these also have the highest proportions of vulnerable households (see Figure 1 and Table S4).

# 5.3. Climate shock impacts on vulnerability and resilience in rural Zambia

Table 3 presents annotated outputs from the first stage probit regressions where we test and confirm that each of the candidate IV strongly correlates with a given endogenous variable. (Full results are available in Table S3 in the supplementary materials.) 17
9 Table 2: **Assumed fiscal consolidation**

|**_Country/regional grouping_**|**_Gross_**<br>**_debt/GDP ratio_**|**_Fiscal consolidation in_**<br>**_primary balance 2010 to 2020_**|
|---|---|---|
||%|% of GDP|
|United States|92.7|11.3|
|Japan|225.8|13.3|
|United Kingdom|76.7|9.3|
|Germany|75.3|2.2|
|Rest of Euro Zone|95.0|8.0|
|Canada|81.7|4.4|
|Australia|21.9|1.0|
|New Zealand|32.1|1.0|
|ROECD|44.5|2.3|
|China|19.1|0.0|
|India|75.1|0.0|
|Other Asia|30.0|0.0|
|Latin America|51.5|0.0|
|Other emerging countries|30.0|0.0|
|Eastern Europe & former Soviet Union|52.0|0.0|
|Oil-exporting & Middle East|12.9|0.0|
|Average advanced (PPP base)|97.3|7.8|
|Average emerging (PPP base)|37.4|0.0|

_Source:_ IMF (2011), Fiscal Monitor, April and author calculations.

# **b) Modelling the effect of infrastructure spending in emerging countries**

Examining the empirical effect of expanding infrastructure spending in emerging countries requires some estimates of the impact of infrastructure spending in the economy. The lack of infrastructure in labour abundant emerging countries can be an important constraint on growth and improving welfare. Besides expanding capital per worker and boosting labour productivity, extra infrastructure can remove bottlenecks and boost productivity more generally in the economy<sup>15</sup> .

The direct impact of increasing infrastructure spending is to build a greater infrastructure capital stock in an economy. The empirical evidence suggests that greater infrastructure increases the marginal product inputs into private sector production. In the G-Cubed model this would show up as an increase in labour augmenting technical change. The model covers regions with various income levels and degrees of economic development. Hence, there is a need to specify the possible effects of infrastructure spending on different economies, especially to distinguish the effects on advanced and emerging countries. It is natural to assume that infrastructure capital return in emerging countries is different from that in advanced countries. It is recognised that infrastructure capital has rapidly diminishing returns (Canning and Bennathan 2000). Therefore, emerging countries that usually have lower infrastructure stock may get more marginal return from increasing infrastructure spending.

15 See Henckel and McKibbin (2010).
replacing these types of macro estimates at the first step of the chain with data and evidence from detailed household survey information, such as that of LSMS, would allow much more precision and nuance, but, to the best of our knowledge, has yet to be attempted.

- Through integration with other data sources, the analytical value and relevance of LSMS-type survey data can be considerably enhanced. To achieve this, however, efforts must be made to increase data accessibility (while at the same time finding solutions to maintain ethical standards and data confidentiality) and enhance interoperability in data collection methods. In operational terms, this means being able to link different data sources through, for example, the use of common identifiers for individuals, households, or administrative areas, geospatial coordinates, time stamps, and common classification standards (Carletto et. al., 2022).

# **4. Empirical guidance on existing surveys**

Based on both the conceptual framework and the methodological agenda set out in the previous sections, we now provide some practical empirical guidelines for interested researchers along with some simple illustrations of why and how to use household surveys to study climate migration. We start with descriptive statistics, drawing on a case study, and carry out some simple back-of-the-envelope calculations about the ‘rare event’ issue; then we provide an empirical checklist for undertaking the research effort.

# **4.1 An illustrative case study to assess ‘rare event’ issues and the migration-adaptation link**

The first step for a researcher interested in leveraging a specific household survey to study climate migration is to understand whether this is feasible in the first place. As we have repeatedly mentioned throughout the paper, in standard surveys which are not _ex ante_ planned for the _ad hoc_ tracking of migrants, there is often a ‘rare event’ issue which can undermine research efforts: small migrant samples compared to the overall sample size. But, since the ‘rare event’ issue depends on the unit of analysis, we argue that apparent ‘rare event’ issues can be drastically attenuated by shifting from the individual to the household as the statistical population of interest and the unit of analysis. To illustrate why this is the case, we leverage the Nigeria Universal Panel Data (NUPD) from the World Bank’s LSMS-ISA collection, which is currently composed of four waves from 2010-2019.<sup>36</sup> To keep things simple, we adopt a very broad and generic definition of migration. We classify an individual as a migrant – regardless of the reasons for the move – if he/she meets the following criteria: i) was present in the previous interview round but not in the current one; ii) his/her current place of residence is either in

> 36 The data are publicly available and can be downloaded here.

24
# Figure 1. Investment shares in total

<!-- Start of picture text --> private environmental costs<br><!-- End of picture text --> <!-- Start of picture text --> (%) 65<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25 1 l l 1lll6<br>1972 194 1976 1978 1980 I 1982 I 1984 1986<br>1973 1975 1977 1979 1981 1983 1 95 1987<br>0 FinlaLnd + Ger many O USA A Austria<br><!-- End of picture text --> Source: OECD (1990).
gies, especially in the form of reduced consumption and drawdown of savings, with only slow improvements through the second year of the pandemic.

In addition to the short-term impacts analyzed in this paper, one of the key pathways of longer-term welfare impacts will be through the learning inequalities that have been accentuated by school closures. It is estimated that globally, about 1.6 billion students in over 190 countries were affected over the pandemic (Munoz-Najar et al. 2022). While a detailed investigation of learning losses throughout the pandemic is beyond the scope of this paper, the HFPS data also show that in LICs, children stopped learning in more than half of households where children had been enrolled prior to the pandemic, in contrast to much lower figures for UMICs where governments and households had more resources available to support distance learning or facilitate in-person learning with containment measures (Table A6.26). Over 20 percent of students in LICs and LMICs had not returned to school in 2021. Moreover, children were less likely to access education if household adults had lower levels of education (Figure A6.6). Evidence from COVID-19 indicates these same households are more vulnerable to economic crises, consistent with negative feedback loops that may worsen future inequalities. The loss in human capital could be detrimental for future poverty among the poorest. Azevedo et al. (2022) estimate that the share of children with more years of – – education than their parents a measure of absolute inter-generational mobility could decline by 8 percentage points or more in UMICs, with the largest declines in the Latin America region.

In conclusion, unrecovered and unmitigated welfare losses from the COVID-19 crisis could worsen inequality in the longer term, an economic version of “long COVID”. Jobs and income losses went largely unmitigated, as reflected in the strong correlation between income losses and food insecurity. The adoption of negative coping strategies during the pandemic, including the use of savings and sale of assets, could hurt households’ productive capacity and ability to recover from the crisis. Combined with the impact of the unequal learning losses, which by some estimate could result in a total of $17 trillion of lost lifetime earnings, equivalent to 14 percent of today’s global annual GDP, the full impact of the crisis may only be seen over the long term.

29
the district’s historical rainfall distribution.<sup>8</sup> Similarly, the dummy _Zero Shock_ equals 1 if the district’s annual rainfall incidence falls between the 20th and the 80th percentile of the district’s historical rainfall distribution.

Table 5 explores how contemporaneous and lagged rainfall shocks affect rural non-farm employment. Panel A examines broad employment categories and employment type, while Panel B considers sectoral employment outcomes. In each instance, the reference category – is _Negative Shock_ years in which the district’s annual rainfall incidence falls in the bottom 20th percentile of the district’s historical rainfall distribution. Unlike the results discussed so far where the regression coefficients capture conditional correlations, we can attribute a causal interpretation to the coefficients corresponding to the rainfall shocks since annual local precipitation is exogenous.

Table 5 shows that rainfall shocks act as shifters between farm and non-farm employment for rural workers. Thus, column (1) of Panel B shows that relative to a negative shock, a positive rainfall shock increases the likelihood of rural workers to participate in farm activities by 3 percentage points, driven primarily through an increase in farm labor [Panel B, column (2)]. This increase comes at the expense of non-farm activities [column (3) of Panel – A] mostly through a reduction in participation in the manufacturing and service sectors [Panel B, columns (3) and (6)], amongst self-employed workers [Panel A, column (4)]. There is however little impact of contemporary or lagged rainfall shocks on labor force participation for rural individuals [Panel A, column (1)], although there is a small decline in the likelihood of being unemployed in response to a positive rainfall shock. Table 5 thus points to the presence of rural workers who shift between the farm and non-farm sectors in response to employment opportunities generated by exogenous increases in precipitation, which positively affects agricultural production and increases the demand for farm labor. In the absence of such labor demand, these workers opt to remain self-employed in manufacturing and service activities.

# **3.2 Heterogeneity of Individual Predictors of Rural Non-Farm Employment across District Characteristics**

## **3.2.1 Heterogeneity by Secondary Education**

The empirical findings in Section 3.1 documented education and social identity to be key predictors of non-farm employment for rural workers. In particular, while secondary educa-

> 8 We use district-level rainfall data from the ICRISAT between 1975 and 2014 to assess the long-term district-specific distribution of annual rainfall.

17
Venezuelan households.

Table 3. Household Descriptive Statistics

||Colombian|Venezuelan|Diff.|
|---|---|---|---|
||(1)|(2)|(1)-(2)|
|Household size|4.05|4.33|-0.28***|
||[1.41]|[1.47]|(0.06)|
|# Adults (3,617)|2.38|2.14|0.25***|
||[1.11]|[0.92]|(0.04)|
|# Children (3,169)|1.48|1.81|-0.32***|
||[0.74]|[0.95]|(0.04)|
|Assets (Max=15)|9.86|5.92|3.94***|
||[4.34]|[2.93]|(0.16)|
|Own dwelling [=1]|0.36|0.01|0.35***|
||[0.48]|[0.10]|(0.02)|
|Observations|1,338|918|2,256|

_Notes:_ Columns (1) and (2) describe the mean values for the households with members born in Colombia and Venezuelan, respectively. The last column illustrates a mean difference test between both groups. Standard errors are reported in brackets. Household size is the number of family members. Total assets is the number of assets owned by the households which can take a maximum number of 15, and own dwelling is a dichotomous variable [=1] if the household owns the place where they live.

We also explore the distribution of wealth for the Venezuelan and Colombian households of our sample. For this purpose, we constructed an index measure of the household’s cumulative living standard constructed following he Demographic and Health Surveys (DHS) methodology. The index combines information on adequate dwelling materials (for floors and walls), asset ownership for 15 items (including telephones, cellphones, TVs, computers, tablets, microwave, washing machine, fridge, blenders, water heater, radio/recorder, cars/taxis, motorcycles, bikes and trailers), and access to 10 services(encompassing energy, gas, garbage, aqueduct, sewage, sewerage, sanitary, kitchen, internet, TV, telephone), using a principal component analysis.

Uniquely, by collecting both current and retrospective information from Venezuelan households about their circumstances while they were in Venezuela, we can examine the wealth distribution before and after displacement episode for this group. This comparative analysis offers valuable insights into the wealth dynamics affecting the displaced population, 24
# **3.4 Implementation fidelity**

We leverage data from the monitoring survey to establish that our experimental design successfully generated different levels of social and geographic decentralization of demonstration activities across arms as Figure 3 illustrates. We present descriptive statistics on the implementation of our experimental design in Table 1. As discussed in Section 3.3, promoted crops are balanced across control and treatment villages and range in number from 1.61 to 1.88 crops. However, no demonstration farmers were designated in control villages, and no demonstration plots were established.

Table 1: Demonstration descriptive statistics

||||Demo in <br>|village<br>|
|---|---|---|---|---|
||Control|Regular|Shared|Decentralized|
||(1)|(2)|(3)|(4)|
|Farmer group|||||
|# of promoted crops|1.88|1.85|1.61|1.85|
|# of demo farmers|0.00|1.52|4.11|8.52|
|Farmer group-crop|||||
|# of demos|0.00|0.82|2.58|4.62|
|# of demo plots|0.00|0.82|1.11|4.62|
|Farmer-crop|||||
|Demo|0.000|0.042|0.129|0.223|
|# of connections to demo|0.000|0.463|0.952|2.683|

_Notes:_ Sample averages of implementation variables by treatment arm across farmer group, farmer group-crop, and farmer-crop observations are presented in this table.

In farmer groups assigned to the regular treatment arm, adhesion to treatment was high with on average 0.82 demonstration plots being set up for each promoted crop. Each farmer in a regular group had a 4.2% chance of demonstrating each promoted crop (roughly one farmer per 25 farmer group members), and has on average 0.46 connections to a demonstration farmer for a given promoted crop.

In farmer groups assigned to the shared treatment arm, implementation fidelity is also high with on average 2.58 demonstration farmers setting up just 1.11 demonstration plots for each promoted crop. The social decentralization of demonstration activities in the shared arm results in each farmer in a shared group having a 12.9% 20
## **_IMPACT_** _OF_ **_INVESTMENT_** _POLICIES ON_ **_GERMAN DIRECT INVESTMENT_** _IN DEVELOPING COUNTRIES: AN EMPIRICAL INVESTIGATION_

# Andrea Gubitz*

# I. Introduction

Over the last few years foreign direct investment<sup>(FDI) has become the most</sup> important single private source of financing<sup>flows to developing countries. In</sup> 1988 it accounted for almost 58 percent<sup>of total private and 18 1/2 percent of</sup> total resource flows to developing countries.<sup>This is partly due to the fact that</sup> international bank lending practically came<sup>to a halt and partly due to an</sup> upswing of FDI in developing countries since<sup>1986, in many cases supported by</sup> debt/equity swap programs. FDI in the industrialized world, however, has grown much faster leaving the developing countries<sup>with a declining share of the cake.</sup> It is thus of obvious interest for researchers<sup>and for policy makers alike, what</sup> the determinants of FDI in developing countries<sup>are, especially the effects of</sup> policy measures, in order to find out,<sup>how the potential interest of foreign</sup> investors can be stabilized and enhanced.

For the developing countries, however,<sup>the main benefit of FDI is not so</sup> much the substitution for private lending,<sup>but the improvement of technological</sup> and managerial standards, the increase in<sup>labor productivity and a bet:er access</sup> to export markets. Another advantage is<sup>that capital costs are closely geared</sup> to the profitability of the capital stock, i.e. no<sup>contractual interest payments</sup> are involved. However there is a price to pay:<sup>earnings on direct investment</sup> 'This paper was written while the author was a<sup>consultant at the World Bank.</sup> It was presented at the seventh conference of the European<sup>Association for</sup> Research in Industrial Economics held in Lisbon<sup>in September of 1990. It is part</sup> of a project on the determinants of German<sup>foreign direct investment in</sup> developing countries in cooperation with the Kiel Institute for World Economics. For stimulating discussions, I am grateful<sup>to Asli Demirguc-Kunt,</sup> J.P. Agarwal and Peter Nunnenkamp. Many thanks also to the<sup>Deutsche Bundesbank and the German</sup> ministry of Economic Affairs for making<sup>available data without which this</sup> research could not have been undertaken.